WO2021196152A1 - Procédé et système de simulation de propagation de maladie infectieuse au sein d'une zone urbaine et dispositif électronique - Google Patents

Procédé et système de simulation de propagation de maladie infectieuse au sein d'une zone urbaine et dispositif électronique Download PDF

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WO2021196152A1
WO2021196152A1 PCT/CN2020/083133 CN2020083133W WO2021196152A1 WO 2021196152 A1 WO2021196152 A1 WO 2021196152A1 CN 2020083133 W CN2020083133 W CN 2020083133W WO 2021196152 A1 WO2021196152 A1 WO 2021196152A1
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individual
data
spatial
simulation
population
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尹凌
万巧
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中国科学院深圳先进技术研究院
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    • 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
    • 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

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  • the invention relates to a method, a system and electronic equipment for simulating the spread of infectious diseases in a city.
  • the existing dengue fever transmission model only considers the influencing factor of the number of asymptomatic infections, ignoring the influence of the spatial location of asymptomatic infections, and there is no research to explore the spatial location of asymptomatic infections;
  • the present invention provides a method for simulating the spread of infectious diseases in a city.
  • the method includes the following steps: a. Initialize the entire population in the city based on the data of imported dengue fever cases; The constructed individual travel chain information constructs an individual mobile network, and calculates the individual’s residence time at work; c. calculates the temporal and spatial distribution of mosquito vectors based on meteorological data including daily average temperature and daily rainfall; d. calculates based on the constructed individual mobile network The obtained residence time and the time and space distribution of mosquito vectors are used to construct a spatial explicit individual transmission model to simulate the spread and spread of infectious diseases.
  • the method further includes the steps before step a:
  • the source data includes multi-source trajectory data, dengue fever input case data, and meteorological data;
  • the multi-source trajectory data includes: census data, travel survey data, building data, and mobile phone positioning data.
  • the method also includes step e:
  • the temporal and spatial distribution patterns of infectious diseases in the city are analyzed.
  • the step a specifically includes:
  • a corresponding number of asymptomatic infections are generated according to the proportion of dengue fever infections and asymptomatic infections, and asymptomatic infections are initialized;
  • a corresponding number of people are selected from the initial population and set to the immune state.
  • the step b specifically includes:
  • census data and building data identify the individual's place of work and residence, and construct a mobile network between the individual's work place and buildings;
  • the residence time of each individual's job residence is calculated.
  • Said step c specifically includes:
  • the daily spatial and temporal distribution of mosquito vectors in each region is calculated through the relationship between the number of mosquito vectors and the above two meteorological factors.
  • the step d specifically includes:
  • a spatial explicit individual model is used to simulate the spread of dengue fever in steps of days. The simulation starts on the first day of infection of the first imported case in the population until the end of the entire dengue transmission simulation process.
  • the step e specifically includes:
  • the scene is simulated N times, and the simulation results of each time are counted on a daily basis, and finally the average value and 95% confidence interval of the N simulations are obtained;
  • the 1km x 1km spatial grid is used as the basic spatial unit of the spatial analysis, and the spatial distribution results of the dengue fever epidemic at the grid scale are analyzed.
  • the scenes are simulated N times, and the simulated dengue fever each time
  • the spatial distribution results of the infected persons are counted according to the grid, and the average value of the number of cases simulated in each grid N times is calculated.
  • the spatial distribution results of the actual cases are compared, and the spatial distribution characteristics of the simulation results and the model's spatial distribution are analyzed. Simulation effect on distribution.
  • the present invention provides a system for simulating the spread of infectious diseases in a city.
  • the system includes a population initialization module, a mobile network building module, a mosquito vector space-time distribution module, and a transmission simulation module.
  • the entire population within the city is initialized;
  • the mobile network construction module is used to construct an individual mobile network based on the individual travel chain information constructed from multi-source trajectory data for the entire population of the initialized city, and calculate the residence time of the individual at work;
  • the mosquito vector time and space distribution module is used to calculate the time and space distribution of mosquito vector based on meteorological data including daily average temperature and daily rainfall;
  • the transmission simulation module is used to calculate the residence time and mosquito vector based on the constructed individual mobile network Spatio-temporal distribution, construct a spatial explicit individual transmission model to simulate the spread and spread of infectious diseases.
  • system also includes:
  • the acquisition module is used to acquire source data;
  • the source data includes multi-source trajectory data, dengue fever input case data, and meteorological data;
  • the multi-source trajectory data includes: census data, travel survey data, building data, and mobile phone location data ;
  • the analysis module is used to analyze the temporal and spatial distribution patterns of infectious diseases in the city according to the simulation results of the spatial explicit individual transmission model.
  • the present invention also provides an electronic device, including:
  • At least one processor At least one processor
  • a memory communicatively connected with the at least one processor; wherein,
  • the memory stores instructions executable by the one processor, and the instructions are executed by the at least one processor, so that the at least one processor can execute the following operations of the method for simulating the spread of infectious diseases within the city :
  • Step a Initialize the entire population in the city based on the imported dengue fever case data
  • Step b For the entire population of the initialized city, construct an individual mobile network based on the individual travel chain information constructed from multi-source trajectory data, and calculate the individual's residence time at work;
  • Step c Calculate the temporal and spatial distribution of mosquito vectors based on meteorological data including daily average temperature and daily rainfall;
  • Step d According to the constructed individual mobile network, the calculated residence time and the space-time distribution of mosquito vectors, construct a spatial explicit individual transmission model to simulate the spread and spread of infectious diseases.
