WO2021196152A1 - 城市内部传染病扩散模拟方法、系统及电子设备 - Google Patents

城市内部传染病扩散模拟方法、系统及电子设备 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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Shenzhen Institute of Advanced Technology of CAS
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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

一种城市内部传染病扩散模拟方法，包括：根据登革热输入病例数据进行城市内部全部人口初始化（S1）；对初始化后的城市的全部人口，根据由多源轨迹数据构造的个体出行链信息构建个体移动网络，并计算个体在职住地停留时间（S2）；根据包括日平均气温、日降雨量的气象数据计算得到蚊媒时空分布（S3）；根据构建的个体移动网络，计算得到的职住地停留时间及蚊媒时空分布，构建空间显式个体传播模型，模拟传染病的传播扩散过程（S4）。城市内部传染病扩散模拟系统及电子设备，能够较真实的反映城市内部个体的移动模式和出行地点，更加准确的模拟个体之间的交互，从而提高传染病传播扩散模拟在空间上的准确性。

Description

城市内部传染病扩散模拟方法、系统及电子设备 技术领域
本发明涉及一种城市内部传染病扩散模拟方法、系统及电子设备。
背景技术
近几十年来，随着气候环境变化、人口流动和城市化进程等因素的综合作用，世界各地登革热等传染病的发病率显著增加，严重威胁人类健康。由于计算机建模和模拟，为理解传染病传播扩散过程、预测传染病扩散态势和科学制定防控措施提供了关键科学支撑，迫切需要精准的传播模型来更加有效的模拟登革热等传染病的扩散过程。
现有技术中，由于大规模的个体移动数据非常稀疏，很多学者通过各种方法来构造个体活动模式。例如，通过利用少部分实际的人与人接触的调查数据，得出个人日常活动的近似模型，然后根据定向和半随机两种活动规则将个体活动进行模拟分配；或者采用随机方法模拟人口移动，即白天个体从居住地随机的选择一个地方出行。这些方法虽然模拟了城市内部人群活动，但是都不能准确的描述个体真实的出行模式。
总体来说，现有技术的缺点主要有：
第一，现有的传染病传播扩散模型中人口移动数据不能准确的描述个体真实的出行模式，存在较大的空间不准确性，因而提取城市内部大规模的居民出行信息存在较大难度；
第二，现有的登革热传播模型中只考虑无症状感染者的数量这一影响因素，忽略了无症状感染者的空间位置的影响，尚未有研究对无症状感染者的空间位置进行探索；
第三，现有的传染病传播模型中利用手机定位数据描述人群移动的研究大多是在国家或者区域等较大的空间尺度上，目前尚未有基于城市内部建筑物这种较小尺度上的研究。
发明内容
有鉴于此，有必要提供一种城市内部传染病扩散模拟方法、系统及电子设备。
本发明提供一种城市内部传染病扩散模拟方法，该方法包括如下步骤：a.根据登革热输入病例数据进行城市内部全部人口初始化；b.对初始化后的城市的全部人口，根据由多源轨迹数据构造的个体出行链信息构建个体移动网络，并计算个体在职住地停留时间；c.根据包括日平均气温、日降雨量的气象数据 计算得到蚊媒时空分布；d.根据构建的个体移动网络，计算得到的职住地停留时间及蚊媒时空分布，构建空间显式个体传播模型，模拟传染病的传播扩散过程。
其中，该方法在步骤a之前还包括步骤：
获取源数据；所述源数据包括多源轨迹数据、登革热输入病例数据、气象数据；所述多源轨迹数据包括：人口普查数据、出行调查数据、建筑物数据及手机定位数据。
该方法还包括步骤e：
根据空间显式个体传播模型模拟结果分析城市内部传染病的时空分布模式。
所述的步骤a具体包括：
根据传染病预防与控制中心提供的登革热输入病例数据，进行有症状感染者的初始化；
根据每一例有症状感染者，依据登革热有、无症状感染者的比例生成相应数量的无症状感染者，进行无症状感染者初始化；
根据城市内部免疫人群的所占比例或者免疫人群年龄分布特征等在初始人口中选择相应数量的人群并将其设置为免疫状态。
所述的步骤b具体包括：
根据所述多源轨迹数据进行大规模的个体移动建模并构造个体出行链信息，所述个体出行链信息是指个体一天24小时的出行地点信息；
根据个体出行链信息、人口普查数据及建筑物数据，识别得到个体的职住地，构建个体在职住地建筑物之间移动的网络；
通过得到的个体的职住地和个体出行链信息，计算得到每个个体在职住地的停留时间。
所述的步骤c具体包括：
根据每个区域每天的日平均气温和降雨量气象因素，分别计算传染病模拟期间内每天每个区域的前一个月的日平均气温和前一个月累计降雨的天数；
根据计算出的每个区域的前一个月的日平均气温和前一个月累计降雨的天数，通过蚊媒数量与上述两个气象因素的关系式计算出每个区域每天的蚊媒时空分布情况。
所述的步骤d具体包括：
以一栋独立的建筑物为模拟单元，结合经典的SEIR模型来建模病毒在人群中的传播以及人群中个体不同状态之间的动态转移；
以天为步长利用空间显式个体模型进行登革热传播扩散的模拟，从人群中第一个输入病例被感染的第一天开始进行模拟直至整个登革热传播模拟过程 结束。
所述的步骤e具体包括：
分析时间分布结果时，将场景分别模拟N次，每次的模拟结果按天进行统计，最终求得N次模拟的均值和95％的置信区间；
