CN114783619A - Infectious disease transmission simulation method, system, terminal and storage medium - Google Patents
Infectious disease transmission simulation method, system, terminal and storage medium Download PDFInfo
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Abstract
The invention relates to an infectious disease transmission simulation method, system, terminal and storage medium. The method comprises the following steps: constructing an individual moving model according to multi-source data in the prevention and control area; constructing an individual-based space-time dynamic contact network based on the individual movement model; modeling for non-drug intervention measures implemented within the prevention and control area; and building an individual-based infectious disease transmission model based on the dynamic contact network and the intervention measure modeling, and simulating the infectious disease transmission intensity in the prevention and control area through the infectious disease transmission model. The invention simulates the infectious disease transmission strength of the prevention and control area through the infectious disease transmission model, can support the fusion modeling of various non-drug intervention measures, is beneficial to evaluating the compliance degree of individuals to various intervention measures, and can accurately evaluate the effectiveness of various non-drug intervention measures for inhibiting the infectious disease transmission, thereby systematically and scientifically assisting in coping with the future epidemic situation prevention and control.
Description
Technical Field
The invention belongs to the technical field of infectious disease calculation, and particularly relates to an infectious disease transmission simulation method, system, terminal and storage medium.
Background
The novel coronavirus (COVID-19) is a virus which can be transmitted by respiratory droplets, and after an individual is infected with the COVID-19 virus, the individual mainly shows symptoms of fever, hypodynamia, dry cough and the like, and has the characteristic of pneumography. According to the latest real-time statistical data of the world health organization, 67780361 cases of global accumulated COVID-19 confirmed cases and 1551214 cases of accumulated death cases are obtained by 2020, 12 months and 9 days of Beijing. The dynamic variation of human activities in time and space is a key driver of the spread of infectious diseases from person to person. Most countries around the world are beginning to implement various non-pharmaceutical intervention measures to deal with the spread of COVID-19 in the country, including closing the city, restricting outages, banning parties, etc., which severely interfere with the normal life needs of people, and at the same time, cause serious economic losses. Therefore, in the real scene that people gradually recover normal living order, it becomes a great challenge to scientifically adopt simple and effective measures to inhibit epidemic outbreaks again.
The method helps track the travel positions of individuals at different moments by constructing a refined infectious disease transmission model based on the individuals, so that a dynamic contact network between the individuals is reconstructed, and the space-time transmission process of the infectious disease between people is accurately simulated. The refined infectious disease propagation model based on the individuals can support space-time diffusion simulation at the individual level and refined prevention and control measure evaluation, but the generated individual movement model depends on detailed individual attribute information and individual activity tracks to construct an individual dynamic contact network. The spreading of infectious diseases in city level is researched, an individual moving model needs to be matched with a research area in the aspects of population attributes, travel characteristics and the like, and the model needs to be verified by using city real infection case data. The evaluation of the effect of the intervention measures can only measure the effect of the intervention measures in epidemic situation diffusion, and a reasonable scheme required for inhibiting the outbreak of the epidemic situation cannot be provided according to the actual situation. Furthermore, the enormous computational resources and computational time required to run an individual-based refined model of infectious disease transmission remains a major challenge.
At the present stage, the number of infected people who wear the high-strength mask and the close-contact tracking countries is obviously lower than that of the countries which wear the anti-wear mask and cannot perform the close-contact tracking, the infected people are detected in time after the disease happens, the propagation time of the infected people is directly shortened, and the effectiveness of the close-contact tracking is also improved. However, there is little research concerning the influence of three intervention measures, namely wearing a mask, performing close-fitting tracking and detecting the speed immediately after the outbreak, on the possibility of epidemic outbreak.
Disclosure of Invention
The invention provides an infectious disease transmission simulation method, system, terminal and storage medium, and aims to solve at least one of the technical problems in the prior art to a certain extent.
In order to solve the above problems, the present invention provides the following technical solutions:
an infectious disease transmission simulation method comprising:
constructing an individual moving model according to multi-source data in the prevention and control area; the multi-source data comprises census data, building census data, travel survey data and mobile phone signaling data;
constructing an individual-based space-time dynamic contact network based on the individual movement model;
modeling for non-drug intervention measures implemented within the prevention and control area;
and building an individual-based infectious disease transmission model based on the dynamic contact network and the intervention measure modeling, and simulating the infectious disease transmission intensity in the prevention and control area through the infectious disease transmission model.
The technical scheme adopted by the embodiment of the invention also comprises the following steps: the specific steps of constructing the individual moving model according to the multi-source data in the prevention and control area are as follows:
generating a synthetic individual corresponding to the population total number by utilizing the census data, and reconstructing individual attribute information of the synthetic individual according to population statistical characteristics; the individual attribute information comprises the attributes of age, gender, family structure and residence;
taking the mobile phone signaling data as a main part, the travel survey data as an auxiliary part and the residence attributes of the synthetic individuals as common semantics, carrying out travel track matching on the synthetic individuals through data mining and fusion, and generating a 24-hour travel activity chain of the synthetic individuals and travel positions corresponding to various types of travel activities; the travel activities include home, work, school and other public place activities.
