WO2022098068A1 - Server and method for predicting infection risk level of pathogen in target area using molecular diagnostic test data, and non-transitory computer-readable storage medium - Google Patents

Server and method for predicting infection risk level of pathogen in target area using molecular diagnostic test data, and non-transitory computer-readable storage medium Download PDF

Info

Publication number
WO2022098068A1
WO2022098068A1 PCT/KR2021/015744 KR2021015744W WO2022098068A1 WO 2022098068 A1 WO2022098068 A1 WO 2022098068A1 KR 2021015744 W KR2021015744 W KR 2021015744W WO 2022098068 A1 WO2022098068 A1 WO 2022098068A1
Authority
WO
WIPO (PCT)
Prior art keywords
pathogen
target area
risk level
infection risk
area
Prior art date
Application number
PCT/KR2021/015744
Other languages
French (fr)
Inventor
Youn Chul Choi
Young Wook Kim
Min Sun SUNG
Original Assignee
Seegene, Inc.
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Seegene, Inc. filed Critical Seegene, Inc.
Priority to KR1020237009021A priority Critical patent/KR20230054405A/en
Publication of WO2022098068A1 publication Critical patent/WO2022098068A1/en

Links

Images

Classifications

    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
    • G16H50/80—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for detecting, monitoring or modelling epidemics or pandemics, e.g. flu
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16B—BIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY
    • G16B25/00—ICT specially adapted for hybridisation; ICT specially adapted for gene or protein expression
    • G16B25/20—Polymerase chain reaction [PCR]; Primer or probe design; Probe optimisation
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16C—COMPUTATIONAL CHEMISTRY; CHEMOINFORMATICS; COMPUTATIONAL MATERIALS SCIENCE
    • G16C20/00—Chemoinformatics, i.e. ICT specially adapted for the handling of physicochemical or structural data of chemical particles, elements, compounds or mixtures
    • G16C20/20—Identification of molecular entities, parts thereof or of chemical compositions
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16C—COMPUTATIONAL CHEMISTRY; CHEMOINFORMATICS; COMPUTATIONAL MATERIALS SCIENCE
    • G16C20/00—Chemoinformatics, i.e. ICT specially adapted for the handling of physicochemical or structural data of chemical particles, elements, compounds or mixtures
    • G16C20/30—Prediction of properties of chemical compounds, compositions or mixtures
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H10/00—ICT specially adapted for the handling or processing of patient-related medical or healthcare data
    • G16H10/40—ICT specially adapted for the handling or processing of patient-related medical or healthcare data for data related to laboratory analysis, e.g. patient specimen analysis
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H20/00—ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
    • G16H50/20—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
    • G16H50/30—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for calculating health indices; for individual health risk assessment
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
    • G16H50/50—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for simulation or modelling of medical disorders
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
    • G16H50/70—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for mining of medical data, e.g. analysing previous cases of other patients
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H70/00—ICT specially adapted for the handling or processing of medical references
    • G16H70/60—ICT specially adapted for the handling or processing of medical references relating to pathologies
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/08—Learning methods
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02A—TECHNOLOGIES FOR ADAPTATION TO CLIMATE CHANGE
    • Y02A90/00—Technologies having an indirect contribution to adaptation to climate change
    • Y02A90/10—Information and communication technologies [ICT] supporting adaptation to climate change, e.g. for weather forecasting or climate simulation