  • residents’ travel activities constructed based on multi-source trajectory data can more truly reflect the movement patterns and travel locations of individuals in the city, and more accurately simulate the interaction between individuals, This improves the spatial accuracy of the simulation of the spread of infectious diseases such as dengue fever, and can also be used to implement more precise interventions in time and space.
  • Three allocation strategies are set up for the spatial location of asymptomatic infections, which is the first time to explore The impact of the spatial location of hidden imported cases on the spread of infectious diseases such as dengue fever, and the temporal and spatial distribution of the simulation results can be used to initially explore the most likely spatial distribution of hidden imported cases; the mobile phone positioning data is used in the present invention to describe the differences in the population within the city
  • the movement between buildings has achieved an important breakthrough in the research of infectious diseases on a smaller scale based on urban internal buildings, and can provide scientific support for the precise prevention and control of infectious diseases.
  • Figure 1 is a flow chart of the method for simulating the spread of infectious diseases within the city of the present invention
  • Figure 2 is a schematic diagram of activities between individuals' employment and residence provided by an embodiment of the present invention.
  • FIG. 3 is a flowchart of infectious disease simulation using days as a step length provided by an embodiment of the present invention
  • FIG. 4 is a hardware architecture diagram of a simulation system for the spread of infectious diseases in a city according to the present invention
  • FIGS 5 to 7 are schematic diagrams of the time series of the spread of dengue fever provided by the embodiments of the present invention.
  • Figures 8-10 are schematic diagrams of daily accumulation of the spread of dengue fever provided by an embodiment of the present invention.
  • 11-13 are schematic diagrams of the spatial distribution results of the spread of dengue fever provided by the embodiments of the present invention.
  • FIG. 14 is a schematic diagram of the spatial distribution of local cases in Shenzhen in 2014 according to an embodiment of the present invention.
  • 15 is a schematic diagram of the hardware device structure of the method for simulating the spread of infectious diseases in a city provided by an embodiment of the present invention.
  • dengue fever is an infectious disease as an example:
  • FIG. 1 it is a flowchart of a preferred embodiment of the method for simulating the spread of infectious diseases in a city according to the present invention.
  • Step S0 Obtain source data.
  • the source data includes multi-source trajectory data, dengue fever input case data, meteorological data and other basic data.
  • the multi-source trajectory data contains a large amount of mobile phone location data and travel survey data.
  • the mobile phone location data refers to more than 7.62 million anonymous user records on a certain day in May 2012.
  • the user records (as shown in Table 1) include the anonymous user ID, timestamp, longitude, and latitude.
  • the data on imported dengue fever cases includes data on 348 local cases and 206 imported cases in Shenzhen in 2014 (imported cases refer to all dengue infected persons who entered Shenzhen from outside Shenzhen).
  • the data on imported dengue fever cases includes Gender, age, home address, date of infection and other information, the case data comes from Shenzhen Medical Information Center.
  • the meteorological data refers to the daily average minimum temperature, rainfall, and average relative humidity of 22 meteorological stations in Shenzhen in 2014, and the meteorological data comes from the Shenzhen Meteorological Bureau.
  • the other basic data includes data from the sixth population census in 2010, Shenzhen Statistical Yearbook and Shenzhen building data.
  • Step S1 Initialize the entire population in the city according to the acquired dengue fever input case data. That is, set the infection status for symptomatic infections; generate asymptomatic infections based on the ratio of asymptomatic infections to asymptomatic infections, and set the infection status of asymptomatic infections; select a corresponding number of people from the initial population and It is set to an immune state.
  • the step S1 includes:
  • Step 101 According to the dengue fever imported case data provided by the Center for Infectious Diseases Prevention and Control, initialize the symptomatic infected persons: that is, in the simulated urban population data, according to the age, gender, home address and other attributes of each imported case The information is similarly matched with all individuals in the population data, and the most relevant individuals are found and set as input cases, and their infection status is set.
  • Step 102 According to each case of symptomatic infection, a corresponding number of asymptomatic infections are generated according to the ratio of dengue fever infections to asymptomatic infections, and asymptomatic infections are initialized.
  • the home address allocation method of asymptomatic infected persons is divided into three strategies: one is based on the temporal and spatial distribution of symptomatic infected persons; the second is based on the population distribution within the city; the third is random distribution. Set the infection status after generating asymptomatic infection.
  • Step 103 Select a corresponding number of people from the initial population according to the proportion of the immunized population in the city or the age distribution characteristics of the immunized population, etc., and set it as an immune state, and the immunized population will not be infected during the entire transmission of dengue fever. , Have lifelong immunity.
  • the initial population is the entire population of the city except those with symptomatic infections and those with asymptomatic infections.