The technical scheme adopted by the embodiment of the invention also comprises the following steps: the method for constructing the space-time dynamic contact network based on the individual movement model specifically comprises the following steps:
according to the 24-hour trip activity chain of the synthesized individuals, taking hours as time granularity, and regarding the individuals performing the same trip activity in the same building in the same hour as space-time co-occurrence individuals;
and respectively constructing a fixed contact circle for a small part of acquaintances and a random contact circle for strangers on the basis of the spatio-temporal co-occurrence individuals.
The technical scheme adopted by the embodiment of the invention also comprises the following steps: the non-drug intervention measures comprise isolation, timely detection, mask wearing, social separation and city closing; the modeling aiming at the non-drug intervention measures implemented in the prevention and control area specifically comprises the following steps:
when the susceptible individual is contacted with an infected individual without any intervention, the probability p that the susceptible individual is infected is:
p＝pTrans×Ic×r
when wearing the mask, the probability of the susceptible individual being infected is:
p＝pTrans×Ic×r×(1-θ)
when the infected person is isolated at home after the disease occurs, the probability of contacting the infected person at each time inside the home is as follows:
p＝pTrans×Ic×r×(1-δ)
in the above formula: pTrans is the propagation probability per contact, IcTo the extent of contact, θ is mask effectiveness, δ is self-housekeeping isolation effectiveness, and r is the ability of the infected to spread the virus before and after the onset of symptoms; when the susceptible individual is contacted with the infected individual, a [0,1 ] is generated]Uniformly distributed random numbers x, comparing the random numbers x with infection probability p, if x ≦ p, indicating that the susceptible individual is infected.
The technical scheme adopted by the embodiment of the invention also comprises the following steps: the modeling and constructing an individual-based model of infectious disease transmission based on the dynamic contact network and the intervention further comprises:
verifying the infectious disease transmission model by adopting real morbidity data in the prevention and control area; what is needed isThe verification process specifically comprises the following steps: based on a given R in the context without any intervention0Fitting the propagation probability pTrans; in a reference epidemic situation scene, fitting two parameters of mask effectiveness theta and household isolation effectiveness delta by taking a root mean square error between a minimized simulation result and a real disease data curve in the prevention and control area as a standard; the reference epidemic situation scene refers to a scene of the first wave infectious disease epidemic situation experienced in the prevention and control area.
The technical scheme adopted by the embodiment of the invention also comprises the following steps: the modeling and constructing an individual-based model of infectious disease transmission based on the dynamic contact network and the intervention further comprises:
aiming at an epidemic situation under a normalized epidemic prevention scene, simulating the outbreak probability of the infectious disease under the combination of three measures of joint tracking, mask wearing and timely detection, and generating an intervention measure combination scheme for inhibiting the spread of the infectious disease; the normalized epidemic prevention scene refers to a scene that the infectious disease is restrained and then the normal life is recovered.
The technical scheme adopted by the embodiment of the invention also comprises the following steps: the outbreak probability of simulating the infectious disease under the combination of three measures of close-contact tracking, mask wearing and timely detection is as follows:
dividing the joint tracking grades into 5 joint tracking grades for not implementing joint tracking, tracking family joint members, tracking fixed contact ring joint members, tracking fixed contact rings and 50% random splicers and tracking all splicers, respectively setting two scenes of low effectiveness and high effectiveness of the mask to simulate the outbreak probability, calculating the outbreak probability of epidemic situation in a set time after a first case is found in each epidemic situation, and generating an intervention measure combination scheme for inhibiting the spread of the infectious disease under the corresponding epidemic situation.
The embodiment of the invention adopts another technical scheme that: an infectious disease transmission simulation system comprising:
a mobile model building module: the system comprises a control area, a motion model and a motion model, wherein the control area is used for controlling the motion of an individual; the multi-source data comprises census data, building census data, travel survey data and mobile phone signaling data;
a contact network construction module: the system is used for constructing an individual-based space-time dynamic contact network based on the individual movement model;
an infection probability calculation module: for modeling non-pharmaceutical intervention measures implemented within the prevention and control area;
a propagation model construction module: and the system is used for building an individual-based infectious disease transmission model based on the dynamic contact network and the intervention measure modeling, and simulating the infectious disease transmission intensity in the control area through the infectious disease transmission model.
The technical scheme adopted by the embodiment of the invention also comprises the following steps: a terminal comprising a processor, a memory coupled to the processor, wherein,
the memory stores program instructions for implementing the infectious disease transmission simulation method described above;
the processor is configured to execute the program instructions stored by the memory to control infectious disease transmission simulation.
The technical scheme adopted by the embodiment of the invention also comprises the following steps: a storage medium stores program instructions executable by a processor to perform the above-described infectious disease transmission simulation method.