Definitions

  • the disclosure relates to a server for predicting the infection risk level of pathogen in a target area using molecular diagnostic test data, a method therefor, and a non-transitory computer-readable storage medium thereof.
  • Infections caused by various pathogens such as respiratory, digestive, and sexually transmitted diseases that occur in the human body may spread through various infection routes.
  • PCR real-time polymerase chain reaction
  • the level of risk of infection with a pathogen in a specific area is predicted based only on molecular diagnostic test data in the specific area.
  • the method for predicting the infection risk level of a pathogen in a specific area does not reflect the infection characteristics of the pathogen because it does not use molecular diagnostic test data for other areas or consider the infection risk level of the pathogen in other areas.
  • a server connected with a communication network and predicting an infection risk level of a pathogen in a target area using molecular diagnostic test data.
  • the server comprises a communication unit receiving the molecular diagnostic test data for the pathogen in the target area and receiving at least one of molecular diagnostic test data for the pathogen in a surrounding area which is likely to spread the pathogen to the target area or an external infection risk level of the pathogen in the target area, the external infection risk level of the pathogen being an infection risk level of the pathogen in the surrounding area, and a processor determining an internal infection risk level of the pathogen in the target area from the molecular diagnostic test data in the target area, determining the external infection risk level of the pathogen in the target area from the molecular diagnostic test data in the surrounding area when only the molecular diagnostic test data in the surrounding area of the molecular diagnostic test data in the surrounding area or the external infection risk level of the pathogen in the target area is received, and predicting a combinatorial infection risk level of the pathogen in the target area using the internal infection risk level of the pathogen in the target area, the external infection risk level of the pathogen, and a first possibility of spreading in which the pathogen is to spread
  • a method for predicting an infection risk level of a pathogen in a target area using molecular diagnostic test data is provided.
  • the method comprises receiving the molecular diagnostic test data in the target area for the pathogen, receiving at least one of molecular diagnostic test data in a surrounding area which is likely to spread the pathogen to the target area or an external infection risk level of the pathogen in the target area, the external infection risk level of the pathogen in the target area being an infection risk level of the pathogen in the surrounding area, determining an internal infection risk level of the pathogen in the target area from the molecular diagnostic test data in the target area, determining the external infection risk level of the pathogen in the target area from the molecular diagnostic test data in the surrounding area when only the molecular diagnostic test data in the surrounding area of the molecular diagnostic test data in the surrounding area or the external infection risk level of the pathogen in the target area is received; and predicting a combinatorial infection risk level of the pathogen in the target area using the internal infection risk level of the pathogen in the target area, the external infection risk level of the pathogen in the target area, and a first possibility of spreading in which the pathogen is to spread from the surrounding area
  • a non-transitory computer-readable storage medium storing, in a computing device, instructions executed by one or more processors to enable the one or more processors to perform a method for predicting an infection risk level of a pathogen in a target area using molecular diagnostic test data.
  • the instructions stored in the computing device perform receiving the molecular diagnostic test data in the target area for the pathogen, receiving at least one of molecular diagnostic test data in a surrounding area which is likely to spread the pathogen to the target area or an external infection risk level of the pathogen in the target area, the external infection risk level of the pathogen in the target area being an infection risk level of the pathogen in the surrounding area and determining an internal infection risk level of the pathogen in the target area from the molecular diagnostic test data in the target area,
  • the server and method for predicting the infection risk level of a pathogen in a target area using molecular diagnostic test data may predict the infection risk level of a specific area using molecular diagnostic test data in other areas geographically adjacent to, or related to, the specific area, as well as molecular diagnostic test data obtained through a molecular diagnostic test in the specific area, or considering the infection risk level of the pathogen in the other areas.
  • FIG. 1 is a block diagram of a communication system including a server that predicts the infection risk level of a pathogen in a target area using molecular diagnostic test data according to an embodiment.
  • FIG. 2 is a view of a configuration of the management server of FIG. 1.
  • FIGS. 3 to 5 illustrate examples of a target area and surrounding areas determined by a processor of the management server of FIG. 1.
  • FIG. 6 is a flowchart of a method for predicting the infection risk level of a pathogen in a target area using molecular diagnostic test data according to another embodiment.
  • FIG. 7 is a flowchart of a method for providing the infection risk level of a pathogen in a target area using molecular diagnostic test data according to another embodiment.
  • Such denotations as “first,” “second,” “A,” “B,” “(a),” “(b),” “(i),” and “(ii)” may be used in describing the components of the present invention. These denotations are provided merely to distinguish a component from another, and the essence of the components is not limited by the denotations in light of order or sequence.
  • a component is described as “connected,” “coupled,” or “linked” to another component, the component may be directly connected or linked to the other component, but it should also be appreciated that other components may be “connected,” “coupled,” or “linked” between the components.
  • FIG. 1 is a block diagram of a communication system including a server that predicts the infection risk level of a pathogen in a target area using molecular diagnostic test data according to an embodiment.
  • the communication system 100 includes user terminals 120 connected with each other through a communication network 110, a management server 130 that predicts the infection risk level of a pathogen in a target area using molecular diagnostic test data according to an embodiment, and inspection servers 140 that provide molecular diagnostic test data.
  • the user terminals 120 are user terminals managed by separate inspection institutions and include two or more user terminals 120a,..., 120m managed by two or more inspection institutions.
  • first terminal of the user terminals 120a,..., 120m and/or some (hereinafter referred to as first server) of the inspection servers 140a,..., 140n may provide molecular diagnostic test data for the pathogen in the target area to the management server 130.
  • the first terminal and/or the first server are exemplarily described as being located in the target area, but may not be located in the target area as long as they may provide the management server 130 with the molecular diagnostic test data for the pathogen in the target area.
  • the first terminal may include not only a general communication terminal but also a storage medium storing molecular diagnostic test data for pathogens.
  • second terminal of the user terminals 120a,..., 120m and/or others (hereinafter referred to as second server) of the inspection servers 140a,..., 140n may provide at least one of molecular diagnostic test data for the pathogen in the surrounding areas or the external infection risk level of the pathogen in the target area, to the management server 130.
  • the second terminal and/or the second server are exemplarily described as being located in the surrounding area, but may not be located in the surrounding area as long as they may provide the management server 130 with at least one of the molecular diagnostic test data for the pathogen in the surrounding area or the external infection risk level of the pathogen in the target area.
  • the second terminal may include not only a general communication terminal but also a storage medium storing at least one of molecular diagnostic test data for pathogens or the external infection risk level of the pathogen in the target area.
  • a specific user first terminal 120a positioned in the target area may provide the molecular diagnostic test data D1 in the target area to the management server 130 through the communication network 110.
  • Another user second terminal 120b positioned in the surrounding area may provide the molecular diagnostic test data D2 in the surrounding area to the management server 130 through the communication network 110.
  • the user terminal 120 may access the management server 130 through the communication network 110 to identify various molecular diagnostic test data provided by the management server 130.
  • the user terminal 120 may download and install specific software through the management server 130 or a sharing platform and may then receive the infection risk level of the pathogen in the target area provided by the management server 130.
  • the management server 130 predicts the infection risk level of the pathogen in the target area using the molecular diagnostic test data.
  • the management server 130 may provide the specific software, e.g., a specific application or app, to the user terminal 120 directly or through a general sharing platform (e.g., Apple Store or Google Store).
  • a general sharing platform e.g., Apple Store or Google Store.
  • the inspection servers 140 are servers managed by separate inspection institutions and include two or more inspection servers 140a,..., 140n managed by two or more inspection institutions.
  • the inspection server 140 stores molecular diagnostic test data obtained by analyzing a sample (e.g., a specimen) obtained from a subject (e.g., a human) using one or more test devices.
  • the inspection server 140 may provide molecular diagnostic test data D1 in the target area to the management server 130.
  • the inspection server 140 may access the management server 130 through the communication network 110 to identify various molecular diagnostic test data provided by the management server 130.
  • the inspection server 140 may also receive the infection risk level of the pathogen in the target area provided by the management server 130.
  • the management server 130 receives molecular diagnostic test data D1 in the target area provided from the user terminal 120a or the inspection server 140 located in the target area.
  • the management server 130 receives molecular diagnostic test data D2 in the surrounding area provided from the user terminal 120b or the inspection server 140 located in the surrounding area.
  • the management server 130 determines the pathogen infection risk level of the target area from the molecular diagnostic test data D1 in the target area.
  • the term “subject” refers to an entity subject to a molecular diagnostic test, such as a real-time PCR test, to identify the presence of a specific pathogen.
  • a molecular diagnostic test such as a real-time PCR test
  • examples of the subject include, but are not limited to, mammals, such as dogs, cats, rodents, primates, and humans, especially humans.
  • the term “subject” also refers to an entity in which the pathogen of interest (e.g., nucleic acid of the pathogen) is identified or suspected of its presence.
  • the term “subject” herein refers to a human patient.
  • sample refers to any analyte (e.g., a specimen) that contains or is suspected of containing the nucleic acid to be analyzed.
  • nucleic acid refers to a single-stranded or double-stranded form of deoxyribonucleotide or ribonucleotide polymer, wherein nucleotide may encompass derivatives of naturally occurring nucleotides, non-natural nucleotides or modified nucleotides that may function in the same manner as naturally occurring nucleotides.
  • target nucleic acid As used herein, the terms “target nucleic acid”, “target nucleic acid sequence” or “target sequence” refer to the nucleic acid sequence to be detected.
  • Target nucleic acids may include the nucleic acids of, e.g., prokaryotic cells(e.g., Mycoplasma pneumoniae, Chlamydophila pneumoniae, Legionella pneumophila, Haemophilus influenzae, Streptococcus pneumoniae, Bordetella pertussis, Bordetella parapertussis, Neisseria meningitidis, Listeria monocytogenes, Streptococcus agalactiae, Campylobacter, Clostridium difficile, Clostridium perfringens, Salmonella, Escherichia coli, Shigella, Vibrio, Yersinia enterocolitica, Aeromonas, Chlamydia trachomatis, Neisseria gonorrhoeae, Trichomonas vaginalis, Mycoplasma hominis, Mycoplasma genitalium, Ureaplasma urealyticum, Ureaplasma parvum, Mycobacter
  • Examples of parasites among the eukaryotic cells may include Giardia lamblia, Entamoeba histolytica, Cryptosporidium, Blastocystis hominis, Dientamoeba fragilis, Cyclospora cayetanensis .
  • influenza A virus influenza A virus
  • influenza B virus influenza B virus
  • respiratory syncytial virus A RSV A
  • respiratory syncytial virus B RSV B
  • parainfluenza virus 1 PV 1
  • parainfluenza virus 2 PV 2
  • parainfluenza virus 3 PV 3
  • parainfluenza virus 4 PV 4
  • metapneumovirus MPV
  • human enteroviruses HEV
  • human bocaviruses HoV
  • human rhinoviruses HRV
  • coronaviruses and adenoviruses coronaviruses and adenoviruses, which cause respiratory diseases
  • noroviruses rotaviruses, adenoviruses, astroviruses and sapoviruses, which cause gastrointestinal diseases.
  • virus examples include, but are not limited to, human papillomavirus (HPV), middle east respiratory syndrome-related coronavirus (MERS-CoV), severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), dengue virus, herpes simplex virus (HSV), human herpes virus (HHV), epstein-barr virus (EMV), varicella zoster virus (VZV), cytomegalovirus (CMV), HIV, hepatitis virus and poliovirus.
  • HPV human papillomavirus
  • MERS-CoV middle east respiratory syndrome-related coronavirus
  • SARS-CoV-2 severe acute respiratory syndrome coronavirus 2
  • dengue virus herpes simplex virus
  • HSV herpes simplex virus
  • HHV human herpes virus
  • EMV epstein-barr virus
  • VZV varicella zoster virus
  • CMV cytomegalovirus
  • HIV hepatitis virus and poliovirus
  • target nucleic acids may be the nucleic acids of pathogens associated with infectious diseases.
  • the nucleic acids may be the nucleic acids of respiratory pathogens, digestive pathogens, and sexually transmitted pathogens.
  • the inspection server 140 may be a server managed by the inspection institution itself or a server managed by a public institution or government. The inspection institution 140 tests the sample (e.g., a specimen). The sample is tested using one or more test devices, and data according to the completion of the test by the test device may be stored in the inspection server 140.
  • the test device may be a nucleic acid amplification device that is intended to encompass an amplification reaction vessel as well as an amplification reaction device including a thermometer and a detector.
  • the nucleic acid amplification device includes various well-known devices capable of temperature adjustment for an amplification reaction. Examples of the device include, but are not limited to, CFX(Bio-Rad), iCycler(Bio-Rad), LightCycler(Roche), StepOne(ABI), 7500(ABI), ViiA7(ABI), QuantStudio(ABI), AriaMx(Agilent), and Eco(Illumina).
  • the amplification reaction vessel includes a tube, a strip, a plate, or other various types of vessels.
  • the test device is preferably a real-time PCR device.
  • the test results obtained by analyzing the sample (e.g., a specimen) obtained from the subject using one or more real-time PCR devices and peripheral devices may be automatically input to the inspection server 140.
  • the inspector may input the data, generated from the test device, to the inspection server 140 using her own user terminal 120.
  • Two or more inspection servers 140a,..., 140n may provide all or part of their respective stored molecular diagnostic test data to the management server 130. If the management server 130 additionally requests the other part of the molecular diagnostic test data, the two or more inspection servers 140a,..., 140n may, or may not, provide the other part of the molecular diagnostic test data requested from the management server 130 according to its respective management policy.
  • a configuration of the management server 130 that predicts the infection risk level of the pathogen in the target area using molecular diagnostic test data according to an embodiment is described below with reference to FIGS. 2 to 6.
  • FIG. 2 is a view of a configuration of the management server of FIG. 1.
  • FIGS. 3 to 5 illustrate examples of a target area and surrounding areas determined by a processor of the management server of FIG. 1.
  • the management server 130 is a server that is connected with the communication network 110 and predicts the infection risk level of the pathogen in the target area using molecular diagnostic test data.
  • the management server 130 includes a communication unit 132 that communicates with a terminal or another server through the communication network 110, a processor 134 controlling the management server 130, and a memory 136 storing data.
  • the communication unit 132 receives, from the first terminal or another first server, molecular diagnostic test data D1 in the target area for the pathogen.
  • the memory 136 stores the molecular diagnostic test data D1 in the target area received from the communication unit 132.
  • the first terminal may be the user terminal 120 located in the target area described above with reference to FIG. 1, and the other first server may be the above-described inspection server 140 located in the target area.
  • the communication unit 132 receives at least one of the molecular diagnostic test data D2 in the surrounding area which is likely to spread the pathogen to the target area, or the external infection risk level of the pathogen in the target area, for the pathogen, from the second terminal or another second server.
  • the external infection risk level of the pathogen may be the infection risk level of the pathogen in the surrounding area, as described below.
  • the memory 136 stores at least one of the molecular diagnostic test data or the external infection risk level of the pathogen in the target area, received from the communication unit 132.
  • the second terminal may be the user terminal 120 located in the surrounding area described above with reference to FIG. 1, and the other second server may be the above-described inspection server 140 located in the surrounding area.
  • the molecular diagnostic test data may include result data obtained from a nucleic acid amplification reaction on the pathogen to be detected in the sample obtained from the subject (e.g., a human patient). Further, the molecular diagnostic test data may include medical data for the subject, but the present invention is not limited thereto.
  • the molecular diagnostic test data may be, e.g., result data for one well (or result data for one sample) or result data for one plate (or result data for multiple samples), result data obtained during a morning, afternoon, or one day, or result data obtained in a specific hospital or a specific inspection institution.
  • the molecular diagnosis result data stored in the memory 136 may be result data for influenza.
  • one well may be configured to detect the presence of one or more nucleic acids.
  • one well may be configured to detect the presence of 1 to 50 nucleic acids, 1 to 40 nucleic acids, 1 to 30 nucleic acids, 1 to 20 nucleic acids, 1 to 10 nucleic acids, 1 to 5 nucleic acids, 1 and 2 nucleic acids, or 1 nucleic acid.
  • one well may be configured to detect a plurality of pathogens having a genetic diversity, such as a virus, or to screen bacteria (e.g., Campylobacter, Salmonella, Shigella, Vibrio, Aeromonas), via nucleic acids.
  • a genetic diversity such as a virus
  • bacteria e.g., Campylobacter, Salmonella, Shigella, Vibrio, Aeromonas
  • the result data may include the name of the pathogen to be detected and information indicating whether the pathogen is positive/negative. If necessary, the result data may optionally further include the cycle threshold (Ct) value in the nucleic acid amplification reaction of the pathogen.
  • Ct cycle threshold
  • result data medical data the name of the pathogen to be detected, whether the pathogen is positive/negative, and the cycle threshold (Ct) value in the nucleic acid amplification reaction of the pathogen (optional)
  • Questionnaire information medication information, treatment methods, symptoms, heritability, medical history, medication history, re-infection status, treatment service code, examination institution type code, past medical history code
  • the nucleic acid amplification reaction of a pathogen may be a nucleic acid amplification method that amplifies a nucleic acid through repetition of a series of reactions with or without a temperature change.
  • the nucleic acid amplification reaction may be a nucleic acid amplification method that amplifies a nucleic acid through repetition of a series of reactions with or without a temperature change.
  • amplification of nucleic acids may be carried out according to various primer-involved nucleic acid amplification methods known in the art, specifically, by polymerase chain reaction (PCR) (U.S. Pat. Nos. 4,683,195, 4,683,202 and 4,800,159).
  • PCR polymerase chain reaction
  • Other examples include ligase chain reaction (LCR) (U.S. Patent Nos.
  • NASBA nucleic acid sequence-based amplification
  • RCA rolling circle amplification
  • Q-beta Replicase Lizardi et al., BiolTechnology 6:1197 (1988)
  • the medical data may include at least one of questionnaire information, medication information, treatment methods, re-infection status, symptoms, heritability, treatment service code, examination institution type code, and past medical history code.
  • the molecular diagnostic test data may further include information regarding the subject.
  • the molecular diagnostic test data may further include personal information for the subject.
  • the subject's personal information may include at least one of the subject's nationality, age, sex, blood type, location (residence), eating habits, working environment, family history, movement information, smoking status, drinking habits, exercise, pregnancy status, and specific information.
  • Subject's personal information Subject's nationality, age, gender, blood type, location (residence), eating habits, working environment, family history, movement information, smoking status, drinking habits, exercise, pregnancy status, and specific information
  • the molecular diagnostic test data may further include the animal's sex, age, habitat, diet, and personal information for the animal's owner.
  • the molecular diagnostic test data may further include the type, collection date and place of the sample, test time, and test place.
  • the processor 134 predicts a combinatorial infection risk level COM of the pathogen in the target area using the internal infection risk level ⁇ 1 of the pathogen in the target area, the external infection risk level ⁇ 1 of the pathogen in the target area, and a first possibility of spreading ⁇ 1 in which the pathogen is to spread from the surrounding area to the target area.
  • the surrounding area may be (i) an area geographically adjacent to the target area or (ii) an area that is traffic-wise, economically, socially, and/or historically related to the target area.
  • A when A is "adjacent to" B, A may be adjacent to B not only geographically but also traffic-wise, economically, socially, and/or historically.
  • the processor134 determines the internal infection risk level ⁇ 1 of the pathogen in the target area from the molecular diagnostic test data D1 in the target area.
