CN113450923A - Method and system for simulating influenza spatiotemporal propagation process by large-scale trajectory data - Google Patents
Method and system for simulating influenza spatiotemporal propagation process by large-scale trajectory data Download PDFInfo
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Abstract
The invention relates to a method for simulating a space-time propagation process of influenza by using large-scale trajectory data, which comprises the following steps: synthesizing city population based on census data and building census data, and endowing individuals synthesizing the city population with corresponding population attributes; constructing an activity chain of an individual for an individual of a synthetic city population endowed with population attributes by taking mobile phone position data as a main part and taking travel survey data as an auxiliary part; dynamically constructing a contact network of 24 time sequences in one day by taking one hour as a time step based on the constructed activity chain of the individual; according to the constructed contact network, an SEIR model is adopted to simulate the propagation of the influenza at high space-time resolution. The invention also relates to a system for simulating the space-time propagation process of the influenza by using the large-scale trajectory data. The method can invert the space-time information of the influenza outbreak on different space-time scales, and effectively solves the problem that the space position of an infected person and a potential high-risk propagation area cannot be really positioned.
Description
Technical Field
The invention relates to a method and a system for simulating a space-time propagation process of influenza by using large-scale trajectory data.
Background
Influenza is an acute respiratory infection disease caused by influenza virus and parainfluenza virus, and the virus is easily spread among people through droplets and particles generated when patients cough or sneeze, thereby seriously harming the life health of people. The temporal and spatial evolution of human activities and the associated fluctuations in population density are key drivers of the dynamics of infectious disease outbreaks. By tracking the contact between urban individuals under high space-time resolution, starting from discrete individuals, the possibly infected individuals are inferred based on time, position information and the like of different individual activities, the method is favorable for accurately expressing the influenza space-time diffusion process, and the accuracy of influenza propagation space mode prediction is improved. In recent years, the large-scale individual trajectory data (mobile phone position data, floating car GPS data, bus card swiping data and the like) has explosive growth, and the space-time information of the mobile individual can be accurately positioned, so that a new opportunity is provided for breaking through the bottleneck of infectious disease prevention and control on space-time accuracy.
At present, an infectious disease model based on an intelligent agent at a city scale reconstructs individual movement and a contact network between individuals mainly through travel survey data, and researches the spreading characteristics of the infectious disease on a time series.
However, the prior art has at least the following disadvantages: building real-world oriented agent models requires a large amount of real individual data, otherwise the exact individual location and contact between individuals cannot be known. Although research attempts have been made to build an infectious disease diffusion model by performing crowd movement modeling based on trajectory data, the existing infectious disease diffusion modeling method based on trajectory data cannot effectively solve the individual modeling requirement for fusing population attributes (such as age, sex, occupation and family structure) and trajectory data movement characteristics, and an effective method for building urban individual space-time proximity relations (such as individuals appearing in the same place at the same time) by fusing trajectory data is lacked, so that the complexity of individual contact spaces in modern densely populated cities is further ignored; meanwhile, the trajectory data has sample bias and cannot represent the whole population of the whole city. Aiming at the infectious disease transmission process, individuals show obvious heterogeneity in the influenza virus transmission process, and the mutual influence among parameters is neglected for simplifying model parameters in the prior art. In addition, the spatial explicit infectious disease model based on the agent is often researched with low spatiotemporal resolution, and it is difficult to reveal the influenza propagation process at the urban scale from the perspective of a spatiotemporal dynamic mechanism, resulting in delay and bias of prediction of the spatiotemporal pattern of influenza outbreak.
Disclosure of Invention
In view of the above, there is a need for a method and system for simulating the spatiotemporal propagation process of influenza by using large-scale trajectory data.
The invention provides a method for simulating a space-time propagation process of influenza by using large-scale trajectory data, which comprises the following steps of: a. synthesizing city population based on census data and building census data, and endowing individuals synthesizing the city population with corresponding population attributes; b. constructing an activity chain of an individual for an individual of a synthetic city population endowed with population attributes by taking mobile phone position data as a main part and taking travel survey data as an auxiliary part; c. dynamically constructing a contact network of 24 time sequences in one day by taking one hour as a time step based on the constructed activity chain of the individual; d. the SEIR model was used to simulate the propagation of influenza at high spatiotemporal resolution based on a constructed contact network of 24 time series over the day.
Wherein, the method also comprises the following steps: the method further comprises step e: and analyzing the simulation result under two visual angles of time and space to obtain a space propagation path between infected persons, and accurately positioning the key space position in the influenza epidemic propagation process.
The census data includes: age, gender, job type, family category, family size, family age component; the building screening data includes: building location information, building height, building area, building function; wherein the building functions include: factories, teaching buildings, residential houses, office buildings and markets.
The population attributes comprise individual attributes, family attributes and whether the mobile phone individuals exist or not; wherein the individual attributes include: age, gender, occupation; the family attributes include: home structure, home address, work place.