  • the specific implementation method of this embodiment includes:
  • the specific implementation method is: find all the individuals in the building according to the home address of the imported case, and calculate the similarity between the gender and age of each individual and the imported case, and then The individual with the highest similarity is selected as the input case. If there are multiple individuals with the highest similarity, then one individual is selected at random as the input case.
  • asymptomatic infections Assume that the ratio of symptomatic infections to asymptomatic infections during the dengue outbreak in Shenzhen is 1:2.2.
  • the specific implementation method is as follows: three scenarios are assumed for the assignment of the home address of asymptomatic infected persons: one is to select according to the home address of the imported case, and it is assumed to be consistent with the spatial distribution of the imported case, that is, for every symptomatic infected person, Randomly generate N asymptomatic infections with a probability of 2.2:1, and randomly select N asymptomatic infections in the same area as the corresponding symptomatic infections; second, select the families of asymptomatic infections according to the population distribution The address is selected based on the probability of the total population of the 10 districts in Shenzhen, that is, the district with a larger total population is more likely to be selected; the third is to select the home address of asymptomatic infected persons based on random distribution, that is, from Shenzhen One district is randomly selected from 10 jurisdictions.
  • the proportion of the initial immunization population of Shenzhen residents is 2.43%, in the total population, after removing all imported cases (including symptomatic and asymptomatic infections), the remaining population is randomized according to the population immunization ratio Select the immunized population and set its state to the recovery state, and assume that the individual has permanent immunity to the dengue virus.
  • Step S2 for the entire population of the initialized city, construct an individual mobile network based on the individual travel chain information constructed from the multi-source trajectory data, and calculate the individual's residence time at work.
  • the individual travel chain information constructed from multi-source trajectory data refers to the 24 hours a day travel location information of individuals simulated based on census data, building data, travel survey data, and a large amount of mobile phone positioning data, and population data is identified and obtained It constructs a mobile network between the individual’s employment and residence and calculates the residence time of each individual’s employment and residence.
  • the step S2 includes:
  • Step 201 Perform large-scale individual movement modeling and construct individual travel chain information based on multi-source trajectory data including census data, travel survey data, building data, and mobile phone positioning data.
  • the individual travel chain information refers to the travel location information of the individual 24 hours a day.
  • Step 202 According to the individual travel chain information, census data and building data, the individual’s place of employment is identified, and a network for moving between the individual’s working place and buildings is constructed (refer to Figure 2). In this model, only the most important individual is considered. The main travel activity, so the main individual travel activity location only considers the building where the home, work place or school is located.
  • Step 203 Calculate the residence time of each individual's employment residence through the obtained individual's job residence and individual travel chain information.
  • the residence time is used for the selection of a newly infected person in the infectious disease simulation process. The longer the stay in the building where the infected person exists, the greater the probability that the individual will be infected.
  • Step S3 Calculate the temporal and spatial distribution of mosquito vectors based on meteorological data including daily average temperature and daily rainfall. That is, using meteorological factors such as daily average temperature and daily rainfall to calculate the daily average temperature in each area in the previous month and the number of days of rainfall in the previous month, and from this, the number of mosquito vectors in each area within the city is calculated The dynamic distribution of time and space.
  • the step S3 includes:
  • Step 301 According to the daily average temperature and rainfall weather factors of each area, calculate the daily average temperature of the previous month and the number of days of accumulated rainfall in the previous month in each area during the infectious disease simulation period.
  • Step 302 According to the calculated daily average temperature of each area in the previous month and the number of days of accumulated rainfall in the previous month, calculate the time and space of mosquito vectors in each area from the relationship between the number of mosquito vectors and the above two meteorological factors Distribution.
  • the specific implementation method of this embodiment includes:
  • M ij 0.05P ij -0.0081T ij 2 +0.5289T ij -5.5461 (1)
  • M ij represents the number of mosquito vectors on the jth day in the i-th zone
  • P ij represents the cumulative rainfall days in the previous month on the j-th day in the i-th zone
  • T ij represents the average temperature of the previous month on the jth day in the i-th zone
  • the value range of i is [1,10], which respectively represents the 10 jurisdictions of Shenzhen
  • the value range of j is [1,365], which represents January 1 to December 31, 2014.
  • Step S4 According to the constructed individual mobile network, the calculated residence time and the space-time distribution of mosquito vectors, construct a spatial explicit individual transmission model to simulate the spreading process of infectious diseases.
  • the spatial explicit individual transmission model refers to a building as a simulation unit, combined with a classic SEIR model to model the spread of the virus in the crowd and the dynamic transfer between different states of individuals in the crowd.
  • each individual is an agent, and the travel activity of the individual refers to the movement between jobs and residences.
  • the step S4 includes:
  • Step 401 Take an independent building as a simulation unit and combine the classic SEIR model to model the spread of the virus in the crowd and the dynamic transfer between different states of the individuals in the crowd.
  • each individual is an agent.
  • Each agent has attributes such as age, gender, home residence, work place, and infection status, and the agent’s travel activities include staying at home, going to work or going to school.
  • the difference of the infection period between symptomatic and asymptomatic infections was distinguished.
  • Step 402 Use a spatial explicit individual model to simulate the spread of dengue fever in steps of days.