Compared with the prior art, the embodiment of the invention has the following beneficial effects: according to the infectious disease propagation simulation method, the system, the terminal and the storage medium, disclosed by the embodiment of the invention, the individual moving model is constructed by fusing multi-source data such as population census data, building census data, travel survey data, mobile phone signaling data and the like, so that the individual moving model is matched with the real environment of a research area, and the space-time dynamic contact network based on individuals is constructed on the basis of the individual moving model, so that the activity track of the individuals under the city scale and the contact network between the individuals are explicitly expressed and tracked, and the inversion and the prediction of the diffusion of infectious diseases in cities are facilitated. And constructing an individual-based infectious disease transmission model based on the dynamic contact network, and simulating the infectious disease transmission strength of the prevention and control area through the infectious disease transmission model. The method is characterized in that the work place and the hourly travel position of an individual are described in detail through an infectious disease transmission model, and contacters possibly met by the individual in various travel activities are tracked, so that the infectious disease transmission intensity of a prevention and control area is simulated. The embodiment of the invention can support the integration modeling of various non-drug intervention measures such as the tracing and isolation of a person who is close to a person, the isolation of foreign people, the wearing of a mask, the reduction of travel, the limitation of intercity traffic, the stopping of learning, the gradual re-work and the like, is not only beneficial to the evaluation of the compliance degree of an individual to the various intervention measures, but also can accurately evaluate the effectiveness of the various non-drug intervention measures in the inhibition of the spread of infectious diseases. In the face of epidemic situation dissemination under a normalized epidemic prevention scene, the embodiment of the invention systematically and scientifically assists in dealing with future epidemic situation prevention and control by further evaluating the outbreak probability of the infectious disease under the combination of three measures of joint tracking, mask wearing and timely detection and generating an intervention measure combination scheme for inhibiting the epidemic situation dissemination.
Drawings
FIG. 1 is a flow chart of an infectious disease transmission simulation method according to an embodiment of the present invention;
FIG. 2 is a diagram of an infectious disease transmission model architecture according to an embodiment of the present invention;
FIG. 3 is a schematic diagram illustrating the effect of different contact tracking levels on the reoccurrence of an epidemic in an embodiment of the present invention;
FIG. 4 is a schematic diagram of reconstruction results of a spatiotemporal contact model of an individual according to an embodiment of the present invention, wherein (a) is a comparison between the age of the synthesized individual and census data, (B) is a comparison between the family structure of the synthesized individual and travel survey data, (c) is a comparison between the number of contacts of the synthesized individual at each age and the survey data of B city, and (d) is a degree distribution of the number of contacts of the synthesized individual;
FIG. 5 is a diagram illustrating fitting results of effectiveness of different masks and effectiveness of household isolation to cumulative numbers of patients in an embodiment of the present invention;
FIG. 6 is a graph showing the results of a fit between actual morbidity data and simulation results observed in an example of the present invention;
FIG. 7 is a schematic diagram of spatial accuracy analysis between simulation results and observation results according to an embodiment of the present invention;
FIG. 8 is a schematic diagram of the outbreak probability of an epidemic within four weeks under the implementation intensity of different intervention measures in the embodiment of the present invention;
FIG. 9 is a schematic diagram of the outbreak probability of an epidemic within eight weeks under the intensity of different intervention measures implemented in the embodiment of the present invention;
FIG. 10 is a schematic diagram of an infectious disease transmission simulation system according to an embodiment of the present invention;
fig. 11 is a schematic structural diagram of a terminal according to an embodiment of the present invention;
FIG. 12 is a schematic structural diagram of a storage medium according to an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
Aiming at the defects of the prior art, the infectious disease propagation simulation method provided by the embodiment of the invention is used for constructing an individual-based infectious disease propagation model containing individual family attributes and family addresses (refined to the building level) based on data such as urban population census data, building census data, family trip survey data, mobile phone position data of mobile phone users and the like. Based on the infectious disease propagation model, the implementation of isolation measures such as household isolation, building isolation, community isolation and the like can be supported. In addition, the infectious disease propagation model is used for describing the working place and the travel position of an individual in detail, tracking colleagues/classmates of the individual and contacts possibly encountered in other activities such as leisure and entertainment, and can support the fusion modeling of various intervention measures such as close-contact person tracking and isolation, foreign person isolation, mask wearing, travel reduction, intercity traffic limitation, stoppage in school, gradual re-work and the like, so that the effect of the implemented intervention measures can be evaluated, and the future epidemic situation prevention and control can be systematically and scientifically assisted. In terms of computing resources and computing efficiency, the embodiment of the invention transplants the infectious disease propagation model onto the eosin super computer computing platform, and the strong computing power of the model meets the propagation simulation computing requirements of various infectious diseases such as COVID-19, influenza, Nore, dengue and the like, so that the model can be applied to large-scale simulation of various scenes, and the effectiveness of various intervention measures in epidemic situation prevention and control is analyzed in detail.
Specifically, please refer to fig. 1, which is a flowchart illustrating an infectious disease transmission simulation method according to an embodiment of the present invention. The infectious disease transmission simulation method provided by the embodiment of the invention comprises the following steps of:
s10: multi-source data such as population census data, building census data, travel survey data, mobile phone signaling data and the like are fused to construct an individual movement model;
in this step, the individual movement model has both individual attribute characteristics and individual movement characteristics. The individual movement model construction mode specifically comprises the following steps:
s11: generating a synthetic individual corresponding to the total population by using census data, and reconstructing individual attribute information of the synthetic individual according to population statistical characteristics;
s12: by taking mobile phone signaling data as a main part, trip survey data as an auxiliary part and individual residence places as common semantics, realizing the trip track matching of the synthesized individuals through data mining and fusion, and generating a 24-hour trip activity chain of the synthesized individuals; the synthetic individual population is equivalent to the total population of the control area, and has individual attribute information, a 24-hour travel activity chain and travel positions in various travel activities. The individual attribute information includes information such as age, sex, family structure, occupation, residence (building level), and the like, and the activity type includes home, work, school, and other public activities.