分析空间分布结果时，将1km x 1km的空间网格作为空间分析的基本空间单元，分析该网格尺度下的登革热流行的空间分布结果，将场景分别模拟N次，并将每次模拟的登革热感染者的空间分布结果按网格进行统计，分别求出每个网格N次模拟的病例数的均值，最后与实际病例的空间分布结果进行比较，分析模拟结果的空间分布特征和模型在空间分布上的模拟效果。
本发明提供一种城市内部传染病扩散模拟系统，该系统包括人口初始化模块、移动网络构建模块、蚊媒时空分布模块、传播模拟模块，其中：所述人口初始化模块用于根据登革热输入病例数据进行城市内部全部人口初始化；所述移动网络构建模块用于对初始化后的城市的全部人口，根据由多源轨迹数据构造的个体出行链信息构建个体移动网络，并计算个体在职住地停留时间；所述蚊媒时空分布模块用于根据包括日平均气温、日降雨量的气象数据计算得到蚊媒时空分布；所述传播模拟模块用于根据构建的个体移动网络，计算得到的职住地停留时间及蚊媒时空分布，构建空间显式个体传播模型，模拟传染病的传播扩散过程。
其中，所述系统还包括：
获取模块，用于获取源数据；所述源数据包括多源轨迹数据、登革热输入病例数据、气象数据；所述多源轨迹数据包括：人口普查数据、出行调查数据、建筑物数据及手机定位数据；
分析模块，用于根据空间显式个体传播模型模拟结果分析城市内部传染病的时空分布模式。
本发明还提供一种电子设备，包括：
至少一个处理器；以及
与所述至少一个处理器通信连接的存储器；其中，
所述存储器存储有可被所述一个处理器执行的指令，所述指令被所述至少一个处理器执行，以使所述至少一个处理器能够执行所述城市内部传染病扩散模拟方法的以下操作：
步骤a：根据登革热输入病例数据进行城市内部全部人口初始化；
步骤b：对初始化后的城市的全部人口，根据由多源轨迹数据构造的个体出行链信息构建个体移动网络，并计算个体在职住地停留时间；
步骤c：根据包括日平均气温、日降雨量的气象数据计算得到蚊媒时空分布；
步骤d：根据构建的个体移动网络，计算得到的职住地停留时间及蚊媒时空分布，构建空间显式个体传播模型，模拟传染病的传播扩散过程。
本发明城市内部传染病扩散模拟方法、系统及电子设备，基于多源轨迹数据构造的居民出行活动能够较真实的反映城市内部个体的移动模式和出行地点，更加准确的模拟个体之间的交互，从而提高登革热等传染病传播扩散模拟在空间上的准确性，也可用于实施时间空间上更加精准的干预措施的模拟评估；针对无症状感染者的空间位置设置了三种分配策略，首次探索了隐性输入病例空间位置对登革热等传染病传播扩散的影响，并通过模拟结果的时空分布可以初步探索隐性输入病例最可能的空间分布情况；本发明中利用手机定位数据描述人群在城市内部不同建筑物之间的移动，实现了目前基于城市内部建筑物这种较小尺度上传染病研究的重要突破，可以为传染病的精准防控提供科学支撑。
附图说明
图1为本发明城市内部传染病扩散模拟方法的流程图；
图2为本发明实施例提供的个体在职住地之间的活动示意图；
图3为本发明实施例提供的以天为步长进行传染病模拟的流程图；
图4为本发明城市内部传染病扩散模拟系统的硬件架构图；
图5-图7为本发明实施例提供的登革热传播扩散的时间序列示意图；
图8-图10为本发明实施例提供的登革热传播扩散的每日累计示意图；
图11-图13为本发明实施例提供的登革热传播扩散的空间分布结果示意图；
图14为本发明实施例提供的2014年深圳市本地病例空间分布示意图；
图15为本发明实施例提供的城市内部传染病扩散模拟方法的硬件设备结构示意图。
具体实施方式
为了使本申请的目的、技术方案及优点更加清楚明白，以下结合附图及实施例，对本申请进行进一步详细说明。应当理解，此处所描述的具体实施例仅用以解释本申请，并不用于限定本申请。
本实施例以登革热这种传染病为例进行说明：
参阅图1所示，是本发明城市内部传染病扩散模拟方法较佳实施例的作业流程图。
步骤S0，获取源数据。所述源数据包括多源轨迹数据、登革热输入病例数据、气象数据和其他基础数据。
其中，多源轨迹数据包含大量的手机定位数据和出行调查数据。在本实施 例中，所述手机定位数据是指2012年5月某一天的超过762万条匿名用户记录，所述用户记录(如表1所示)包括匿名用户ID、时间戳、经度和维度。所述登革热输入病例数据包括2014年深圳市的348例本地病例数据和206例外来输入病例(输入病例是指所有从深圳市以外地区进入深圳市的登革热感染者)，所述登革热输入病例数据包含性别、年龄、家庭地址，感染日期等信息，该病例数据来源于深圳市医学信息中心。所述气象数据是指2014年深圳市22个气象站点每日的平均最低气温、降雨量及平均相对湿度，该气象数据来源于深圳市气象局。所述其他基础数据包括2010年第六次人口普查数据、深圳市统计年鉴及深圳市建筑物数据。
表1
Figure PCTCN2020083133-appb-000001
步骤S1，根据获取的登革热输入病例数据进行城市内部全部人口初始化。也即是，对有症状感染者设置其感染状态；依据有、无症状感染者的比例生成无症状感染者，并设置无症状感染者的感染状态；在初始人口中选择相应数量的人群并将其设置为免疫状态。
具体而言，所述步骤S1包括：
步骤101，根据传染病预防与控制中心提供的登革热输入病例数据，进行有症状感染者的初始化：即在模拟的城市内部全部人口数据中，根据每一个输入病例的年龄、性别、家庭住址等属性信息与人口数据中所有个体进行相似性匹配，找出最相关的个体将其设置为输入病例，并设置其感染状态。
步骤102，根据每一例有症状感染者，依据登革热有、无症状感染者的比例生成相应数量的无症状感染者，进行无症状感染者初始化。无症状感染者的家庭地址分配方法分为三种策略：一是根据有症状感染者的时空分布；二是根据城市内部人口分布；三是随机分布。生成无症状感染者后设置其感染状态。