  • the simulation starts on the first day when the first imported case in the population is infected until the end of the entire dengue transmission simulation process.
  • a new infected person is generated in a building, first select the newly generated infected person in the building according to the weight of the individual being infected, and then determine the infection according to the probability of the proportion of symptomatic and asymptomatic infected persons The infection symptoms of the infected person, and finally the incubation period, infection period and recovery period of the infected person are respectively set.
  • the specific implementation method of this embodiment includes:
  • each individual is an agent, and each agent has attributes such as age, gender, home residence, work place, and infection status, and the movement of agents between different buildings leads to the spread of dengue fever virus.
  • the difference of the infection period of people with and without symptoms was distinguished. Entire population divided susceptible H S, latent H E, symptomatic infection H Is, H Ia and asymptomatic infection restorer H R.
  • H S When a susceptible individual (H S ) is bitten by a virus-carrying Aedes mosquito, it will be transformed into a latent state ( HE ). The probability depends on the susceptible individual being bitten by a virus-carrying mosquito to get infected. The probability ( ⁇ H ) and the number of Aedes mosquitoes in the building where the individual is currently located. Individuals in the latent state (H E ) will become infected (H Is or H Ia ) after the incubation period ( ⁇ H ).
  • Aedes mosquito bites an infected individual dengue virus will infect Aedes probability ⁇ V to a healthy, virus-carrying Aedes mosquitoes may bite in susceptible individuals throughout their lifetime to spread the virus.
  • the probability of a susceptible individual being infected is:
  • m represents the number of Aedes per capita
  • k is a proportional coefficient
  • M represents the number of mosquito vectors per simulation unit.
  • the space explicit individual model is used to simulate the spread of dengue fever with the step of days.
  • the simulation starts on the first day of infection of the first imported case in the population until the end of the entire dengue transmission simulation process.
  • the weight of each individual in the building being infected is equal to the sum of the normalized individual incidence rate and the normalized residence time.
  • the final selection of newly infected individuals should try their best to select individuals with a high incidence rate (related to age) and a longer residence time (accumulated residence time in a building where the infected person exists).
  • Step S5 Analyze the temporal and spatial distribution pattern of infectious diseases in the city according to the simulation result of the spatial explicit individual transmission model. That is, according to the actual local case data and the simulation results are compared, the effect of the model is analyzed from the time distribution and the spatial distribution, and the time and space distribution of infectious diseases is analyzed.
  • the step S5 includes:
  • Step 501 When analyzing the time distribution results, in order to solve the uncertainty problem caused by the random selection of the parameters of a simulation, multiple scenarios are simulated N times, and the simulation results of each time are counted on a daily basis, and finally obtained The mean of N simulations and 95% confidence interval.
  • the time distribution results of local cases and simulated symptomatic infections are displayed through time series graphs and daily cumulative graphs respectively, and then the time distribution characteristics of the results and the simulation effect of the model on the time distribution are analyzed, and the time under different strategies The distribution results are used to explore the most likely spatial distribution of asymptomatic infections.
  • Step 502 When analyzing the spatial distribution result, a 1km x 1km spatial grid is used as the basic spatial unit of the spatial analysis, and the spatial distribution result of the dengue fever epidemic at the grid scale is analyzed. Similarly, in order to solve the uncertainty problem caused by the random selection of the parameters of a simulation, a variety of scenarios were simulated N times, and the spatial distribution results of the dengue infected persons in each simulation were counted on a grid. Calculate the mean value of the number of cases simulated for each grid N times, and finally compare with the spatial distribution results of actual cases, analyze the spatial distribution characteristics of the simulation results and the simulation effect of the model on the spatial distribution, and adopt different strategies The spatial distribution results explore the most likely spatial distribution of asymptomatic infections.
  • the specific implementation method of this embodiment includes:
  • the three scenarios were simulated 100 times, and the results of each simulation were counted on a daily basis, and finally the average value and 95% confidence interval of 100 simulations were obtained.
  • the time distribution results of local cases and simulated symptomatic infections are shown in time series diagrams ( Figure 5-7) and daily cumulative diagrams ( Figure 8-10).
  • the black curve in the figure represents the actual local cases in Shenzhen in 2014;
  • the red curve represents the average of the number of symptomatic infections in 100 simulations;
  • the gray area represents the 95% confidence interval. From the simulation results of Figures 5 to 7 and Figure 8 to Figure 10, it can be found that this model can better simulate the propagation and diffusion process of dengue fever in the time dimension.
  • the results of 100 simulations are counted according to the spatial grid of 1km x 1km as the basic spatial unit, and then the average value of the number of cases in each grid 100 simulations is calculated, and finally with The spatial distribution results of actual cases are compared, and the spatial distribution characteristics of the simulation results and the simulation effect of the model on the spatial distribution are analyzed. From the spatial distribution of the simulation results in Figure 11-13, it can be seen that symptomatic infections are mainly concentrated in Baoan District (adjacent to Nanshan District), Nanshan District, Futian, Luohu District and Longhua District. The spatial distribution of actual local cases is generally consistent (as shown in Figure 14).