The step b specifically comprises the following steps: constructing a travel track of an individual with a mobile phone based on mobile phone position data: sequencing data of the same mobile phone number according to time to form a one-day travel track of the mobile phone user, dividing a Thiessen polygon based on a mobile phone base station, and taking a plurality of buildings which are positioned in the same Thiessen polygon with the mobile phone base station as a candidate set of individual positions; constructing a travel track of the individual without the mobile phone based on travel survey data: constructing each piece of travel information of the travel survey to obtain a candidate set with a plurality of buildings of the same traffic cell as individual positions; and constructing an activity chain of the individual according to the obtained candidate set of the individual positions.
The step c specifically comprises the following steps: according to the constructed activity chain of the individuals, the activity types of different individuals in one day are compared by taking hours as time granularity, the individuals which perform the same activity at the same time and are located at the same building position are set as space-time co-occurrence individuals, different contact probabilities are given to the space-time co-occurrence individuals on the basis of the activity types, and a dynamic contact network with the resolution of hours between intelligent agents is generated under the constraint of the contact probabilities.
The activity categories include: at home, at work/school, leisure and recreation.
The step d specifically comprises the following steps: in the unit of hours, tracking the time and place where the infection event occurs and who infects the infection event, in the process of influenza transmission, the probability of the infection of an individual is called as effective infection probability P, and the formula of the effective infection probability is as follows:
P＝Pc×Pi×r
wherein, PcIs the probability of contact between individuals, PiIs the probability of infection of an individual, and r is the relative infectivity of the individual;
finally, the Monte Carlo method is used to determine whether the individual is infected.
Said determining whether the individual is infected by the Monte Carlo method comprises: and generating uniformly distributed pseudo random numbers based on a computer, comparing the pseudo random numbers with the effective infection probability, if the pseudo random numbers are less than or equal to the effective infection probability, infecting the individual, and repeating the processes until the trend of the number of newly infected persons is consistent with the real data every day and the calculated basic regeneration number is more than 1.
The step e specifically comprises the following steps: performing comparative analysis on a time sequence under two scales of city scale and administrative division scale, dividing a city into grids of 1km multiplied by 1km, and analyzing the number of newly infected cases per day of each grid;
an epidemic situation tree is constructed by constructing a transmission topological relation between a parent infectious agent and an offspring infectious agent, and an epidemic situation forest is formed by a plurality of epidemic situation trees, so that a spatial transmission path between the parent infectious agent and the offspring infected agent is obtained, and a key spatial position in the influenza epidemic situation transmission process is accurately positioned.
The invention provides a system for simulating a space-time propagation process of influenza by using large-scale trajectory data, which comprises a population attribute endowing module, an activity chain construction module, a contact network construction module and a propagation simulation module, wherein the population attribute endowing module comprises: the population attribute assigning module is used for synthesizing city population based on census data and building census data and assigning corresponding population attributes to individuals synthesizing the city population; the activity chain construction module is used for constructing an activity chain of an individual for an individual of a synthetic city population endowed with population attributes, mainly by taking mobile phone position data as a main part and taking travel survey data as an auxiliary part; the contact network construction module is used for dynamically constructing contact networks of 24 time sequences in one day by taking one hour as a time step based on the constructed activity chain of the individual; the propagation simulation module is used for simulating the propagation of the influenza at high space-time resolution by adopting an SEIR model according to the constructed contact network of 24 time sequences in one day.
The method and the system for simulating the space-time propagation process of the influenza by the large-scale trajectory data have the beneficial effects that:
(1) after large-scale mobile phone position data are integrated, individual mobility is restored more truly, mobility aggregation among areas is stronger, and activity space is increased compared with trip survey data. The trip positions of individuals in the mobile model are represented by building coordinates, so that the spatial explicit infectious disease model based on the intelligent agent can invert the spatiotemporal information of influenza outbreaks on different spatiotemporal scales.
(2) A large number of parameters related to the spread of the influenza are set in the space explicit infectious disease model based on the intelligent agent, so that the model interpretability is improved, and the propagation mechanism of the influenza under high space-time resolution is disclosed. The simulation result can be matched with the trend of the original influenza data in the urban scale and the administrative division scale, and the problem that the spatial position of an infected person and a potential high-risk propagation area cannot be really positioned is effectively solved by the spatial information of the epidemic situation depicted by the simulation result.
(3) Epidemic situation forests are innovatively adopted to depict the propagation rule of the influenza in a small-scale space range, the propagation characteristics of the influenza in grids and among grids are analyzed, and a high-risk area in the influenza propagation process is located. The model can reflect the propagation strength of the influenza in the region and describe the propagation rule of the influenza between the regions, and is favorable for accurately positioning the key spatial position in the influenza epidemic propagation process.
