KR101728506B1 - Prediction of hpai outbreak route systme and method - Google Patents

Prediction of hpai outbreak route systme and method Download PDF

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KR101728506B1
KR101728506B1 KR1020150182960A KR20150182960A KR101728506B1 KR 101728506 B1 KR101728506 B1 KR 101728506B1 KR 1020150182960 A KR1020150182960 A KR 1020150182960A KR 20150182960 A KR20150182960 A KR 20150182960A KR 101728506 B1 KR101728506 B1 KR 101728506B1
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avian influenza
onset
highly pathogenic
pathogenic avian
processor
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박대희
서정순
정용화
이종욱
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고려대학교 산학협력단
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    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
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Abstract

The present invention relates to an outbreak route prediction system and method for high pathogenic avian influenza (HPAI). The HPAI outbreak route prediction system according to an embodiment of the present invention includes: a network transmission/reception unit for receiving the onset data of the HPAI; a database for receiving and storing the onset data; a memory for storing the outbreak route prediction program for the HPAI; and a processor for executing the program. The processor derives a sequential pattern rule from the onset data of the received HPAI according to the execution of the program, and predicts the outbreak route of the HPAI based on the sequential pattern rule. The present invention has the effect of promptly anticipating the outbreak route at the onset of the HPAI, thereby enabling prompt responding.

Description

[0001] PREDICTION OF HPAI OUTBREAK [0002] ROUTE SYSTME AND METHOD [

The present invention relates to a diffusion path prediction system for highly pathogenic avian influenza virus and a diffusion path prediction method therefor.

The highly pathogenic avian influenza virus (HPAI) has been reported to occur periodically in Korea after the first outbreak in December 2003 in Chungbuk Province.

The outbreak of highly pathogenic avian influenza threatens human health as well as economic losses, causing environmental pollution problems. In addition, highly pathogenic avian influenza has a high mortality rate, very high infectivity, and causes of the disease are very diverse. Therefore, it is very difficult to predict the diffusion range from the initial onset area and the next diffusion area.

Therefore, although various studies on the highly pathogenic avian influenza are being carried out recently, studies on the pathogenesis of infectious diseases are very limited.

Based on the Susceptible Infected Recovered (SIR) model, Kim et al. Published a paper (Korea Institute of Information Science and Technology (KIIS)) on the integrated information system for tracking and predicting pathology of infectious diseases. We propose a tracking and prediction system for infectious disease pathway for real - time monitoring and management.

Prediction methods using vulnerable infectious disease recovery model are applied to computational fluid dynamics to predict the spread of infectious disease. However, it is difficult to predict natural phenomena effectively because there are various factors related to the spread of infectious diseases in the actual environment.

Disclosure of Invention Technical Problem [6] The present invention provides a diffusion path prediction system and a diffusion path prediction method for highly pathogenic avian influenza.

It should be understood, however, that the technical scope of the present invention is not limited to the above-described technical problems, and other technical problems may exist.

According to an aspect of the present invention, there is provided a highly pathogenic avian influenza path prediction system comprising: a network transmission / reception unit for receiving onset data of highly pathogenic avian influenza; a database for receiving and storing onset data; And a processor for executing a program and a memory for storing the spread path prediction program of the highly pathogenic avian influenza. At this time, the processor derives a sequential pattern rule from the onset data of the received highly pathogenic avian influenza according to the execution of the program, and predicts the diffusion path of the highly pathogenic avian influenza based on the sequential pattern rule.

According to another aspect of the present invention, there is provided a method for predicting a diffusion path of a highly pathogenic avian influenza virus diffusion path prediction system, comprising: collecting onset data of a highly pathogenic avian influenza virus; Performing pre-processing and statistical analysis of data for deriving a sequential pattern rule from the collected data; Deriving a sequential pattern rule from onset data of highly pathogenic avian influenza; And predicting the diffusion pathway of highly pathogenic avian influenza. At this time, the step of deriving the sequential pattern rule derives a sequential pattern rule considering at least one of the onset statistics information of the highly pathogenic avian influenza, seasonal latency period, and seasonal latency period.