  • the processor 134 may determine the internal infection risk level ⁇ 1 of the pathogen in the target area using the positive result value (e.g., the positive number or positive rate) of the pathogen obtained from the molecular diagnostic test data D1 in the target area of Tables 1 to 3 and, optionally, the load of the pathogen.
  • the positive result value e.g., the positive number or positive rate
  • the processor 134 may determine the internal infection risk level ⁇ 1 of the pathogen in the target area further using pathogen-specific risk factors in the target area.
  • the pathogen-specific risk factors may include at least one of the transmissibility or the propagability, transmission speed and route, survival and/or proliferation conditions, mutation rate, lethality, incubation period, reinfection rate, animal-human cross infection, and/or drug resistance of the pathogen.
  • the transmissibility refers to the ability of the nucleic acid introduced into the cell by infection or artificial method to, after replication, form infectious particles or a complex equivalent thereto and propagate to separate cells.
  • Transmission route refers to the transmission path of the pathogen and may be divided into droplet transmission, air transmission, and contact transmission (direct or indirect).
  • Survival and/or proliferation conditions are factors for an environment in which pathogens may proliferate, including the humidity, temperature, or surface of the place where the pathogen is located. This pathogen-specific risk factors may be known information or may be information newly identified by the molecular diagnostic data of Tables 1 to 3.
  • the processor 134 may further use the preventive treatment factors of the target area to determine the internal infection risk level ⁇ 1 of the pathogen in the target area.
  • the preventive treatment factors may include the presence of a prophylaxis, the presence of a therapy, the efficacy of the prophylaxis, the efficacy of the therapy, and cure rate of the prophylactic therapy, and/or the level of quarantine for the target area for infection of the pathogen.
  • This preventive treatment factors may be known information or may be information newly identified by the molecular diagnostic data of Tables 1 to 3.
  • the presence of a prophylaxis may include the presence of a vaccine against the pathogen, the presence of a diagnostic kit for testing the pathogen, the number of molecular diagnostic tests per unit population, the number of molecular diagnostic tests per unit population relative to the positive rate, the prevalence of masks.
  • the presence of a therapy may include the presence or absence of a treatment facility, the number of treatment facilities per unit population (e.g., number of isolation treatment beds per unit population), and the number of medical personnel per unit population.
  • the processor 134 may determine the internal infection risk level ⁇ 1 of the pathogen in the target area by using pathogen-specific risk factors alone or preventive treatment factors alone for the target area.
  • the processor 340 may determine the internal infection risk level ⁇ 1 of the pathogen in the target area depending on a pathogen-specific risk factor, e.g., whether the pathogen is a class 1 transmissible disease pathogen or a class 2 transmissible disease pathogen, together with the positive result value of the particular pathogen and optionally the load of the pathogen.
  • a pathogen-specific risk factor e.g., whether the pathogen is a class 1 transmissible disease pathogen or a class 2 transmissible disease pathogen, together with the positive result value of the particular pathogen and optionally the load of the pathogen.
  • the internal infection risk level ⁇ 1 of the pathogen in the target area may be relatively high if the pathogen is the class 1 transmissible disease pathogen and may be relatively low if the pathogen is the class 2 transmissible disease pathogen.
  • the processor 134 determines the internal infection risk level ⁇ 1 of the pathogen in the target area using at least two of the positive result value of the pathogen in the target area and optionally, the load of the pathogen, the pathogen-specific risk factors and the preventive treatment factors, and the processor 134 may determine the internal infection risk level ⁇ 1 of the pathogen in the target area by comprehensively considering two or more determination factors.
  • the processor 134 may score, or divide into two or more infection risk level grades, the internal infection risk level of the pathogen for use in predicting the combinatorial infection risk level of the pathogen in the target area.
  • the processor 134 may score the internal infection risk level ⁇ 1 of the pathogen as an absolute value. For example, when the processor 134 scores the internal infection risk level ⁇ 1 of the pathogen in the target area using the positive rate, which is one of the positive result values of the pathogen, the processor 340 may determine "0.5" points for a positive rate of the specific pathogen of 2%, "1" point for a positive rate of the specific pathogen of 3.5%, and "2" points for a positive rate of the specific pathogen exceeding 5%.
  • the processor 340 may determine "0.5" points for a positive number of the specific pathogen not less than 1 and less than 50, "1" point for a positive number of the specific pathogen not less than 50 and less than 100, and "2" points for a positive number of the specific pathogen not less than 100.
  • the processor 134 may divide the internal infection risk level ⁇ 1 of the pathogen into two or more grades (stages).
  • the pathogen infection risk level may be classified into two stages, e.g., high (dangerous) and low (not dangerous), three stages, e.g., high, medium, and low, or N stages, e.g., risk I stage, risk II stage,..., risk N stage.
  • the processor 340 may divide it into the risk I stage when the positive rate of a specific pathogen is 2% to 3%, the risk II stage when the positive rate is 3% to 5 %, and the risk III stage when the positive rate is more than 5%.
  • the processor 340 may divide it into the risk I stage when the positive number of the specific pathogen is not less than 1 and less than 50, the risk II stage when the positive number is not less than 50 and less than 100, and the risk III stage when the positive number is not less than 100.
  • the communication unit 132 may receive the external infection risk level ⁇ 1 of the pathogen in the target area from the second terminal or another second server.
  • the processor 134 may predict the combinatorial infection risk level of the pathogen in the target area using the received external infection risk level ⁇ 1 of the pathogen in the target area.
  • the communication unit 132 may receive both the molecular diagnostic test data D2 in the surrounding area for the pathogen and the external infection risk level ⁇ 1 of the pathogen in the target area, from the second terminal or another second server.
  • the processor 134 may predict the combinatorial infection risk level of the pathogen in the target area using the received external infection risk level ⁇ 1 of the pathogen in the target area.
  • the processor 134 may calculate the external infection risk level ⁇ 1 of the pathogen in the target area from the molecular diagnostic test data D2 in the surrounding area and compare it with the received external infection risk level ⁇ 1 of the pathogen in the target area. When the two are different, the processor 134 may use the average or selected one of the two.
  • the communication unit 132 may receive only the molecular diagnostic test data D2 in the surrounding area for the pathogen from the second terminal or another second server.
  • the processor 134 determines the external infection risk level ⁇ 1 of the pathogen in the target area from the molecular diagnostic test data D2 in the surrounding area.
  • the external infection risk level ⁇ 1 of the pathogen in the target area is the infection risk level of the pathogen in the surrounding area.
  • the external infection risk level ⁇ 1 of the pathogen in the target area may be the internal infection risk level ⁇ 2 of the pathogen in the surrounding area.
  • the external infection risk level ⁇ 1 of the pathogen in the target area may be the combinatorial infection risk level COM2 of the pathogen in the surrounding area.
  • the external infection risk level ⁇ 1 of the pathogen may be the combinatorial infection risk level COM2 of the pathogen in the surrounding area, predicted using the internal infection risk level ⁇ 2 of the pathogen in the surrounding area, the external infection risk level ⁇ 2 of the pathogen in another surrounding area for the surrounding area, and the second possibility of spreading ⁇ 2 in which the pathogen is to spread from the other surrounding area for the surrounding area to the surrounding area.
  • the external infection risk level ⁇ 1 of the pathogen in the target area may be the sum of the internal infection risk levels ⁇ 2 of the pathogen in the surrounding areas or the sum of the combinatorial infection risk levels COM2 of the pathogen in the surrounding areas.
  • ⁇ 1 ⁇ 21+ ⁇ 22+...+ ⁇ 2n
  • ⁇ 1 COM21+COM22+...+ COM2n
  • the processor 134 may determine the infection risk level of the pathogen in the surrounding area in the same manner as (i) determining the internal infection risk level ⁇ 1 of the pathogen in the target area or (ii) predicting the combinatorial infection risk level COM1 of the pathogen in the target area.
  • the processor 134 may calculate the external infection risk level ⁇ 2 of the pathogen in the surrounding area in the same manner as calculating the external infection risk level ⁇ 1 of the pathogen in the target area.
  • the processor 134 may score the first possibility of spreading ⁇ 1 of the pathogen or divide it into two or more grades to be utilized in predicting the combinatorial infection risk level COM1 of the pathogen in the target area.
  • the processor 134 may determine the first possibility of spreading ⁇ 1 using the distance between the surrounding area and the target area. When the surrounding areas are determined according to the distances from the target area to concentric circles as shown in FIG. 4, the processor 134 may classify the first possibility of spreading ⁇ 1 as a specific score or specific grade using the distance between a specific surrounding area and the target area.
  • the processor 134 may classify the first possibility of spreading ⁇ 1 as grade I when the distance between the specific surrounding area and the target area is r1, as grade II when the distance between the specific surrounding area and the target area is r2 (r2>r1), and as grade III when the distance between the specific surrounding area and the target area is r3 (r3>r1).
  • the distance between the surrounding area and the target area may be a geographic, traffic-wise, economic, social, and/or historical distance from the first area.
  • the reason why the processor 134 considers the traffic-wise, economic, social, and/or historical distance between the surrounding area and the target area in determining the first possibility of spreading ⁇ 1 using the distance between the surrounding area and the target area is that the spread of a specific pathogen is possible between subjects in the areas that are sufficiently apart from each other in geographical distance but have active traffic, economic, social, and/or historical exchanges.
  • the surrounding area may include a single geographical area or two or more areas geographically separated from each other.
  • the area geographically adjacent to the target area may be an area adjacent to the target area in terms of administrative district, an area separated by an infectious disease management entity for infectious disease management, or an area separated by a doctor association or organization considering the degree of proximity of hospitals.
  • An area that is traffic-wise, economically, socially, and/or historically related to the target area may be an area where traffic, human or material exchanges with the target area are active.
  • the target area may have large-scale factory facilities and workers in the facilities move to a specific area by shuttle buses
  • the specific area may be the surrounding area although not geographically adjacent to the target area.
  • the surrounding area may correspond to an area that is not geographically adjacent to the target area but is connected with the target area through an infrastructure through which physical exchange is possible, such as a highway.
  • an infrastructure through which physical exchange is possible
  • the surrounding area may be determined relative to the target area.
  • the surrounding area may be subdivided into narrow units, and if a specific area with insufficient medical infrastructure has few inspection institutions, the range covered by the surrounding area may be widened, and the surrounding area may be subdivided into wider units.
  • the target area may be a first hospital and the surrounding area may be a second hospital located within a predetermined radius from the first hospital as shown in FIG. 4.
  • the target area may be a specific administrative area, and the surrounding area may be another administrative area adjacent to the specific administrative area.
  • the target area may be a specific country, and the surrounding area may be another country adjacent to the specific country.
  • the target area may be a specific continent, and the surrounding area may be another continent adjacent to the specific continent.
  • the processor 134 may calculate the second possibility of spreading ⁇ 2 of the pathogen in the surrounding area in the same manner as calculating the first possibility of spreading ⁇ 1 of the pathogen in the target area.
  • the processor 134 may determine, every first period T1, the internal infection risk level ⁇ 1 of the pathogen in the target area from the molecular diagnostic test data D1 in the target area and predict, every second period T2 different from the first period T1, the combinatorial infection risk level COM1 of the pathogen in the target area using the internal infection risk level ⁇ 1 of the pathogen in the target area, the external infection risk level ⁇ 1 of the pathogen in the target area, and the first possibility of spreading ⁇ 1.
  • the first period T1 may be shorter than the second period T2.
  • the processor 134 may determine the internal infection risk level ⁇ 1 of the pathogen in the target area, from the molecular diagnostic test data D1 in the target area, in a relatively shorter period. Further, the processor 134 may determine the combinatorial infection risk level COM1 of the pathogen in the target area, in a relatively long period.
  • the first period T1 may be one day
  • the second period T2 may be one week.
  • the processor 134 may determine the internal infection risk level ⁇ 1 of the pathogen in the target area every day and predict the combinatorial infection risk level COM1 of the pathogen in the target area every week.
  • the processor 134 may predict, for one week, the combinatorial infection risk level COM1 of the pathogen in the target area using, e.g., the average, minimum value, or maximum value of the internal infection risk levels ⁇ 1 of the pathogen in the target area determined every day for one week.
  • the first period T1 may be longer than the second period T1, and as another example, the first period T1 may be the same as the second period T2.
  • the processor 134 may predict the combinatorial infection risk level COM1 of the pathogen in the target area using the internal infection risk level ⁇ 1 of the pathogen in the target area for the same period.
  • the processor 134 may predict the combinatorial infection risk level COM1 of the pathogen in the target area by giving the same or different weights to the internal infection risk level ⁇ 1 of the pathogen in the target area, the external infection risk level ⁇ 1 of the pathogen in the target area, and the first possibility of spreading ⁇ 1.
  • the combinatorial infection risk level COM1 of the pathogen in the target area may be represented as in Equation 1 below.
  • the combinatorial infection risk level COM1 of the pathogen in the target area may be represented as in Equation 2.
  • the combinatorial infection risk level COM1 of the pathogen in the target area may be calculated using Equation 2, i.e., the weighted average.
  • influenza target area (Gyeonggi-do), surrounding area (Seoul) January February March ⁇ 1 1 2 1 ⁇ 1 1 1 2 ⁇ 1 1 1 1 COM1 0.50 0.77 0.57
  • the combinatorial infection risk level COM1 of the pathogen in the target area may be represented as in Equation 3.
  • influenza target area (Gyeonggi-do), surrounding area (Seoul) January February March ⁇ 1 1 2 1 ⁇ 1 1 1 2 ⁇ 1 1 1 1 COM1 2 2.8 2.2
  • the processor 134 may calculate the combinatorial infection risk level COM2 of the pathogen in the surrounding area from the molecular diagnostic test data D2 in the surrounding area, using one of Equations 1 to 3.
  • the processor 134 may predict the combinatorial infection risk level COM1 of the pathogen in the target area using the internal infection risk level ⁇ 1 of the pathogen in the target area, the external infection risk level ⁇ 1 of the pathogen in the target area, and the first possibility of spreading ⁇ 1 in which the pathogen is to spread from the surrounding area to the target area, based on an artificial neural network.
  • the processor 134 may determine the combinatorial infection risk level COM of the pathogen in the target area although no separate determination element is presented.
  • the processor 134 may determine at least one of the internal infection risk level ⁇ 2 of the pathogen in the surrounding area, the external infection risk level ⁇ 2 of the pathogen in the surrounding area, the second possibility of spreading ⁇ 2 in which the pathogen is to spread from another surrounding area to the surrounding area, or the combinatorial infection risk level COM2 of the pathogen in the surrounding area, based on the artificial neural network.
  • the server for predicting the infection risk level of a pathogen in a target area using molecular diagnostic test data may predict the infection risk level of a specific area using molecular diagnostic test data in other areas geographically adjacent to, or related to, the specific area, as well as molecular diagnostic test data obtained through a molecular diagnostic test in the specific area, or considering the infection risk level of the pathogen in the other areas.
  • a configuration of the management server 130 that predicts the infection risk level of the pathogen in the target area using molecular diagnostic test data according to an embodiment has been described above with reference to FIGS. 2 to 5.
  • a method for predicting the infection risk level of a pathogen in a target area using molecular diagnostic test data according to another embodiment is described below with reference to FIG. 6.
  • FIG. 6 is a flowchart of a method for predicting the infection risk level of a pathogen in a target area using molecular diagnostic test data according to another embodiment.
  • a method 200 for predicting an infection risk level of a pathogen in a target area using molecular diagnostic test data includes the step S210 of receiving data in the target data, the step S220 of receiving data in a surrounding area, the step S230 of determining an internal infection risk level of the pathogen, the step S240 of determining an external infection risk level of the pathogen, and the step S250 of predicting a combinatorial infection risk level of the pathogen.
  • the method 200 for predicting the infection risk level of the pathogen in the target area may be performed by the management server 130 described with reference to FIG. 1 or may be performed by the user terminal 120 and the inspection server 140. Some of the steps may be performed by either the user terminal 120 or the inspection server 140 while the others by the other.
  • the above-described steps may be performed sequentially, repeated for a specific step, or performed in a different order.
  • the management server 130 performs the method 200 for predicting the infection risk level of the pathogen in the target area using the molecular diagnostic test data according to an embodiment, the disclosure is not limited thereto.
  • the step S210 of receiving the data in the target area receives, from the first terminal or another first server, molecular diagnostic test data D1 in the target area for the pathogen.
  • the first terminal may be the user terminal 120 located in the target area described above with reference to FIG. 1, and the other first server may be the above-described inspection server 140 located in the target area.
  • the step S220 of receiving the data in the surrounding area receives at least one of the molecular diagnostic test data D2 in the surrounding area or the external infection risk level of the pathogen in the target area, for the pathogen, from the second terminal or another second server.
  • the external infection risk level of the pathogen may be the infection risk level of the pathogen in the surrounding area, as described above.
  • the second terminal may be the user terminal 120 located in the surrounding area described above with reference to FIG. 1, and the other second server may be the above-described inspection server 140 located in the surrounding area.
  • the step S220 of receiving the data in the surrounding area may receive both the molecular diagnostic test data D2 in the surrounding area and the external infection risk level ⁇ 1 of the pathogen in the target area, for the pathogen, from the second terminal or another second server.
  • the step S220 of receiving the data in the surrounding area may receive only the molecular diagnostic test data D2 in the surrounding area for the pathogen from the second terminal or another second server.
  • the step S220 of receiving only the data in the surrounding area may receive the external infection risk level ⁇ 1 of the pathogen in the target area from the second terminal or another second server.
  • the molecular diagnostic test data may include result data obtained from a nucleic acid amplification reaction on the pathogen to be detected in the sample obtained from the subject (e.g., a human patient). Further, the molecular diagnostic test data may include medical data for the subject, but the present invention is not limited thereto.
  • the result data may include the name of the pathogen to be detected and information indicating whether the pathogen is positive/negative. If necessary, the result data may optionally further include the cycle threshold (Ct) value in the nucleic acid amplification reaction of the pathogen.
  • Ct cycle threshold
  • the molecular diagnostic test data may further include information regarding the subject.
  • the molecular diagnostic test data may further include personal information for the subject.
  • the subject's personal information may include at least one of the subject's nationality, age, sex, blood type, location (residence), eating habits, working environment, family history, movement information, smoking status, drinking habits, exercise, pregnancy status, and specific information.
  • the step S230 of determining the internal infection risk level of the pathogen determines the internal infection risk level ⁇ 1 of the pathogen in the target area from the molecular diagnostic test data D1 in the target area.
  • the internal infection risk level ⁇ 1 of the pathogen in the target area it is possible to determine the internal infection risk level ⁇ 1 of the pathogen in the target area using the positive result value (e.g., the positive number or positive rate) of the pathogen obtained from the molecular diagnostic test data D1 in the target area of Tables 1 to 3 and, optionally, the load of the pathogen.
  • the positive result value e.g., the positive number or positive rate
  • the pathogen-specific risk factors may include at least one of the transmissibility, transmission speed and route, survival and/or proliferation conditions, mutation rate, lethality, incubation period, reinfection rate, animal-human cross infection, and/or drug resistance of the pathogen.
  • the transmissibility refers to the ability of the nucleic acid introduced into the cell by infection or artificial method to, after replication, form infectious particles or a complex equivalent thereto and propagate to separate cells.