步骤103，根据城市内部免疫人群的所占比例或者免疫人群年龄分布特征等在初始人口中选择相应数量的人群并将其设置为免疫状态，该免疫人群在整个登革热的传播过程中不会被感染，拥有终身免疫。其中，所述初始人口为除有症状感染者和无症状感染者之外的城市的全部人口。
进一步地，本实施例的具体实现方法包括：
首先，对于有症状感染者进行初始化，具体实现方法为：根据输入病例的家庭地址找出该建筑物内所有的个体，计算每个个体与输入病例的性别和年龄两个属性的相似度，然后选择相似度最高的个体匹配为输入病例，如果存在多 个最高的相似度相等的个体，则在这些个体中随机选择一个个体匹配为输入病例。
然后，对于无症状感染者进行初始化：假设深圳市登革热暴发流行的过程中有症状感染者和无症状感染者的比例是1：2.2。具体实现方法为：对于无症状感染者的家庭住址的分配假定了3种场景：一是根据输入病例的家庭地址进行选取，假设与输入病例的空间分布一致，即对于每一个有症状感染者，以2.2:1的概率随机生成N个无症状感染者，在与对应的有症状感染者相同的区内随机选择N个的无症状感染者；二是根据人口数量分布选择无症状感染者的家庭地址，即依据深圳市10个辖区的总人口数为概率进行选择，即总人口数越多的区选中的概率越大；三是根据随机分布选择无症状感染者的家庭地址，即从深圳市10个辖区中随机选择一个区，无症状感染者的家庭地址确定后，分别设置其潜伏期、感染期和恢复期。
最后，假设深圳市居民的初始免疫人群比例为2.43％，在总的人群中，去除所有输入病例(包括有症状感染者和无症状感染者)后，在剩下的人群中根据人群免疫比例随机的选取免疫人群，并将其状态设置为恢复态，且假设个体获得该登革热病毒的永久免疫力。
步骤S2，对初始化后的城市的全部人口，根据由多源轨迹数据构造的个体出行链信息构建个体移动网络，并计算个体在职住地停留时间。
其中，所述由多源轨迹数据构造的个体出行链信息是指根据人口普查数据、建筑物数据、出行调查数据以及大量手机定位数据模拟出来的个体一天24小时的出行地点信息，识别得到人口数据的职住地和出行链信息，构造个体在职住地之间的移动网络，并计算出每个个体在职住地的停留时间。
具体而言，所述步骤S2包括：
步骤201：根据包括人口普查数据、出行调查数据、建筑物数据及手机定位数据的多源轨迹数据进行大规模的个体移动建模并构造个体出行链信息。所述个体出行链信息是指个体一天24小时的出行地点信息。
步骤202：根据个体出行链信息、人口普查数据及建筑物数据，识别得到个体的职住地，构建个体在职住地建筑物之间移动的网络(请参阅图2)，其中本模型中仅考虑个体最主要的出行活动，所以主要的个体出行活动地点仅考虑家庭、工作地或者学校所在建筑物。
步骤203：通过得到的个体的职住地和个体出行链信息，计算得到每个个体在职住地的停留时间。所述停留时间用于传染病模拟过程中新发感染者的选择，在有感染者存在的建筑物中停留的时间越久，则该个体被感染的概率越大。
步骤S3，根据包括日平均气温、日降雨量的气象数据计算得到蚊媒时空分布。也即是，利用日平均气温和日降雨量等气象因素来计算每个区域前一个 月内的日平均气温和前一个月内降雨的天数，并由此计算出城市内部每个区域蚊媒数量的时空动态分布。
具体而言，所述步骤S3包括：
步骤301：根据每个区域每天的日平均气温和降雨量气象因素，分别计算传染病模拟期间内每天每个区域的前一个月的日平均气温和前一个月累计降雨的天数。
步骤302：根据计算出的每个区域的前一个月的日平均气温和前一个月累计降雨的天数，通过蚊媒数量与上述两个气象因素的关系式计算出每个区域每天的蚊媒时空分布情况。
进一步地，本实施例的具体实现方法包括：
由深圳市22个气象站点的数据分别计算每个区域每一天的前一个月的日平均气温和前一个月累计降雨的天数，根据公式(1)计算每个区域每天的蚊媒时空分布情况：
M ij＝0.05P ij-0.0081T ij 2+0.5289T ij-5.5461    (1)
其中M ij表示第i区第j天的蚊媒数量，P ij表示第i区第j天的前一个月累计降雨的天数，T ij表示第i区第j天的的前一个月平均气温，i的取值范围是[1,10]，分别表示深圳市10个辖区，j的取值范围是[1,365]，表示2014年1月1日至12月31日。
步骤S4，根据构建的个体移动网络，计算得到的职住地停留时间及蚊媒时空分布，构建空间显式个体传播模型，模拟传染病的传播扩散过程。
其中，所述空间显式个体传播模型是指以一栋建筑物为模拟单元，结合经典的SEIR模型来建模病毒在人群中的传播以及人群中个体不同状态之间的动态转移。在模型中每个个体是一个智能体，并且个体的出行活动是指职住地之间的移动。
具体而言，所述步骤S4包括：
步骤401：以一栋独立的建筑物为模拟单元，结合经典的SEIR模型来建模病毒在人群中的传播以及人群中个体不同状态之间的动态转移。在模型中每个个体是一个智能体，每个智能体具有年龄、性别、家庭住地、工作地及感染状态等属性，并且智能体的出行活动包括待在家里、去工作或者去学校。此外，在模拟的过程中，区分了有症状感染者、无症状感染者感染期的差异性。
步骤402：以天为步长利用空间显式个体模型进行登革热传播扩散的模拟，从人群中第一个输入病例被感染的第一天开始进行模拟直至整个登革热传播模拟过程结束。以天为单位模拟的流程请参阅图3，在每天的模拟过程中，遍历所有职住地建筑物查看是否出现新的感染者。若建筑物内产生了新的感染者，首先根据个体被感染的权重大小来选择该建筑物内的新产生的感染者，然后根据有症状感染者和无症状感染者的比例为概率来确定感染者的感染症状，最后分别设置感染者的潜伏期、感染期和恢复期。
