WO2021189516A1 - Procédé et système pour simuler le processus de circulation temporelle et spatiale de la grippe avec des données de trajectoire massives - Google Patents

Procédé et système pour simuler le processus de circulation temporelle et spatiale de la grippe avec des données de trajectoire massives Download PDF

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WO2021189516A1
WO2021189516A1 PCT/CN2020/082708 CN2020082708W WO2021189516A1 WO 2021189516 A1 WO2021189516 A1 WO 2021189516A1 CN 2020082708 W CN2020082708 W CN 2020082708W WO 2021189516 A1 WO2021189516 A1 WO 2021189516A1
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individual
influenza
individuals
data
spatial
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PCT/CN2020/082708
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Chinese (zh)
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尹凌
张�浩
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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
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W4/00—Services specially adapted for wireless communication networks; Facilities therefor
    • H04W4/02—Services making use of location information
    • H04W4/029—Location-based management or tracking services
    • 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 and a system for simulating the time-space propagation process of influenza with large-scale trajectory data.
  • Influenza is an acute respiratory infection caused by influenza virus and parainfluenza virus. Through the droplets and particles produced when the infected person coughs or sneezes, the virus can easily spread from person to person and seriously endanger the lives and health of the people. .
  • the temporal and spatial evolution of human activities and related fluctuations in population density are the key driving factors for the dynamics of infectious disease outbreaks. By tracing the contact between urban individuals at high spatial and temporal resolution, starting from discrete individuals, inferring potentially infected individuals based on the time and location information of the activities of different individuals, it helps to accurately express the spatiotemporal spread of influenza and improve the spread of influenza Accuracy of spatial model prediction.
  • Bottlenecks provide new opportunities.
  • the agent-based infectious disease model at the urban scale mainly reconstructs the movement of individuals and the contact network between individuals through travel survey data, and studies the transmission characteristics of infectious diseases in time series.
  • the prior art has at least the following shortcomings: building a real-world oriented agent model requires a large amount of real individual data, otherwise it is impossible to know precise individual positions and contacts between individuals.
  • existing studies have attempted to model population movement based on trajectory data to build an infectious disease spread model, the existing trajectory data-based infectious disease spread modeling method still cannot effectively solve the integration of population attributes (such as age, gender, occupation, etc.).
  • Family structure and individual modeling needs of the movement characteristics of trajectory data, lack of effective methods for fusing trajectory data to construct temporal and spatial proximity relationships of urban individuals (for example, individuals appearing in the same place at the same time), and even neglecting individuals in densely populated modern cities
  • the complexity of the contact space; at the same time, the trajectory data has sample bias and cannot represent the entire population of the entire city.
  • the present invention provides a method for simulating the spatiotemporal transmission process of influenza with large-scale trajectory data.
  • the method includes the following steps: a. Synthesize urban population based on census data and building census data, and assign corresponding population attributes to individuals of the synthesized urban population B. For the individuals of the synthetic urban population given demographic attributes, use mobile phone location data as the main and travel survey data as a supplement to construct an individual activity chain; c. Based on the constructed individual activity chain, one hour is the time Step size, dynamically construct a contact network of 24 time series in a day; d. According to the constructed contact network of 24 time series in a day, use the SEIR model to simulate the spread of influenza at high spatial and temporal resolution.
  • the method also includes the steps: the method also includes the step e: analyzing the simulation results from the two perspectives of time and space to obtain the spatial transmission path between the infected persons, and accurately locate the key spatial locations in the spread of the influenza epidemic .
  • the population census data includes: age, gender, occupation type, family category, family size, and family age components;
  • the building census data includes: building location information, building height, building area, and building function; wherein, the Building functions include: factories, teaching buildings, residential buildings, office buildings, and shopping malls.
  • the demographic attributes include individual attributes, family attributes, and whether they are individuals with mobile phones; wherein, the individual attributes include age, gender, and occupation; the family attributes include family structure, home address, and work place.
  • Said step b specifically includes the following steps: constructing the travel trajectory of an individual with a mobile phone based on mobile phone location data: sorting the data of the same mobile phone number by time to form a day’s travel trajectory of the mobile phone user, and dividing Tyson based on the mobile phone base station Polygon, multiple buildings located in the same Tyson polygon as the mobile phone base station are the candidate sets of individual positions; the travel trajectory of individuals without mobile phones is constructed based on travel survey data: the same The multiple buildings in the traffic area are candidate sets of individual locations; according to the candidate set of individual locations obtained above, an individual activity chain is constructed.