  • Transmission route refers to the transmission path of the pathogen and may be divided into droplet transmission, air transmission, and contact transmission (direct or indirect).
  • Survival and/or proliferation conditions are factors for an environment in which pathogens may proliferate, including the humidity, temperature, or surface of the place where the pathogen is located. This pathogen-specific risk factors may be known information or may be information newly identified by the molecular diagnostic data of Tables 1 to 3.
  • the preventive treatment factors may include the presence of a prophylaxis, the presence of a therapy, the efficacy of the prophylaxis, the efficacy of the therapy, and cure rate of the prophylactic therapy, and/or the level of quarantine for the target area for infection of the pathogen.
  • This preventive treatment factors may be known information or may be information newly identified by the molecular diagnostic data of Tables 1 to 3.
  • the presence of a prophylaxis may include the presence of a vaccine against the pathogen, the presence of a diagnostic kit for testing the pathogen, the number of molecular diagnostic tests per unit population, the number of molecular diagnostic tests per unit population relative to the positive rate, the prevalence of masks.
  • the presence of a therapy may include the presence or absence of a treatment facility, the number of treatment facilities per unit population (e.g., number of isolation treatment beds per unit population), and the number of medical personnel per unit population.
  • determining the internal infection risk level ⁇ 1 of the pathogen in the target area it is possible to determine the internal infection risk level ⁇ 1 of the pathogen in the target area depending on a pathogen-specific risk factor, e.g., whether the pathogen is a class 1 transmissible disease pathogen or a class 2 transmissible disease pathogen, together with the positive result value of the particular pathogen and optionally the load of the pathogen.
  • a pathogen-specific risk factor e.g., whether the pathogen is a class 1 transmissible disease pathogen or a class 2 transmissible disease pathogen, together with the positive result value of the particular pathogen and optionally the load of the pathogen.
  • the internal infection risk level ⁇ 1 of the pathogen in the target area may be relatively high if the pathogen is the class 1 transmissible disease pathogen and may be relatively low if the pathogen is the class 2 transmissible disease pathogen.
  • step S230 of determining the internal infection risk level of the pathogen when determining the internal infection risk level ⁇ 1 of the pathogen in the target area using at least two of the positive result value of the pathogen in the target area and optionally, the load of the pathogen, the pathogen-specific risk factors and the preventive treatment factors, it is possible to determine the internal infection risk level ⁇ 1 of the pathogen in the target area by comprehensively considering two or more determination factors.
  • the step S230 of determining the internal infection risk level of the pathogen may score, or divide into two or more infection risk level grades, the internal infection risk level of the pathogen for use in predicting the combinatorial infection risk level of the pathogen in the target area.
  • the internal infection risk level ⁇ 1 of the pathogen may be scored as an absolute value. For example, when the processor 134 scores the internal infection risk level ⁇ 1 of the pathogen in the target area using the positive rate, which is one of the positive result values of the pathogen, the processor 340 may determine "0.5" points for a positive rate of the specific pathogen of 2%, "1" point for a positive rate of the specific pathogen of 3.5%, and "2" points for a positive rate of the specific pathogen exceeding 5%.
  • the processor 340 may determine "0.5" points for a positive number of the specific pathogen not less than 1 and less than 50, "1" point for a positive number of the specific pathogen not less than 50 and less than 100, and "2" points for a positive number of the specific pathogen not less than 100.
  • the step S230 of determining the internal infection risk level of the pathogen may divide the internal infection risk level ⁇ 1 of the pathogen into two or more grades (stages).
  • the pathogen infection risk level may be classified into two stages, e.g., high (dangerous) and low (not dangerous), three stages, e.g., high, medium, and low, or N stages, e.g., risk I stage, risk II stage,..., risk N stage.
  • the processor 340 may divide it into the risk I stage when the positive rate of a specific pathogen is 2% to 3%, the risk II stage when the positive rate is 3% to 5 %, and the risk III stage when the positive rate is more than 5%.
  • the processor 134 determines the infection risk level of the pathogen in the target area using the positive number of the pathogen
  • the processor 340 may divide it into the risk I stage when the positive number of the specific pathogen is not less than 1 and less than 50, the risk II stage when the positive number is not less than 50 and less than 100, and the risk III stage when the positive number is not less than 100.
  • the step S240 of determining the external infection risk level of the pathogen determines the external infection risk level of the pathogen using the received molecular diagnostic test data D2 in the surrounding area or the external infection risk level of the pathogen in the target area.
  • the step S220 of receiving the data in the surrounding area may receive both the molecular diagnostic test data D2 in the surrounding area and the external infection risk level ⁇ 1 of the pathogen in the target area, for the pathogen, from the second terminal or another second server.
  • the step S240 of determining the external infection risk level of the pathogen may calculate the external infection risk level ⁇ 1 of the pathogen in the target area from the molecular diagnostic test data D2 in the surrounding area and compare it with the received external infection risk level ⁇ 1 of the pathogen in the target area.
  • the step S240 of determining the external infection risk level of the pathogen may use the average or selected one of the two.
  • the step S220 of receiving the data in the surrounding area may receive only the molecular diagnostic test data D2 in the surrounding area for the pathogen from the second terminal or another second server.
  • the step S240 of determining the external infection risk level of the pathogen may calculate the external infection risk level ⁇ 1 of the pathogen in the target area from the molecular diagnostic test data D2 in the surrounding area.
  • the external infection risk level ⁇ 1 of the pathogen in the target area is the infection risk level of the pathogen in the surrounding area.
  • the external infection risk level ⁇ 1 of the pathogen in the target area may be the internal infection risk level ⁇ 2 of the pathogen in the surrounding area.
  • the external infection risk level ⁇ 1 of the pathogen in the target area may be the combinatorial infection risk level COM2 of the pathogen in the surrounding area.
  • the external infection risk level ⁇ 1 of the pathogen may be the combinatorial infection risk level COM2 of the pathogen in the surrounding area, predicted using the internal infection risk level ⁇ 2 of the pathogen in the surrounding area, the external infection risk level ⁇ 2 of the pathogen in another surrounding area for the surrounding area, and the second possibility of spreading ⁇ 2 in which the pathogen is to spread from the other surrounding area for the surrounding area to the surrounding area.
  • the external infection risk level ⁇ 1 of the pathogen in the target area may be the sum of the internal infection risk levels ⁇ 2 of the pathogen in the surrounding areas or the sum of the combinatorial infection risk levels COM2 of the pathogen in the surrounding areas.
  • ⁇ 1 ⁇ 21+ ⁇ 22+...+ ⁇ 2n
  • ⁇ 1 COM21+COM22+...+ COM2n
  • the step S240 of determining the external infection risk level of the pathogen may determine the infection risk level of the pathogen in the surrounding area in the same manner as (i) determining the internal infection risk level ⁇ 1 of the pathogen in the target area or (ii) predicting the combinatorial infection risk level COM1 of the pathogen in the target area.
  • the processor 134 may calculate the external infection risk level ⁇ 2 of the pathogen in the surrounding area in the same manner as calculating the external infection risk level ⁇ 1 of the pathogen in the target area.
  • the step S250 of predicting the combinatorial infection risk level of the pathogen predicts a combinatorial infection risk level COM of the pathogen in the target area using the internal infection risk level ⁇ 1 of the pathogen in the target area, the external infection risk level ⁇ 1 of the pathogen, and a first possibility of spreading ⁇ 1 in which the pathogen is to spread from the surrounding area to the target area.
  • the step S250 of predicting the combinatorial infection risk level of the pathogen may score the first possibility of spreading ⁇ 1 of the pathogen or divide it into two or more grades to be utilized in predicting the combinatorial infection risk level COM1 of the pathogen in the target area.
  • the step S250 of predicting the combinatorial infection risk level of the pathogen may determine the first possibility of spreading ⁇ 1 using the distance between the surrounding area and the target area.
  • the processor 134 may classify the first possibility of spreading ⁇ 1 as a specific score or specific grade using the distance between a specific surrounding area and the target area.
  • the step S250 of predicting the combinatorial infection risk level of the pathogen may classify the first possibility of spreading ⁇ 1 as grade I when the distance between the specific surrounding area and the target area is r1, as grade II when the distance between the specific surrounding area and the target area is r2 (r2>r1), and as grade III when the distance between the specific surrounding area and the target area is r3 (r3>r1).
  • the surrounding area may be (i) an area geographically adjacent to the target area or (ii) an area that is traffic-wise, economically, socially, and/or historically related to the target area.
  • A when A is "adjacent to" B, A may be adjacent to B not only geographically but also traffic-wise, economically, socially, and/or historically.
  • the distance between the surrounding area and the target area may be a geographic, traffic-wise, economic, social, and/or historical distance from the first area.
  • the reason for considering the traffic-wise, economic, social, and/or historical distance between the surrounding area and the target area in determining the first possibility of spreading ⁇ 1 using the distance between the surrounding area and the target area is that the spread of a specific pathogen is possible between subjects in the areas that are sufficiently apart from each other in geographical distance but have active traffic, economic, social, and/or historical exchanges.
  • the surrounding area may include a single geographical area or two or more areas geographically separated from each other.
  • the area geographically adjacent to the target area may be an area adjacent to the target area in terms of administrative district, an area separated by an infectious disease management entity for infectious disease management, or an area separated by a doctor association or organization considering the degree of proximity of hospitals.
  • An area that is traffic-wise, economically, socially, and/or historically related to the target area may be an area where traffic, human or material exchanges with the target area are active.
  • the target area may have large-scale factory facilities and workers in the facilities move to a specific area by shuttle buses
  • the specific area may be the surrounding area although not geographically adjacent to the target area.
  • the surrounding area may correspond to an area that is not geographically adjacent to the target area but is connected with the target area through an infrastructure through which physical exchange is possible, such as a highway.
  • an infrastructure through which physical exchange is possible
  • the surrounding area may be determined relative to the target area.
  • the surrounding area may be subdivided into narrow units, and if a specific area with insufficient medical infrastructure has few inspection institutions, the range covered by the surrounding area may be widened, and the surrounding area may be subdivided into wider units.
  • the target area may be a first hospital and the surrounding area may be a second hospital located within a predetermined radius from the first hospital as shown in FIG. 4.
  • the target area may be a specific administrative area, and the surrounding area may be another administrative area adjacent to the specific administrative area.
  • the target area may be a specific country, and the surrounding area may be another country adjacent to the specific country.
  • the target area may be a specific continent, and the surrounding area may be another continent adjacent to the specific continent.
  • the step S250 of predicting the combinatorial infection risk level of the pathogen may calculate the second possibility of spreading ⁇ 2 of the pathogen in the surrounding area in the same manner as calculating the first possibility of spreading ⁇ 1 of the pathogen in the target area.
  • the step S250 of predicting the combinatorial infection risk level of the pathogen may determine, every first period T1, the internal infection risk level ⁇ 1 of the pathogen in the target area from the molecular diagnostic test data D1 in the target area and predict, every second period T2 different from the first period T1, the combinatorial infection risk level COM1 of the pathogen in the target area using the internal infection risk level ⁇ 1 of the pathogen in the target area, the external infection risk level ⁇ 1 of the pathogen in the target area, and the first possibility of spreading ⁇ 1.
  • the first period T1 may be shorter than the second period T2.
  • the processor 134 may determine the internal infection risk level ⁇ 1 of the pathogen in the target area, from the molecular diagnostic test data D1 in the target area, in a relatively shorter period. Further, the processor 134 may determine the combinatorial infection risk level COM1 of the pathogen in the target area, in a relatively long period.
  • the first period T1 may be one day
  • the second period T2 may be one week.
  • the processor 134 may determine the internal infection risk level ⁇ 1 of the pathogen in the target area every day and predict the combinatorial infection risk level COM1 of the pathogen in the target area every week.
  • the processor 134 may predict, for one week, the combinatorial infection risk level COM1 of the pathogen in the target area using, e.g., the average, minimum value, or maximum value of the internal infection risk levels ⁇ 1 of the pathogen in the target area determined every day for one week.
  • the first period T1 may be longer than the second period T1, and as another example, the first period T1 may be the same as the second period T2.
  • the processor 134 may predict the combinatorial infection risk level COM1 of the pathogen in the target area using the internal infection risk level ⁇ 1 of the pathogen in the target area for the same period.
  • the step S250 of predicting the combinatorial infection risk level of the pathogen may predict the combinatorial infection risk level COM1 of the pathogen in the target area by giving the same or different weights to the internal infection risk level ⁇ 1 of the pathogen in the target area, the external infection risk level ⁇ 1 of the pathogen in the target area, and the first possibility of spreading ⁇ 1.
  • the combinatorial infection risk level COM1 of the pathogen in the target area may be represented as in Equation 1 above.
  • the combinatorial infection risk level COM1 of the pathogen in the target area may be represented as in Equation 2 above.
  • the combinatorial infection risk level COM1 of the pathogen in the target area may be calculated using Equation 2, ie, the weighted average.
  • the combinatorial infection risk level COM1 of the pathogen in the target area may be represented as in Equation 3 as described above.
  • the step S250 of predicting the combinatorial infection risk level of the pathogen may calculate the combinatorial infection risk level COM2 of the pathogen in the surrounding area from the molecular diagnostic test data D2 in the surrounding area, using one of Equations 1 to 3.
  • the step S250 of predicting the combinatorial infection risk level of the pathogen may predict the combinatorial infection risk level COM1 of the pathogen in the target area using the internal infection risk level ⁇ 1 of the pathogen in the target area, the external infection risk level ⁇ 1 of the pathogen in the target area, and the first possibility of spreading ⁇ 1 in which the pathogen is to spread from the surrounding area to the target area, based on an artificial neural network.
  • the processor 134 may determine the combinatorial infection risk level COM of the pathogen in the target area although no separate determination element is presented.
  • the step S250 of predicting the combinatorial infection risk level of the pathogen may determine at least one of the internal infection risk level ⁇ 2 of the pathogen in the surrounding area, the external infection risk level ⁇ 2 of the pathogen in the surrounding area, the second possibility of spreading ⁇ 2 in which the pathogen is to spread from another surrounding area to the surrounding area, or the combinatorial infection risk level COM2 of the pathogen in the surrounding area, based on the artificial neural network.
  • the method for predicting the infection risk level of a pathogen in a target area using molecular diagnostic test data may predict the infection risk level of a specific area using molecular diagnostic test data in other areas geographically adjacent to, or related to, the specific area, as well as molecular diagnostic test data obtained through a molecular diagnostic test in the specific area, or considering the infection risk level of the pathogen in the other areas.
  • a server comprises a memory and a processor and performs a method 200 for predicting an infection risk level of a pathogen in a target area using molecular diagnostic test data according to another embodiment described with reference to FIG. 6 using one or more programs stored in the memory and configured to be executed by the processor and includes instructions to perform the step S210 of receiving molecular diagnostic test data D1 in the target area for the pathogen the step S220 of receiving molecular diagnostic test data D2 in a surrounding area, which is likely to spread the pathogen to the target area, or an external infection risk level ⁇ 1 of the pathogen in the target area , the step S230 of determining an internal infection risk level ⁇ 1 of the pathogen in the target area from the molecular diagnostic test data D1 in the target area, the step S240 of determining the external infection risk level ⁇ 1 of the pathogen in the target area, the external infection risk level ⁇ 1 of the pathogen in the target area being an infection risk level of the pathogen in the surrounding area, and the step S250 of predicting a combinatorial
  • the external infection risk level ⁇ 1 of the pathogen in the target area may be determined from the received molecular diagnostic test data D2 in the surrounding area or be the received external infection risk level of the pathogen in the target area.
  • a non-transitory computer-readable storage medium storing, in a computing device, instructions executed by one or more processors to enable the one or more processors to perform a method for predicting an infection risk level of a pathogen in a target area using molecular diagnostic test data
  • the instructions executed by the one or more processors to enable the computing device to perform the step S210 of receiving molecular diagnostic test data D1 in the target area for the pathogen, the step S220 of receiving molecular diagnostic test data D2 in a surrounding area, which is likely to spread the pathogen to the target area, or an external infection risk level ⁇ 1 of the pathogen in the target area
  • the external infection risk level ⁇ 1 of the pathogen in the target area may be determined from the received molecular diagnostic test data D2 in the surrounding area or be the received external infection risk level of the pathogen in the target area.
  • the instructions may perform other steps or operations included in the method for predicting the infection risk level of the pathogen in the target area using the molecular diagnostic test data according to other embodiments described above in connection with FIG. 6.
  • FIG. 7 is a flowchart of a method for providing the infection risk level of a pathogen in a target area using molecular diagnostic test data according to another embodiment.
  • a method 300 for providing an infection risk level of a pathogen in a target area using molecular diagnostic test data includes the step S310 of receiving data in the target area, the step S320 of receiving data in a surrounding area, and the step S330 of providing a combinatorial infection risk level of the pathogen.
  • the method 300 for providing the infection risk level of the pathogen in the target area may be performed by the management server 130 described with reference to FIG. 1 or may be performed by the user terminal 120 and the inspection server 140. Some of the steps may be performed by either the user terminal 120 or the inspection server 140 while the others by the other.
  • the step S310 of receiving the data in the target area may be substantially the same as the step S210 of receiving the data in the target area described above in connection with FIG. 6.
  • the step S320 of receiving the data in the surrounding area may be substantially the same as the step S220 of receiving the data in the surrounding area described above in connection with FIG. 6.
  • the step S330 of providing the combinatorial infection risk level of the pathogen may provide the terminal or another server with the combinatorial infection risk level of the pathogen, predicted in the step S250 of predicting the combinatorial infection risk level of the pathogen, described above in connection with FIG. 6.
  • the step S330 of providing the combinatorial infection risk level of the pathogen may provide the terminal or the other server with the combinatorial infection risk level COM of the pathogen in the target area predicted using the internal infection risk level ⁇ 1 of the pathogen in the target area, the external infection risk level ⁇ 1 of the pathogen in the target area, and a first possibility of spreading ⁇ 1 in which the pathogen is to spread from the surrounding area to the target area.
  • the terminal may be the user terminal 120 described above with reference to FIG. 1, and the other server may be the inspection server 140 described above with reference to FIG. 1.
  • the method for providing the infection risk level of the pathogen in the target area using the molecular diagnostic test data may maximally reflect the infection characteristics of the pathogen when predicting the infection risk level of the pathogen in a specific area and provide the infection risk level of the pathogen in the specific area with enhanced accuracy.
  • the examples described herein relate to the use of a management server for predicting an infection risk level of a pathogen in a target area using molecular diagnostic test data for implementing the techniques described herein.
  • the techniques are performed by the management server in response to the processor executing one or more sequences of one or more instructions included in the memory.
  • the instructions may be read from other machine-readable medium, such as a storage medium, to the memory.
  • the execution of the instruction sequence included in the memory enables the process steps described herein to be carried out.
  • hardwired circuitry along with software instructions may be used instead of the software instructions so as to implement the examples described herein.
  • the described examples are not limited to any specific combination of the hardware circuitry and software.