  • the simulation results of the home address assignment scenarios of the three asymptomatic imported cases show that the simulation results of assigning the home locations of asymptomatic infections according to the spatial distribution of the imported cases are relatively accurate (Figure 11), which is most consistent with the actual local cases, especially
  • the simulation results of Nanshan District, Luohu District, Bao'an District and the neighboring areas of Nanshan District are consistent with the actual local cases, and they are all areas where dengue fever cases are frequent and dense.
  • FIG. 4 is a hardware architecture diagram of the system 10 for simulating the spread of infectious diseases in a city according to the present invention.
  • the system includes: an acquisition module 100, a population initialization module 101, a mobile network construction module 102, a mosquito vector temporal and spatial distribution module 103, a propagation simulation module 104, and an analysis module 105.
  • the acquisition module 100 is used to acquire source data.
  • the source data includes multi-source trajectory data, dengue fever input case data, meteorological data and other basic data.
  • the multi-source trajectory data contains a large amount of mobile phone location data and travel survey data.
  • the mobile phone location data refers to more than 7.62 million anonymous user records on a certain day in May 2012.
  • the user records (as shown in Table 1) include the anonymous user ID, timestamp, longitude, and latitude.
  • the data on imported dengue fever cases includes data on 348 local cases and 206 imported cases in Shenzhen in 2014 (imported cases refer to all dengue infected persons who entered Shenzhen from outside Shenzhen).
  • the data on imported dengue fever cases includes Gender, age, home address, date of infection and other information, the case data comes from Shenzhen Medical Information Center.
  • the meteorological data refers to the daily average minimum temperature, rainfall, and average relative humidity of 22 meteorological stations in Shenzhen in 2014, and the meteorological data comes from the Shenzhen Meteorological Bureau.
  • the other basic data includes data from the sixth population census in 2010, Shenzhen Statistical Yearbook and Shenzhen building data.
  • the population initialization module 101 is used to initialize all the population in the city according to the acquired dengue fever input case data. That is, set the infection status for symptomatic infections; generate asymptomatic infections based on the ratio of asymptomatic infections to asymptomatic infections, and set the infection status of asymptomatic infections; select a corresponding number of people from the initial population and It is set to an immune state.
  • the population initialization module 101 initializes symptomatic infected persons based on the dengue fever imported case data provided by the Center for Infectious Disease Prevention and Control: that is, in the simulated population data of the entire city, according to the age and gender of each imported case Attribute information such as home address and home address are similarly matched with all individuals in the population data, find the most relevant individual and set it as an input case, and set its infection status.
  • the population initialization module 101 generates a corresponding number of asymptomatic infections according to each case of symptomatic infections and the ratio of dengue fever infections to asymptomatic infections, and initializes asymptomatic infections.
  • the home address allocation method of asymptomatic infected persons is divided into three strategies: one is based on the temporal and spatial distribution of symptomatic infected persons; the second is based on the population distribution within the city; the third is random distribution. Set the infection status after generating asymptomatic infection.
  • the population initialization module 101 selects a corresponding number of people in the initial population according to the proportion of the immunized population in the city or the age distribution characteristics of the immunized population, and sets them to an immune state.
  • the immunized population is in the entire transmission process of dengue fever. It will not be infected and has lifelong immunity.
  • the initial population is the entire population of the city except those with symptomatic infections and those with asymptomatic infections.
  • the population initialization module 101 in this embodiment is specifically implemented as:
  • the population initialization module 101 initializes symptomatic infected persons, specifically: finding all individuals in the building according to the home address of the imported case, and calculating the gender and age of each individual and the imported case. Similarity, and then select the individual with the highest similarity to match the imported case. If there are multiple individuals with the highest similarity, then randomly select one of these individuals to match the imported case.
  • the population initialization module 101 initializes asymptomatic infections: suppose that the ratio of symptomatic infections to asymptomatic infections during the dengue fever outbreak in Shenzhen is 1:2.2.
  • the specific implementation method is as follows: three scenarios are assumed for the assignment of the home address of asymptomatic infected persons: one is to select according to the home address of the imported case, and it is assumed to be consistent with the spatial distribution of the imported case, that is, for every symptomatic infected person, Randomly generate N asymptomatic infections with a probability of 2.2:1, and randomly select N asymptomatic infections in the same area as the corresponding symptomatic infections; second, select the families of asymptomatic infections according to the population distribution The address is selected based on the probability of the total population of the 10 districts in Shenzhen, that is, the district with a larger total population is more likely to be selected; the third is to select the home address of asymptomatic infected persons based on random distribution, that is, from Shenzhen One district is randomly selected from 10
  • the proportion of the initial immunization population of Shenzhen residents is 2.43%, in the total population, after removing all imported cases (including symptomatic and asymptomatic infections), the remaining population is randomized according to the population immunization ratio Select the immunized population and set its state to the recovery state, and assume that the individual has permanent immunity to the dengue virus.
  • the mobile network construction module 102 is used to construct an individual mobile network based on the individual travel chain information constructed from multi-source trajectory data for the entire population of the initialized city, and calculate the individual's residence time at work.
  • the individual travel chain information constructed from multi-source trajectory data refers to the 24 hours a day travel location information of individuals simulated based on census data, building data, travel survey data, and a large amount of mobile phone positioning data, and population data is identified and obtained It constructs a mobile network between the individual’s employment and residence and calculates the residence time of each individual’s employment and residence.