进一步地，本实施例的具体实现方法包括：
以一栋独立的建筑物为模拟单元，结合经典的SEIR模型来建模病毒在人群中的传播以及人群中个体不同状态之间的动态转移。在模型中每个个体是一个智能体，每个智能体具有年龄、性别、家庭住地、工作地及感染状态等属性，并且智能体在不同建筑物之间的移动导致登革热病毒的传播。此外，在模拟的过程中，区分了有无症状感染者感染期的差异性。整个人群分为易感者H S、潜伏者H E、有症状感染者H Is、无症状感染者H Ia和恢复者H R。
当一个处于易感状态的个体(H S)被一个携带病毒的伊蚊叮咬后，将会有可能转化为潜伏状态(H E)，这个概率取决于易感个体被带病毒蚊虫叮咬一次获得感染的概率(β H)和当前个体所在建筑物内伊蚊的数量。处于潜伏态的个体(H E)经过潜伏期(δ H)后将转为感染态(H Is或H Ia)，处于潜伏状态的个体不具备传染性，但如果伊蚊叮咬了处于感染态的个体，登革热病毒将会以概率β V传染给健康的伊蚊，携带病毒的伊蚊在其整个生存时间内都可能叮咬易感个体来传播病毒。一个处于易感态的个体被感染的概率为：
Figure PCTCN2020083133-appb-000002
其中m＝k·M,m表示人均伊蚊数，k是一个比例系数，M表示每个模拟单元蚊媒的数量。
然后以天为步长利用空间显式个体模型进行登革热传播扩散的模拟，从人群中第一个输入病例的被感染的第一天开始进行模拟直至整个登革热传播模拟过程结束。在模拟过程中，遍历所有职住地建筑物查看是否出现新的感染者。若建筑物内产生了新的感染者，首先根据个体被感染的权重大小来选择该建筑物内的新产生的感染者，然后根据有症状感染者和无症状感染者的比例为概率来确定感染者的感染症状，最后分别设置感染者的潜伏期、感染期和恢复期。其中建筑物内每个个体被感染的权重等于归一化后的个体发病率与归一化后的停留时间之和。最终选择的新发感染者个体尽可能的选择那些具有高发病率(与年龄有关)和停留时间(在有感染者存在的建筑物内累计停留时间)比较长的个体。
步骤S5：根据空间显式个体传播模型模拟结果分析城市内部传染病的时空分布模式。也即是，根据实际的本地病例数据与模拟结果进行对比，从时间分布和空间分布上来分析模型的效果，并分析传染病的时空分布情况。
具体而言，所述步骤S5包括：
步骤501：分析时间分布结果时，为解决一次模拟的参数随机取值过程中带来的不确定性问题，将多种场景分别模拟N次，每次的模拟结果按天进行统计，最终求得N次模拟的均值和95％的置信区间。本地病例与模拟有症状感染者的时间分布结果分别通过时间序列图和每日累计图进行结果展示，然后分析结果的时间分布特征和模型在时间分布上的模拟效果，并通过不同策略下的时间分布结果来探索无症状感染者最有可能的空间分布情况。
步骤502：分析空间分布结果时，将1km x 1km的空间网格作为空间分析的基本空间单元，分析该网格尺度下的登革热流行的空间分布结果。同样地，为解决一次模拟的参数随机取值过程中带来的不确定性问题，将多种场景分别模拟N次，并将每次模拟的登革热感染者的空间分布结果按网格进行统计，分别求出每个网格N次模拟的病例数的均值，最后与实际病例的空间分布结果进行比较，分析模拟结果的空间分布特征和模型在空间分布上的模拟效果，并通过不同策略下的空间分布结果探索无症状感染者最有可能的空间分布情况。
进一步地，本实施例的具体实现方法包括：
将三种场景分别模拟100次，每次的模拟结果按天进行统计，最终求得100次模拟的均值和95％的置信区间。本地病例与模拟有症状感染者的时间分布结果分别通过时间序列图(图5-图7)和每日累计图(图8-图10)进行展示。其中，图中黑色曲线代表2014年深圳市实际本地病 例；红色曲线代表100次模拟的有症状感染者数量的均值；灰色的区域表示95％的置信区间。通过图5-图7、图8-图10的模拟结果可以发现，本模型可以较好的模拟出登革热在时间维度上的传播扩散过程。尽管由于100次模拟的结果经过均值处理后，高峰期的天数和峰值的大小与本地实际病例相比有一些差异，但是总体上模拟效果很好。同时，通过三种场景的时间序列图可以发现，无症状感染者的家庭住址根据输入病例的空间分布(图5)进行分配时，模型的模拟效果更好一些，高峰期天数和峰值的大小都与本地实际病例更符合。
同样地，分析空间分布结果时，将100次模拟的结果按照1km x 1km的空间网格作为基本空间单元进行统计，然后再分别求出每个网格100次模拟的病例数的均值，最后与实际病例的空间分布结果进行比较，分析模拟结果的空间分布特征和模型在空间分布上的模拟效果。通过图11-图13模拟结果的空间分布可以看出，有症状感染者主要集中在宝安区(与南山区相邻处)、南山区、福田、罗湖区和龙华区，这与2014年深圳市实际的本地病例的空间分布总体上是一致的(如图14所示)。三种无症状输入病例的家庭地址的分配场景模拟结果表明，根据输入病例的空间分布来分配无症状感染者家庭位置的模拟结果相对较准确(图11)，与实际的本地病例最符合，尤其是南山区、罗湖区、宝安区与南山区相邻处等区域的模拟结果与实际本地病例相符，都是登革热病例出现较多且密集的区域。
参阅图4所示，是本发明城市内部传染病扩散模拟系统10的硬件架构图。该系统包括：获取模块100、人口初始化模块101、移动网络构建模块102、蚊媒时空分布模块103、传播模拟模块104以及分析模块105。
所述获取模块100用于获取源数据。所述源数据包括多源轨迹数据、登革热输入病例数据、气象数据和其他基础数据。