Drawings
FIG. 1 is a flow chart of a method of the present invention for modeling the spatiotemporal propagation of influenza with large-scale trajectory data;
FIG. 2 is a schematic view of the natural history of influenza under the SEIR model;
FIG. 3 is a schematic diagram of an epidemic forest according to an embodiment of the present invention;
FIG. 4 is a hardware architecture diagram of a system for large scale trajectory data simulation of the influenza spatiotemporal propagation process of the present invention;
FIG. 5 is a schematic diagram of the age and gender distribution of synthetic individuals according to one embodiment of the present invention;
FIG. 6 is a schematic diagram of a synthetic home structure distribution provided by an embodiment of the present invention;
fig. 7 is a schematic diagram illustrating comparison between simulation results of an influenza spreading process at an urban scale and real cases according to an embodiment of the present invention;
FIG. 8 is a schematic diagram illustrating the distribution of the regeneration numbers generated by 100 simulation results on a city scale according to an embodiment of the present invention;
fig. 9 is a schematic diagram comparing the simulation result of the influenza spreading process provided by the embodiment of the present invention with the real case on the scale of 10 zones;
FIG. 10 is a schematic view of the spatiotemporal distribution of influenza spread within a city provided by an embodiment of the present invention;
FIG. 11 is a schematic diagram of the intensity of the propagation of influenza within a space cell provided by an embodiment of the present invention;
FIG. 12 is a diagram illustrating the number of cells that can be affected by a cell during the propagation of a flu infection according to an embodiment of the present invention;
fig. 13 is a schematic diagram illustrating the number of events (connection strength) in which the influenza propagates between two grids according to an embodiment of the present invention;
fig. 14 is a schematic diagram of a spatial aggregation phenomenon in an influenza propagation process according to an embodiment of the present invention.
Detailed Description
The present invention will be described in further detail with reference to the accompanying drawings and specific embodiments.
Referring to fig. 1, a flow chart of the operation of the preferred embodiment of the method for simulating the space-time propagation process of the influenza by using the large-scale trajectory data is shown.
And step S1, synthesizing city population based on census data and building census data, and endowing individuals of the synthesized city population with corresponding population attributes. Namely, the multi-source data is fused to construct the urban population movement model. Wherein:
the census data includes: age, gender, job type, family category, family size, family age component; the building screening data includes: building location information, building height, building area, building function; wherein the building functions include: factories, teaching buildings, residential houses, office buildings and markets.
The population attributes comprise individual attributes, family attributes and whether the mobile phone individuals exist or not; wherein the individual attributes include: age, gender, occupation; the family attributes include: home structure, home address, work place.
Specifically, the method comprises the following steps:
first, according to the probability distribution of individual attributes such as age, gender, occupation type and the like in census data, corresponding individual attributes are allocated to the individuals of each synthetic city population through Monte Carlo simulation. And secondly, constructing a synthetic family according to the probability of family attributes such as family category, family scale, family age component and the like in the census data, and filling the individuals of the population of the synthetic city into the synthetic family. And finally, carrying out Monte Carlo simulation according to the mobile phone use rates of different genders and ages, giving the attribute of whether the individuals of the synthetic city population are the individuals with the mobile phones or not, and dividing the individuals of the synthetic city population into the individuals with the mobile phones and the individuals without the mobile phones.
In step S2, for an individual (hereinafter referred to as an "individual") of the composite city population to which the population attribute is assigned, an activity chain of the individual is constructed mainly from the mobile phone position data and with the assistance of the travel survey data. Namely, the mobile phone data is taken as the main data, the trip survey data is taken as the auxiliary data, and the individual mobile modeling of the building scale is realized based on the combination of the space-time characteristics of individual trip and the building general survey data. Specifically, the method comprises the following steps:
and reconstructing the travel track of the individual with the mobile phone based on the mobile phone position data, and constructing the travel track of the individual without the mobile phone based on travel survey data. At this time, the space range of the place where the mobile phone is located, which is identified by the mobile phone data, is refined to the service range of the mobile phone base station, and the space range of the place where the mobile phone is not located, which is recorded based on the travel survey, is refined to the traffic cell.
Further, the mobile phone position data includes: and the anonymous mobile phone number, the time, the longitude and latitude of the base station, and sequencing the data of the same mobile phone number according to the time to form a one-day travel track of the mobile phone user, wherein the travel position of the user is the position of the mobile phone base station. Then, a Thiessen polygon is divided based on the mobile phone base station (the range of the Thiessen polygon is the service range of the mobile phone base station), and a plurality of buildings located in the same Thiessen polygon with the mobile phone base station are candidate sets of individual positions.
The trip survey is a survey of individual trip behaviors, and the trip survey data comprises: individual work units, departure places, destinations, departure times, end times, travel manners, travel purposes, and the like, but the location information is in units of traffic cells. The travel track of the cell phone-free individual is constructed by each piece of travel information of the travel survey, and a plurality of buildings of the same traffic cell are obtained and are a candidate set of individual positions.
And constructing an activity chain of the individual according to the obtained candidate set of the individual positions.
And step S3, dynamically constructing a contact network of 24 time sequences in one day by taking one hour as a time step based on the constructed activity chain of the individual.