  • the mobile network construction module 102 performs large-scale individual movement modeling and constructs individual travel chain information based on multi-source trajectory data including census data, travel survey data, building data, and mobile phone positioning data.
  • the individual travel chain information refers to the travel location information of the individual 24 hours a day.
  • the mobile network construction module 102 identifies the individual’s place of employment and residence based on the individual’s travel chain information, census data, and building data, and constructs a network that moves between the individual’s working place and buildings (see Figure 2). In this model, Only the most important travel activities of the individual are considered, so the main individual travel location only considers the building where the home, work or school is located.
  • the mobile network construction module 102 calculates the residence time of each individual's employment residence through the obtained individual's job residence and individual travel chain information.
  • the residence time is used for the selection of a newly infected person in the infectious disease simulation process. The longer the stay in the building where the infected person exists, the greater the probability that the individual will be infected.
  • the mosquito vector temporal and spatial distribution module 103 is used to calculate the temporal and spatial distribution of mosquito vectors based on meteorological data including daily average temperature and daily rainfall. That is, using meteorological factors such as daily average temperature and daily rainfall to calculate the daily average temperature in each area in the previous month and the number of days of rainfall in the previous month, and from this, the number of mosquito vectors in each area within the city is calculated The dynamic distribution of time and space.
  • the mosquito vector space-time distribution module 103 calculates the daily average temperature of the previous month and the number of days of accumulated rainfall in the previous month in each area during the infectious disease simulation period according to the daily average temperature and rainfall meteorological factors of each area. .
  • the mosquito vector temporal and spatial distribution module 103 calculates each region based on the calculated daily average temperature of each region in the previous month and the number of days of accumulated rainfall in the previous month through the relationship between the number of mosquito vectors and the above two meteorological factors Daily distribution of mosquito vectors in time and space.
  • mosquito vector temporal and spatial distribution module 103 in this embodiment is specifically implemented as follows:
  • M ij 0.05P ij -0.0081T ij 2 +0.5289T ij -5.5461 (1)
  • M ij represents the number of mosquito vectors on the jth day in the i-th zone
  • P ij represents the cumulative rainfall days in the previous month on the j-th day in the i-th zone
  • T ij represents the average temperature of the previous month on the jth day in the i-th zone
  • the value range of i is [1,10], which respectively represents the 10 jurisdictions of Shenzhen
  • the value range of j is [1,365], which represents January 1 to December 31, 2014.
  • the transmission simulation module 104 is used to calculate the residence time and the space-time distribution of mosquito vectors based on the constructed individual mobile network, construct a spatial explicit individual transmission model, and simulate the spread and spread process of infectious diseases.
  • the spatial explicit individual transmission model refers to a building as a simulation unit, combined with a classic SEIR model to model the spread of the virus in the crowd and the dynamic transfer between different states of individuals in the crowd.
  • each individual is an agent, and the travel activity of the individual refers to the movement between jobs and residences.
  • the transmission simulation module 104 uses an independent building as a simulation unit and combines the classic SEIR model to model the spread of the virus in the crowd and the dynamic transfer between different states of the individuals in the crowd.
  • each individual is an agent.
  • Each agent has attributes such as age, gender, home residence, work place, and infection status, and the agent’s travel activities include staying at home, going to work or going to school.
  • the difference of the infection period between symptomatic and asymptomatic infections was distinguished.
  • the transmission simulation module 104 uses a spatial explicit individual model to simulate the transmission and spread of dengue fever in steps of days.
  • the simulation starts on the first day when the first imported case in the population is infected until the end of the entire dengue transmission simulation process. Refer to Figure 3 for the simulation process on a day-to-day basis. In the daily simulation process, traverse all the buildings in the occupation and residence to see if there are new infected persons.
  • a new infected person is generated in a building, first select the newly generated infected person in the building according to the weight of the individual being infected, and then determine the infection according to the probability of the proportion of symptomatic and asymptomatic infected persons The infection symptoms of the infected person, and finally the incubation period, infection period and recovery period of the infected person are respectively set.
  • propagation simulation module 104 in this embodiment is specifically implemented as:
  • the transmission simulation module 104 uses an independent building as a simulation unit and combines the classic SEIR model to model the spread of the virus in the crowd and the dynamic transfer between different states of individuals in the crowd.
  • each individual is an agent, and each agent has attributes such as age, gender, home residence, work place, and infection status, and the movement of agents between different buildings leads to the spread of dengue fever virus.
  • the difference in the infection period of patients with and without symptoms was distinguished. Entire population divided susceptible H S, latent H E, symptomatic infection H Is, H Ia and asymptomatic infection restorer H R.
  • H S When a susceptible individual (H S ) is bitten by a virus-carrying Aedes mosquito, it will be transformed into a latent state ( HE ). The probability depends on the susceptible individual being bitten by a virus-carrying mosquito to get infected. The probability ( ⁇ H ) and the number of Aedes mosquitoes in the building where the individual is currently located. Individuals in the latent state (H E ) will become infected (H Is or H Ia ) after the incubation period ( ⁇ H ).