  • Said step c specifically includes the following steps: according to the activity chain of the constructed individual, with the hour as the time granularity, compare the activity categories performed by different individuals in a day, and set individuals who perform the same activities at the same time and are in the same building location For individuals with co-occurrence in time and space, based on the activity category, different contact probabilities are assigned to individuals in co-occurrence in time and space. Under the constraint of contact probability, a dynamic contact network with hourly resolution between agents is generated.
  • the activity categories include: home, work/school, leisure and entertainment activities.
  • the step d specifically includes the following steps: using hours as the unit of time, tracking the time and place of the infection, and from whom to whom.
  • the probability of an individual being infected is called the effective infection probability P,
  • the formula for effective infection probability is as follows:
  • P c is the contact probability between individuals
  • P i is the infection probability of the individual
  • r is the relative infectivity of the individual
  • the Monte Carlo method to determine whether the individual is infected includes: generating a uniformly distributed pseudo-random number based on a computer, comparing the pseudo-random number with the effective infection probability, and if the pseudo-random number is less than or equal to the effective infection probability, the individual is infected , Repeat the above process until the final trend of the number of new infections per day is consistent with the real data and the calculated basic reproductive number is greater than 1.
  • the step e specifically includes the following steps: a comparative analysis of the city scale and the administrative division scale on the time series, divide the city into 1km ⁇ 1km grids, and analyze the number of newly infected cases in each grid every day ;
  • the epidemic tree is constructed by constructing the spread topological relationship between the parent infected person and the offspring infected person, and then the epidemic forest is composed of multiple epidemic trees to obtain the spatial transmission path between the parent infected person and the offspring infected person, and accurately locate the flu The key spatial location in the spread of the epidemic.
  • the present invention provides a system for simulating the spatiotemporal transmission process of influenza with large-scale trajectory data.
  • the system includes a population attribute assignment module, an activity chain building module, a contact network building module, and a transmission simulation module.
  • the census data and building census data synthesize the urban population, and assign corresponding population attributes to the individuals of the composite urban population;
  • the activity chain building module is used to assign the population attributes to the individuals of the composite urban population, mainly based on mobile phone location data ,
  • the travel survey data is supplemented to construct an individual's activity chain;
  • the contact network building module is used to dynamically construct a contact network of 24 time series in a day based on the constructed individual's activity chain, taking one hour as the time step
  • the spread simulation module is used to simulate the spread of influenza at high temporal and spatial resolution by using the SEIR model according to the constructed contact network of 24 time series in a day.
  • the agent-based spatial explicit epidemic model sets a large number of parameters related to the spread of influenza, improves the interpretability of the model, and helps to reveal the spreading mechanism of influenza at high spatial and temporal resolution.
  • the simulation results can match the trend of the original influenza data at the urban scale and administrative division scale.
  • the spatial information of the epidemic situation described by the simulation results effectively solves the problem of the inability to truly locate the spatial location of the infected person and the potential high-risk transmission area.
  • Fig. 1 is a flow chart of the method for simulating the temporal and spatial propagation process of influenza with large-scale trajectory data according to the present invention
  • Figure 2 is a schematic diagram of the natural history of influenza under the SEIR model
  • Figure 3 is a schematic diagram of an epidemic forest provided by an embodiment of the present invention.
  • Figure 4 is a hardware architecture diagram of the system for simulating the spread of influenza in time and space with large-scale trajectory data according to the present invention
  • FIG. 5 is a schematic diagram of the age and gender distribution of synthetic individuals provided by an embodiment of the present invention.
  • FIG. 6 is a schematic diagram of the structure distribution of a synthetic family provided by an embodiment of the present invention.
  • FIG. 7 is a schematic diagram of the comparison between the simulation results of the influenza diffusion process at the urban scale and the real cases according to the embodiment of the present invention.
  • FIG. 8 is a schematic diagram of the distribution of reproduction numbers generated by 100 simulation results at a city scale according to an embodiment of the present invention.
  • Fig. 9 is a schematic diagram of the comparison between the simulation results of the influenza spreading process provided by the embodiment of the present invention and real cases on a scale of 10 regions;
  • FIG. 10 is a schematic diagram of the temporal and spatial distribution of influenza spread in a city according to an embodiment of the present invention.