Abstract

The disclosure relates to a server for predicting the infection risk level of pathogen in a target area using molecular diagnostic test data, a method therefor, and a non-transitory computer-readable storage medium thereof.

Description

SERVER AND METHOD FOR PREDICTING INFECTION RISK LEVEL OF PATHOGEN IN TARGET AREA USING MOLECULAR DIAGNOSTIC TEST DATA, AND NON-TRANSITORY COMPUTER-READABLE STORAGE MEDIUM
The disclosure relates to a server for predicting the infection risk level of pathogen in a target area using molecular diagnostic test data, a method therefor, and a non-transitory computer-readable storage medium thereof.
Infections caused by various pathogens, such as respiratory, digestive, and sexually transmitted diseases that occur in the human body may spread through various infection routes.
It is very important to control these infections and to identify the causative pathogen for proper patient management. Pathogen testing for most infections has grown rapidly with the development of molecular diagnostic technology, especially real-time polymerase chain reaction (PCR). Real-time PCR is more sensitive than traditional methods and may detect a variety of pathogens simultaneously and quickly.
Recently, a real-time PCR test is essential to quickly identify the infection of SARS CoV-2, a respiratory virus that is prevalent around the world and to minimize its spread.
Not only does it provide information regarding the presence or absence of pathogens of interest in a specific subject (whether infected or not) and the infection rate of the pathogens of interest in a population, but molecular diagnostic test data obtained by such a real-time PCR test also helps identify mutations of pathogens of interest or enhance test reagents and methods.
Due to the nature of the infection with a pathogen, when a specific subject in a specific area is infected with a specific pathogen, the risk of infection with the pathogen also increases for subjects in other areas geographically adjacent to or related to the specific area.
However, the level of risk of infection with a pathogen in a specific area is predicted based only on molecular diagnostic test data in the specific area. The method for predicting the infection risk level of a pathogen in a specific area does not reflect the infection characteristics of the pathogen because it does not use molecular diagnostic test data for other areas or consider the infection risk level of the pathogen in other areas.
Accordingly, there is a need for a device and method for predicting the infection risk level of a pathogen in a specific area using molecular diagnostic test data in other areas adjacent to the specific area or considering the infection risk level of the pathogen in the other areas.
According to the disclosure, there is provided a server and method for predicting the infection risk level of a pathogen in a specific area using molecular diagnostic test data in other areas geographically adjacent to, or related to, the specific area, as well as molecular diagnostic test data obtained through a molecular diagnostic test in the specific area, or considering the infection risk level of the pathogen in the other areas.
According to an embodiment, there is provided a server connected with a communication network and predicting an infection risk level of a pathogen in a target area using molecular diagnostic test data.
The server comprises a communication unit receiving the molecular diagnostic test data for the pathogen in the target area and receiving at least one of molecular diagnostic test data for the pathogen in a surrounding area which is likely to spread the pathogen to the target area or an external infection risk level of the pathogen in the target area, the external infection risk level of the pathogen being an infection risk level of the pathogen in the surrounding area, and a processor determining an internal infection risk level of the pathogen in the target area from the molecular diagnostic test data in the target area, determining the external infection risk level of the pathogen in the target area from the molecular diagnostic test data in the surrounding area when only the molecular diagnostic test data in the surrounding area of the molecular diagnostic test data in the surrounding area or the external infection risk level of the pathogen in the target area is received, and predicting a combinatorial infection risk level of the pathogen in the target area using the internal infection risk level of the pathogen in the target area, the external infection risk level of the pathogen, and a first possibility of spreading in which the pathogen is to spread from the surrounding area to the target area.
According to an embodiment, there is provided a method for predicting an infection risk level of a pathogen in a target area using molecular diagnostic test data.
The method comprises receiving the molecular diagnostic test data in the target area for the pathogen, receiving at least one of molecular diagnostic test data in a surrounding area which is likely to spread the pathogen to the target area or an external infection risk level of the pathogen in the target area, the external infection risk level of the pathogen in the target area being an infection risk level of the pathogen in the surrounding area, determining an internal infection risk level of the pathogen in the target area from the molecular diagnostic test data in the target area, determining the external infection risk level of the pathogen in the target area from the molecular diagnostic test data in the surrounding area when only the molecular diagnostic test data in the surrounding area of the molecular diagnostic test data in the surrounding area or the external infection risk level of the pathogen in the target area is received; and predicting a combinatorial infection risk level of the pathogen in the target area using the internal infection risk level of the pathogen in the target area, the external infection risk level of the pathogen in the target area, and a first possibility of spreading in which the pathogen is to spread from the surrounding area to the target area.
According to an embodiment, there is provided a non-transitory computer-readable storage medium storing, in a computing device, instructions executed by one or more processors to enable the one or more processors to perform a method for predicting an infection risk level of a pathogen in a target area using molecular diagnostic test data.
The instructions stored in the computing device perform receiving the molecular diagnostic test data in the target area for the pathogen, receiving at least one of molecular diagnostic test data in a surrounding area which is likely to spread the pathogen to the target area or an external infection risk level of the pathogen in the target area, the external infection risk level of the pathogen in the target area being an infection risk level of the pathogen in the surrounding area and determining an internal infection risk level of the pathogen in the target area from the molecular diagnostic test data in the target area,
determining the external infection risk level of the pathogen in the target area from the molecular diagnostic test data in the surrounding area when only the molecular diagnostic test data in the surrounding area of the molecular diagnostic test data in the surrounding area or the external infection risk level of the pathogen in the target area is received; and predicting a combinatorial infection risk level of the pathogen in the target area using the internal infection risk level of the pathogen in the target area, the external infection risk level of the pathogen in the target area, and a first possibility of spreading in which the pathogen is to spread from the surrounding area to the target area.
The server and method for predicting the infection risk level of a pathogen in a target area using molecular diagnostic test data, according to the above-described embodiments, may predict the infection risk level of a specific area using molecular diagnostic test data in other areas geographically adjacent to, or related to, the specific area, as well as molecular diagnostic test data obtained through a molecular diagnostic test in the specific area, or considering the infection risk level of the pathogen in the other areas.
Therefore, when predicting the infection risk level of a pathogen in a specific area, it is possible to maximally reflect the infection characteristics of the pathogen and enhance the accuracy of the infection risk level of the pathogen in the specific area.
FIG. 1 is a block diagram of a communication system including a server that predicts the infection risk level of a pathogen in a target area using molecular diagnostic test data according to an embodiment.
FIG. 2 is a view of a configuration of the management server of FIG. 1.
FIGS. 3 to 5 illustrate examples of a target area and surrounding areas determined by a processor of the management server of FIG. 1.
FIG. 6 is a flowchart of a method for predicting the infection risk level of a pathogen in a target area using molecular diagnostic test data according to another embodiment.
FIG. 7 is a flowchart of a method for providing the infection risk level of a pathogen in a target area using molecular diagnostic test data according to another embodiment.
The disclosure is now described in further detail in connection with embodiments thereof, taken in conjunction with the drawings. The embodiments are provided merely to specifically describe the present invention, and it is obvious to one of ordinary skill in the art that the scope of the present invention is not limited to the embodiments.
Such denotations as "first," "second," "A," "B," "(a)," "(b)," "(i)," and "(ii)" may be used in describing the components of the present invention. These denotations are provided merely to distinguish a component from another, and the essence of the components is not limited by the denotations in light of order or sequence. When a component is described as "connected," "coupled," or "linked" to another component, the component may be directly connected or linked to the other component, but it should also be appreciated that other components may be "connected," "coupled," or "linked" between the components.
FIG. 1 is a block diagram of a communication system including a server that predicts the infection risk level of a pathogen in a target area using molecular diagnostic test data according to an embodiment.
Referring to FIG. 1, the communication system 100 includes user terminals 120 connected with each other through a communication network 110, a management server 130 that predicts the infection risk level of a pathogen in a target area using molecular diagnostic test data according to an embodiment, and inspection servers 140 that provide molecular diagnostic test data.
The user terminals 120 are user terminals managed by separate inspection institutions and include two or more user terminals 120a,..., 120m managed by two or more inspection institutions.
Some (hereinafter referred to as first terminal) of the user terminals 120a,..., 120m and/or some (hereinafter referred to as first server) of the inspection servers 140a,..., 140n may provide molecular diagnostic test data for the pathogen in the target area to the management server 130. The first terminal and/or the first server are exemplarily described as being located in the target area, but may not be located in the target area as long as they may provide the management server 130 with the molecular diagnostic test data for the pathogen in the target area. The first terminal may include not only a general communication terminal but also a storage medium storing molecular diagnostic test data for pathogens.
Others (hereinafter referred to as second terminal) of the user terminals 120a,..., 120m and/or others (hereinafter referred to as second server) of the inspection servers 140a,..., 140n may provide at least one of molecular diagnostic test data for the pathogen in the surrounding areas or the external infection risk level of the pathogen in the target area, to the management server 130. The second terminal and/or the second server are exemplarily described as being located in the surrounding area, but may not be located in the surrounding area as long as they may provide the management server 130 with at least one of the molecular diagnostic test data for the pathogen in the surrounding area or the external infection risk level of the pathogen in the target area. The second terminal may include not only a general communication terminal but also a storage medium storing at least one of molecular diagnostic test data for pathogens or the external infection risk level of the pathogen in the target area.
For example, a specific user first terminal 120a positioned in the target area may provide the molecular diagnostic test data D1 in the target area to the management server 130 through the communication network 110. Another user second terminal 120b positioned in the surrounding area may provide the molecular diagnostic test data D2 in the surrounding area to the management server 130 through the communication network 110.
The user terminal 120 may access the management server 130 through the communication network 110 to identify various molecular diagnostic test data provided by the management server 130. The user terminal 120 may download and install specific software through the management server 130 or a sharing platform and may then receive the infection risk level of the pathogen in the target area provided by the management server 130.
The management server 130 predicts the infection risk level of the pathogen in the target area using the molecular diagnostic test data. The management server 130 may provide the specific software, e.g., a specific application or app, to the user terminal 120 directly or through a general sharing platform (e.g., Apple Store or Google Store).
The inspection servers 140 are servers managed by separate inspection institutions and include two or more inspection servers 140a,..., 140n managed by two or more inspection institutions.
The inspection server 140 stores molecular diagnostic test data obtained by analyzing a sample (e.g., a specimen) obtained from a subject (e.g., a human) using one or more test devices. The inspection server 140 may provide molecular diagnostic test data D1 in the target area to the management server 130. The inspection server 140 may access the management server 130 through the communication network 110 to identify various molecular diagnostic test data provided by the management server 130. The inspection server 140 may also receive the infection risk level of the pathogen in the target area provided by the management server 130.
The management server 130 receives molecular diagnostic test data D1 in the target area provided from the user terminal 120a or the inspection server 140 located in the target area. The management server 130 receives molecular diagnostic test data D2 in the surrounding area provided from the user terminal 120b or the inspection server 140 located in the surrounding area.
The management server 130 determines the pathogen infection risk level of the target area from the molecular diagnostic test data D1 in the target area.
As used herein, the term "subject" refers to an entity subject to a molecular diagnostic test, such as a real-time PCR test, to identify the presence of a specific pathogen. Examples of the subject include, but are not limited to, mammals, such as dogs, cats, rodents, primates, and humans, especially humans. Herein, the term "subject" also refers to an entity in which the pathogen of interest (e.g., nucleic acid of the pathogen) is identified or suspected of its presence. In particular, the term "subject" herein refers to a human patient.
As used herein, the term "sample" refers to any analyte (e.g., a specimen) that contains or is suspected of containing the nucleic acid to be analyzed.
As used herein, the terms "nucleic acid", "nucleic acid sequence" or "nucleic acid molecule" refer to a single-stranded or double-stranded form of deoxyribonucleotide or ribonucleotide polymer, wherein nucleotide may encompass derivatives of naturally occurring nucleotides, non-natural nucleotides or modified nucleotides that may function in the same manner as naturally occurring nucleotides.
As used herein, the terms "target nucleic acid", "target nucleic acid sequence" or "target sequence" refer to the nucleic acid sequence to be detected.
Target nucleic acids may include the nucleic acids of, e.g., prokaryotic cells(e.g., Mycoplasma pneumoniae, Chlamydophila pneumoniae, Legionella pneumophila, Haemophilus influenzae, Streptococcus pneumoniae, Bordetella pertussis, Bordetella parapertussis, Neisseria meningitidis, Listeria monocytogenes, Streptococcus agalactiae, Campylobacter, Clostridium difficile, Clostridium perfringens, Salmonella, Escherichia coli, Shigella, Vibrio, Yersinia enterocolitica, Aeromonas, Chlamydia trachomatis, Neisseria gonorrhoeae, Trichomonas vaginalis, Mycoplasma hominis, Mycoplasma genitalium, Ureaplasma urealyticum, Ureaplasma parvum, Mycobacterium tuberculosis), eukaryotic cells (e.g., protozoa and parasites, fungi, yeast, higher plants, lower animals and higher animals including mammals and humans), viruses or viroids. Examples of parasites among the eukaryotic cells may include Giardia lamblia, Entamoeba histolytica, Cryptosporidium, Blastocystis hominis, Dientamoeba fragilis, Cyclospora cayetanensis. Examples of the virus may include influenza A virus (Flu A), influenza B virus (Flu B), respiratory syncytial virus A (RSV A), respiratory syncytial virus B (RSV B), parainfluenza virus 1 (PIV 1), parainfluenza virus 2 (PIV 2), parainfluenza virus 3 (PIV 3), parainfluenza virus 4 (PIV 4), metapneumovirus (MPV), human enteroviruses (HEV), human bocaviruses (HBoV), human rhinoviruses (HRV), coronaviruses and adenoviruses, which cause respiratory diseases, and noroviruses, rotaviruses, adenoviruses, astroviruses and sapoviruses, which cause gastrointestinal diseases. Examples of the virus may include, but are not limited to, human papillomavirus (HPV), middle east respiratory syndrome-related coronavirus (MERS-CoV), severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), dengue virus, herpes simplex virus (HSV), human herpes virus (HHV), epstein-barr virus (EMV), varicella zoster virus (VZV), cytomegalovirus (CMV), HIV, hepatitis virus and poliovirus.
According to an embodiment, target nucleic acids may be the nucleic acids of pathogens associated with infectious diseases. For example, the nucleic acids may be the nucleic acids of respiratory pathogens, digestive pathogens, and sexually transmitted pathogens. The inspection server 140 may be a server managed by the inspection institution itself or a server managed by a public institution or government. The inspection institution 140 tests the sample (e.g., a specimen). The sample is tested using one or more test devices, and data according to the completion of the test by the test device may be stored in the inspection server 140.
The test device may be a nucleic acid amplification device that is intended to encompass an amplification reaction vessel as well as an amplification reaction device including a thermometer and a detector.
The nucleic acid amplification device includes various well-known devices capable of temperature adjustment for an amplification reaction. Examples of the device include, but are not limited to, CFX(Bio-Rad), iCycler(Bio-Rad), LightCycler(Roche), StepOne(ABI), 7500(ABI), ViiA7(ABI), QuantStudio(ABI), AriaMx(Agilent), and Eco(Illumina).
The amplification reaction vessel includes a tube, a strip, a plate, or other various types of vessels.
The test device is preferably a real-time PCR device. The test results obtained by analyzing the sample (e.g., a specimen) obtained from the subject using one or more real-time PCR devices and peripheral devices may be automatically input to the inspection server 140. However, the inspector may input the data, generated from the test device, to the inspection server 140 using her own user terminal 120.
Two or more inspection servers 140a,..., 140n may provide all or part of their respective stored molecular diagnostic test data to the management server 130. If the management server 130 additionally requests the other part of the molecular diagnostic test data, the two or more inspection servers 140a,..., 140n may, or may not, provide the other part of the molecular diagnostic test data requested from the management server 130 according to its respective management policy.
A configuration of the management server 130 that predicts the infection risk level of the pathogen in the target area using molecular diagnostic test data according to an embodiment is described below with reference to FIGS. 2 to 6.
FIG. 2 is a view of a configuration of the management server of FIG. 1. FIGS. 3 to 5 illustrate examples of a target area and surrounding areas determined by a processor of the management server of FIG. 1.
Referring to FIG. 2, the management server 130 is a server that is connected with the communication network 110 and predicts the infection risk level of the pathogen in the target area using molecular diagnostic test data.
The management server 130 includes a communication unit 132 that communicates with a terminal or another server through the communication network 110, a processor 134 controlling the management server 130, and a memory 136 storing data.
The communication unit 132 receives, from the first terminal or another first server, molecular diagnostic test data D1 in the target area for the pathogen. The memory 136 stores the molecular diagnostic test data D1 in the target area received from the communication unit 132.
The first terminal may be the user terminal 120 located in the target area described above with reference to FIG. 1, and the other first server may be the above-described inspection server 140 located in the target area.
The communication unit 132 receives at least one of the molecular diagnostic test data D2 in the surrounding area which is likely to spread the pathogen to the target area, or the external infection risk level of the pathogen in the target area, for the pathogen, from the second terminal or another second server. The external infection risk level of the pathogen may be the infection risk level of the pathogen in the surrounding area, as described below. The memory 136 stores at least one of the molecular diagnostic test data or the external infection risk level of the pathogen in the target area, received from the communication unit 132.
The second terminal may be the user terminal 120 located in the surrounding area described above with reference to FIG. 1, and the other second server may be the above-described inspection server 140 located in the surrounding area.
Referring to Table 1, the molecular diagnostic test data may include result data obtained from a nucleic acid amplification reaction on the pathogen to be detected in the sample obtained from the subject (e.g., a human patient). Further, the molecular diagnostic test data may include medical data for the subject, but the present invention is not limited thereto.
Molecular diagnostic test data
result data medical data
The molecular diagnostic test data (result data) may be, e.g., result data for one well (or result data for one sample) or result data for one plate (or result data for multiple samples), result data obtained during a morning, afternoon, or one day, or result data obtained in a specific hospital or a specific inspection institution. As an example, the molecular diagnosis result data stored in the memory 136 may be result data for influenza.
According to an embodiment, one well may be configured to detect the presence of one or more nucleic acids. For example, one well may be configured to detect the presence of 1 to 50 nucleic acids, 1 to 40 nucleic acids, 1 to 30 nucleic acids, 1 to 20 nucleic acids, 1 to 10 nucleic acids, 1 to 5 nucleic acids, 1 and 2 nucleic acids, or 1 nucleic acid.
According to an embodiment, one well may be configured to detect a plurality of pathogens having a genetic diversity, such as a virus, or to screen bacteria (e.g., Campylobacter, Salmonella, Shigella, Vibrio, Aeromonas), via nucleic acids.
Referring to Table 2, the result data may include the name of the pathogen to be detected and information indicating whether the pathogen is positive/negative. If necessary, the result data may optionally further include the cycle threshold (Ct) value in the nucleic acid amplification reaction of the pathogen.
result data medical data
the name of the pathogen to be detected, whether the pathogen is positive/negative, and the cycle threshold (Ct) value in the nucleic acid amplification reaction of the pathogen (optional) Questionnaire information, medication information, treatment methods, symptoms, heritability, medical history, medication history, re-infection status, treatment service code, examination institution type code, past medical history code
The nucleic acid amplification reaction of a pathogen may be a nucleic acid amplification method that amplifies a nucleic acid through repetition of a series of reactions with or without a temperature change.
The nucleic acid amplification reaction may be a nucleic acid amplification method that amplifies a nucleic acid through repetition of a series of reactions with or without a temperature change. For example, amplification of nucleic acids may be carried out according to various primer-involved nucleic acid amplification methods known in the art, specifically, by polymerase chain reaction (PCR) (U.S. Pat. Nos. 4,683,195, 4,683,202 and 4,800,159). Other examples include ligase chain reaction (LCR) (U.S. Patent Nos. 4,683,195 and 4,683,202; PCR Protocols: A Guide to Methods and Applications (Innis et al., eds, 1990)), strand displacement amplification (SDA)) (Walker, et al. Nucleic Acids Res. 20(7):1691-6 (1992); Walker PCR Methods Appl 3(1):1-6 (1993)), transcription-mediated amplification (Phyffer, et al., J. Clin. Microbiol. 34:834-841 (1996); Vuorinen, et al., J. Clin. Microbiol. 33:1856-1859 (1995)), nucleic acid sequence-based amplification (NASBA) (Compton, Nature 350(6313):91-2 (1991)), rolling circle amplification (RCA) (Lisby, Mol. Biotechnol. 12(1):75-99 (1999); Hatch et al., Genet. Anal. 15(2):35-40 (1999)) and Q-beta Replicase (Lizardi et al., BiolTechnology 6:1197 (1988)).
The medical data may include at least one of questionnaire information, medication information, treatment methods, re-infection status, symptoms, heritability, treatment service code, examination institution type code, and past medical history code.
Referring to Table 3, the molecular diagnostic test data may further include information regarding the subject. For example, if the subject is a human, the molecular diagnostic test data may further include personal information for the subject. The subject's personal information may include at least one of the subject's nationality, age, sex, blood type, location (residence), eating habits, working environment, family history, movement information, smoking status, drinking habits, exercise, pregnancy status, and specific information.
Subject's personal information
Subject's nationality, age, gender, blood type, location (residence), eating habits, working environment, family history, movement information, smoking status, drinking habits, exercise, pregnancy status, and specific information
When the subject is an animal, the molecular diagnostic test data may further include the animal's sex, age, habitat, diet, and personal information for the animal's owner. The molecular diagnostic test data may further include the type, collection date and place of the sample, test time, and test place.
Referring back to FIGS. 2 and 3, the processor 134 predicts a combinatorial infection risk level COM of the pathogen in the target area using the internal infection risk level α1 of the pathogen in the target area, the external infection risk level β1 of the pathogen in the target area, and a first possibility of spreading γ1 in which the pathogen is to spread from the surrounding area to the target area.
As an example, the surrounding area may be (i) an area geographically adjacent to the target area or (ii) an area that is traffic-wise, economically, socially, and/or historically related to the target area. As used herein, when A is "adjacent to" B, A may be adjacent to B not only geographically but also traffic-wise, economically, socially, and/or historically.
First, the processor134 determines the internal infection risk level α1 of the pathogen in the target area from the molecular diagnostic test data D1 in the target area.
As an example, when determining the internal infection risk level α1 of the pathogen in the target area, the processor 134 may determine the internal infection risk level α1 of the pathogen in the target area using the positive result value (e.g., the positive number or positive rate) of the pathogen obtained from the molecular diagnostic test data D1 in the target area of Tables 1 to 3 and, optionally, the load of the pathogen.
As another example, when determining the internal infection risk level α1 of the pathogen in the target area, the processor 134 may determine the internal infection risk level α1 of the pathogen in the target area further using pathogen-specific risk factors in the target area.