The present invention has the effect of promptly anticipating the diffusion pathway at the onset of a highly pathogenic avian influenza virus.

In addition, it is possible to promptly identify candidates that are highly likely to develop the disease, thereby enabling a rapid evacuation operation, thereby minimizing the additional damage caused by the highly pathogenic avian influenza.

FIG. 1 is a block diagram of a diffusion path prediction system for highly pathogenic avian influenza according to an embodiment of the present invention. Referring to FIG.
2 is a flowchart illustrating a method for predicting a diffusion path of highly pathogenic avian influenza according to an embodiment of the present invention.
FIG. 3 shows an example of onset data of highly pathogenic avian influenza collected according to an embodiment of the present invention.
FIG. 4 illustrates an example of a data set generated to derive a sequential pattern rule in the method for predicting the pathogenesis of highly pathogenic avian influenza according to an embodiment of the present invention.
FIG. 5 illustrates a partial result of a sequential pattern rule derived in accordance with an embodiment of the present invention.
Figure 6 shows a time semantic graph of the onset of highly pathogenic avian influenza viruses generated according to one embodiment of the present invention.
FIG. 7 shows a result of expressing a diffusion path of a time semantic graph generated according to an embodiment of the present invention using a geographic information system.

Hereinafter, embodiments of the present invention will be described in detail with reference to the accompanying drawings, which will be readily apparent to those skilled in the art. The present invention may, however, be embodied in many different forms and should not be construed as limited to the embodiments set forth herein. In order to clearly illustrate the present invention, parts not related to the description are omitted, and similar parts are denoted by like reference characters throughout the specification.

Throughout the specification, when a part is referred to as being "connected" to another part, it includes not only "directly connected" but also "electrically connected" with another part in between . Also, when an element is referred to as "comprising ", it means that it can include other elements as well, without departing from the other elements unless specifically stated otherwise.

Hereinafter, a diffusion path prediction system and a prediction method for highly pathogenic avian influenza according to an embodiment of the present invention will be described in detail with reference to the drawings.

FIG. 1 is a block diagram of a diffusion path prediction system for highly pathogenic avian influenza according to an embodiment of the present invention.

1, a highly pathogenic avian influenza diffusion path prediction system 100 according to an embodiment of the present invention includes a network transmission / reception unit 110, a memory 120, a database 140, and a processor 130 .

The network transmission / reception unit 110 receives the onset data of the highly pathogenic avian influenza. For example, the outbreak data for highly pathogenic avian influenza can be, but is not limited to, forensic disease data provided by the national animal anti-virus integration system.

Here, the network transmission / reception unit 110 may be a wired network such as a local area network (LAN), a wide area network (WAN) or a value added network (VAN) ) Or a satellite communication network, or the like.

In the memory 120, a spread path prediction program of a highly pathogenic avian influenza virus is stored. The memory 120 may be a nonvolatile memory device such as a cache, a read only memory (ROM), a programmable ROM (PROM), an erasable programmable ROM (EPROM), an electrically erasable programmable ROM (EEPROM) But is not limited to, a volatile memory device such as a RAM (Random Access Memory) or a storage medium such as a hard disk drive (HDD) and a CD-ROM.

The database 140 may store a set of data for the year of onset, date of onset, and outbreak area generated from the data of the highly pathogenic avian influenza.

The processor 130 generates a data set, such as an address related to the year of onset, the date of onset, and the onset area, from the collected onset data, and stores the data set in the database 140.

The processor 130 may derive a sequential pattern rule by analyzing the association between the respective data using the data set described above. Here, the sequential pattern rule is an association analysis of the above-described data set according to a time series, for example, a rule indicating a temporal order of an event or an action.

The processor 130 predicts the diffusion path of the highly pathogenic avian influenza based on the derived sequential pattern rule. At this time, according to the embodiment of the present invention, the processor 130 can predict a diffusion path by deriving a sequential pattern rule in consideration of at least one of statistical information on onset of highly pathogenic avian influenza, seasonal latency period, and seasonal latency period .

The processor 130 may visualize the predicted pathogenesis of the highly pathogenic avian influenza by using a temporal semantic graph or a geographic information system (GIS).