  • FIG. 11 is a schematic diagram of the intensity of the spread of influenza within the space unit according to an embodiment of the present invention.
  • FIG. 12 is a schematic diagram of the number of grids that can be affected by one grid during the spread of influenza according to an embodiment of the present invention.
  • FIG. 13 is a schematic diagram of the number of events (connection strength) in which influenza transmission occurs between two grids according to an embodiment of the present invention
  • FIG. 14 is a schematic diagram of a spatial aggregation phenomenon during the spread of influenza according to an embodiment of the present invention.
  • FIG. 1 it is a flowchart of a preferred embodiment of the method for simulating the time-space propagation process of influenza with large-scale trajectory data according to the present invention.
  • Step S1 Synthesize the urban population based on the population census data and the building census data, and assign corresponding population attributes to individuals of the synthesized urban population. That is, fusion of multi-source data to build a model of urban population movement.
  • the population census data includes: age, gender, occupation type, family category, family size, and family age components;
  • the building census data includes: building location information, building height, building area, and building function; wherein, the Building functions include: factories, teaching buildings, residential buildings, office buildings, and shopping malls.
  • the demographic attributes include individual attributes, family attributes, and whether they are individuals with mobile phones; wherein, the individual attributes include age, gender, and occupation; the family attributes include family structure, home address, and work place.
  • Monte Carlo simulation is used to assign corresponding individual attributes to the individuals of each composite urban population.
  • family attributes such as family category, family size, family age composition and so on in the census data
  • a synthetic family is constructed, and individuals of the synthetic urban population are filled into the synthetic family.
  • Monte Carlo simulation is performed to give the individuals of the synthetic urban population the attributes of whether they are individuals with mobile phones.
  • the individuals of the synthetic urban population are divided into two types: individuals with mobile phones and individuals without mobile phones. .
  • Step S2 for individuals (hereinafter referred to as "individuals") of synthetic urban population assigned population attributes, using mobile phone location data as the main and travel survey data as the supplement to construct an individual activity chain. That is to say, based on the combination of mobile phone data and travel survey data supplemented by the time and space characteristics of individual travel and building census data, building-scale individual movement modeling is realized. in particular:
  • the travel trajectory of individuals with mobile phones is reconstructed based on mobile phone location data, and the travel trajectories of individuals without mobile phones are constructed based on travel survey data.
  • the spatial range of the work and residence of individuals with mobile phones identified through the mobile phone data is refined to the service range of the mobile phone base station, and the spatial range of the work and residence of individuals without mobile phones based on travel survey records is refined to the traffic community.
  • the mobile phone location data includes: anonymous mobile phone number, time, base station latitude and longitude.
  • the data of the same mobile phone number is sorted by time to form a day's travel trajectory of the mobile phone user.
  • the user's travel location is the location of the mobile phone base station.
  • the Thiessen polygon is divided (the range of the Thiessen polygon is the service range of the mobile phone base station), and multiple buildings located in the same Thiessen polygon as the mobile phone base station are the candidate sets of individual positions.
  • Travel survey is an investigation of individual travel behavior.
  • the travel survey data includes: individual work unit, place of departure, destination, departure time, end time, travel mode, travel purpose, etc., but the location information is based on the traffic community.
  • the travel trajectory of an individual without a mobile phone is constructed with each travel information in the travel survey, and multiple buildings in the same traffic area are obtained as a candidate set of individual locations.
  • an individual's activity chain is constructed.
  • Step S3 based on the constructed individual activity chain, with one hour as the time step, dynamically constructing a contact network of 24 time series in a day.
  • a contact network is dynamically constructed based on the individual activity chain of the urban population.
  • the constructed activity chain of individuals with the hour as the time granularity, compare the types of activities performed by different individuals within a day, and set individuals who perform the same activities at the same time and are in the same building location as individuals with the same time and space.
  • different contact probabilities are assigned to individuals who co-occur in time and space.
  • a dynamic contact network with hourly resolution between agents is generated.
  • the activity categories include: home, work/school, leisure and entertainment activities.
  • the vertices represent traveling individuals, and individuals appearing in the same location are connected by edges.
  • the locations are in units of home (home), work (work), and building address (leisure).
  • Individuals appearing at the same location at the same time have a certain probability of contact, and this probability of contact is recorded as p c .
  • An individual has 24 contact networks in a day. Take a contact network as an example.