S20: constructing a space-time dynamic contact network based on individuals based on an individual movement model;
in this step, the space-time dynamic contact network construction method specifically includes:
s21: according to a 24-hour trip activity chain of the synthesized individuals, taking hours as time granularity, and regarding the individuals performing the same type of activities in the same building in the same hour as space-time co-occurrence individuals;
s22: respectively constructing fixed contact rings aiming at a small part of acquaintances and random contact rings aiming at strangers on the basis of space-time co-occurrence individuals; according to the practical situation of a prevention and control area, 5 contact environments facing families, kindergartens, primary and secondary schools, work units and public places are respectively provided with fixed contact rings of the former four contact environments, and the fixed contact rings respectively comprise family members, personnel in the same class of kindergartens (upper limit 25 people), personnel in the same class of primary and secondary schools (upper limit 50 people) and personnel in the same work group in the work units (upper limit 10 people). According to the existing urban research result, the contact between every two family members is set to be generated every day; children in the kindergarten make contact with 10 classmates in the fixed contact ring every day, and make random contact with 2 children of other classes in the same kindergarten; each student in the school of the primary and middle schools contacts with 20 classmates in the fixed contact ring and randomly contacts with 5 classmates of other classes; each individual of the work unit made contact with 7 colleagues within a fixed contact circle, and 3 other groups of colleagues made random contact. The specific contact population of the space-time co-occurrence individuals in the public places in random contact is totally restricted by the long-tail distribution of the total contact population of the individuals every day.
S30: modeling is carried out aiming at non-drug intervention measures such as isolation, timely detection, mask wearing, social isolation, city closing and the like implemented in a prevention and control area;
in this step, the non-pharmaceutical intervention is not limited to isolation, timely detection, wearing of a mask, social isolation, city sealing, but also includes other intervention measures such as travel restriction, environmental disinfection and the like. The starting and stopping time of each intervention measure can be set in the model according to the actual situation of the prevention and control area, taking CODVID-19 (novel coronavirus) as an example, and the concrete modeling contents comprise:
a) isolation of each type: the simulated dominant infected person and the recessive infected person are isolated after the diagnosis is confirmed, the close contact person of the confirmed patient is isolated for 14 days in a centralized way, the neighbor living in the building where the confirmed patient is located is isolated for 14 days by the home, the immigration population entering the prevention and control area is isolated for 14 days by the home, and the dominant infected person is isolated from the self-living before the diagnosis after the disease occurs;
b) and (3) timely detection: according to the flow regulation report, the model shortens the time from onset to diagnosis in stages;
c) wearing a mask: according to actual conditions, the mask wearing probability before the COVID-19 outbreak is 0, and after the COVID-19 outbreak, citizens gradually start wearing the mask during the outgoing activities. The wearing probability of the mask in the model changes along with time, and is specifically described as Pt＝1/(1+e-0.5*(t-t0) In which t) is01 month and 23 days.
d) Social separation: the model simulates the social separation of canceling the outgoing activities, closing the school, gradually reworking in batches, canceling the amateur activities and the like.
e) Closing the city: and the model simulates the urban migratory and migratory population according to the Baidu migration index.
The susceptible individual is contacted with the infected individual without any intervention, with the probability p of being infected being:
p＝pTrans×Ic×r (1)
when wearing the mask, the probability of the susceptible individual being infected is adjusted according to the effectiveness of the mask:
p＝pTrans×Ic×r×(1-θ) (2)
when the infected person is isolated at home after the disease occurs, the probability of contacting the infected person each time inside the home is adjusted to be:
p＝pTrans×Ic×r×(1-δ) (3)
in formulas (1), (2), and (3): pTrans is the propagation probability per contact, IcTo the extent of contact, θ is mask effectiveness, δ is self-housekeeping isolation effectiveness, and r is the ability of the infected person to spread the virus before and after the onset of symptoms. Finally, Bernoulli test (Bernoulli trial) is adopted to determine whether the susceptible individual is infected after contacting with the infected individual, and the specific description is as follows: when a susceptible individual is exposed to an infected individual, a [0,1 ] is generated]And comparing the random number x with the infection probability p, wherein if x is less than or equal to p, the susceptible person is infected, and otherwise, the susceptible person is not infected.
S40: establishing an individual-based infectious disease transmission model based on a dynamic contact network and intervention measure modeling, verifying the infectious disease transmission model according to real epidemic situation data, and simulating the infectious disease transmission strength of a prevention and control area through the infectious disease transmission model;
in this step, the infectious disease transmission model is configured as shown in fig. 2. Wherein, S represents susceptible population, L represents population in latent period, I represents population with transmission power, Q represents population in centralized isolation/diagnosis state, R represents population removed after rehabilitation or death, and Inflow and Outflow represent respectively immigration population and immigration population of each day in the prevention and control area. Susceptible individuals S will have a 25% chance of becoming recessive after infection, otherwise they will develop symptoms after the end of the latent period and become dominant. The dominant infected patients experience a latent period of 5.2 days on average and have the ability to transmit the virus 2 days before onset, and the transmission stage before onset of the patients is marked as Ipre_symThe transmission stage after onset is marked as Ionset. Recessive infectors began to have the ability to transmit virus after a period of 4.6 days of latent infection and entered infection stage IasymAnd continued for 9.5 days.