其中，多源轨迹数据包含大量的手机定位数据和出行调查数据。在本实施例中，所述手机定位数据是指2012年5月某一天的超过762万条匿名用户记录，所述用户记录(如表1所示)包括匿名用户ID、时间戳、经度和维度。所述登革热输入病例数据包括2014年深圳市的348例本地病例数据和206例外来输入病例(输入病例是指所有从深圳市以外地区进入深圳市的登革热感染者)，所述登革热输入病例数据包含性别、年龄、家庭地址，感染日期等信息，该病例数据来源于深圳市医学信息中心。所述气象数据是指2014年深圳市22个气象站点每日的平均最低气温、降雨量及平均相对湿度，该气象数据来源于深圳市气象局。所述其他基础数据包括2010年第六次人口普查数据、深圳市统计年鉴及深圳市建筑物数据。
表1
Figure PCTCN2020083133-appb-000003
所述人口初始化模块101用于根据获取的登革热输入病例数据进行城市内部全部人口初始化。也即是，对有症状感染者设置其感染状态；依据有、无症状感染者的比例生成无症状感染者，并设置无症状感染者的感染状态；在初始人口中选择相应数量的人群并将其设置为免疫状态。
具体而言：
首先，所述人口初始化模块101根据传染病预防与控制中心提供的登革热输入病例数据，进行有症状感染者的初始化：即在模拟的城市内部全部人口数据中，根据每一个输入病例的年龄、性别、家庭住址等属性信息与人口数据中所有个体进行相似性匹配，找出最相关的个体将其设置为输入病例，并设置其感染状态。
然后，所述人口初始化模块101根据每一例有症状感染者，依据登革热有、无症状感染者的比例生成相应数量的无症状感染者，进行无症状感染者初始化。无症状感染者的家庭地址分配方法分为三种策略：一是根据有症状感染者的时空分布；二是根据城市内部人口分布；三是随机分布。生成无症状感染者后设置其感染状态。
最后，所述人口初始化模块101根据城市内部免疫人群的所占比例或者免疫人群年龄分布特征等在初始人口中选择相应数量的人群并将其设置为免疫状态，该免疫人群在整个登革热的传播过程中不会被感染，拥有终身免疫。其中，所述初始人口为除有症状感染者和无症状感染者之外的城市的全部人口。
进一步地，本实施例中所述人口初始化模块101具体实现为：
首先，所述人口初始化模块101对于有症状感染者进行初始化，具体为：根据输入病例的家庭地址找出该建筑物内所有的个体，计算每个个体与输入病例的性别和年龄两个属性的相似度，然后选择相似度最高的个体匹配为输入病例，如果存在多个最高的相似度相等的个体，则在这些个体中随机选择一个个体匹配为输入病例。
然后，所述人口初始化模块101对于无症状感染者进行初始化：假设深圳市登革热暴发流行的过程中有症状感染者和无症状感染者的比例是1：2.2。具体实现方法为：对于无症状感染者的家庭住址的分配假定了3种场景：一是根据输入病例的家庭地址进行选取，假设与输入病例的空 间分布一致，即对于每一个有症状感染者，以2.2:1的概率随机生成N个无症状感染者，在与对应的有症状感染者相同的区内随机选择N个的无症状感染者；二是根据人口数量分布选择无症状感染者的家庭地址，即依据深圳市10个辖区的总人口数为概率进行选择，即总人口数越多的区选中的概率越大；三是根据随机分布选择无症状感染者的家庭地址，即从深圳市10个辖区中随机选择一个区，无症状感染者的家庭地址确定后，分别设置其潜伏期、感染期和恢复期。
最后，假设深圳市居民的初始免疫人群比例为2.43％，在总的人群中，去除所有输入病例(包括有症状感染者和无症状感染者)后，在剩下的人群中根据人群免疫比例随机的选取免疫人群，并将其状态设置为恢复态，且假设个体获得该登革热病毒的永久免疫力。
所述移动网络构建模块102用于对初始化后的城市的全部人口，根据由多源轨迹数据构造的个体出行链信息构建个体移动网络，并计算个体在职住地停留时间。
其中，所述由多源轨迹数据构造的个体出行链信息是指根据人口普查数据、建筑物数据、出行调查数据以及大量手机定位数据模拟出来的个体一天24小时的出行地点信息，识别得到人口数据的职住地和出行链信息，构造个体在职住地之间的移动网络，并计算出每个个体在职住地的停留时间。
具体而言：
所述移动网络构建模块102根据包括人口普查数据、出行调查数据、建筑物数据及手机定位数据的多源轨迹数据进行大规模的个体移动建模并构造个体出行链信息。所述个体出行链信息是指个体一天24小时的出行地点信息。
所述移动网络构建模块102根据个体出行链信息、人口普查数据及建筑物数据，识别得到个体的职住地，构建个体在职住地建筑物之间移动的网络(请参阅图2)，其中本模型中仅考虑个体最主要的出行活动，所以主要的个体出行活动地点仅考虑家庭、工作地或者学校所在建筑物。
所述移动网络构建模块102通过得到的个体的职住地和个体出行链信息，计算得到每个个体在职住地的停留时间。所述停留时间用于传染病模拟过程中新发感染者的选择，在有感染者存在的建筑物中停留的时间越久，则该个体被感染的概率越大。
所述蚊媒时空分布模块103用于根据包括日平均气温、日降雨量的气象数据计算得到蚊媒时空分布。也即是，利用日平均气温和日降雨量等气象因素来计算每个区域前一个月内的日平均气温和前一个月内降雨的 天数，并由此计算出城市内部每个区域蚊媒数量的时空动态分布。
具体而言：
所述蚊媒时空分布模块103根据每个区域每天的日平均气温和降雨量气象因素，分别计算传染病模拟期间内每天每个区域的前一个月的日平均气温和前一个月累计降雨的天数。
所述蚊媒时空分布模块103根据计算出的每个区域的前一个月的日平均气温和前一个月累计降雨的天数，通过蚊媒数量与上述两个气象因素的关系式计算出每个区域每天的蚊媒时空分布情况。