That is, contact networks are dynamically constructed based on individual activity chains of the city population. According to the constructed activity chain of the individuals, the activity categories of different individuals in one day are compared by taking hours as time granularity, and the individuals which perform the same activity at the same moment and are located at the same building position are set as the individuals with simultaneous space-time occurrence. Different contact probabilities are assigned to spatio-temporally co-occurring individuals based on activity categories. Under the constraint of contact probability, a dynamic contact network with resolution of hours between agents is generated. Wherein the activity categories include: at home, at work/school, leisure and recreation.
Specifically, the method comprises the following steps:
in a contact network for one time period, individuals who travel are represented by vertexes, and individuals who appear at the same position are connected with edges, and the positions are in units of home (at home), work (at work), and building address (leisure). Contact between individuals who occur at the same time and at the same location occurs with a certain probability, which is denoted as pc。
An individual has 24 contact networks in a day, and taking one contact network as an example, the top points in the network represent the individual, the individuals appearing at the same position are connected by edges, and the travel positions of the two individuals at the same time are the same. Then, it is judged whether or not two individuals present at the same time in the same position actually make contact based on the contact probabilities in table 1, for example, when the infected person is an adult, the infected person comes into contact with a minor at a probability of 0.25 at home and comes into contact with an adult at a probability of 0.4.
When the individuals do different activities, the contact probability among the individuals is different (see table 1), a contact network among the individuals is generated every 1 hour according to the contact probability, and 24 contact networks form a final dynamic contact network in one day.
TABLE 1 inter-individual contact probability
Step S4, simulating the propagation of the influenza at high spatiotemporal resolution using the SEIR model according to the constructed contact network of 24 time series during the day. Specifically, the method comprises the following steps:
the SEIR model classifies individuals into four states based on natural history of influenza (fig. 2): susceptible phase, latent phase, infection phase, convalescent phase. As shown in fig. 2, a portion of the susceptible population is immune to influenza virus due to vaccination and autoantibody production; a susceptible individual is infected into a latent period with a certain probability; after the virus is parasitized in vivo for a plurality of times, the individual is infectious and enters the infectious stage; an infected person in the infectious phase may or may not exhibit symptoms associated with influenza. Finally, the individual is cured and enters a recovery state.
The simulation process is in hours to track when and where an infection event occurred, who was infected with it. During the course of influenza transmission, the probability that an individual is infected is referred to as the effective infection probability P. In the model, the formula for the probability of effective infection is as follows:
P＝Pc×Pi×r
wherein P iscIs the probability of contact between individuals, PiIs the probability of infection of an individual, and r is the relative infectivity of that individual. Finally, it is decided whether the individual is infected by the monte carlo method: based on the computer generating a uniformly distributed pseudo random number, the pseudo random number is compared to an effective probability of infection, and if the pseudo random number is less than or equal to the effective probability of infection, the individual is infected. The above process is repeated until the final daily trend of the number of newly infected persons is consistent with the real data and the calculated basic regeneration number is more than 1.
Step S5: and (3) analyzing the simulation result under two visual angles of time and space, exploring the outbreak trend and strength of the influenza, analyzing a high-risk area in the influenza spreading process, and accurately positioning the key space position in the influenza epidemic situation spreading process.
Wherein, the influenza outbreak trend and intensity refer to: the outbreak trend of influenza is mainly represented by a curve generated by newly infected cases every day in time, the curve is approximately in a normal distribution, if the amplitude of the curve is narrower, the outbreak speed is higher, and conversely, the outbreak speed is milder, the peak value of the curve represents the severity of the outbreak, and the peak value is higher and more severe.
The high risk areas are areas with a high cumulative number of infected persons, indicating that the risk of infection of susceptible persons in these areas is high.
The key space position is a position with strong transmission connectivity among the regions, namely the key space position is intervened, so that the transmission of influenza viruses to other regions can be reduced, and the transmission risk of infectious diseases in cities is further reduced.
The method specifically comprises the following steps:
and carrying out comparative analysis on the time sequence under two scales of city scale and administrative division scale. To accurately express the temporal and spatial diffusion process of influenza, a city was divided into 1km × 1km grids, and the number of newly infected cases per day was analyzed for each grid, which is the average of 100 simulation results. An epidemic situation tree is constructed by constructing a transmission topological relation between a parent infectious agent and a child infectious agent, and an epidemic situation forest (figure 3) is formed by a plurality of epidemic situation trees so as to reveal a space transmission path between the parent infectious agent and the child infected agent, accurately position a key space position in the influenza epidemic situation transmission process and reveal the relation between the transmission distance and the transmission intensity and the space aggregation effect when the influenza is transmitted in a city.
The key spatial position in the influenza epidemic situation spreading process is mainly analyzed by analyzing the spreading strength of the influenza viruses among spatial grids, the stronger the spreading connectivity of one grid and other grids (fig. 13), and the larger the number of grids related to the grid (fig. 12), the more important the position is, and the more important the intervention is needed.