  • the vertices in the network represent individuals. Individuals appearing at the same location are connected by edges, which means that two individuals travel at the same location at the same time. Then, according to the contact probability in Table 1, it is judged whether two individuals appearing at the same place at the same time actually have contact. For example, when the infected person is an adult, he will have contact with a minor at a probability of 0.25 when he is at home. 0.4 probability of contact with an adult.
  • Step S4 According to the constructed contact network of 24 time series in a day, the SEIR model is used to simulate the spread of influenza at high temporal and spatial resolution. in particular:
  • the SEIR model divides individuals into four states based on the natural history of influenza (Figure 2): susceptible period, incubation period, infection period, and recovery period.
  • Figure 2 due to the vaccination and the production of autoantibodies, a part of the susceptible population has immunity to influenza virus; a susceptible individual is infected with a certain probability and enters the incubation period; after the virus is parasitic in the body for a certain period of time, the individual has Infectious, entering the infectious period; an infected person in the infectious period may show flu-corresponding symptoms, or there may be no obvious symptoms. Finally, the individual was cured and entered a recovery state.
  • the simulation process takes hours as the time unit to track when and where the infection occurred, and from whom to whom.
  • the probability of an individual being infected is called the effective probability of infection P.
  • the formula for effective infection probability is as follows:
  • P c is equal probability of contact between individuals
  • P i is the probability of infected individuals
  • r is the relative infectivity for the individual.
  • the Monte Carlo method is used to determine whether the individual is infected: based on a computer-generated uniformly distributed pseudo-random number, the pseudo-random number is compared with the effective infection probability, if the pseudo-random number is less than or equal to the effective infection probability, the individual is infected. Repeat the above process until the final trend of the number of new infections per day is consistent with the real data and the calculated basic reproductive number is greater than 1.
  • Step S5 Analyze the simulation results from the two perspectives of time and space, explore the trend and intensity of influenza outbreaks, analyze high-risk areas in the process of influenza transmission, and accurately locate key spatial locations in the process of influenza epidemic transmission.
  • influenza outbreak trend and intensity refers to: the influenza outbreak trend is mainly reflected in the time curve of newly infected cases every day, and the curve is roughly normal distribution. If the curve amplitude is narrow, it indicates that the outbreak speed is relatively high. Fast, on the contrary, the speed of the outbreak is relatively slow, the peak of the curve indicates the severity of the outbreak, the higher the peak, the more serious.
  • the high-risk areas are areas where the cumulative number of infected persons is large, which means that susceptible persons have a high risk of being infected in these areas.
  • the key spatial location is a location with strong transmission connectivity between regions, that is, intervention in the key spatial location can reduce the spread of influenza virus to other regions, thereby reducing the risk of transmission of infectious diseases within the city.
  • the epidemic tree is constructed by constructing the transmission topology relationship between the parent infected and the offspring infected, and then the epidemic forest is composed of multiple epidemic trees (Figure 3) to reveal the spatial transmission between the parent infected and the offspring infected Path, accurately locate the key spatial locations during the spread of influenza epidemics, reveal the relationship between the spread distance and the transmission intensity of influenza in the city, and the spatial agglomeration effect.
  • the key spatial position in the process of locating the influenza epidemic is mainly used to analyze the transmission intensity of influenza virus between spatial grids.
  • the spatial distance between the grids represents the transmission distance of influenza
  • the connection weight between the grids represents the transmission intensity of influenza between the grids.
  • the Pearson between the two is calculated in this embodiment.
  • the correlation coefficient r, r -0.098, shows that the propagation distance and the propagation strength are extremely weakly negatively correlated.
  • FIG. 4 is a hardware architecture diagram of the system 10 for simulating the time-space propagation process of influenza with large-scale trajectory data according to the present invention.
  • the system includes: a population attribute assignment module 101, an activity chain building module 102, a contact network building module 103, a propagation simulation module 104, and an analysis module 105.
  • the population attribute assignment module 101 is used to synthesize urban population based on census data and building census data, and assign corresponding population attributes to individuals who synthesize urban population. That is, fusion of multi-source data to build a model of urban population movement. in:
  • the population census data includes: age, gender, occupation type, family category, family size, and family age components;
  • the building census data includes: building location information, building height, building area, and building function; wherein, the Building functions include: factories, teaching buildings, residential buildings, office buildings, and shopping malls.
  • the demographic attributes include individual attributes, family attributes, and whether they are individuals with mobile phones; wherein, the individual attributes include age, gender, and occupation; the family attributes include family structure, home address, and work place.