Furthermore, the embodiment of the invention adopts the real disease data of the prevention and control area to check the transmission model of the infectious diseases; the model checking process specifically comprises the following steps: based on a given R in the context without any intervention0Fitting the propagation probability pTrans with the value of (c); in a reference epidemic situation scene, fitting two unknown parameters of mask effectiveness theta and household isolation effectiveness delta by taking Root Mean Square Error (RMSE) between a minimized simulation result and a real disease curve of a prevention and control area as a standard; according to the relevant research, the theta value range is assumed to be 0.5-0.9, the delta value range is assumed to be 0-0.9, simulation is carried out in a grid search mode (firstly, 0.1 is used as a step length, and then, 0.05 is used as the step length), and each group of parameters is simulated for 1000 times so as to reduce the randomness of the simulation result. And analyzing the secondary morbidity inside the family, the age distribution of the infected persons and the spatial position precision of the infected persons based on the verified model, and verifying the simulation capability of the model again.
In the embodiment of the invention, heterogeneity among individuals can be displayed by constructing an individual-based infectious disease transmission model. The individual work place and the travel position of each hour are described in detail based on the individual infectious disease transmission model, the colleagues/classmates in contact with the individual infectious disease transmission model can be tracked, the contacters possibly encountered in other activities such as leisure and entertainment can be tracked, and the fusion modeling of various intervention measures such as tracking and isolation of the close contact person, isolation of foreign people, mask wearing, travel reduction, intercity traffic limitation, stopping of school, gradual re-work and the like can be supported. Meanwhile, the non-drug intervention measures implemented in the real world are corresponding to each individual, so that the method is not only beneficial to evaluating the compliance degree of each individual to each intervention measure, but also capable of accurately evaluating the effectiveness of each intervention measure in inhibiting the spread of infectious diseases.
S50: aiming at an epidemic situation under a normalized epidemic prevention scene, simulating the outbreak probability of the infectious disease under the combination of three measures of joint tracking, mask wearing and timely detection, and generating an intervention measure combination scheme for inhibiting the spread of the infectious disease;
in the step, because the effective rate of the mask is uncertain, two scenes, namely low (0.5) and high (0.7) mask effectiveness, are respectively set for simulating the outbreak probability. The mask wearing rate and the timely detection rate are set from 0 to 1 at intervals of 0.2, and 6 sets of parameters are set. The tight tracking grades are divided into 5 grades for not performing tight tracking (grade 0), tracking family tight members (grade I), tracking fixed contact circle (family/work unit/school) tight members (grade II), tracking fixed contact circle and 50% random close splicers (grade III), and tracking all close splicers (grade IV). Based on the parameters, 360 (6 × 5 × 6 × 2) kinds of epidemic prevention scenes are simulated in total, the epidemic situation outbreak probability of each epidemic prevention scene in 4 weeks or 8 weeks after the first case is found is calculated respectively, and then an intervention measure combination scheme for inhibiting the spread of the infectious disease under the corresponding epidemic prevention scene is generated for reference of a decision maker. Specifically, as shown in fig. 3, it is a schematic diagram showing the effect of different contact tracking levels on the reoccurrence of epidemic. In the embodiment of the invention, the epidemic situation suppression effect is defined as follows: if the number of effective regeneration RtWhen the time drops to 1 or less within 4 weeks (or 8 weeks, which may be set according to actual conditions) after the first case is found, it indicates that the epidemic is effectively controlled.
It can be understood that the invention can be widely applied to the transmission simulation of various infectious diseases such as COVID-19, influenza, nore, dengue and the like.
Based on the above, the infectious disease propagation simulation method in the embodiment of the present invention constructs an individual movement model by fusing multi-source data such as population census data, building census data, travel survey data, mobile phone signaling data, and the like, so that the individual movement model is matched with the real environment of a research area, and a space-time dynamic contact network based on an individual is constructed based on the individual movement model, so that an activity trajectory of the individual in a city scale and a contact network between individuals are explicitly expressed and tracked, which is beneficial for inversion and prediction of the diffusion of infectious diseases in cities. The method is characterized in that an individual-based infectious disease transmission model is established based on a dynamic contact network, the working place and the hourly travel position of the individual are described in detail through the infectious disease transmission model, and contacters possibly met by the individual in various travel activities are tracked, so that fusion modeling of various non-drug intervention measures such as tracking and isolation of splicers, isolation of foreign people, mask wearing, travel reduction, intercity traffic limitation, stopping, gradual re-work and the like can be supported. In addition, by corresponding the non-drug intervention measures implemented in the real world to each individual, the method is not only helpful for evaluating the compliance degree of the individual to the intervention measures, but also can accurately evaluate the effectiveness of the non-drug intervention measures for inhibiting the spread of infectious diseases. In the face of epidemic situation dissemination under a normalized epidemic prevention scene, the embodiment of the invention systematically and scientifically assists in dealing with future epidemic situation prevention and control by further evaluating the outbreak probability of the infectious disease under the combination of three measures of close-contact tracking, mask wearing and timely detection and generating an intervention measure combination scheme for inhibiting the epidemic disease from disseminating the epidemic situation.