进一步地，本实施例中所述蚊媒时空分布模块103具体实现为：
由深圳市22个气象站点的数据分别计算每个区域每一天的前一个月的日平均气温和前一个月累计降雨的天数，根据公式(1)计算每个区域每天的蚊媒时空分布情况：
M ij＝0.05P ij-0.0081T ij 2+0.5289T ij-5.5461     (1)
其中M ij表示第i区第j天的蚊媒数量，P ij表示第i区第j天的前一个月累计降雨的天数，T ij表示第i区第j天的的前一个月平均气温，i的取值范围是[1,10]，分别表示深圳市10个辖区，j的取值范围是[1,365]，表示2014年1月1日至12月31日。
所述传播模拟模块104用于根据构建的个体移动网络，计算得到的职住地停留时间及蚊媒时空分布，构建空间显式个体传播模型，模拟传染病的传播扩散过程。
其中，所述空间显式个体传播模型是指以一栋建筑物为模拟单元，结合经典的SEIR模型来建模病毒在人群中的传播以及人群中个体不同状态之间的动态转移。在模型中每个个体是一个智能体，并且个体的出行活动是指职住地之间的移动。
具体而言：
所述传播模拟模块104以一栋独立的建筑物为模拟单元，结合经典的SEIR模型来建模病毒在人群中的传播以及人群中个体不同状态之间的动态转移。在模型中每个个体是一个智能体，每个智能体具有年龄、性别、家庭住地、工作地及感染状态等属性，并且智能体的出行活动包括待在家里、去工作或者去学校。此外，在模拟的过程中，区分了有症状感染者、无症状感染者感染期的差异性。
所述传播模拟模块104以天为步长利用空间显式个体模型进行登革热 传播扩散的模拟，从人群中第一个输入病例被感染的第一天开始进行模拟直至整个登革热传播模拟过程结束。以天为单位模拟的流程请参阅图3，在每天的模拟过程中，遍历所有职住地建筑物查看是否出现新的感染者。若建筑物内产生了新的感染者，首先根据个体被感染的权重大小来选择该建筑物内的新产生的感染者，然后根据有症状感染者和无症状感染者的比例为概率来确定感染者的感染症状，最后分别设置感染者的潜伏期、感染期和恢复期。
进一步地，本实施例中所述传播模拟模块104具体实现为：
所述传播模拟模块104以一栋独立的建筑物为模拟单元，结合经典的SEIR模型来建模病毒在人群中的传播以及人群中个体不同状态之间的动态转移。在模型中每个个体是一个智能体，每个智能体具有年龄、性别、家庭住地、工作地及感染状态等属性，并且智能体在不同建筑物之间的移动导致登革热病毒的传播。此外，在模拟的过程中，区分了有无症状感染者感染期的差异性。整个人群分为易感者H S、潜伏者H E、有症状感染者H Is、无症状感染者H Ia和恢复者H R。
当一个处于易感状态的个体(H S)被一个携带病毒的伊蚊叮咬后，将会有可能转化为潜伏状态(H E)，这个概率取决于易感个体被带病毒蚊虫叮咬一次获得感染的概率(β H)和当前个体所在建筑物内伊蚊的数量。处于潜伏态的个体(H E)经过潜伏期(δ H)后将转为感染态(H Is或H Ia)，处于潜伏状态的个体不具备传染性，但如果伊蚊叮咬了处于感染态的个体，登革热病毒将会以概率β V传染给健康的伊蚊，携带病毒的伊蚊在其整个生存时间内都可能叮咬易感个体来传播病毒。一个处于易感态的个体被感染的概率为：
Figure PCTCN2020083133-appb-000004
其中m＝k·M,m表示人均伊蚊数，k是一个比例系数，M表示每个模拟单元蚊媒的数量。
然后所述传播模拟模块104以天为步长利用空间显式个体模型进行登革热传播扩散的模拟，从人群中第一个输入病例的被感染的第一天开始进行模拟直至整个登革热传播模拟过程结束。在模拟过程中，遍历所有职住地建筑物查看是否出现新的感染者。若建筑物内产生了新的感染者，首先根据个体被感染的权重大小来选择该建筑物内的新产生的感染者，然后根据有症状感染者和无症状感染者的比例为概率来确定感染者的感染症状，最后分别设置感染者的潜伏期、感染期和恢复期。其中建筑物内每个个体被感染的权重等于归一化后的个体发病率与归一化后的停留时间之和。最终选择的新发感染者个体尽可能的选择那些具有高发病率(与年龄有关)和停留时间(在有感染者存在的建筑物内累计停留时间)比较长的个体。
所述分析模块105用于根据空间显式个体传播模型模拟结果分析城市内部传染病的时空分布模式。也即是，根据实际的本地病例数据与模拟结果进行对比，从时间分布和空间分布上来分析模型的效果，并分析传染病的时空分布情况。
具体而言：
所述分析模块105分析时间分布结果时，为解决一次模拟的参数随机取值过程中带来的不确定性问题，将多种场景分别模拟N次，每次的模拟结果按天进行统计，最终求得N次模拟的均值和95％的置信区间。本地病例与模拟有症状感染者的时间分布结果分别通过时间序列图和每日累计图进行结果展示，然后分析结果的时间分布特征和模型在时间分布上的模拟效果，并通过不同策略下的时间分布结果来探索无症状感染者最有可能的空间分布情况。
所述分析模块105分析空间分布结果时，将1km x 1km的空间网格作为空间分析的基本空间单元，分析该网格尺度下的登革热流行的空间分布结果。同样地，为解决一次模拟的参数随机取值过程中带来的不确定性问题，将多种场景分别模拟N次，并将每次模拟的登革热感染者的空间分布结果按网格进行统计，分别求出每个网格N次模拟的病例数的均值，最后与实际病例的空间分布结果进行比较，分析模拟结果的空间分布特征和模型在空间分布上的模拟效果，并通过不同策略下的空间分布结果探索无症状感染者最有可能的空间分布情况。
进一步地，本实施例中所述分析模块105具体实现为：