  • the population attribute assignment module 101 first assigns corresponding individual attributes to individuals of each composite urban population through Monte Carlo simulation based on the probability distribution of individual attributes such as age, gender, and occupation type in the census data. Then a composite family is constructed according to the probability of family attributes such as family category, family size, and family age composition in the census data, and the individuals of the composite urban population are filled into the composite family. Finally, Monte Carlo simulation is carried out according to the mobile phone usage rate of different genders and age groups, and the attributes of the synthetic urban population are assigned to individuals with mobile phones. The synthetic urban population individuals are divided into two categories: mobile phone individuals and mobile phone individuals.
  • the activity chain construction module 102 is used to construct an individual activity chain for individuals (hereinafter referred to as "individuals") of the synthetic urban population assigned population attributes, using mobile phone location data as the main and travel survey data as the supplement. That is to say, based on the combination of mobile phone data and travel survey data supplemented by the time and space characteristics of individual travel and building census data, building-scale individual movement modeling is realized. in particular:
  • the activity chain construction module 102 reconstructs the travel trajectory of individuals with mobile phones based on mobile phone location data, and constructs the travel trajectories of individuals without mobile phones based on travel survey data. At this time, the spatial range of the work and residence of individuals with mobile phones identified through the mobile phone data is refined to the service range of the mobile phone base station, and the spatial range of the work and residence of individuals without mobile phones based on travel survey records is refined to the traffic community.
  • the mobile phone location data includes: anonymous mobile phone number, time, base station latitude and longitude.
  • the data of the same mobile phone number is sorted by time to form a day's travel trajectory of the mobile phone user.
  • the user's travel location is the location of the mobile phone base station.
  • the cell phone base station is divided into the Thiessen polygon (the range of the cell phone base station is the service range of the cell phone base station), and multiple buildings located in the same Thiessen polygon as the cell phone base station are the candidate sets of individual positions.
  • a travel survey is an investigation of individual travel behavior.
  • the travel survey data includes: individual work unit, departure place, destination, departure time, end time, travel mode, travel purpose, etc., but the location information is based on the traffic community.
  • the travel trajectory of an individual without a mobile phone is constructed with each travel information in the travel survey, and multiple buildings in the same traffic area are obtained as a candidate set of individual locations.
  • the activity chain construction module 102 constructs an individual activity chain according to the candidate set of individual positions obtained above.
  • the contact network construction module 103 is used to dynamically construct a contact network of 24 time series in a day based on the activity chain of the constructed individual, with a time step of one hour. That is, the contact network construction module 103 dynamically constructs a contact network based on the individual activity chain of the urban population. According to the constructed activity chain of individuals, with the hour as the time granularity, compare the types of activities performed by different individuals within a day, and set individuals who perform the same activities at the same time and are in the same building location as individuals with the same time and space. Based on the activity category, different contact probabilities are assigned to individuals who co-occur in time and space. Under the constraint of contact probability, a dynamic contact network with hourly resolution between agents is generated. Wherein, the activity categories include: home, work/school, leisure and entertainment activities.
  • the vertices represent traveling individuals, and individuals appearing in the same location are connected by edges.
  • the locations are in units of home (home), work (work), and building address (leisure).
  • Individuals appearing at the same location at the same time have a certain probability of contact, and this probability of contact is recorded as p c .
  • An individual has 24 contact networks in a day. Take a contact network as an example.
  • the vertices in the network represent individuals. Individuals appearing in the same position are connected by edges, which means that two individuals travel at the same location at the same time. Then, according to the contact probability in Table 1, it is judged whether two individuals appearing at the same place at the same time actually have contact. For example, when the infected person is an adult, he will have contact with a minor at a probability of 0.25 when he is at home. 0.4 probability of contact with an adult.
  • the spread simulation module 104 is used to simulate the spread of influenza at high temporal and spatial resolution by using the SEIR model according to the constructed contact network of 24 time series in a day. in particular:
  • the SEIR model divides individuals into four states based on the natural history of influenza (Figure 2): susceptible period, incubation period, infection period, and recovery period.
  • Figure 2 due to the vaccination and the production of autoantibodies, a part of the susceptible population has immunity to influenza virus; a susceptible individual is infected with a certain probability and enters the incubation period; after the virus is parasitic in the body for a certain period of time, the individual has Infectious, entering the infectious period; an infected person in the infectious period may show flu-corresponding symptoms, or there may be no obvious symptoms. Finally, the individual was cured and entered a recovery state.