Thereafter, the processor 130 may predict the next onset risk candidate region based on the predicted spread path.

Meanwhile, according to an embodiment of the present invention, the processor 130 may transmit the predicted next occurrence risk candidate region to the user terminal through the network transmission / reception unit 110. [

The user terminal may be a portable terminal or a computer. For example, a portable terminal is a wireless communication device that is guaranteed to be portable and mobility. The portable terminal includes a PCS (Personal Communication System), a GSM (Global System for Mobile communications), a PDC (Personal Digital Cellular), a PHS Personal Digital Assistant), IMT (International Mobile Telecommunication) -2000, Code Division Multiple Access (CDMA) -2000, W-Code Division Multiple Access (W-CDMA), WiBro (Wireless Broadband Internet) , A smart pad, and the like, for example. Further, the computer may include, for example, a desktop, a laptop, a tablet PC, and the like on which a WEB Browser is mounted.

2 is a flowchart illustrating a method for predicting a diffusion path of highly pathogenic avian influenza according to an embodiment of the present invention.

Referring to FIG. 2, the method for predicting a highly pathogenic avian influenza pathway according to an embodiment of the present invention includes: collecting onset data of highly pathogenic avian influenza (S110); Performing a preprocessing and statistical analysis of data for deriving a sequential pattern rule (S120); Deriving a sequential pattern rule from the onset data of the collected highly pathogenic avian influenza (S130); And estimating the diffusion path of the highly pathogenic avian influenza (S140).

FIG. 3 shows an example of onset data of highly pathogenic avian influenza collected according to an embodiment of the present invention.

FIG. 4 illustrates an example of a data set generated to derive a sequential pattern rule in the method for predicting the pathogenesis of highly pathogenic avian influenza according to an embodiment of the present invention.

FIG. 5 illustrates a partial result of a sequential pattern rule derived in accordance with an embodiment of the present invention.

First, in step S110 of collecting the onset data of the highly pathogenic avian influenza, the processor 130 may collect the onset data of avian influenza provided by a public institution or a private agency through the network transmitting / receiving unit 110. [ For example, the outbreak data for the highly pathogenic avian influenza can be, but is not limited to, the outbreak data provided by the national animal anti-virus integration system.

At this time, the onset data of the highly pathogenic avian influenza collected include the name of the disease, the name of the farm, the location of the farm, the date of diagnosis, the axis number, The basic statistical results of the onset data for a long term may include the year of avian influenza, the onset season, the number of onset cases, the first onset area, and the onset area, as shown in Fig. In addition to the information shown in FIG. 3, the avian influenza virus may include information on the spreading path including the date of onset and the local information on the occurrence of the avian influenza after the first occurrence in a specific area.

Meanwhile, the processor 130 may collect statistical data related to the highly pathogenic avian influenza in addition to the onset data described above, and store the collected statistical data in the database 140. The statistical data herein may include, but is not limited to, the region where the most highly pathogenic avian influenza is most likely to occur, the onset season, and the seasonal latency period.

Next, the processor 130 generates a new data set through a preprocessing process on data stored in the database 140 in step S120 of performing preprocessing and statistical analysis of data for deriving a sequential pattern rule, Statistical analysis is performed using the collected data.

For example, the processor 130 may define the sequence of correlations of patterns according to time series, then construct each sequence as a list of events, each event may be organized as a set of items, And extracts a subsequence having support.

Specifically, referring to FIG. 4, processor 130 generates a set of data for sequential pattern analysis, from the collected onset data. In detail, a data set can be generated from the collected onset data by dividing the year, the date of onset, and the address of the onset area of the time dimension into a city / province, a city / town, a town / village, Then, the transaction ID is set for each year of occurrence, the date of onset is assigned to the sorting ID in the same transaction, and the affected area is converted into an item.

Meanwhile, terms such as item, transaction, and transaction ID have the same meaning as items used in a general data mining technique, a transaction (a set of items), and a transaction ID (transaction identifier) The detailed description will be omitted.

In addition, the processor 130 may perform a statistical analysis on the state of the invention of avian influenza by year or the latency period by season. In addition, the processor 130 may map the incidence rate by region to perform clustering around a specific region.