In order to verify the feasibility and the effectiveness of the embodiment of the invention, in the following embodiment, a market a is taken as a research area, and a propagation simulation of COVID-19 is taken as an example, so that an individual-hour-building level COVID-19 propagation model is constructed, and the propagation of an epidemic under a standard epidemic situation scene and a normalized epidemic prevention scene is simulated respectively, so as to test the scheme. The standard epidemic situation scene is a first wave COVID-19 epidemic situation experienced by the market A, and the normalized epidemic prevention scene is a scene in which people recover to normal life after the epidemic situation is restrained. The method comprises the following specific steps:
(1) firstly, population census data, hundred-degree migration data, signaling data after 580 million mobile phone users are desensitized, travel survey records of 98 million residents, social contact survey data of 1000 households and 60 million building census data are fused to construct an individual movement model of 1120 million individual residents facing complex urban environment and crowd heterogeneity. And generating a synthetic individual corresponding to the population number of the city A by utilizing the census data, and reconstructing the attributes of the synthetic individual, such as age, gender, family structure, residence and the like according to the population statistical characteristics. Secondly, the mobile phone signaling data is used as the main data, the travel survey data is used as the auxiliary data, the individual residence is used as the common semantic meaning, the travel track matching of the synthetic individuals is realized through data mining and fusion, and a 24-hour travel activity chain is generated. And finally, according to the synthesized individual trip activity chain, taking hours as time granularity, regarding individuals who perform the same type of activity in the same building in the same hour as spatio-temporal co-occurrence individuals, and constructing fixed contact aiming at a small part of acquaintances and random contact aiming at strangers according to survey data in the city B on the basis of the spatio-temporal co-occurrence individuals. Specifically, as shown in fig. 4, a schematic diagram of reconstruction results of an individual spatiotemporal contact model is shown, where (a) is a comparison between the age of a synthetic individual and census data, (B) is a comparison between the family structure of the synthetic individual and travel survey data, (c) is a comparison between the number of contacts of the synthetic individual at each age and survey data in city B, and (d) is a degree distribution of the number of contacts of the synthetic individual.
(2) And (3) constructing an individual-level COVID-19 transmission model by combining the disease transmission characteristics of the COVID-19, and verifying the COVID-19 transmission model by adopting the actual morbidity data of the market A. Based first on R0The pTrans were fitted 2.4. In the absence of any non-drug intervention, 1000 individuals were randomly selected as initial cases, and pTrans ═ 0.165 was obtained by systematic simulation. Through 1000 times of simulation in a reference epidemic situation scene, the root mean square error RMSE is minimized, and a model simulation result is insensitive to a value theta, so that the accurate value of the model is difficult to determine; fig. 5 is a schematic diagram showing the fitting result of the effectiveness of different masks and the effectiveness of isolation of a house to the cumulative number of people suffering from diseases. Finally, the model average yields 416 cases of significanceSexually infected persons, the data of 418 real cases in A city are highly consistent. The observed number of new cases per day and the average number of new cases per day of the simulation result are well fitted, and the root mean square error is 1.354; specifically, as shown in fig. 6, a diagram of the fitting result between the observed actual onset data and the simulation result is shown. The average secondary infection rate at home was 11.02%, consistent with epidemiological investigations. In addition, the number of cases in each administrative district in the model simulation highly matches the number of cases reported in each district (R)20.95) with high spatial accuracy, specifically as shown in fig. 7, which is a schematic diagram of spatial accuracy analysis between simulation results and observation results.
In the above, in the reference epidemic situation scene, for the actual situation of city a, the moving population (Inflow) and moving population (Outflow) of the city are considered in the model based on the population flow data of the city with the hectic emigration. In addition, due to the high vigilance of the population in the first wave epidemic situation and the high-intensity intervention implemented by the government, some patients will seek medical advice immediately after symptoms appear, and some patients will have self-home isolation before seeking medical advice, according to the flow regulation report. Therefore, it is assumed that patients in the first wave epidemic will take hospitalization or home isolation measures immediately after the symptoms appear. In a normalized epidemic prevention scene, as the compliance of people on non-drug intervention measures is gradually reduced, the delay time for taking measures (home self-isolation or detection/hospitalization) by a patient after symptoms appears is assumed to be 1-2 days. Based on city scale and individual activity intensity in city A, different contact degrees I are given to the space-time co-occurrence individuals according to the average contact time length of the activity typescSpecifically, the degree of contact between family members in the household activity was 0.37, the degree of contact between kindergarten and primary and secondary school students was 0.25, the degree of contact between adults in the work activity was 0.26, and the degree of contact between other public place activities was 0.1.
(3) On the basis of a COVID-19 propagation model, the combined intervention effects of three non-drug intervention measures of close-contact tracking, mask wearing and timely detection and the risk of epidemic situation reoccurrence under different intervention strengths are simulated, and scientific and quantitative guidance is provided for the normalized epidemic prevention of large cities. On the basis of the verified reference model, in the face of epidemic situation under a normalized epidemic prevention scene, the invention further evaluates the outbreak probability of the COVID-19 epidemic situation under the combination of three measures of close-contact tracking, mask wearing and timely detection. By respectively setting two scenes, namely low (0.5) and high (0.7) of mask effectiveness, the two scenes show that the effect of joint sealing tracking on epidemic situation inhibition is most obvious, and then the mask wearing and the timely detection are carried out.