  • the simulation process takes hours as the time unit to track when and where the infection occurred, and from whom it was transmitted to whom.
  • the probability of an individual being infected is called the effective probability of infection P.
  • the formula for effective infection probability is as follows:
  • P c is equal probability of contact between individuals
  • P i is the probability of infected individuals
  • r is the relative infectivity for the individual.
  • the Monte Carlo method is used to determine whether the individual is infected: based on a computer-generated uniformly distributed pseudo-random number, the pseudo-random number is compared with the effective infection probability, if the pseudo-random number is less than or equal to the effective infection probability, the individual is infected. Repeat the above process until the final trend of the number of new infections per day is consistent with the real data and the calculated basic reproductive number is greater than 1.
  • the analysis module 105 is used to analyze the simulation results from two perspectives of time and space, explore the trend and intensity of influenza outbreaks, analyze high-risk areas in the process of influenza transmission, and accurately locate key spatial locations in the process of influenza epidemic transmission.
  • influenza outbreak trend and intensity refers to: the influenza outbreak trend is mainly reflected in the time curve of newly infected cases every day, and the curve is roughly normal distribution. If the curve amplitude is narrow, it indicates that the outbreak speed is relatively high. Fast, on the contrary, the speed of the outbreak is relatively slow, the peak of the curve indicates the severity of the outbreak, the higher the peak, the more serious.
  • the high-risk areas are areas where the cumulative number of infected persons is large, which means that susceptible persons have a high risk of being infected in these areas.
  • the key spatial location is a location with strong transmission connectivity between regions, that is, intervention in the key spatial location can reduce the spread of influenza virus to other regions, thereby reducing the risk of transmission of infectious diseases within the city.
  • the analysis module 105 performs a comparative analysis on the city scale and the administrative division scale from the time series. In order to accurately express the spread of influenza in time and space, the city is divided into 1km ⁇ 1km grids, and the number of newly infected cases in each grid every day is analyzed. The number of cases is the average of 100 simulation results.
  • the analysis module 105 constructs an epidemic tree by constructing the transmission topology relationship between the parent infected person and the offspring infected person, and then the epidemic forest is composed of multiple epidemic trees (Figure 3) to reveal the parent infected person and the offspring infected person
  • Figure 3 The spatial transmission path between the two, accurately locates the key spatial locations in the spread of influenza epidemics, and reveals the relationship between the transmission distance and the transmission intensity of influenza in the city and the spatial agglomeration effect.
  • the key spatial position in the process of locating the influenza epidemic is mainly used to analyze the transmission intensity of influenza virus between spatial grids.
  • the spatial distance between the grids represents the transmission distance of influenza
  • the connection weight between the grids represents the transmission intensity of influenza between the grids.
  • the Pearson between the two is calculated in this embodiment.
  • the correlation coefficient r, r -0.098, shows that the propagation distance and the propagation strength are extremely weakly negatively correlated.
  • Modeling of population attributes According to the probability distribution of individual attributes such as age, gender, and occupation type in the census data, the corresponding individual attributes are assigned to each synthetic individual through Monte Carlo simulation. The age and gender distribution of the synthetic individual are shown in Figure 5.
  • a synthetic family is constructed according to the probability of family attributes such as family category, family size, and family age components in the census data, and synthetic individuals are filled into the synthetic family.
  • Figure 6 shows the size distribution of the synthetic family constructed in this embodiment. Compared with the population data of each district released by the 2017 Shenzhen Statistical Yearbook, the synthetic population constructed in this embodiment basically matches the population.
  • Figure 10 is the result of simulating the temporal and spatial distribution of influenza spread in Shenzhen.
  • the value in each grid is the average of 100 simulation results.
  • the agent-based spatial explicit infectious disease model constructed by fusing mobile phone location data proposed in this embodiment can support tracking the location information of infected persons and their interaction with other susceptible persons, effectively solving the inability to truly locate the space of infected persons. Location and potential high-risk transmission areas.
  • Figure 11-14 reflects the number of influenza spreading within each spatial unit, that is, the homes of the parents of the infected and the offspring are located in the same grid, and such infections often occur between neighbors in the same community. It can be seen from Figure 11 that there is a high-risk area in the southeast of Luohu District. Influenza is more likely to spread infection within this location, which can remind residents living in this area to prevent the spread of influenza among neighbors.