所述分析模块105将三种场景分别模拟100次，每次的模拟结果按天进行统计，最终求得100次模拟的均值和95％的置信区间。本地病例与模拟有症状感染者的时间分布结果分别通过时间序列图(图5-图7)和每日累计图(图8-图10)进行展示。其中，图中黑色曲线代表2014年深 圳市实际本地病例；红色曲线代表100次模拟的有症状感染者数量的均值；灰色的区域表示95％的置信区间。通过图5-图7、图8-图10的模拟结果可以发现，本模型可以较好的模拟出登革热在时间维度上的传播扩散过程。尽管由于100次模拟的结果经过均值处理后，高峰期的天数和峰值的大小与本地实际病例相比有一些差异，但是总体上模拟效果很好。同时，通过三种场景的时间序列图可以发现，无症状感染者的家庭住址根据输入病例的空间分布(图5)进行分配时，模型的模拟效果更好一些，高峰期天数和峰值的大小都与本地实际病例更符合。
同样地，所述分析模块105分析空间分布结果时，将100次模拟的结果按照1km x 1km的空间网格作为基本空间单元进行统计，然后再分别求出每个网格100次模拟的病例数的均值，最后与实际病例的空间分布结果进行比较，分析模拟结果的空间分布特征和模型在空间分布上的模拟效果。通过图11-图13模拟结果的空间分布可以看出，有症状感染者主要集中在宝安区(与南山区相邻处)、南山区、福田、罗湖区和龙华区，这与2014年深圳市实际的本地病例的空间分布总体上是一致的(如图14所示)。三种无症状输入病例的家庭地址的分配场景模拟结果表明，根据输入病例的空间分布来分配无症状感染者家庭位置的模拟结果相对较准确(图11)，与实际的本地病例最符合，尤其是南山区、罗湖区、宝安区与南山区相邻处等区域的模拟结果与实际本地病例相符，都是登革热病例出现较多且密集的区域。
图15是本申请实施例提供的城市内部传染病扩散模拟方法的硬件设备结构示意图。如图15所示，该设备包括一个或多个处理器以及存储器。以一个处理器为例，该设备还可以包括：输入系统和输出系统。
处理器、存储器、输入系统和输出系统可以通过总线或者其他方式连接，图15中以通过总线连接为例。
存储器作为一种非暂态计算机可读电子设备，可用于存储非暂态软件程序、非暂态计算机可执行程序以及模块。处理器通过运行存储在存储器中的非暂态软件程序、指令以及模块，从而执行电子设备的各种功能应用以及数据处理，即实现上述方法实施例的处理方法。
存储器可以包括存储程序区和存储数据区，其中，存储程序区可存储操作系统、至少一个功能所需要的应用程序；存储数据区可存储数据等。此外，存储器可以包括高速随机存取存储器，还可以包括非暂态存储器，例如至少一个磁盘存储器件、闪存器件、或其他非暂态固态存储器件。在一些实施例中，存储器可选包括相对于处理器远程设置的存储器，这些远程存储器可以通过网络连接至处理系统。上述网络的实例包括但不限于互联网、企业内部网、局域网、移动通信网及其组合。
输入系统可接收输入的数字或字符信息，以及产生信号输入。输出系统可包括显示屏等显示设备。
所述一个或者多个模块存储在所述存储器中，当被所述一个或者多个处理器执行时，执行上述任一方法实施例的以下操作：
步骤a：根据登革热输入病例数据进行城市内部全部人口初始化；
步骤b：对初始化后的城市的全部人口，根据由多源轨迹数据构造的个体出行链信息构建个体移动网络，并计算个体在职住地停留时间；
步骤c：根据包括日平均气温、日降雨量的气象数据计算得到蚊媒时空分布；
步骤d：根据构建的个体移动网络，计算得到的职住地停留时间及蚊媒时空分布，构建空间显式个体传播模型，模拟传染病的传播扩散过程。
上述产品可执行本申请实施例所提供的方法，具备执行方法相应的功能模块和有益效果。未在本实施例中详尽描述的技术细节，可参见本申请实施例提供的方法。
本申请实施例提供了一种非暂态(非易失性)计算机电子设备，所述计算机电子设备存储有计算机可执行指令，该计算机可执行指令可执行以下操作：
步骤a：根据登革热输入病例数据进行城市内部全部人口初始化；
步骤b：对初始化后的城市的全部人口，根据由多源轨迹数据构造的个体出行链信息构建个体移动网络，并计算个体在职住地停留时间；
步骤c：根据包括日平均气温、日降雨量的气象数据计算得到蚊媒时空分布；
步骤d：根据构建的个体移动网络，计算得到的职住地停留时间及蚊媒时空分布，构建空间显式个体传播模型，模拟传染病的传播扩散过程。
本申请实施例提供了一种计算机程序产品，所述计算机程序产品包括存储在非暂态计算机可读电子设备上的计算机程序，所述计算机程序包括程序指令，当所述程序指令被计算机执行时，使所述计算机执行以下操作：
步骤a：根据登革热输入病例数据进行城市内部全部人口初始化；
步骤b：对初始化后的城市的全部人口，根据由多源轨迹数据构造的个体出行链信息构建个体移动网络，并计算个体在职住地停留时间；
步骤c：根据包括日平均气温、日降雨量的气象数据计算得到蚊媒时空分布；
步骤d：根据构建的个体移动网络，计算得到的职住地停留时间及蚊媒时空分布，构建空间显式个体传播模型，模拟传染病的传播扩散过程。 本发明基于多源轨迹数据构造的居民出行活动能够较真实的反映城市内部个体的移动模式和出行地点，更加准确的模拟个体间的交互，从而提高登革热等传染病传播扩散模拟在空间上的准确性，也可用于实施时间空间上更加精准的干预措施的模拟评估。其中基于多源轨迹数据的空间显式个体模型为城市内部登革热等传染病传播扩散模拟提供一个框架，为传染病的精准模拟和防控策略的制定提供科学支撑。
需要说明的是，本发明能够针对多种以蚊媒为传播途径的传染病的传播进行扩散模拟，例如：登革热、疟疾等传染病。