Next, in step S130 of deriving the sequential pattern rule from the onset data of the collected highly pathogenic avian influenza, the sequential pattern rule can be derived using the data set stored in the database 140 and the statistical analysis data. At this time, the processor 130 can analyze the association rule between each data through the package (arulesSequence) of the R program based on the SPADE algorithm. After extracting the results satisfying the minimum reliability of 0.4 and the minimum reliability of 1, the association rules can be sorted as shown in FIG. 5 based on the specific region.

Here, the minimum support value is defined by the user. When the initial value of the minimum support is exceeded, it is referred to as a frequent itemset. If the initial value is not exceeded, a non-frequent itemset ).

That is, according to the diffusion path prediction method for highly pathogenic avian influenza according to an embodiment of the present invention, a frequent item set having a minimum support of 0.4 or more and a minimum reliability of 1 is extracted to search for an association rule.

As shown in FIG. 5, for example, the association rule derived according to an embodiment of the present invention indicates the time for the first onset of the highly pathogenic avian influenza in the region after each region, For patterns spreading to the area, record the time of onset in the nearest epoch of the epidemic area. Therefore, the diffusion pathway of the highly pathogenic avian influenza virus over a certain period of time can be confirmed.

Thereafter, the processor may combine the statistical analysis data of the highly pathogenic avian influenza analyzed in step S120 into the association rule shown in Fig. 5, to derive a final sequential pattern rule.

Finally, in step S140 of predicting the spread path of the highly pathogenic avian influenza, the processor 130 calculates a time semantic graph such as that shown in Fig. 6 using the final sequential pattern rule derived in step S130 . Then, using this, it is possible to predict the spread path of the highly pathogenic avian influenza by sequentially displaying the areas of the highly pathogenic avian influenza on the map.

Figure 6 shows a time semantic graph of the onset of highly pathogenic avian influenza viruses generated according to one embodiment of the present invention.

FIG. 7 shows a result of expressing a diffusion path of a time semantic graph generated according to an embodiment of the present invention using a geographic information system.

6, using the time semantic graph generated according to the method for predicting the spread path of highly pathogenic avian influenza according to an embodiment of the present invention, after the first highly pathogenic avian influenza was invented on Jan. 16 at Gochang, It is possible to visually confirm the pattern of spreading over time. Specifically, during period 1, which represents the first two weeks after the outbreak of the highly pathogenic avian influenza, the highly pathogenic avian influenza has already spread nationwide, and during the period 2, which represents a period of four weeks thereafter, It can be seen that diffusion has spread to the region. In other words, as can be seen from FIG. 6, in the case of highly pathogenic avian influenza, since the risk of spreading nationwide is high after two weeks from the first invention, it is necessary to anticipate the spreading pathway, More important than anything else.

In addition, according to an embodiment of the present invention, as shown in FIG. 7, the spread path of the highly pathogenic avian influenza is displayed on a map using a geographic information system and visualized, whereby the distance between each onset area and the next spreading danger zone candidate Can be quickly identified.

As described above, the diffusion path predicting method of the highly pathogenic avian influenza diffusion path prediction system according to an embodiment of the present invention enables early prediction of the diffusion path at the onset of the highly pathogenic avian influenza, thereby enabling quick response. Therefore, it is possible to minimize the additional damage caused by the highly pathogenic avian influenza.

One embodiment of the present invention may also be embodied in the form of a recording medium including instructions executable by a computer, such as program modules, being executed by a computer. Computer readable media can be any available media that can be accessed by a computer and includes both volatile and nonvolatile media, removable and non-removable media. In addition, the computer readable medium may include both computer storage media. Computer storage media includes both volatile and nonvolatile, removable and non-removable media implemented in any method or technology for storage of information such as computer readable instructions, data structures, program modules or other data.

While the methods and systems of the present invention have been described in connection with specific embodiments, some or all of those elements or operations may be implemented using a computer system having a general purpose hardware architecture.