In order to reveal the relationship between the propagation distance and the propagation strength, the present example calculates a pearson correlation coefficient r between the propagation distance and the propagation strength, where r is-0.098, and shows that the propagation distance and the propagation strength have an extremely weak negative correlation.
The spatial clustering effect is manifested in that the regions with the connection weight more than 100 have obvious spatial clustering phenomenon (figure 14), which shows that the regions have extremely strong mutual propagation capacity.
Referring to FIG. 4, there is shown a hardware architecture diagram of a system 10 for modeling the spatiotemporal propagation process of influenza using large-scale trajectory data in accordance with the present invention. The system comprises: demographic attributes assignment module 101, activity chain construction module 102, contact network construction module 103, propagation simulation module 104, and analysis module 105.
The population attribute assigning module 101 is configured to synthesize a city population based on census data and building census data, and assign a corresponding population attribute to an individual synthesizing the city population. Namely, the multi-source data is fused to construct the urban population movement model. Wherein:
the census data includes: age, gender, job type, family category, family size, family age component; the building screening data includes: building location information, building height, building area, building function; wherein the building functions include: factories, teaching buildings, residential houses, office buildings and markets.
The population attributes comprise individual attributes, family attributes and whether the mobile phone individuals exist or not; wherein the individual attributes include: age, gender, occupation; the family attributes include: home structure, home address, work place.
Specifically, the method comprises the following steps:
the population attribute assigning module 101 first assigns corresponding individual attributes to individuals of each synthetic city population through monte carlo simulation according to probability distribution of individual attributes such as age, gender, occupation type and the like in census data. And then constructing a synthetic family according to the probability of family attributes such as family category, family scale, family age component and the like in the census data, and filling the individuals of the population of the synthetic city into the synthetic family. And finally, carrying out Monte Carlo simulation according to the mobile phone use rates of different genders and ages, giving the attribute of whether the individuals of the synthetic city population are the individuals with the mobile phones or not, and dividing the individuals of the synthetic city population into the individuals with the mobile phones and the individuals without the mobile phones.
The activity chain construction module 102 is configured to construct an activity chain of an individual (hereinafter, referred to as an "individual") of a composite city population with population attributes, mainly using mobile phone location data and assisting travel survey data. Namely, the mobile phone data is taken as the main data, the trip survey data is taken as the auxiliary data, and the individual mobile modeling of the building scale is realized based on the combination of the space-time characteristics of individual trip and the building general survey data. Specifically, the method comprises the following steps:
the active chain construction module 102 reconstructs the travel track of the individual with the mobile phone based on the mobile phone position data, and the travel track of the individual without the mobile phone is constructed based on the travel survey data. At this time, the space range of the place where the mobile phone is located, which is identified by the mobile phone data, is refined to the service range of the mobile phone base station, and the space range of the place where the mobile phone is not located, which is recorded based on the travel survey, is refined to the traffic cell.
Further, the mobile phone position data includes: and the anonymous mobile phone number, the time, the longitude and latitude of the base station, and sequencing the data of the same mobile phone number according to the time to form a one-day travel track of the mobile phone user, wherein the travel position of the user is the position of the mobile phone base station. Then, a Thiessen polygon is divided based on the mobile phone base station (the range of the Thiessen polygon is the service range of the mobile phone base station), and a plurality of buildings located in the same Thiessen polygon with the mobile phone base station are candidate sets of individual positions.
The trip survey is a survey of individual trip behaviors, and the trip survey data comprises: individual work units, departure places, destinations, departure times, end times, travel manners, travel purposes, and the like, but the location information is in units of traffic cells. The travel track of the cell phone-free individual is constructed by each piece of travel information of the travel survey, and a plurality of buildings of the same traffic cell are obtained and are a candidate set of individual positions.
The active chain constructing module 102 constructs an active chain of an individual according to the obtained candidate set of the individual position.
The contact network construction module 103 is used for dynamically constructing a contact network of 24 time sequences in one day by taking one hour as a time step based on the constructed activity chain of the individual. That is, the contact network construction module 103 dynamically constructs a contact network based on the individual activity chain of the city population. According to the constructed activity chain of the individuals, the activity categories of different individuals in one day are compared by taking hours as time granularity, and the individuals which perform the same activity at the same moment and are located at the same building position are set as the individuals with simultaneous space-time occurrence. Different contact probabilities are assigned to spatio-temporally co-occurring individuals based on activity categories. Under the constraint of contact probability, a dynamic contact network with resolution of hours between agents is generated. Wherein the activity categories include: at home, at work/school, leisure and recreation.
Specifically, the method comprises the following steps:
in a contact network for one time period, individuals who travel are represented by vertexes, and individuals who appear at the same position are connected with edges, and the positions are in units of home (at home), work (at work), and building address (leisure). Contact between individuals who occur at the same time and at the same location occurs with a certain probability, which is denoted as pc。
An individual has 24 contact networks in a day, and taking one contact network as an example, the top points in the network represent the individual, the individuals appearing at the same position are connected by edges, and the travel positions of the two individuals at the same time are the same. Then, it is judged whether or not two individuals present at the same time in the same position actually make contact based on the contact probabilities in table 1, for example, when the infected person is an adult, the infected person comes into contact with a minor at a probability of 0.25 at home and comes into contact with an adult at a probability of 0.4.