以上所述仅是本发明的优选实施方式，应当指出，对于本技术领域的普通技术人员来说，在不脱离本发明原理的前提下，还可以做出若干改进和润饰，这些改进和润饰也应视为本发明的保护范围。

Claims (11)

  1. 一种城市内部传染病扩散模拟方法，其特征在于，该方法包括如下步骤：
    a.根据登革热输入病例数据进行城市内部全部人口初始化；
    b.对初始化后的城市的全部人口，根据由多源轨迹数据构造的个体出行链信息构建个体移动网络，并计算个体在职住地停留时间；
    c.根据包括日平均气温、日降雨量的气象数据计算得到蚊媒时空分布；
    d.根据构建的个体移动网络，计算得到的职住地停留时间及蚊媒时空分布，构建空间显式个体传播模型，模拟传染病的传播扩散过程。
  2. 如权利要求1所述的方法，其特征在于，该方法在步骤a之前还包括步骤：
    获取源数据；所述源数据包括多源轨迹数据、登革热输入病例数据、气象数据；所述多源轨迹数据包括：人口普查数据、出行调查数据、建筑物数据及手机定位数据。
  3. 如权利要求1或2所述的方法，其特征在于，该方法还包括步骤e：
    根据空间显式个体传播模型模拟结果分析城市内部传染病的时空分布模式。
  4. 如权利要求3所述的方法，其特征在于，所述的步骤a具体包括：
    根据传染病预防与控制中心提供的登革热输入病例数据，进行有症状感染者的初始化；
    根据每一例有症状感染者，依据登革热有、无症状感染者的比例生成相应数量的无症状感染者，进行无症状感染者初始化；
    根据城市内部免疫人群的所占比例或者免疫人群年龄分布特征等在初始人口中选择相应数量的人群并将其设置为免疫状态。
  5. 如权利要求4所述的方法，其特征在于，所述的步骤b具体包括：
    根据所述多源轨迹数据进行大规模的个体移动建模并构造个体出行链信息，所述个体出行链信息是指个体一天24小时的出行地点信息；
    根据个体出行链信息、人口普查数据及建筑物数据，识别得到个体的职住地，构建个体在职住地建筑物之间移动的网络；
    通过得到的个体的职住地和个体出行链信息，计算得到每个个体在职住地的停留时间。
  6. 如权利要求5所述的方法，其特征在于，所述的步骤c具体包括：
    根据每个区域每天的日平均气温和降雨量气象因素，分别计算传染病模拟期间内每天每个区域的前一个月的日平均气温和前一个月累计降雨的天数；
    根据计算出的每个区域的前一个月的日平均气温和前一个月累计降雨 的天数，通过蚊媒数量与上述两个气象因素的关系式计算出每个区域每天的蚊媒时空分布情况。
  7. 如权利要求6所述的方法，其特征在于，所述的步骤d具体包括：
    以一栋独立的建筑物为模拟单元，结合经典的SEIR模型来建模病毒在人群中的传播以及人群中个体不同状态之间的动态转移；
    以天为步长利用空间显式个体模型进行登革热传播扩散的模拟，从人群中第一个输入病例被感染的第一天开始进行模拟直至整个登革热传播模拟过程结束。
  8. 如权利要求7所述的方法，其特征在于，所述的步骤e具体包括：
    分析时间分布结果时，将场景分别模拟N次，每次的模拟结果按天进行统计，最终求得N次模拟的均值和95％的置信区间；
    分析空间分布结果时，将1km x 1km的空间网格作为空间分析的基本空间单元，分析该网格尺度下的登革热流行的空间分布结果，将场景分别模拟N次，并将每次模拟的登革热感染者的空间分布结果按网格进行统计，分别求出每个网格N次模拟的病例数的均值，最后与实际病例的空间分布结果进行比较，分析模拟结果的空间分布特征和模型在空间分布上的模拟效果。
  9. 一种城市内部传染病扩散模拟系统，其特征在于，该系统包括人口初始化模块、移动网络构建模块、蚊媒时空分布模块、传播模拟模块，其中：
    所述人口初始化模块用于根据登革热输入病例数据进行城市内部全部人口初始化；
    所述移动网络构建模块用于对初始化后的城市的全部人口，根据由多源轨迹数据构造的个体出行链信息构建个体移动网络，并计算个体在职住地停留时间；
    所述蚊媒时空分布模块用于根据包括日平均气温、日降雨量的气象数据计算得到蚊媒时空分布；
    所述传播模拟模块用于根据构建的个体移动网络，计算得到的职住地停留时间及蚊媒时空分布，构建空间显式个体传播模型，模拟传染病的传播扩散过程。
  10. 如权利要求9所述的系统，其特征在于，所述系统还包括：
    获取模块，用于获取源数据；所述源数据包括多源轨迹数据、登革热输入病例数据、气象数据；所述多源轨迹数据包括：人口普查数据、出行调查数据、建筑物数据及手机定位数据；
    分析模块，用于根据空间显式个体传播模型模拟结果分析城市内部传染病的时空分布模式。
  11. 一种电子设备，包括：
    至少一个处理器；以及
    与所述至少一个处理器通信连接的存储器；其中，
    所述存储器存储有可被所述一个处理器执行的指令，所述指令被所述至少一个处理器执行，以使所述至少一个处理器能够执行上述1至8任一项所述的城市内部传染病扩散模拟方法的以下操作：
    步骤a：根据登革热输入病例数据进行城市内部全部人口初始化；
    步骤b：对初始化后的城市的全部人口，根据由多源轨迹数据构造的个体出行链信息构建个体移动网络，并计算个体在职住地停留时间；
    步骤c：根据包括日平均气温、日降雨量的气象数据计算得到蚊媒时空分布；
    步骤d：根据构建的个体移动网络，计算得到的职住地停留时间及蚊媒时空分布，构建空间显式个体传播模型，模拟传染病的传播扩散过程。
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