The large-scale simulation result based on the model shows that the 'close contact II +80+ 40' strategy is adopted as the lowest prevention and control level of the A city and other large cities, namely: tracking the close contact ring of the fixed contact ring, wearing 80% of the mask by the public and detecting 40% of patients in time after the diseases occur. Other cities can practice different joint tracking levels, mask wearing degrees and timely detection proportions according to own prevention and control targets and referring to the risk simulation results shown in fig. 8 and 9 so as to adapt to local medical treatment capacity and national conditions. Fig. 8 is a schematic diagram showing the outbreak probability of an epidemic within four weeks under different intervention implementation strengths (the black dashed line indicates the boundary of the 5% outbreak rate), and fig. 9 is a schematic diagram showing the outbreak probability of an epidemic within eight weeks under different intervention implementation strengths (the black dashed line indicates the boundary of the 5% outbreak rate).
Please refer to fig. 10, which is a schematic structural diagram of an infectious disease transmission simulation system according to an embodiment of the present invention. The infectious disease transmission simulation system 40 according to the embodiment of the present invention includes:
the movement model construction module 41: the system is used for fusing multi-source data such as census data, building census data, trip survey data, mobile phone signaling data and the like to construct an individual movement model;
contact network construction module 42: the system comprises a database, a database and a database, wherein the database is used for establishing an individual-based space-time dynamic contact network based on an individual movement model;
infection probability calculation module 43: the system is used for modeling aiming at non-drug intervention measures such as isolation, timely detection, mask wearing, social isolation, city closing and the like carried out in a prevention and control area;
propagation model construction module 44: the system is used for establishing an individual-based infectious disease transmission model based on a dynamic contact network and intervention measure modeling, verifying the infectious disease transmission model according to real epidemic situation data, and simulating the infectious disease transmission intensity of a prevention and control area through the infectious disease transmission model;
the burst probability simulation module 45: the method is used for simulating the outbreak probability of the infectious disease under the combination of three measures of close contact tracking, mask wearing and timely detection aiming at the epidemic situation under the normalized epidemic prevention scene, and generating an intervention measure combination scheme for inhibiting the spread of the infectious disease.
Fig. 11 is a schematic diagram of a terminal structure according to an embodiment of the present invention. The terminal 50 comprises a processor 51, a memory 52 coupled to the processor 51.
The memory 52 stores program instructions for implementing the infectious disease transmission simulation method described above.
The processor 51 is operable to execute program instructions stored in the memory 52 to control infectious disease transmission simulation.
The processor 51 may also be referred to as a CPU (Central Processing Unit). The processor 51 may be an integrated circuit chip having signal processing capabilities. The processor 51 may also be a general purpose processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), an off-the-shelf programmable gate array (FPGA) or other programmable logic device, discrete gate or transistor logic, discrete hardware components. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
Fig. 12 is a schematic structural diagram of a storage medium according to an embodiment of the invention. The storage medium of the embodiment of the present invention stores a program file 61 capable of implementing all the methods described above, wherein the program file 61 may be stored in the storage medium in the form of a software product, and includes several instructions to enable a computer device (which may be a personal computer, a server, or a network device) or a processor (processor) to execute all or part of the steps of the methods of the embodiments of the present invention. And the aforementioned storage medium includes: various media capable of storing program codes, such as a usb disk, a portable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, or terminal devices such as a computer, a server, a mobile phone, and a tablet.
The previous description of the disclosed embodiments is provided to enable any person skilled in the art to make or use the present invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the invention. Thus, the present invention is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.
Claims (10)
1. An infectious disease transmission simulation method, comprising:
constructing an individual moving model according to multi-source data in a prevention and control area; the multi-source data comprises census data, building census data, travel survey data and mobile phone signaling data;
constructing an individual-based space-time dynamic contact network based on the individual movement model;
modeling for non-drug intervention measures implemented within the prevention and control area;
and building an individual-based infectious disease transmission model based on the dynamic contact network and the intervention measure modeling, and simulating the infectious disease transmission intensity in the control area through the infectious disease transmission model.
2. An infectious disease transmission simulation method as claimed in claim 1, wherein the building of the individual movement model based on the multi-source data in the prevention and control area is specifically:
generating a synthetic individual corresponding to the population total number by utilizing the census data, and reconstructing individual attribute information of the synthetic individual according to population statistical characteristics; the individual attribute information comprises the attributes of age, gender, family structure and residence;
taking the mobile phone signaling data as a main part, taking travel survey data as an auxiliary part, taking the residence property of the synthesized individual as a common semantic, performing travel track matching on the synthesized individual through data mining and fusion, and generating a 24-hour travel activity chain of the synthesized individual and travel positions corresponding to various types of travel activities; the travel activities include home, work, school and other public place activities.