It will be understood by those skilled in the art that the foregoing description of the present invention is for illustrative purposes only and that those of ordinary skill in the art can readily understand that various changes and modifications may be made without departing from the spirit or essential characteristics of the present invention. will be. It is therefore to be understood that the above-described embodiments are illustrative in all aspects and not restrictive. For example, each component described as a single entity may be distributed and implemented, and components described as being distributed may also be implemented in a combined form.

The scope of the present invention is defined by the appended claims rather than the detailed description and all changes or modifications derived from the meaning and scope of the claims and their equivalents are to be construed as being included within the scope of the present invention do.

100: Pathway prediction system for highly pathogenic avian influenza
110: Network transmission /
120: Memory
130: Processor
140: Database

Claims (10)

A system for predicting a highly pathogenic avian influenza pathway,
A network transmission / reception section for receiving the onset data of the highly pathogenic avian influenza;
A database for receiving and storing the onset data,
The spread path prediction program of highly pathogenic avian influenza is stored in memory and
And a processor for executing the program,
According to the execution of the program, the processor executes a plurality of sequences including an item corresponding to an onset area and a transaction corresponding to the onset time information from the onset data of the received highly pathogenic avian influenza Determining a frequent itemset from among the plurality of sequences based on a predetermined minimum support, deriving a sequential pattern rule from the frequent item set, and based on the sequential pattern rule, determining whether the highly pathogenic avian influenza virus Of the diffusion path,
Pathway prediction system for highly pathogenic avian influenza. The method according to claim 1,
Wherein the onset time information includes information on at least one of a year of onset and a date of onset,
The processor
Wherein said plurality of sequences are stored in a database. The method according to claim 1,
The processor
Wherein the sequential pattern rule is derived in consideration of at least one of statistical information on onset of the highly pathogenic avian influenza, seasonal latency period, and seasonal latency period. The method according to claim 1,
The processor
And predicting a next onset risk candidate region based on the predicted spread path of the highly pathogenic avian influenza. 5. The method of claim 4,
The processor
Through the network transmitting / receiving unit,
And transmits information on the predicted next oncoming risk candidate region to the user terminal. A method for predicting the spread path of avian influenza by a processor of a system for predicting the pathogenesis of highly pathogenic avian influenza,
Collecting onset data of highly pathogenic avian influenza;
Generating a plurality of sequences including an item corresponding to an onset area and a transaction corresponding to onset time information from onset data of the highly pathogenic avian influenza virus;
Determining a frequent itemset among the plurality of sequences based on a predetermined minimum support;
Deriving a sequential pattern rule from the frequent item set; And
And predicting the diffusion path of the highly pathogenic avian influenza,
The step of deriving the sequential pattern rule
Wherein the sequential pattern rule is derived in consideration of at least one of statistical information on onset of highly pathogenic avian influenza, seasonal latency period, and seasonal latency period,
Diffusion path prediction method. delete The method according to claim 6,
Further comprising predicting a next onset risk candidate region based on the predicted spread path of the highly pathogenic avian influenza. A computer-readable recording medium recording a program for performing the method according to any one of claims 6 to 8 on a computer. The method according to claim 1,
The processor
And generating a time semantic graph for predicting a diffusion path of an onset area of the highly pathogenic avian influenza based on the sequential pattern rule.
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Cited By (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2019227711A1 (en) * 2018-05-31 2019-12-05 平安科技（深圳）有限公司 Method and apparatus for generating influenza prediction model, and computer-readable storage medium
KR102140096B1 (en) * 2019-09-20 2020-07-31 한국과학기술정보연구원 Prediction apparatus for spreading of epidemic, and control method thereof
KR20240002682A (en) 2022-06-29 2024-01-05 고려대학교 산학협력단 Apparatus and method for predicting influenza through reginal similarity

Cited By (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2019227711A1 (en) * 2018-05-31 2019-12-05 平安科技（深圳）有限公司 Method and apparatus for generating influenza prediction model, and computer-readable storage medium
KR102140096B1 (en) * 2019-09-20 2020-07-31 한국과학기술정보연구원 Prediction apparatus for spreading of epidemic, and control method thereof
KR20240002682A (en) 2022-06-29 2024-01-05 고려대학교 산학협력단 Apparatus and method for predicting influenza through reginal similarity

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