  • Aedes mosquito bites an infected individual dengue virus will infect Aedes probability ⁇ V to a healthy, virus-carrying Aedes mosquitoes may bite in susceptible individuals throughout their lifetime to spread the virus.
  • the probability of a susceptible individual being infected is:
  • m represents the number of Aedes per capita
  • k is a proportional coefficient
  • M represents the number of mosquito vectors per simulation unit.
  • the transmission simulation module 104 uses a spatial explicit individual model to simulate the spread of dengue fever in steps of days.
  • the simulation starts on the first day of infection of the first imported case in the population until the end of the entire dengue transmission simulation process. .
  • During the simulation traverse all the buildings of the residence and check whether there is a new infected person. If a new infected person is generated in a building, first select the newly generated infected person in the building according to the weight of the individual being infected, and then determine the infection according to the probability of the proportion of symptomatic and asymptomatic infected persons The infection symptoms of the infected person, and finally the incubation period, infection period and recovery period of the infected person are respectively set.
  • the weight of each individual in the building being infected is equal to the sum of the normalized individual incidence rate and the normalized residence time.
  • the final selection of newly infected individuals should try their best to select individuals with a high incidence rate (related to age) and a longer residence time (accumulated residence time in a building where the infected person exists).
  • the analysis module 105 is used to analyze the temporal and spatial distribution pattern of infectious diseases in the city according to the simulation result of the spatial explicit individual transmission model. That is, according to the actual local case data and the simulation results are compared, the effect of the model is analyzed from the time distribution and the spatial distribution, and the time and space distribution of infectious diseases is analyzed.
  • the analysis module 105 analyzes the time distribution results, in order to solve the uncertainty problem caused by the random value of the parameters of a simulation, multiple scenarios are simulated N times, and the simulation results of each time are counted on a daily basis, and finally Calculate the mean value and 95% confidence interval of N simulations.
  • the time distribution results of local cases and simulated symptomatic infections are displayed through time series graphs and daily cumulative graphs respectively, and then the time distribution characteristics of the results and the simulation effect of the model on the time distribution are analyzed, and the time under different strategies The distribution results are used to explore the most likely spatial distribution of asymptomatic infections.
  • a 1km x 1km spatial grid is used as the basic spatial unit of the spatial analysis, and the spatial distribution result of the dengue fever epidemic at the grid scale is analyzed.
  • a variety of scenarios were simulated N times, and the spatial distribution results of the dengue infected persons in each simulation were counted on a grid. Calculate the mean value of the number of cases simulated for each grid N times, and finally compare with the spatial distribution results of actual cases, analyze the spatial distribution characteristics of the simulation results and the simulation effect of the model on the spatial distribution, and adopt different strategies
  • the spatial distribution results explore the most likely spatial distribution of asymptomatic infections.
  • analysis module 105 in this embodiment is specifically implemented as:
  • the analysis module 105 simulates the three scenarios 100 times respectively, and the simulation results of each time are counted on a daily basis, and finally the average value and the 95% confidence interval of the 100 simulations are obtained.
  • the time distribution results of local cases and simulated symptomatic infections are shown in time series diagrams ( Figure 5-7) and daily cumulative diagrams ( Figure 8-10).
  • the black curve in the figure represents the actual local cases in Shenzhen in 2014;
  • the red curve represents the average number of symptomatic infections in 100 simulations;
  • the gray area represents the 95% confidence interval. From the simulation results of Figures 5 to 7 and Figure 8 to Figure 10, it can be found that this model can better simulate the propagation and diffusion process of dengue fever in the time dimension.
  • the analysis module 105 analyzes the spatial distribution results, it counts the results of 100 simulations according to the 1km x 1km spatial grid as the basic spatial unit, and then calculates the number of cases for each grid 100 simulations. Finally, it is compared with the spatial distribution results of actual cases to analyze the spatial distribution characteristics of the simulation results and the simulation effect of the model on the spatial distribution. From the spatial distribution of the simulation results in Figure 11-13, it can be seen that symptomatic infections are mainly concentrated in Baoan District (adjacent to Nanshan District), Nanshan District, Futian, Luohu District and Longhua District. The spatial distribution of actual local cases is generally consistent (as shown in Figure 14).
  • the simulation results of the home address assignment scenarios of the three asymptomatic imported cases show that the simulation results of assigning the home locations of asymptomatic infections according to the spatial distribution of the imported cases are relatively accurate (Figure 11), which is most consistent with the actual local cases, especially
  • the simulation results of Nanshan District, Luohu District, Bao'an District and the neighboring areas of Nanshan District are consistent with the actual local cases, and they are all areas where dengue fever cases are frequent and dense.
  • the device includes one or more processors and memory. Taking a processor as an example, the device may also include: an input system and an output system.
  • the processor, the memory, the input system, and the output system may be connected by a bus or in other ways.
  • the connection by a bus is taken as an example.
  • the memory can be used to store non-transitory software programs, non-transitory computer executable programs, and modules.
  • the processor executes various functional applications and data processing of the electronic device by running non-transitory software programs, instructions, and modules stored in the memory, that is, realizing the processing methods of the foregoing method embodiments.