When the individuals do different activities, the contact probability among the individuals is different (see table 1), a contact network among the individuals is generated every 1 hour according to the contact probability, and 24 contact networks form a final dynamic contact network in one day.
TABLE 1 inter-individual contact probability
The propagation simulation module 104 is configured to simulate the propagation of the influenza at high spatiotemporal resolution using an SEIR model according to a constructed contact network of 24 time series of a day. Specifically, the method comprises the following steps:
the SEIR model classifies individuals into four states based on natural history of influenza (fig. 2): susceptible phase, latent phase, infection phase, convalescent phase. As shown in fig. 2, a portion of the susceptible population is immune to influenza virus due to vaccination and autoantibody production; a susceptible individual is infected into a latent period with a certain probability; after the virus is parasitized in vivo for a plurality of times, the individual is infectious and enters the infectious stage; an infected person in the infectious phase may or may not exhibit symptoms associated with influenza. Finally, the individual is cured and enters a recovery state.
The simulation process is in hours to track when and where an infection event occurred, who was infected with it. During the course of influenza transmission, the probability that an individual is infected is referred to as the effective infection probability P. In the model, the formula for the probability of effective infection is as follows:
P＝Pc×Pi×r
wherein P iscIs the probability of contact between individuals, PiIs the probability of infection of an individual, and r is the relative infectivity of that individual. Finally, it is decided whether the individual is infected by the monte carlo method: based on the computer generating a uniformly distributed pseudo random number, the pseudo random number is compared to an effective probability of infection, and if the pseudo random number is less than or equal to the effective probability of infection, the individual is infected. The above process is repeated until the final daily trend of the number of newly infected persons is consistent with the real data and the calculated basic regeneration number is more than 1.
The analysis module 105 is used for analyzing the simulation result under two visual angles of time and space, exploring the outbreak trend and strength of the influenza, analyzing a high risk area in the influenza spreading process and accurately positioning the key space position in the influenza epidemic situation spreading process.
Wherein, the influenza outbreak trend and intensity refer to: the outbreak trend of influenza is mainly represented by a curve generated by newly infected cases every day in time, the curve is approximately in a normal distribution, if the amplitude of the curve is narrower, the outbreak speed is higher, and conversely, the outbreak speed is milder, the peak value of the curve represents the severity of the outbreak, and the peak value is higher and more severe.
The high risk areas are areas with a high cumulative number of infected persons, indicating that the risk of infection of susceptible persons in these areas is high.
The key space position is a position with strong transmission connectivity among the regions, namely the key space position is intervened, so that the transmission of influenza viruses to other regions can be reduced, and the transmission risk of infectious diseases in cities is further reduced.
The method specifically comprises the following steps:
the analysis module 105 performs comparative analysis on the time series under the two scales of city scale and administrative division scale. To accurately express the temporal and spatial diffusion process of influenza, a city was divided into 1km × 1km grids, and the number of newly infected cases per day was analyzed for each grid, which is the average of 100 simulation results. The analysis module 105 constructs an epidemic situation tree by constructing a transmission topological relation between a parent infectious agent and a child infectious agent, and then an epidemic situation forest (fig. 3) is composed of a plurality of epidemic situation trees to reveal a spatial transmission path between the parent infectious agent and the child infected agent, accurately position a key spatial position in the influenza epidemic situation transmission process, and reveal a relation between a transmission distance and a transmission intensity and a spatial aggregation effect when influenza is transmitted in a city.
The key spatial position in the influenza epidemic situation spreading process is mainly analyzed by analyzing the spreading strength of the influenza viruses among spatial grids, the stronger the spreading connectivity of one grid and other grids (fig. 13), and the larger the number of grids related to the grid (fig. 12), the more important the position is, and the more important the intervention is needed.
In order to reveal the relationship between the propagation distance and the propagation strength, the present example calculates a pearson correlation coefficient r between the propagation distance and the propagation strength, where r is-0.098, and shows that the propagation distance and the propagation strength have an extremely weak negative correlation.
The spatial clustering effect is manifested in that the regions with the connection weight more than 100 have obvious spatial clustering phenomenon (figure 14), which shows that the regions have extremely strong mutual propagation capacity.
The first embodiment of the application:
(1) and modeling population attributes. According to the probability distribution of individual attributes such as age, gender and occupation type in census data, corresponding individual attributes are assigned to each synthesized individual through Monte Carlo simulation, and the distribution of age and gender of the synthesized individual is shown in FIG. 5. A synthetic family is constructed according to the probability of family attributes such as family category, family scale, family age component and the like in census data, and synthetic individuals are filled in the synthetic family, and fig. 6 shows the size distribution of the synthetic family constructed by the embodiment. Compared with population data of each region published by the Shenzhen city statistical yearbook in 2017, the population quantity of the synthetic population constructed by the embodiment is basically consistent.