3. An infectious disease transmission simulation method according to claim 2, wherein the constructing of the individual-based spatiotemporal dynamic contact network based on the individual movement model is specifically:
according to the 24-hour trip activity chain of the synthesized individuals, taking hours as time granularity, and regarding the individuals performing the same trip activity in the same building in the same hour as space-time co-occurrence individuals;
and respectively constructing a fixed contact circle aiming at a small part of acquaintances and a random contact circle aiming at strangers on the basis of the space-time co-occurrence individuals.
4. An infectious disease transmission simulation method as defined in claim 1, wherein the non-drug intervention measures include isolation, timely detection, mask wearing, social detachment, city containment; the modeling for the non-drug intervention measures implemented in the prevention and control area specifically comprises the following steps:
when the susceptible individual is contacted with an infected individual without any intervention, the probability p that the susceptible individual is infected is:
p＝pTrans×Ic×r
when wearing the mask, the probability of the susceptible individual being infected is:
p＝pTrans×Ic×r×(1-θ)
when the infected person is isolated at home after the disease occurs, the probability of contacting the infected person in the home every time is as follows:
p＝pTrans×Ic×r×(1-δ)
in the above formula: pTrans is the propagation probability per contact, IcTo the extent of contact, θ is mask effectiveness, δ is self-housekeeping isolation effectiveness, and r is the transmitted virus before and after the infected person develops symptoms(ii) ability of; when the susceptible individual is contacted with an infected individual, a [0,1 ] is generated]Uniformly distributed random numbers x, comparing the random numbers x with the infection probability p, and if x is less than or equal to p, indicating that the susceptible individuals are infected.
5. An infectious disease transmission simulation method as defined in claim 4, wherein the modeling and constructing an individual-based infectious disease transmission model based on the dynamic contact network and the intervention measure further comprises:
verifying the infectious disease transmission model by adopting real morbidity data in the prevention and control area; the verification process specifically comprises the following steps: based on a given R in a scenario without any intervention0Fitting the propagation probability pTrans; in a reference epidemic situation scene, fitting two parameters of mask effectiveness theta and household isolation effectiveness delta by taking a root mean square error between a minimized simulation result and a real disease data curve in the prevention and control area as a standard; the reference epidemic situation scene is a scene of the epidemic situation of the first wave infectious disease experienced in the prevention and control area.
6. An infectious disease transmission simulation method according to claim 4 or 5, wherein the building of the individual-based infectious disease transmission model based on the dynamic contact network and the intervention measure modeling further comprises:
aiming at an epidemic situation under a normalized epidemic prevention scene, simulating the outbreak probability of the infectious disease under the combination of three measures of joint tracking, mask wearing and timely detection, and generating an intervention measure combination scheme for inhibiting the spread of the infectious disease; the normalized epidemic prevention scene refers to a scene that the infectious disease is restrained and then the normal life is recovered.
7. An infectious disease transmission simulation method according to claim 6, wherein the probability of outbreak of the infectious disease under the combination of three measures of close-contact tracking, mask wearing and timely detection is:
dividing the joint tracking grades into 5 joint tracking grades for not implementing joint tracking, tracking family joint members, tracking fixed contact ring joint members, tracking fixed contact rings and 50% random splicers and tracking all splicers, respectively setting two scenes of low effectiveness and high effectiveness of the mask to simulate the outbreak probability, calculating the outbreak probability of epidemic situation in a set time after a first case is found in each epidemic situation, and generating an intervention measure combination scheme for inhibiting the spread of the infectious disease under the corresponding epidemic situation.
8. An infectious disease transmission simulation system, comprising:
a mobile model construction module: the individual movement model is constructed according to multi-source data in the prevention and control area; the multi-source data comprises census data, building census data, travel survey data and mobile phone signaling data;
a contact network construction module: the space-time dynamic contact network is used for constructing a space-time dynamic contact network based on the individual movement model;
an infection probability calculation module: for modeling non-pharmaceutical intervention measures implemented within the controlled area;
a propagation model construction module: and the system is used for building an individual-based infectious disease transmission model based on the dynamic contact network and the intervention measure modeling, and simulating the infectious disease transmission intensity in the control area through the infectious disease transmission model.
9. A terminal, comprising a processor, a memory coupled to the processor, wherein,
the memory stores program instructions for implementing an infectious disease transmission simulation method according to any one of claims 1 to 7;
the processor is configured to execute the program instructions stored by the memory to control infectious disease transmission simulation.
10. A storage medium having stored thereon program instructions executable by a processor to perform the infectious disease transmission simulation method according to any one of claims 1 to 7.
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| CN115331833A (en) * | 2022-08-23 | 2022-11-11 | 四川大学 | Multilayer network, construction method thereof and infectious disease modeling simulation method |
| CN115831388A (en) * | 2023-02-17 | 2023-03-21 | 南京市疾病预防控制中心 | Infectious disease simulation early warning method and system based on big data |
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| CN111863271B (en) * | 2020-06-08 | 2024-03-12 | 浙江大学 | Early warning and prevention and control analysis system for major infectious disease transmission risk of new coronaries |
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| CN115331833A (en) * | 2022-08-23 | 2022-11-11 | 四川大学 | Multilayer network, construction method thereof and infectious disease modeling simulation method |
| CN115831388A (en) * | 2023-02-17 | 2023-03-21 | 南京市疾病预防控制中心 | Infectious disease simulation early warning method and system based on big data |
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