The pathogen-specific risk factors may include at least one of the transmissibility or the propagability, transmission speed and route, survival and/or proliferation conditions, mutation rate, lethality, incubation period, reinfection rate, animal-human cross infection, and/or drug resistance of the pathogen. The transmissibility refers to the ability of the nucleic acid introduced into the cell by infection or artificial method to, after replication, form infectious particles or a complex equivalent thereto and propagate to separate cells. Transmission route refers to the transmission path of the pathogen and may be divided into droplet transmission, air transmission, and contact transmission (direct or indirect). Survival and/or proliferation conditions are factors for an environment in which pathogens may proliferate, including the humidity, temperature, or surface of the place where the pathogen is located. This pathogen-specific risk factors may be known information or may be information newly identified by the molecular diagnostic data of Tables 1 to 3.
As another example, when determining the internal infection risk level α1 of the pathogen in the target area, the processor 134 may further use the preventive treatment factors of the target area to determine the internal infection risk level α1 of the pathogen in the target area.
The preventive treatment factors may include the presence of a prophylaxis, the presence of a therapy, the efficacy of the prophylaxis, the efficacy of the therapy, and cure rate of the prophylactic therapy, and/or the level of quarantine for the target area for infection of the pathogen. This preventive treatment factors may be known information or may be information newly identified by the molecular diagnostic data of Tables 1 to 3. In particular, the presence of a prophylaxis may include the presence of a vaccine against the pathogen, the presence of a diagnostic kit for testing the pathogen, the number of molecular diagnostic tests per unit population, the number of molecular diagnostic tests per unit population relative to the positive rate, the prevalence of masks. The presence of a therapy may include the presence or absence of a treatment facility, the number of treatment facilities per unit population (e.g., number of isolation treatment beds per unit population), and the number of medical personnel per unit population.
As another example, when determining the internal infection risk level α1 of the pathogen in the target area, the processor 134 may determine the internal infection risk level α1 of the pathogen in the target area by using pathogen-specific risk factors alone or preventive treatment factors alone for the target area.
Further, the processor 340 may determine the internal infection risk level α1 of the pathogen in the target area depending on a pathogen-specific risk factor, e.g., whether the pathogen is a class 1 transmissible disease pathogen or a class 2 transmissible disease pathogen, together with the positive result value of the particular pathogen and optionally the load of the pathogen.
Even with the same positive result value of the specific pathogen and optionally the same load of pathogen, the internal infection risk level α1 of the pathogen in the target area may be relatively high if the pathogen is the class 1 transmissible disease pathogen and may be relatively low if the pathogen is the class 2 transmissible disease pathogen.
When the processor 134 determines the internal infection risk level α1 of the pathogen in the target area using at least two of the positive result value of the pathogen in the target area and optionally, the load of the pathogen, the pathogen-specific risk factors and the preventive treatment factors, and the processor 134 may determine the internal infection risk level α1 of the pathogen in the target area by comprehensively considering two or more determination factors.
The processor 134 may score, or divide into two or more infection risk level grades, the internal infection risk level of the pathogen for use in predicting the combinatorial infection risk level of the pathogen in the target area.
The processor 134 may score the internal infection risk level α1 of the pathogen as an absolute value. For example, when the processor 134 scores the internal infection risk level α1 of the pathogen in the target area using the positive rate, which is one of the positive result values of the pathogen, the processor 340 may determine "0.5" points for a positive rate of the specific pathogen of 2%, "1" point for a positive rate of the specific pathogen of 3.5%, and "2" points for a positive rate of the specific pathogen exceeding 5%. As another example, when the processor 134 scores the internal infection risk level α1 of the pathogen in the target area using the positive number, which is one of the positive result values of the pathogen, the processor 340 may determine "0.5" points for a positive number of the specific pathogen not less than 1 and less than 50, "1" point for a positive number of the specific pathogen not less than 50 and less than 100, and "2" points for a positive number of the specific pathogen not less than 100.
The processor 134 may divide the internal infection risk level α1 of the pathogen into two or more grades (stages). For example, the pathogen infection risk level may be classified into two stages, e.g., high (dangerous) and low (not dangerous), three stages, e.g., high, medium, and low, or N stages, e.g., risk I stage, risk II stage,..., risk N stage.
For example, when the processor 134 determines the infection risk level of the pathogen in the target area based on the positive rate of the pathogen, the processor 340 may divide it into the risk I stage when the positive rate of a specific pathogen is 2% to 3%, the risk II stage when the positive rate is 3% to 5 %, and the risk III stage when the positive rate is more than 5%. As another example, when the processor 134 determines the infection risk level of the pathogen in the target area using the positive number of the pathogen, the processor 340 may divide it into the risk I stage when the positive number of the specific pathogen is not less than 1 and less than 50, the risk II stage when the positive number is not less than 50 and less than 100, and the risk III stage when the positive number is not less than 100.
Referring back to FIGS. 2 and 3, the communication unit 132 may receive the external infection risk level β1 of the pathogen in the target area from the second terminal or another second server. In this case, the processor 134 may predict the combinatorial infection risk level of the pathogen in the target area using the received external infection risk level β1 of the pathogen in the target area.
Further, the communication unit 132 may receive both the molecular diagnostic test data D2 in the surrounding area for the pathogen and the external infection risk level β1 of the pathogen in the target area, from the second terminal or another second server. The processor 134 may predict the combinatorial infection risk level of the pathogen in the target area using the received external infection risk level β1 of the pathogen in the target area.
Even in this case, the processor 134 may calculate the external infection risk level β1 of the pathogen in the target area from the molecular diagnostic test data D2 in the surrounding area and compare it with the received external infection risk level β1 of the pathogen in the target area. When the two are different, the processor 134 may use the average or selected one of the two.
Meanwhile, the communication unit 132 may receive only the molecular diagnostic test data D2 in the surrounding area for the pathogen from the second terminal or another second server.
When the communication unit 132 receives only the molecular diagnostic test data D2 in the surrounding area from the second terminal or the other second server, the processor 134 determines the external infection risk level β1 of the pathogen in the target area from the molecular diagnostic test data D2 in the surrounding area.
For example, the external infection risk level β1 of the pathogen in the target area is the infection risk level of the pathogen in the surrounding area. The external infection risk level β1 of the pathogen in the target area may be the internal infection risk level α2 of the pathogen in the surrounding area.
As another example, the external infection risk level β1 of the pathogen in the target area may be the combinatorial infection risk level COM2 of the pathogen in the surrounding area. In this case, the external infection risk level β1 of the pathogen may be the combinatorial infection risk level COM2 of the pathogen in the surrounding area, predicted using the internal infection risk level α2 of the pathogen in the surrounding area, the external infection risk level β2 of the pathogen in another surrounding area for the surrounding area, and the second possibility of spreading γ2 in which the pathogen is to spread from the other surrounding area for the surrounding area to the surrounding area.
When there are two surrounding areas, the external infection risk level β1 of the pathogen in the target area may be the sum of the internal infection risk levels α2 of the pathogen in the surrounding areas or the sum of the combinatorial infection risk levels COM2 of the pathogen in the surrounding areas.
For example, when the number of the surrounding areas is n, β1= α21+α22+...+ α2n, or β1= COM21+COM22+...+ COM2n.
The processor 134 may determine the infection risk level of the pathogen in the surrounding area in the same manner as (i) determining the internal infection risk level α1 of the pathogen in the target area or (ii) predicting the combinatorial infection risk level COM1 of the pathogen in the target area.
For example, when calculating the combinatorial infection risk levels COM2 of the pathogen in the surrounding areas, the processor 134 may calculate the external infection risk level β2 of the pathogen in the surrounding area in the same manner as calculating the external infection risk level β1 of the pathogen in the target area. When the number of the surrounding areas of a specific surrounding area is n, the external infection risk level β2 of the pathogen in the surrounding area may be β2= α31+α32+...+ α3n or β2= COM31+COM32+...+ COM3n.
Referring back to FIGS. 2 and 3, the processor 134 may score the first possibility of spreading γ1 of the pathogen or divide it into two or more grades to be utilized in predicting the combinatorial infection risk level COM1 of the pathogen in the target area.
The processor 134 may determine the first possibility of spreading γ1 using the distance between the surrounding area and the target area. When the surrounding areas are determined according to the distances from the target area to concentric circles as shown in FIG. 4, the processor 134 may classify the first possibility of spreading γ1 as a specific score or specific grade using the distance between a specific surrounding area and the target area.
For example, the processor 134 may classify the first possibility of spreading γ1 as grade I when the distance between the specific surrounding area and the target area is r1, as grade II when the distance between the specific surrounding area and the target area is r2 (r2>r1), and as grade III when the distance between the specific surrounding area and the target area is r3 (r3>r1).
The distance between the surrounding area and the target area may be a geographic, traffic-wise, economic, social, and/or historical distance from the first area.
The reason why the processor 134 considers the traffic-wise, economic, social, and/or historical distance between the surrounding area and the target area in determining the first possibility of spreading γ1 using the distance between the surrounding area and the target area is that the spread of a specific pathogen is possible between subjects in the areas that are sufficiently apart from each other in geographical distance but have active traffic, economic, social, and/or historical exchanges.
The surrounding area may include a single geographical area or two or more areas geographically separated from each other.
In this case, (i) the area geographically adjacent to the target area may be an area adjacent to the target area in terms of administrative district, an area separated by an infectious disease management entity for infectious disease management, or an area separated by a doctor association or organization considering the degree of proximity of hospitals.
(ii) An area that is traffic-wise, economically, socially, and/or historically related to the target area may be an area where traffic, human or material exchanges with the target area are active.
As an example of the area where traffic and/or human exchanges are active with the target area, if the target area has large-scale factory facilities and workers in the facilities move to a specific area by shuttle buses, the specific area may be the surrounding area although not geographically adjacent to the target area.
For example, as shown in FIG. 5, the surrounding area may correspond to an area that is not geographically adjacent to the target area but is connected with the target area through an infrastructure through which physical exchange is possible, such as a highway. In consideration of not only a specific highway but also the connection of railways or express railways, topographic features, such as rivers, seas, and mountains, inter-city relationships, such as core cities and satellite cities, schools, hospitals, and locations of local governments, the surrounding area may be determined relative to the target area.
Large cities may have many inspection institutions. Thus, the surrounding area may be subdivided into narrow units, and if a specific area with insufficient medical infrastructure has few inspection institutions, the range covered by the surrounding area may be widened, and the surrounding area may be subdivided into wider units.
For example, the target area may be a first hospital and the surrounding area may be a second hospital located within a predetermined radius from the first hospital as shown in FIG. 4. As another example, the target area may be a specific administrative area, and the surrounding area may be another administrative area adjacent to the specific administrative area. As another example, the target area may be a specific country, and the surrounding area may be another country adjacent to the specific country. As another example, if a Russian sailor is infected with a specific pathogen in a specific area of Korea, the target area may be Korea and the surrounding area may be Russia, and vice versa. As another example, the target area may be a specific continent, and the surrounding area may be another continent adjacent to the specific continent.
The processor 134 may calculate the second possibility of spreading γ2 of the pathogen in the surrounding area in the same manner as calculating the first possibility of spreading γ1 of the pathogen in the target area.
The processor 134 may determine, every first period T1, the internal infection risk level α1 of the pathogen in the target area from the molecular diagnostic test data D1 in the target area and predict, every second period T2 different from the first period T1, the combinatorial infection risk level COM1 of the pathogen in the target area using the internal infection risk level α1 of the pathogen in the target area, the external infection risk level β1 of the pathogen in the target area, and the first possibility of spreading γ1.
For example, the first period T1 may be shorter than the second period T2. In other words, the processor 134 may determine the internal infection risk level α1 of the pathogen in the target area, from the molecular diagnostic test data D1 in the target area, in a relatively shorter period. Further, the processor 134 may determine the combinatorial infection risk level COM1 of the pathogen in the target area, in a relatively long period.
For example, the first period T1 may be one day, and the second period T2 may be one week. Accordingly, the processor 134 may determine the internal infection risk level α1 of the pathogen in the target area every day and predict the combinatorial infection risk level COM1 of the pathogen in the target area every week. The processor 134 may predict, for one week, the combinatorial infection risk level COM1 of the pathogen in the target area using, e.g., the average, minimum value, or maximum value of the internal infection risk levels α1 of the pathogen in the target area determined every day for one week.
As another example, the first period T1 may be longer than the second period T1, and as another example, the first period T1 may be the same as the second period T2. When the first period T1 is the same as the second period T2, the processor 134 may predict the combinatorial infection risk level COM1 of the pathogen in the target area using the internal infection risk level α1 of the pathogen in the target area for the same period.
Further, the processor 134 may predict the combinatorial infection risk level COM1 of the pathogen in the target area by giving the same or different weights to the internal infection risk level α1 of the pathogen in the target area, the external infection risk level β1 of the pathogen in the target area, and the first possibility of spreading γ1.
For example, when the respective weights of the infection risk level α1 of the pathogen in the target area, external infection risk level β1 of the pathogen in the target area, and the first possibility of spreading γ1 are kα, kβ, and kγ, the combinatorial infection risk level COM1 of the pathogen in the target area may be represented as in Equation 1 below.
[Equation 1]
COM1= F(α1, β1, γ1, kα, kβ, kγ)
For example, when weighted-averaging the infection risk level α1 of the pathogen, external infection risk level β1 of the pathogen, and the first possibility of spreading γ1 in the target area in Equation 1, the combinatorial infection risk level COM1 of the pathogen in the target area may be represented as in Equation 2.
[Equation 2]
COM1= (kα*α1+ kβ*β1+kγ*γ1)/3
As described above, for comparison between the combinatorial infection risk level COM1 of the pathogen in the target area and each of the infection risk level α1 of the pathogen, external infection risk level β1 of the pathogen, and the first possibility of spreading γ1 in the target area when scoring or grading the infection risk level α1 of the pathogen, external infection risk level β1 of the pathogen, and the first possibility of spreading γ1 in the target area as the same one, the combinatorial infection risk level COM1 of the pathogen in the target area may be calculated using Equation 2, i.e., the weighted average.
Table 4 shows the values resultant from calculating the combinatorial infection risk level COM1 of the pathogen in the target area using Equation 2 when kα=0.8, kβ=0.2, and kγ=0.5.
influenza target area (Gyeonggi-do), surrounding area (Seoul)
January February March
α1 1 2 1
β1 1 1 2
γ1 1 1 1
COM1 0.50 0.77 0.57
As another example, when summating the results obtained by multiplying the infection risk level α1 of the pathogen, external infection risk level β1 of the pathogen, and the first possibility of spreading γ1 in the target area by their respective weights in Equation 1, the combinatorial infection risk level COM1 of the pathogen in the target area may be represented as in Equation 3.
[Equation 3]
COM1= kα*α1+ kβ*β1+kγ*γ1
Table 5 shows the values resultant from calculating the combinatorial infection risk level COM1 of the pathogen in the target area using Equation 3 when kα=0.8, kβ=0.2, and kγ=1.
influenza target area (Gyeonggi-do), surrounding area (Seoul)
January February March
α1 1 2 1
β1 1 1 2
γ1 1 1 1
COM1 2 2.8 2.2
In a case where the communication unit 132 receives only the molecular diagnostic test data D2 in the surrounding area from the second terminal or another second server, and the external infection risk level β1 of the pathogen in the target area is the combinatorial infection risk level COM2 of the pathogen in the surrounding area, the processor 134 may calculate the combinatorial infection risk level COM2 of the pathogen in the surrounding area from the molecular diagnostic test data D2 in the surrounding area, using one of Equations 1 to 3.
The processor 134 may predict the combinatorial infection risk level COM1 of the pathogen in the target area using the internal infection risk level α1 of the pathogen in the target area, the external infection risk level β1 of the pathogen in the target area, and the first possibility of spreading γ1 in which the pathogen is to spread from the surrounding area to the target area, based on an artificial neural network.
For example, all or part of previously stored molecular diagnostic test data is split into a training set and a test set. After a specific deep learning model may be trained with the training set, the combinatorial infection risk level COM of the pathogen in the target area may be predicted using the test set. Thus, the processor 134 may determine the combinatorial infection risk level COM of the pathogen in the target area although no separate determination element is presented.
The processor 134 may determine at least one of the internal infection risk level α2 of the pathogen in the surrounding area, the external infection risk level β2 of the pathogen in the surrounding area, the second possibility of spreading γ2 in which the pathogen is to spread from another surrounding area to the surrounding area, or the combinatorial infection risk level COM2 of the pathogen in the surrounding area, based on the artificial neural network. The server for predicting the infection risk level of a pathogen in a target area using molecular diagnostic test data, according to the above-described embodiments, may predict the infection risk level of a specific area using molecular diagnostic test data in other areas geographically adjacent to, or related to, the specific area, as well as molecular diagnostic test data obtained through a molecular diagnostic test in the specific area, or considering the infection risk level of the pathogen in the other areas.
Therefore, when predicting the infection risk level of a pathogen in a specific area, it is possible to maximally reflect the infection characteristics of the pathogen and enhance the accuracy of the infection risk level of the pathogen in the specific area.
A configuration of the management server 130 that predicts the infection risk level of the pathogen in the target area using molecular diagnostic test data according to an embodiment has been described above with reference to FIGS. 2 to 5. A method for predicting the infection risk level of a pathogen in a target area using molecular diagnostic test data according to another embodiment is described below with reference to FIG. 6.
FIG. 6 is a flowchart of a method for predicting the infection risk level of a pathogen in a target area using molecular diagnostic test data according to another embodiment.
Referring to FIG. 6, a method 200 for predicting an infection risk level of a pathogen in a target area using molecular diagnostic test data according to another embodiment includes the step S210 of receiving data in the target data, the step S220 of receiving data in a surrounding area, the step S230 of determining an internal infection risk level of the pathogen, the step S240 of determining an external infection risk level of the pathogen, and the step S250 of predicting a combinatorial infection risk level of the pathogen.
According to another embodiment, the method 200 for predicting the infection risk level of the pathogen in the target area may be performed by the management server 130 described with reference to FIG. 1 or may be performed by the user terminal 120 and the inspection server 140. Some of the steps may be performed by either the user terminal 120 or the inspection server 140 while the others by the other.
According to another embodiment, in the method 200 for predicting the infection risk level of the pathogen in the target area using the molecular diagnostic test data, the above-described steps may be performed sequentially, repeated for a specific step, or performed in a different order.
Although it is described below with reference to FIG. 2 that the management server 130 performs the method 200 for predicting the infection risk level of the pathogen in the target area using the molecular diagnostic test data according to an embodiment, the disclosure is not limited thereto.
Referring to FIG. 6, the step S210 of receiving the data in the target area receives, from the first terminal or another first server, molecular diagnostic test data D1 in the target area for the pathogen.
The first terminal may be the user terminal 120 located in the target area described above with reference to FIG. 1, and the other first server may be the above-described inspection server 140 located in the target area.
The step S220 of receiving the data in the surrounding area receives at least one of the molecular diagnostic test data D2 in the surrounding area or the external infection risk level of the pathogen in the target area, for the pathogen, from the second terminal or another second server. The external infection risk level of the pathogen may be the infection risk level of the pathogen in the surrounding area, as described above.
The second terminal may be the user terminal 120 located in the surrounding area described above with reference to FIG. 1, and the other second server may be the above-described inspection server 140 located in the surrounding area.
As an example, the step S220 of receiving the data in the surrounding area may receive both the molecular diagnostic test data D2 in the surrounding area and the external infection risk level β1 of the pathogen in the target area, for the pathogen, from the second terminal or another second server.
As another example, the step S220 of receiving the data in the surrounding area may receive only the molecular diagnostic test data D2 in the surrounding area for the pathogen from the second terminal or another second server.
As another example, the step S220 of receiving only the data in the surrounding area may receive the external infection risk level β1 of the pathogen in the target area from the second terminal or another second server.
Referring to Table 1, the molecular diagnostic test data may include result data obtained from a nucleic acid amplification reaction on the pathogen to be detected in the sample obtained from the subject (e.g., a human patient). Further, the molecular diagnostic test data may include medical data for the subject, but the present invention is not limited thereto.
Referring to Table 2, the result data may include the name of the pathogen to be detected and information indicating whether the pathogen is positive/negative. If necessary, the result data may optionally further include the cycle threshold (Ct) value in the nucleic acid amplification reaction of the pathogen.
Referring to Table 3, the molecular diagnostic test data may further include information regarding the subject. For example, if the subject is a human, the molecular diagnostic test data may further include personal information for the subject. The subject's personal information may include at least one of the subject's nationality, age, sex, blood type, location (residence), eating habits, working environment, family history, movement information, smoking status, drinking habits, exercise, pregnancy status, and specific information.
The step S230 of determining the internal infection risk level of the pathogen determines the internal infection risk level α1 of the pathogen in the target area from the molecular diagnostic test data D1 in the target area.
As an example, when determining the internal infection risk level α1 of the pathogen in the target area, it is possible to determine the internal infection risk level α1 of the pathogen in the target area using the positive result value (e.g., the positive number or positive rate) of the pathogen obtained from the molecular diagnostic test data D1 in the target area of Tables 1 to 3 and, optionally, the load of the pathogen.
As another example, when determining the internal infection risk level α1 of the pathogen in the target area, it is possible to determine the internal infection risk level α1 of the pathogen in the target area further using pathogen-specific risk factors in the target area.
The pathogen-specific risk factors may include at least one of the transmissibility, transmission speed and route, survival and/or proliferation conditions, mutation rate, lethality, incubation period, reinfection rate, animal-human cross infection, and/or drug resistance of the pathogen. The transmissibility refers to the ability of the nucleic acid introduced into the cell by infection or artificial method to, after replication, form infectious particles or a complex equivalent thereto and propagate to separate cells. Transmission route refers to the transmission path of the pathogen and may be divided into droplet transmission, air transmission, and contact transmission (direct or indirect). Survival and/or proliferation conditions are factors for an environment in which pathogens may proliferate, including the humidity, temperature, or surface of the place where the pathogen is located. This pathogen-specific risk factors may be known information or may be information newly identified by the molecular diagnostic data of Tables 1 to 3.
As another example, when determining the internal infection risk level α1 of the pathogen in the target area, it is possible to further use the preventive treatment factors of the target area to determine the internal infection risk level α1 of the pathogen in the target area.
The preventive treatment factors may include the presence of a prophylaxis, the presence of a therapy, the efficacy of the prophylaxis, the efficacy of the therapy, and cure rate of the prophylactic therapy, and/or the level of quarantine for the target area for infection of the pathogen. This preventive treatment factors may be known information or may be information newly identified by the molecular diagnostic data of Tables 1 to 3. In particular, the presence of a prophylaxis may include the presence of a vaccine against the pathogen, the presence of a diagnostic kit for testing the pathogen, the number of molecular diagnostic tests per unit population, the number of molecular diagnostic tests per unit population relative to the positive rate, the prevalence of masks. The presence of a therapy may include the presence or absence of a treatment facility, the number of treatment facilities per unit population (e.g., number of isolation treatment beds per unit population), and the number of medical personnel per unit population.