  • Figures 12, 13, and 14 reflect the situation where the infected parent and the infected offspring are in two spatial units during the spread of influenza.
  • the linear propagation paths of the infected parent and the infected offspring of different spatial units constitute the connection relationship between the grids, and the connection relationship between the grids and the grids forms a social network.
  • the value of the grid described in Figure 12 It is the degree of the grid in the social network, reflecting the number of grids that a grid can affect. It can be seen that the spatial unit at the junction of Futian District and Luohu District will have an impact on more geographic spaces in Shenzhen than other spatial units.
  • the weight of the connection relationship between the grids is different (the connection weight represents the number of events in which influenza transmission occurs between the two grids).
  • connection weight between 99.9% of the grids is less than 100 (Figure 13), and the area where the connection weight is greater than 100 has obvious spatial aggregation ( Figure 14).
  • Figure 13 The connection weight between 99.9% of the grids is less than 100
  • Figure 14 The connection weight between 99.9% of the grids is less than 100
  • Figure 14 The connection weight between 99.9% of the grids is less than 100
  • Figure 14 The connection weight between 99.9% of the grids is less than 100
  • Figure 14 The connection weight between 99.9% of the grids is less than 100
  • Figure 14 The connection weight between 99.9% of the grids is less than 100
  • Figure 14 The connection weight between 99.9% of the grids is less than 100
  • Figure 14 The connection weight between 99.9% of the grids has obvious spatial aggregation
  • the spatial distance between the grids represents the transmission distance of influenza
  • the connection weight between the grids represents the transmission intensity of influenza between the grids.
  • the invention establishes a spatial explicit infectious disease model based on an agent. Based on the traditional method of individual movement modeling based on statistical data, a new spatio-temporal framework that integrates large-scale mobile phone location data for individual movement modeling is proposed, and the chain of individual urban activities is reconstructed.
  • the model includes the entire population in the urban area (both demographic attributes and mobile behavior), the individual travel location is estimated to the building, and the individual activity place is based on the family (home), work unit (work), and building (entertainment). Construct a dynamic contact network between individuals. Then the SEIR model is used to simulate the time and space propagation process of influenza in the city. The simulation time step is 1 hour, the spatial scale is in the unit of building, and the heterogeneity of individual response to influenza virus is fully considered in the simulation process.
  • the present invention is not limited to the fusion of data such as mobile phone data, travel survey data, population census data, and building census data.
  • the present invention can simulate the spread of a variety of infectious diseases, such as: influenza, dengue fever and other close-transmitted diseases.
  • the present invention has good analysis capabilities for the spread of infectious diseases in time and space, especially spatial analysis. ability.

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Abstract

La présente invention concerne un procédé et un système pour simuler le processus de circulation temporelle et spatiale de la grippe avec des données de trajectoire massives. Ledit procédé consiste à : synthétiser une population urbaine sur la base de données de recensement de la population et de données de recensement de bâtiments, et affecter un attribut de population correspondant à un individu de la population urbaine synthétisée (S1) ; pour l'individu de la population urbaine synthétisée auquel l'attribut de population a été affecté, construire une chaîne d'activité individuelle constituée principalement de données de position de téléphone mobile et assistée par des données d'enquête sur les déplacements (S2) ; sur la base de la chaîne d'activité individuelle construite, construire dynamiquement, avec une heure comme pas de temps, un réseau de contacts de 24 séries chronologiques par jour (S3) ; selon le réseau de contacts construit, utiliser un modèle SEIR pour simuler la circulation d'une grippe à une résolution temporelle et spatiale élevée (S4) ; et analyser un résultat de simulation à partir de deux perspectives de temps et d'espace, de façon à analyser une zone à haut risque pendant la circulation de la grippe et à positionner avec précision une position spatiale clé pendant la circulation de la situation épidémique de la grippe (S5). Ledit procédé peut inverser les informations temporelles et spatiales d'éclosions de la grippe à différentes échelles temporelles et spatiales, résolvant efficacement le problème d'incapacité à réaliser une localisation réelle de la position spatiale d'une personne infectée et d'une zone de circulation potentiellement à haut risque.
PCT/CN2020/082708 2020-03-27 2020-04-01 Procédé et système pour simuler le processus de circulation temporelle et spatiale de la grippe avec des données de trajectoire massives WO2021189516A1 (fr)

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