(2) On the overall level of a city, the fit degree of the simulation curve and the real influenza data on the trend is high, and important information such as the influenza outbreak intensity, outbreak duration and peak value can be reflected. Fig. 7 shows the comparison of the simulation result of the influenza spreading process with the real cases in time series, and fig. 8 shows the regeneration number generated by the model in the whole simulation period, wherein the regeneration number is more than 1, which indicates that the epidemic is spreading, and less than 1, which indicates that the epidemic tends to die. When the regeneration number is greater than 1, the regeneration number rises first and then falls, and the 100-time simulation result shows that the regeneration number has larger volatility in the part greater than 1. When the regeneration number is less than 1, the regeneration number is slightly reduced in the early stage and basically tends to be smooth in the later stage, and 100 times of simulation results show that the regeneration number has small volatility in a part less than 1.
(3) Under the scale of the administrative district, most administrative districts can still better reflect the influenza outbreak strength and the outbreak trend (figure 9). The hospitals and social health are less in the plateau mountain area, the Longhua area, the Guangming area and the Roc area, and the space-time density of influenza cases is sparse (the last line in FIG. 9), so that the deviation of the simulation result and the real influenza data is large in trend.
(4) FIG. 10 is the result of simulating the Shenzhen City flu diffusion spatio-temporal distribution, with the values in each grid being the average of 100 simulated results. The intelligent-agent-based spatial explicit infectious disease model constructed by fusing the mobile phone position data can support tracking of the position information of an infected person and the interaction situation of the infected person with other susceptible persons, and effectively solves the problem that the spatial position of the infected person and a potential high-risk transmission area cannot be really positioned.
(5) An epidemic tree is constructed according to 100 times of simulation results, and the importance of each spatial unit in the epidemic propagation process is analyzed in the embodiment under the spatial scale of 1Km multiplied by 1Km (FIGS. 11-14). Fig. 11 reflects the number of influenza transmissions within each spatial unit, i.e., the home locations of parent and child infectors are located on the same grid, and such infection events tend to occur between neighbors of the same cell. As can be seen from fig. 11, there is a high-risk area in the southeast of laohu, and influenza is easy to spread inside the area, so that residents living in the area can be reminded to prevent the spread of influenza between neighbors. Fig. 12, 13, and 14 reflect the case where the parent infected person and the child infected person are respectively in two spatial units during the propagation of influenza. The straight propagation paths of parent infectors and child infectors of different space units form the connection relationship between grids, the connection relationship between the grids forms a social network, the value of the grid described in fig. 12 is the degree of the grid in the social network, and the number of the grids which can be influenced by one grid is reflected. It can be seen that the spatial units at the junction of the futian region and the luohu region have an influence on more geographic spaces of Shenzhen city compared with other spatial units. As a result of the analysis, it was found that when a flow propagates between grids, the weight of the inter-grid connection relationship is different (the connection weight indicates the number of events in which the flow propagates between two grids). 99.9% of the inter-grid connection weights are less than 100 (FIG. 13), and the regions with connection weights greater than 100 have significant spatial clustering (FIG. 14). Although the area ratio with the connection weight more than 100 is very small, the identification of the part of the area is important because the part of the area has a large influence on the spread of the influenza, the spreading probability of the influenza in the area is high, and the obvious spatial aggregation phenomenon of the part of the area provides convenience for implementing the epidemic prevention measures. In order to reveal the relationship between the propagation distance and the propagation strength, the present example calculates a pearson correlation coefficient r between the propagation distance and the propagation strength, where r is-0.098, and shows that the propagation distance and the propagation strength have an extremely weak negative correlation.
The invention establishes a spatial explicit infectious disease model based on an agent. On the basis of a traditional individual mobile modeling method based on statistical data, a novel space-time framework which integrates large-scale mobile phone position data to carry out individual mobile modeling is provided, and an urban individual activity chain is reconstructed. The model comprises all population (both population attribute and mobile behavior) in urban areas, the individual traveling position is estimated to a building, and the individual activity site, namely family (at home), work unit (work) and building (entertainment) are taken as units, so that a dynamic contact network among individuals is constructed. And then simulating the space-time propagation process of influenza in a city by adopting an SEIR model, wherein the time step of the simulation is 1 hour, the space scale is in units of buildings, and the heterogeneity of individual response to the influenza virus is fully considered in the simulation process.
It should be noted that the present invention is not limited to the fusion of data such as mobile phone data, travel survey data, census data, building census data, and the like. The invention can simulate the transmission of various infectious diseases, such as: influenza, dengue and other closely transmitted diseases, and in addition, the invention has good analysis capability on the transmission of the infectious diseases in time and space, especially the analysis capability in space.