As another example, when determining the internal infection risk level α1 of the pathogen in the target area, it is possible to determine the internal infection risk level α1 of the pathogen in the target area by using pathogen-specific risk factors alone or preventive treatment factors alone for the target area.
Further, in determining the internal infection risk level α1 of the pathogen in the target area, it is possible to determine the internal infection risk level α1 of the pathogen in the target area depending on a pathogen-specific risk factor, e.g., whether the pathogen is a class 1 transmissible disease pathogen or a class 2 transmissible disease pathogen, together with the positive result value of the particular pathogen and optionally the load of the pathogen.
Even with the same positive result value of the specific pathogen and optionally the same load of pathogen, the internal infection risk level α1 of the pathogen in the target area may be relatively high if the pathogen is the class 1 transmissible disease pathogen and may be relatively low if the pathogen is the class 2 transmissible disease pathogen.
In the step S230 of determining the internal infection risk level of the pathogen, when determining the internal infection risk level α1 of the pathogen in the target area using at least two of the positive result value of the pathogen in the target area and optionally, the load of the pathogen, the pathogen-specific risk factors and the preventive treatment factors, it is possible to determine the internal infection risk level α1 of the pathogen in the target area by comprehensively considering two or more determination factors.
The step S230 of determining the internal infection risk level of the pathogen may score, or divide into two or more infection risk level grades, the internal infection risk level of the pathogen for use in predicting the combinatorial infection risk level of the pathogen in the target area.
In the step S230 of determining the internal infection risk level of the pathogen, the internal infection risk level α1 of the pathogen may be scored as an absolute value. For example, when the processor 134 scores the internal infection risk level α1 of the pathogen in the target area using the positive rate, which is one of the positive result values of the pathogen, the processor 340 may determine "0.5" points for a positive rate of the specific pathogen of 2%, "1" point for a positive rate of the specific pathogen of 3.5%, and "2" points for a positive rate of the specific pathogen exceeding 5%. As another example, when the processor 134 scores the internal infection risk level α1 of the pathogen in the target area using the positive number, which is one of the positive result values of the pathogen, the processor 340 may determine "0.5" points for a positive number of the specific pathogen not less than 1 and less than 50, "1" point for a positive number of the specific pathogen not less than 50 and less than 100, and "2" points for a positive number of the specific pathogen not less than 100.
The step S230 of determining the internal infection risk level of the pathogen may divide the internal infection risk level α1 of the pathogen into two or more grades (stages). For example, the pathogen infection risk level may be classified into two stages, e.g., high (dangerous) and low (not dangerous), three stages, e.g., high, medium, and low, or N stages, e.g., risk I stage, risk II stage,..., risk N stage.
For example, when determining the infection risk level of the pathogen in the target area based on the positive rate of the pathogen, the processor 340 may divide it into the risk I stage when the positive rate of a specific pathogen is 2% to 3%, the risk II stage when the positive rate is 3% to 5 %, and the risk III stage when the positive rate is more than 5%. As another example, when the processor 134 determines the infection risk level of the pathogen in the target area using the positive number of the pathogen, the processor 340 may divide it into the risk I stage when the positive number of the specific pathogen is not less than 1 and less than 50, the risk II stage when the positive number is not less than 50 and less than 100, and the risk III stage when the positive number is not less than 100.
The step S240 of determining the external infection risk level of the pathogen determines the external infection risk level of the pathogen using the received molecular diagnostic test data D2 in the surrounding area or the external infection risk level of the pathogen in the target area.
As described above, the step S220 of receiving the data in the surrounding area may receive both the molecular diagnostic test data D2 in the surrounding area and the external infection risk level β1 of the pathogen in the target area, for the pathogen, from the second terminal or another second server.
Even in this case, the step S240 of determining the external infection risk level of the pathogen may calculate the external infection risk level β1 of the pathogen in the target area from the molecular diagnostic test data D2 in the surrounding area and compare it with the received external infection risk level β1 of the pathogen in the target area. When the two are different, the step S240 of determining the external infection risk level of the pathogen may use the average or selected one of the two.
As described above, the step S220 of receiving the data in the surrounding area may receive only the molecular diagnostic test data D2 in the surrounding area for the pathogen from the second terminal or another second server.
In this case, the step S240 of determining the external infection risk level of the pathogen may calculate the external infection risk level β1 of the pathogen in the target area from the molecular diagnostic test data D2 in the surrounding area.
For example, the external infection risk level β1 of the pathogen in the target area is the infection risk level of the pathogen in the surrounding area. The external infection risk level β1 of the pathogen in the target area may be the internal infection risk level α2 of the pathogen in the surrounding area.
As another example, the external infection risk level β1 of the pathogen in the target area may be the combinatorial infection risk level COM2 of the pathogen in the surrounding area. In this case, the external infection risk level β1 of the pathogen may be the combinatorial infection risk level COM2 of the pathogen in the surrounding area, predicted using the internal infection risk level α2 of the pathogen in the surrounding area, the external infection risk level β2 of the pathogen in another surrounding area for the surrounding area, and the second possibility of spreading γ2 in which the pathogen is to spread from the other surrounding area for the surrounding area to the surrounding area.
When there are two surrounding areas, the external infection risk level β1 of the pathogen in the target area may be the sum of the internal infection risk levels α2 of the pathogen in the surrounding areas or the sum of the combinatorial infection risk levels COM2 of the pathogen in the surrounding areas.
For example, when the number of the surrounding areas is n, β1= α21+α22+...+ α2n, or β1= COM21+COM22+...+ COM2n.
The step S240 of determining the external infection risk level of the pathogen may determine the infection risk level of the pathogen in the surrounding area in the same manner as (i) determining the internal infection risk level α1 of the pathogen in the target area or (ii) predicting the combinatorial infection risk level COM1 of the pathogen in the target area.
For example, when calculating the combinatorial infection risk levels COM2 of the pathogen in the surrounding areas, the processor 134 may calculate the external infection risk level β2 of the pathogen in the surrounding area in the same manner as calculating the external infection risk level β1 of the pathogen in the target area. When the number of the surrounding areas of a specific surrounding area is n, the external infection risk level β2 of the pathogen in the surrounding area may be β2= α31+α32+...+ α3n or β2= COM31+COM32+...+ COM3n.
The step S250 of predicting the combinatorial infection risk level of the pathogen predicts a combinatorial infection risk level COM of the pathogen in the target area using the internal infection risk level α1 of the pathogen in the target area, the external infection risk level β1 of the pathogen, and a first possibility of spreading γ1 in which the pathogen is to spread from the surrounding area to the target area.
The step S250 of predicting the combinatorial infection risk level of the pathogen may score the first possibility of spreading γ1 of the pathogen or divide it into two or more grades to be utilized in predicting the combinatorial infection risk level COM1 of the pathogen in the target area.
The step S250 of predicting the combinatorial infection risk level of the pathogen may determine the first possibility of spreading γ1 using the distance between the surrounding area and the target area. When the surrounding areas are determined according to the distances from the target area to concentric circles as shown in FIG. 4, the processor 134 may classify the first possibility of spreading γ1 as a specific score or specific grade using the distance between a specific surrounding area and the target area.
For example, the step S250 of predicting the combinatorial infection risk level of the pathogen may classify the first possibility of spreading γ1 as grade I when the distance between the specific surrounding area and the target area is r1, as grade II when the distance between the specific surrounding area and the target area is r2 (r2>r1), and as grade III when the distance between the specific surrounding area and the target area is r3 (r3>r1).
As an example, the surrounding area may be (i) an area geographically adjacent to the target area or (ii) an area that is traffic-wise, economically, socially, and/or historically related to the target area. As used herein, when A is "adjacent to" B, A may be adjacent to B not only geographically but also traffic-wise, economically, socially, and/or historically.
The distance between the surrounding area and the target area may be a geographic, traffic-wise, economic, social, and/or historical distance from the first area.
The reason for considering the traffic-wise, economic, social, and/or historical distance between the surrounding area and the target area in determining the first possibility of spreading γ1 using the distance between the surrounding area and the target area is that the spread of a specific pathogen is possible between subjects in the areas that are sufficiently apart from each other in geographical distance but have active traffic, economic, social, and/or historical exchanges.
The surrounding area may include a single geographical area or two or more areas geographically separated from each other.
In this case, (i) the area geographically adjacent to the target area may be an area adjacent to the target area in terms of administrative district, an area separated by an infectious disease management entity for infectious disease management, or an area separated by a doctor association or organization considering the degree of proximity of hospitals.
(ii) An area that is traffic-wise, economically, socially, and/or historically related to the target area may be an area where traffic, human or material exchanges with the target area are active.
As an example of the area where traffic and/or human exchanges are active with the target area, if the target area has large-scale factory facilities and workers in the facilities move to a specific area by shuttle buses, the specific area may be the surrounding area although not geographically adjacent to the target area.
For example, as shown in FIG. 5, the surrounding area may correspond to an area that is not geographically adjacent to the target area but is connected with the target area through an infrastructure through which physical exchange is possible, such as a highway. In consideration of not only a specific highway but also the connection of railways or express railways, topographic features, such as rivers, seas, and mountains, inter-city relationships, such as core cities and satellite cities, schools, hospitals, and locations of local governments, the surrounding area may be determined relative to the target area.
Large cities may have many inspection institutions. Thus, the surrounding area may be subdivided into narrow units, and if a specific area with insufficient medical infrastructure has few inspection institutions, the range covered by the surrounding area may be widened, and the surrounding area may be subdivided into wider units.
For example, the target area may be a first hospital and the surrounding area may be a second hospital located within a predetermined radius from the first hospital as shown in FIG. 4. As another example, the target area may be a specific administrative area, and the surrounding area may be another administrative area adjacent to the specific administrative area. As another example, the target area may be a specific country, and the surrounding area may be another country adjacent to the specific country. As another example, if a Russian sailor is infected with a specific pathogen in a specific area of Korea, the target area may be Korea and the surrounding area may be Russia, and vice versa. As another example, the target area may be a specific continent, and the surrounding area may be another continent adjacent to the specific continent.
The step S250 of predicting the combinatorial infection risk level of the pathogen may calculate the second possibility of spreading γ2 of the pathogen in the surrounding area in the same manner as calculating the first possibility of spreading γ1 of the pathogen in the target area.
The step S250 of predicting the combinatorial infection risk level of the pathogen may determine, every first period T1, the internal infection risk level α1 of the pathogen in the target area from the molecular diagnostic test data D1 in the target area and predict, every second period T2 different from the first period T1, the combinatorial infection risk level COM1 of the pathogen in the target area using the internal infection risk level α1 of the pathogen in the target area, the external infection risk level β1 of the pathogen in the target area, and the first possibility of spreading γ1.
For example, the first period T1 may be shorter than the second period T2. In other words, the processor 134 may determine the internal infection risk level α1 of the pathogen in the target area, from the molecular diagnostic test data D1 in the target area, in a relatively shorter period. Further, the processor 134 may determine the combinatorial infection risk level COM1 of the pathogen in the target area, in a relatively long period.
For example, the first period T1 may be one day, and the second period T2 may be one week. Accordingly, the processor 134 may determine the internal infection risk level α1 of the pathogen in the target area every day and predict the combinatorial infection risk level COM1 of the pathogen in the target area every week. The processor 134 may predict, for one week, the combinatorial infection risk level COM1 of the pathogen in the target area using, e.g., the average, minimum value, or maximum value of the internal infection risk levels α1 of the pathogen in the target area determined every day for one week.
As another example, the first period T1 may be longer than the second period T1, and as another example, the first period T1 may be the same as the second period T2. When the first period T1 is the same as the second period T2, the processor 134 may predict the combinatorial infection risk level COM1 of the pathogen in the target area using the internal infection risk level α1 of the pathogen in the target area for the same period.
Further, the step S250 of predicting the combinatorial infection risk level of the pathogen may predict the combinatorial infection risk level COM1 of the pathogen in the target area by giving the same or different weights to the internal infection risk level α1 of the pathogen in the target area, the external infection risk level β1 of the pathogen in the target area, and the first possibility of spreading γ1.
As described above, when the respective weights of the infection risk level of the pathogen in the target area, external infection risk level β1 of the pathogen in the target area, and the first possibility of spreading γ1 are kα, kβ, and kγ, the combinatorial infection risk level COM1 of the pathogen in the target area may be represented as in Equation 1 above.
As described above, when weighted-averaging the infection risk level α1 of the pathogen, external infection risk level β1 of the pathogen, and the first possibility of spreading γ1 in the target area in Equation 1, the combinatorial infection risk level COM1 of the pathogen in the target area may be represented as in Equation 2 above.
As described above, for comparison between the combinatorial infection risk level COM1 of the pathogen in the target area and each of the infection risk level α1 of the pathogen, external infection risk level β1 of the pathogen, and the first possibility of spreading γ1 in the target area when scoring or grading the infection risk level 5 α1 of the pathogen, external infection risk level β1 of the pathogen, and the first possibility of spreading γ1 in the target area as the same one, the combinatorial infection risk level COM1 of the pathogen in the target area may be calculated using Equation 2, ie, the weighted average.
As another example, when summating the results obtained by multiplying the infection risk level α1 of the pathogen, external infection risk level β1 of the pathogen, and the first possibility of spreading γ1 in the target area by their respective weights in Equation 1, the combinatorial infection risk level COM1 of the pathogen in the target area may be represented as in Equation 3 as described above.
In a case where only the molecular diagnostic test data D2 in the surrounding area is received from the second terminal or another second server, and the external infection risk level β1 of the pathogen in the target area is the combinatorial infection risk level COM2 of the pathogen in the surrounding area, the step S250 of predicting the combinatorial infection risk level of the pathogen may calculate the combinatorial infection risk level COM2 of the pathogen in the surrounding area from the molecular diagnostic test data D2 in the surrounding area, using one of Equations 1 to 3.
The step S250 of predicting the combinatorial infection risk level of the pathogen may predict the combinatorial infection risk level COM1 of the pathogen in the target area using the internal infection risk level α1 of the pathogen in the target area, the external infection risk level β1 of the pathogen in the target area, and the first possibility of spreading γ1 in which the pathogen is to spread from the surrounding area to the target area, based on an artificial neural network.
For example, all or part of previously stored molecular diagnostic test data is split into a training set and a test set. After a specific deep learning model may be trained with the training set, the combinatorial infection risk level COM of the pathogen in the target area may be predicted using the test set. Thus, the processor 134 may determine the combinatorial infection risk level COM of the pathogen in the target area although no separate determination element is presented.
The step S250 of predicting the combinatorial infection risk level of the pathogen may determine at least one of the internal infection risk level α2 of the pathogen in the surrounding area, the external infection risk level β2 of the pathogen in the surrounding area, the second possibility of spreading γ2 in which the pathogen is to spread from another surrounding area to the surrounding area, or the combinatorial infection risk level COM2 of the pathogen in the surrounding area, based on the artificial neural network. The method for predicting the infection risk level of a pathogen in a target area using molecular diagnostic test data, according to the above-described embodiments, may predict the infection risk level of a specific area using molecular diagnostic test data in other areas geographically adjacent to, or related to, the specific area, as well as molecular diagnostic test data obtained through a molecular diagnostic test in the specific area, or considering the infection risk level of the pathogen in the other areas.
Therefore, when predicting the infection risk level of a pathogen in a specific area, it is possible to maximally reflect the infection characteristics of the pathogen and enhance the accuracy of the infection risk level of the pathogen in the specific area.
Another embodiment of the disclosure may be described below.
A server comprises a memory and a processor and performs a method 200 for predicting an infection risk level of a pathogen in a target area using molecular diagnostic test data according to another embodiment described with reference to FIG. 6 using one or more programs stored in the memory and configured to be executed by the processor and includes instructions to perform the step S210 of receiving molecular diagnostic test data D1 in the target area for the pathogen the step S220 of receiving molecular diagnostic test data D2 in a surrounding area, which is likely to spread the pathogen to the target area, or an external infection risk level β1 of the pathogen in the target area , the step S230 of determining an internal infection risk level α1 of the pathogen in the target area from the molecular diagnostic test data D1 in the target area, the step S240 of determining the external infection risk level β1 of the pathogen in the target area, the external infection risk level β1 of the pathogen in the target area being an infection risk level of the pathogen in the surrounding area, and the step S250 of predicting a combinatorial infection risk level COM1 of the pathogen in the target area using the internal infection risk level α1 of the pathogen in the target area, the external infection risk level β1 of the pathogen, and a first possibility of spreading γ1 in which the pathogen is to spread from the surrounding area to the target area.
The external infection risk level β1 of the pathogen in the target area may be determined from the received molecular diagnostic test data D2 in the surrounding area or be the received external infection risk level of the pathogen in the target area.
Another embodiment of the disclosure may be described as follows.
There is provided a non-transitory computer-readable storage medium storing, in a computing device, instructions executed by one or more processors to enable the one or more processors to perform a method for predicting an infection risk level of a pathogen in a target area using molecular diagnostic test data according to another embodiment, the instructions executed by the one or more processors to enable the computing device to perform the step S210 of receiving molecular diagnostic test data D1 in the target area for the pathogen, the step S220 of receiving molecular diagnostic test data D2 in a surrounding area, which is likely to spread the pathogen to the target area, or an external infection risk level β1 of the pathogen in the target area, the step S230 of determining an internal infection risk level α1 of the pathogen in the target area from the molecular diagnostic test data D1 in the target area, the step S240 of determining the external infection risk level β1 of the pathogen in the target area, the external infection risk level β1 of the pathogen in the target area being an infection risk level of the pathogen in the surrounding area, and the step S250 of predicting a combinatorial infection risk level COM1 of the pathogen in the target area using the internal infection risk level α1 of the pathogen in the target area, the external infection risk level β1 of the pathogen, and a first possibility of spreading γ1 in which the pathogen is to spread from the surrounding area to the target area.
The external infection risk level β1 of the pathogen in the target area may be determined from the received molecular diagnostic test data D2 in the surrounding area or be the received external infection risk level of the pathogen in the target area.
In the non-transitory computer-readable storage medium, the instructions may perform other steps or operations included in the method for predicting the infection risk level of the pathogen in the target area using the molecular diagnostic test data according to other embodiments described above in connection with FIG. 6.
FIG. 7 is a flowchart of a method for providing the infection risk level of a pathogen in a target area using molecular diagnostic test data according to another embodiment.
Referring to FIG. 7, according to another embodiment, a method 300 for providing an infection risk level of a pathogen in a target area using molecular diagnostic test data includes the step S310 of receiving data in the target area, the step S320 of receiving data in a surrounding area, and the step S330 of providing a combinatorial infection risk level of the pathogen.
According to another embodiment, the method 300 for providing the infection risk level of the pathogen in the target area may be performed by the management server 130 described with reference to FIG. 1 or may be performed by the user terminal 120 and the inspection server 140. Some of the steps may be performed by either the user terminal 120 or the inspection server 140 while the others by the other.
The step S310 of receiving the data in the target area may be substantially the same as the step S210 of receiving the data in the target area described above in connection with FIG. 6.
The step S320 of receiving the data in the surrounding area may be substantially the same as the step S220 of receiving the data in the surrounding area described above in connection with FIG. 6.
The step S330 of providing the combinatorial infection risk level of the pathogen may provide the terminal or another server with the combinatorial infection risk level of the pathogen, predicted in the step S250 of predicting the combinatorial infection risk level of the pathogen, described above in connection with FIG. 6.
In other words, the step S330 of providing the combinatorial infection risk level of the pathogen may provide the terminal or the other server with the combinatorial infection risk level COM of the pathogen in the target area predicted using the internal infection risk level α1 of the pathogen in the target area, the external infection risk level β1 of the pathogen in the target area, and a first possibility of spreading γ1 in which the pathogen is to spread from the surrounding area to the target area.
The terminal may be the user terminal 120 described above with reference to FIG. 1, and the other server may be the inspection server 140 described above with reference to FIG. 1.
The method for providing the infection risk level of the pathogen in the target area using the molecular diagnostic test data according to the above-described embodiments may maximally reflect the infection characteristics of the pathogen when predicting the infection risk level of the pathogen in a specific area and provide the infection risk level of the pathogen in the specific area with enhanced accuracy.
The examples described herein relate to the use of a management server for predicting an infection risk level of a pathogen in a target area using molecular diagnostic test data for implementing the techniques described herein. According to an embodiment, the techniques are performed by the management server in response to the processor executing one or more sequences of one or more instructions included in the memory. The instructions may be read from other machine-readable medium, such as a storage medium, to the memory. The execution of the instruction sequence included in the memory enables the process steps described herein to be carried out. In an alternative implementation, hardwired circuitry along with software instructions may be used instead of the software instructions so as to implement the examples described herein. Thus, the described examples are not limited to any specific combination of the hardware circuitry and software.
The examples described herein may be expanded to individual elements and concepts described herein, independently from other concepts, ideas, or systems and may be combined with elements cited anywhere in the disclosure. Although some examples have been described in detail with reference to the accompanying drawings, the concept is not limited to such examples. Thus, the scope of the concept is intended to be defined by the appended claims and their equivalents. Further, specific features described individually or as some examples may be combined with other features described individually or other examples although not specifically mentioned for the specific features. Thus, the absence of a description of such combination should not be interpreted as excluding such combination from the scope of the disclosure.
[CROSS-REFERENCE TO RELATED APPLICATION(S)]
This application claims priority to Korean Patent Application No. 10-2020-0144882, filed on November 03, 2020, in the Korean Intellectual Property Office, the disclosures of which are incorporated by reference herein in their entireties.