  • the memory may include a program storage area and a data storage area, where the program storage area can store an operating system and an application program required by at least one function; the data storage area can store data and the like.
  • the memory may include a high-speed random access memory, and may also include a non-transitory memory, such as at least one magnetic disk storage device, a flash memory device, or other non-transitory solid-state storage devices.
  • the memory may optionally include a memory remotely provided with respect to the processor, and these remote memories may be connected to the processing system through a network. Examples of the aforementioned networks include, but are not limited to, the Internet, corporate intranets, local area networks, mobile communication networks, and combinations thereof.
  • the input system can receive input digital or character information, and generate signal input.
  • the output system may include display devices such as a display screen.
  • the one or more modules are stored in the memory, and when executed by the one or more processors, the following operations of any of the foregoing method embodiments are performed:
  • Step a Initialize the entire population in the city based on the imported dengue fever case data
  • Step b For the entire population of the initialized city, construct an individual mobile network based on the individual travel chain information constructed from multi-source trajectory data, and calculate the individual's residence time at work;
  • Step c Calculate the temporal and spatial distribution of mosquito vectors based on meteorological data including daily average temperature and daily rainfall;
  • Step d According to the constructed individual mobile network, the calculated residence time and the space-time distribution of mosquito vectors, construct a spatial explicit individual transmission model to simulate the spread and spread of infectious diseases.
  • the embodiments of the present application provide a non-transitory (non-volatile) computer electronic device.
  • the computer electronic device stores computer-executable instructions, and the computer-executable instructions can perform the following operations:
  • Step a Initialize the entire population in the city based on the imported dengue fever case data
  • Step b For the entire population of the initialized city, construct an individual mobile network based on the individual travel chain information constructed from multi-source trajectory data, and calculate the individual's residence time at work;
  • Step c Calculate the temporal and spatial distribution of mosquito vectors based on meteorological data including daily average temperature and daily rainfall;
  • Step d According to the constructed individual mobile network, the calculated residence time and the space-time distribution of mosquito vectors, construct a spatial explicit individual transmission model to simulate the spread and spread of infectious diseases.
  • the embodiment of the present application provides a computer program product, the computer program product includes a computer program stored on a non-transitory computer-readable electronic device, the computer program includes program instructions, when the program instructions are executed by a computer To make the computer do the following:
  • Step a Initialize the entire population in the city based on the imported dengue fever case data
  • Step b For the entire population of the initialized city, construct an individual mobile network based on the individual travel chain information constructed from multi-source trajectory data, and calculate the individual's residence time at work;
  • Step c Calculate the temporal and spatial distribution of mosquito vectors based on meteorological data including daily average temperature and daily rainfall;
  • Step d According to the constructed individual mobile network, the calculated residence time and the space-time distribution of mosquito vectors, construct a spatial explicit individual transmission model to simulate the spread and spread of infectious diseases.
  • the residents travel activities constructed based on the multi-source trajectory data of the present invention can more truly reflect the movement patterns and travel locations of individuals within the city, and more accurately simulate the interaction between individuals, thereby improving the spatial accuracy of the simulation of the spread of infectious diseases such as dengue fever. It can also be used to implement simulation evaluation of more precise interventions in time and space.
  • the spatial explicit individual model based on multi-source trajectory data provides a framework for the simulation of the spread of infectious diseases such as dengue fever in the city, and provides scientific support for the precise simulation of infectious diseases and the formulation of prevention and control strategies.
  • the present invention can simulate the spread of a variety of infectious diseases that use mosquito vectors as a transmission route, such as dengue fever, malaria and other infectious diseases.

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Abstract

Procédé de simulation de propagation de maladie infectieuse au sein d'une zone urbaine, consistant : à effectuer une initialisation sur l'ensemble de la population de la zone urbaine en fonction de données importées de cas de dengue (S1) ; à construire un réseau de mobilité des individus de l'ensemble de la population urbaine initialisée en fonction d'informations de chaînes de déplacements effectués par les individus construites selon des données de trajectoires d'une pluralité de sources, et à calculer le temps passé au travail par un individu (S2) ; à calculer une distribution spatio-temporelle de moustiques vecteurs en fonction de données météorologiques comprenant la température moyenne quotidienne et la pluie quotidienne (S3) ; et à construire un modèle spatialement explicite de propagation par les individus selon le réseau de mobilité des individus construit, le temps passé au travail calculé, et la distribution spatio-temporelle de moustiques vecteurs calculée, et à simuler le processus de propagation d'une maladie infectieuse (S4). Un système de simulation de propagation de maladie infectieuse au sein d'une zone urbaine et un dispositif électronique peuvent refléter de manière plus réaliste les tendances dominantes en termes de mouvements et les lieux de déplacement d'individus au sein de la zone urbaine et simuler des interactions entre individus de manière plus précise, ce qui permet d'améliorer la précision spatiale de la propagation et de la simulation de la propagation de la maladie infectieuse.
PCT/CN2020/083133 2020-04-01 2020-04-03 Procédé et système de simulation de propagation de maladie infectieuse au sein d'une zone urbaine et dispositif électronique WO2021196152A1 (fr)

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