Although the present invention has been described with reference to the presently preferred embodiments, it will be understood by those skilled in the art that the foregoing description is illustrative only and is not intended to limit the scope of the invention, as claimed.
Claims (10)
1. A method for simulating a space-time propagation process of influenza by using large-scale trajectory data is characterized by comprising the following steps:
a. synthesizing city population based on census data and building census data, and endowing individuals synthesizing the city population with corresponding population attributes;
b. constructing an activity chain of an individual for an individual of a synthetic city population endowed with population attributes by taking mobile phone position data as a main part and taking travel survey data as an auxiliary part;
c. dynamically constructing a contact network of 24 time sequences in one day by taking one hour as a time step based on the constructed activity chain of the individual;
d. the SEIR model was used to simulate the propagation of influenza at high spatiotemporal resolution based on a constructed contact network of 24 time series over the day.
2. The method of claim 1, further comprising the step of e:
and analyzing the simulation result under two visual angles of time and space to obtain a space propagation path between infected persons, and accurately positioning the key space position in the influenza epidemic propagation process.
3. The method of claim 1,
the census data includes: age, gender, job type, family category, family size, family age component; the building screening data includes: building location information, building height, building area, building function; wherein the building functions include: factories, teaching buildings, residential houses, office buildings and markets;
the population attributes comprise individual attributes, family attributes and whether the mobile phone individuals exist or not; wherein the individual attributes include: age, gender, occupation; the family attributes include: home structure, home address, work place.
4. The method according to claim 3, wherein said step b comprises the steps of:
constructing a travel track of an individual with a mobile phone based on mobile phone position data: sequencing data of the same mobile phone number according to time to form a one-day travel track of the mobile phone user, dividing a Thiessen polygon based on a mobile phone base station, and taking a plurality of buildings which are positioned in the same Thiessen polygon with the mobile phone base station as a candidate set of individual positions;
constructing a travel track of the individual without the mobile phone based on travel survey data: constructing each piece of travel information of the travel survey to obtain a candidate set with a plurality of buildings of the same traffic cell as individual positions;
and constructing an activity chain of the individual according to the obtained candidate set of the individual positions.
5. The method according to claim 4, wherein said step c comprises the steps of:
according to the constructed activity chain of the individuals, the activity types of different individuals in one day are compared by taking hours as time granularity, the individuals which perform the same activity at the same time and are located at the same building position are set as space-time co-occurrence individuals, different contact probabilities are given to the space-time co-occurrence individuals on the basis of the activity types, and a dynamic contact network with the resolution of hours between intelligent agents is generated under the constraint of the contact probabilities.
6. The method of claim 5, wherein the activity categories include: at home, at work/school, leisure and recreation.
7. The method according to claim 5, wherein said step d comprises the steps of:
in the unit of hours, tracking the time and place where the infection event occurs and who infects the infection event, in the process of influenza transmission, the probability of the infection of an individual is called as effective infection probability P, and the formula of the effective infection probability is as follows:
P＝Pc×Pi×r
wherein, PcIs the probability of contact between individuals, PiIs the probability of infection of an individual, and r is the relative infectivity of the individual;
finally, the Monte Carlo method is used to determine whether the individual is infected.
8. The method of claim 7, wherein determining whether the individual is infected by the Monte Carlo method comprises:
and generating uniformly distributed pseudo random numbers based on a computer, comparing the pseudo random numbers with the effective infection probability, if the pseudo random numbers are less than or equal to the effective infection probability, infecting the individual, and repeating the processes until the trend of the number of newly infected persons is consistent with the real data every day and the calculated basic regeneration number is more than 1.
9. The method according to claim 8, wherein said step e comprises the steps of:
performing comparative analysis on a time sequence under two scales of city scale and administrative division scale, dividing a city into grids of 1km multiplied by 1km, and analyzing the number of newly infected cases per day of each grid;
an epidemic situation tree is constructed by constructing a transmission topological relation between a parent infectious agent and an offspring infectious agent, and an epidemic situation forest is formed by a plurality of epidemic situation trees, so that a spatial transmission path between the parent infectious agent and the offspring infected agent is obtained, and a key spatial position in the influenza epidemic situation transmission process is accurately positioned.
10. A system for simulating a space-time propagation process of a flu by using large-scale trajectory data is characterized by comprising a population attribute assigning module, an activity chain constructing module, a contact network constructing module and a propagation simulating module, wherein:
the population attribute assigning module is used for synthesizing city population based on census data and building census data and assigning corresponding population attributes to individuals synthesizing the city population;
the activity chain construction module is used for constructing an activity chain of an individual for an individual of a synthetic city population endowed with population attributes, mainly by taking mobile phone position data as a main part and taking travel survey data as an auxiliary part;
the contact network construction module is used for dynamically constructing contact networks of 24 time sequences in one day by taking one hour as a time step based on the constructed activity chain of the individual;
the propagation simulation module is used for simulating the propagation of the influenza at high space-time resolution by adopting an SEIR model according to the constructed contact network of 24 time sequences in one day.
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