Claims (27)

  1. A server for predicting an infection risk level of a pathogen in a target area using molecular diagnostic test data, the server comprising:
    a communication unit:
    receiving the molecular diagnostic test data for the pathogen in the target area; and
    receiving at least one of molecular diagnostic test data for the pathogen in a surrounding area which is likely to spread the pathogen to the target area or an external infection risk level of the pathogen in the target area, the external infection risk level of the pathogen being an infection risk level of the pathogen in the surrounding area; and
    a processor:
    determining an internal infection risk level of the pathogen in the target area from the molecular diagnostic test data in the target area;
    determining the external infection risk level of the pathogen in the target area from the molecular diagnostic test data in the surrounding area when only the molecular diagnostic test data in the surrounding area of the molecular diagnostic test data in the surrounding area or the external infection risk level of the pathogen in the target area is received; and
    predicting a combinatorial infection risk level of the pathogen in the target area using the internal infection risk level of the pathogen in the target area, the external infection risk level of the pathogen in the target area, and a first possibility of spreading in which the pathogen is to spread from the surrounding area to the target area.
  2. The server of claim 1, wherein the molecular diagnostic test data includes result data obtained from a nucleic acid amplification reaction for a pathogen to be detected in a sample obtained from a subject.
  3. The server of claim 2, wherein the molecular diagnostic test data includes a name of the pathogen to be detected and whether the pathogen is positive or negative and further includes, optionally, a cycle threshold (Ct) value in the nucleic acid amplification reaction of the pathogen.
  4. The server of claim 1, wherein the processor determines the internal infection risk level of the pathogen in the target area using a positive result value of the pathogen obtained from the molecular diagnostic test data in the target area and optionally a load of the pathogen.
  5. The server of claim 4, wherein the processor determines the internal infection risk level of the pathogen in the target area further using a pathogen-specific risk factor in the target area.
  6. The server of claim 5, wherein the pathogen-specific risk factor includes a transmissibility, transmission speed, transmission route, mutation rate, survival and/or proliferation condition, lethality, incubation period, reinfection rate, animal-human cross infection, and/or drug resistance of the pathogen.
  7. The server of claim 4, wherein the processor determines the internal infection risk level of the pathogen in the target area further using a preventive treatment factor in the target area.
  8. The server of claim 6, wherein the preventive treatment factor includes a presence of a prophylaxis, a presence of a therapy, an efficacy of the prophylaxis, an efficacy of the therapy, a cure rate, and/or a level of quarantine of the target area for infection of the pathogen.
  9. The server of claim 1, wherein the external infection risk level of the pathogen in the target area is a combinatorial infection risk level of the pathogen in the surrounding area.
  10. The server of claim 1, wherein the external infection risk level of the pathogen in the target area is an internal infection risk level of the pathogen in the surrounding area.
  11. The server of claim 1, wherein the external infection risk level of the pathogen in the target area is a combinatorial infection risk level of the pathogen in the surrounding area predicted using an internal infection risk level of the pathogen in the surrounding area, an external infection risk level of the pathogen in another surrounding area for the surrounding area, and a second possibility of spreading in which the pathogen is to spread from the other surrounding area for the surrounding area to the surrounding area.
  12. The server of claim 1, wherein the processor determines the infection risk level of the pathogen in the surrounding area in the same manner as (i) determining the internal infection risk level of the pathogen in the target area or (ii) predicting a combinatorial infection risk level of the pathogen in the target area.
  13. The server of claim 1, wherein the surrounding area is an area geographically adjacent to the target area or is an area related to the target area traffic-wise, economically, socially and/or historically.
  14. The server of claim 1, wherein the processor determines the first possibility of spreading using a distance between the surrounding area and the target area.
  15. The server of claim 13, wherein a distance between the surrounding area and the target area is a geographical distance or a traffic-wise, economic, social and/or historical distance between the surrounding area and the target area.
  16. The server of claim 13, wherein the surrounding area includes a single geographical area or two or more areas geographically separated from each other.
  17. The server of claim 1, wherein the processor determines, every first period, the internal infection risk level of the pathogen in the target area from the molecular diagnostic test data in the target area and predicts, every second period different from the first period, a combinatorial infection risk level of the pathogen in the target area using the internal infection risk level of the pathogen in the target area, the external infection risk level of the pathogen in the target area, and the first possibility of spreading.
  18. The server of claim 1, wherein the processor assigns the same or a different weight to each of the internal infection risk level of the pathogen in the target area, the external infection risk level of the pathogen in the target area, and the first possibility of spreading and predicts a combinatorial infection risk level of the pathogen in the target area.
  19. A method for predicting an infection risk level of a pathogen in a target area using molecular diagnostic test data, the method comprising:
    receiving the molecular diagnostic test data for the pathogen in the target area;
    receiving at least one of molecular diagnostic test data for the pathogen in a surrounding area which is likely to spread the pathogen to the target area or an external infection risk level of the pathogen in the target area, the external infection risk level of the pathogen in the target area being an infection risk level of the pathogen in the surrounding area; and
    determining an internal infection risk level of the pathogen in the target area from the molecular diagnostic test data in the target area;
    determining the external infection risk level of the pathogen in the target area from the molecular diagnostic test data in the surrounding area when only the molecular diagnostic test data in the surrounding area of the molecular diagnostic test data in the surrounding area or the external infection risk level of the pathogen in the target area is received; and
    predicting a combinatorial infection risk level of the pathogen in the target area using the internal infection risk level of the pathogen in the target area, the external infection risk level of the pathogen in the target area, and a first possibility of spreading in which the pathogen is to spread from the surrounding area to the target area.
  20. The method of claim 19, wherein the external infection risk level of the pathogen in the target area is a combinatorial infection risk level of the pathogen in the surrounding area.
  21. The method of claim 19, wherein the external infection risk level of the pathogen in the target area is an internal infection risk level of the pathogen in the surrounding area.
  22. The method of claim 19, wherein the external infection risk level of the pathogen in the target area is a combinatorial infection risk level of the pathogen in the surrounding area predicted using the internal infection risk level of the pathogen in the surrounding area, an external infection risk level of the pathogen in the surrounding area, the external infection risk level of the pathogen in the surrounding area being an infection risk level of the pathogen in another surrounding area for the surrounding area, and a second possibility of spreading in which the pathogen is to spread from the other surrounding area for the surrounding area to the surrounding area.
  23. The method of claim 19, wherein the first possibility of spreading is determined using a distance between the surrounding area and the target area.
  24. The method of claim 19, wherein when determining the internal infection risk level of the pathogen in the target area, the internal infection risk level of the pathogen in the target area is determined, every first period, from the molecular diagnostic test data in the target area, and wherein when predicting the combinatorial infection risk level of the pathogen in the target area, the combinatorial infection risk level of the pathogen in the target area is predicted, every second period different from the first period, using the internal infection risk level of the pathogen in the target area, the external infection risk level of the pathogen in the target area, and the first possibility of spreading.
  25. The method of claim 19, wherein when determining the external infection risk level of the pathogen in the target area, the infection risk level of the pathogen in the surrounding area is determined in the same manner as determining the internal infection risk level of the pathogen in the target area or predicting the combinatorial infection risk level of the pathogen in the target area.
  26. The method of claim 19, wherein when determining the combinatorial infection risk level of the pathogen in the target area, the same or a different weight is assigned to each of the internal infection risk level of the pathogen in the target area, the external infection risk level of the pathogen in the target area, and the first possibility of spreading, and the combinatorial infection risk level of the pathogen in the target area is predicted.
  27. A non-transitory computer-readable storage medium storing, in a computing device, instructions executed by one or more processors to enable the one or more processors to perform a method for predicting an infection risk level of a pathogen in a target area using molecular diagnostic test data, wherein the instructions perform:
    receiving the molecular diagnostic test data in the target area for the pathogen;
    receiving at least one of molecular diagnostic test data in a surrounding area which is likely to spread the pathogen to the target area or an external infection risk level of the pathogen in the target area, the external infection risk level of the pathogen in the target area being an infection risk level of the pathogen in the surrounding area; and
    determining an internal infection risk level of the pathogen in the target area from the molecular diagnostic test data in the target area;
    determining the external infection risk level of the pathogen in the target area from the molecular diagnostic test data in the surrounding area when only the molecular diagnostic test data in the surrounding area of the molecular diagnostic test data in the surrounding area or the external infection risk level of the pathogen in the target area is received; and
    predicting a combinatorial infection risk level of the pathogen in the target area using the internal infection risk level of the pathogen in the target area, the external infection risk level of the pathogen in the target area, and a first possibility of spreading in which the pathogen is to spread from the surrounding area to the target area.
PCT/KR2021/015744 2020-11-03 2021-11-03 Server and method for predicting infection risk level of pathogen in target area using molecular diagnostic test data, and non-transitory computer-readable storage medium WO2022098068A1 (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
KR1020237009021A KR20230054405A (en) 2020-11-03 2021-11-03 A server and method for predicting the infection risk level of a pathogen in a target area using molecular diagnostic test data, and a non-transitory computer readable storage medium thereof

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
KR20200144882 2020-11-03
KR10-2020-0144882 2020-11-03

Publications (1)

Publication Number Publication Date
WO2022098068A1 true WO2022098068A1 (en) 2022-05-12

Family

ID=81458124

Family Applications (1)

Application Number Title Priority Date Filing Date
PCT/KR2021/015744 WO2022098068A1 (en) 2020-11-03 2021-11-03 Server and method for predicting infection risk level of pathogen in target area using molecular diagnostic test data, and non-transitory computer-readable storage medium

Country Status (2)

Country Link
KR (1) KR20230054405A (en)
WO (1) WO2022098068A1 (en)

Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20040122787A1 (en) * 2002-12-18 2004-06-24 Avinash Gopal B. Enhanced computer-assisted medical data processing system and method
US20110093249A1 (en) * 2009-10-19 2011-04-21 Theranos, Inc. Integrated health data capture and analysis system
KR20140122359A (en) * 2013-04-09 2014-10-20 한국과학기술연구원 Remote monitoring service system for realtime management of respiratory infectious disease and a method thereof
KR101960504B1 (en) * 2017-12-18 2019-07-15 세종대학교산학협력단 Apparatus and method for epidemic spread prediction modeling

Patent Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20040122787A1 (en) * 2002-12-18 2004-06-24 Avinash Gopal B. Enhanced computer-assisted medical data processing system and method
US20110093249A1 (en) * 2009-10-19 2011-04-21 Theranos, Inc. Integrated health data capture and analysis system
KR20140122359A (en) * 2013-04-09 2014-10-20 한국과학기술연구원 Remote monitoring service system for realtime management of respiratory infectious disease and a method thereof
KR101960504B1 (en) * 2017-12-18 2019-07-15 세종대학교산학협력단 Apparatus and method for epidemic spread prediction modeling

Non-Patent Citations (1)

* Cited by examiner, † Cited by third party
Title
JANG MI, LEE BOK-JU, KANG BONG-GU, SEO KYUNG-MIN: "Suggestion of Corona Virus Infection Data-based Simulation Model Update Method", THE KOREA INFORMATION PROCESSING SOCIETY SPRING ON-LINE CONFERENCE 2020. KIPS, vol. 27, 29 May 2020 (2020-05-29), pages 334 - 337, XP055928811, DOI: 10.3745/PKIPS.y2020m05a.334 *

Also Published As

Publication number Publication date
KR20230054405A (en) 2023-04-24

Similar Documents

Publication Publication Date Title
Soskic et al. Immune disease risk variants regulate gene expression dynamics during CD4+ T cell activation
Nograles et al. New insights into the pathogenesis and genetics of psoriatic arthritis
Rödel et al. Production of basic fibroblast growth factor and interleukin 6 by human smooth muscle cells following infection with Chlamydia pneumoniae
Rodríguez-Domínguez et al. Clinical and epidemiological characterization of a lymphogranuloma venereum outbreak in Madrid, Spain: co-circulation of two variants
Porcella et al. Transcriptional profiling of human epithelial cells infected with plasmid-bearing and plasmid-deficient Chlamydia trachomatis
Waters et al. Interleukin-17A as a biomarker for bovine tuberculosis
Tenner-Racz et al. Early protection against pathogenic virus infection at a mucosal challenge site after vaccination with attenuated simian immunodeficiency virus
Nakano et al. Evaluation of a multiplex strip PCR test for infectious uveitis: a prospective multicenter study
Harding-Esch et al. Active trachoma and ocular Chlamydia trachomatis infection in two Gambian regions: on course for elimination by 2020?
Crespo-Bermejo et al. Persistent low-level viremia in persons living with HIV undertreatment: an unresolved status
Yang et al. The Chlamydia-secreted protease CPAF promotes chlamydial survival in the mouse lower genital tract
Rizzo et al. Is Epstein-Barr virus infection associated with the pathogenesis of microscopic colitis?
WO2022098068A1 (en) Server and method for predicting infection risk level of pathogen in target area using molecular diagnostic test data, and non-transitory computer-readable storage medium
Reuschl et al. Enhanced innate immune suppression by SARS-CoV-2 Omicron subvariants BA. 4 and BA. 5
Mohseni et al. Frequency of cytomegalovirus in fertile and infertile men, referring to Afzalipour Hospital IVF Research Center, Kerman, IRAN: A case-control study
Refaat et al. Role of activins and inducible nitric oxide in the pathogenesis of ectopic pregnancy in patients with or without Chlamydia trachomatis infection
Brummaier et al. A prospective cohort for the investigation of alteration in temporal transcriptional and microbiome trajectories preceding preterm birth: a study protocol
Donati et al. Tetracycline susceptibility in Chlamydia suis pig isolates
Hui et al. Advances in the epidemiology, clinical features, diagnosis, clinical management and prevention of coronavirus disease 2019
Girón-Pérez et al. Saliva pooling strategy for the large-scale detection of SARS-CoV-2, through working-groups testing of asymptomatic subjects for potential applications in different workplaces
Groen-Hakan et al. The usefulness of aqueous fluid analysis for Epstein–Barr virus in patients with uveitis
Buder et al. Contribution of high viral loads, detection of viral antigen and seroconversion to severe acute respiratory syndrome coronavirus 2 infectivity
WO2022146046A1 (en) Server for predicting prevalence of infectious disease of interest in target area using molecular diagnostic test data, method therefor, and non-transitory computer-readable storage medium thereof
Li et al. SARS-CoV-2 viral clearance and evolution varies by type and severity of immunodeficiency
Surasi et al. Effectiveness of Abbott BinaxNOW rapid antigen test for detection of SARS-CoV-2 infections in outbreak among horse racetrack workers, California, USA

Legal Events

Date Code Title Description
121 Ep: the epo has been informed by wipo that ep was designated in this application

Ref document number: 21889533

Country of ref document: EP

Kind code of ref document: A1

ENP Entry into the national phase

Ref document number: 20237009021

Country of ref document: KR

Kind code of ref document: A

NENP Non-entry into the national phase

Ref country code: DE

122 Ep: pct application non-entry in european phase

Ref document number: 21889533

Country of ref document: EP

Kind code of ref document: A1