CN114496265A - Urban internal infectious disease space-time diffusion modeling method and system - Google Patents
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Abstract
The invention relates to a space-time diffusion modeling method for urban internal infectious diseases, which comprises the following steps: collecting infectious disease related data and preprocessing the collected data, wherein the data comprises: mobile phone positioning data; constructing a space-time spreading model of the infectious diseases in the city according to the preprocessed data and the pathogenesis and transmission mechanism of the infectious diseases; and determining and estimating parameters of the constructed urban internal infectious disease space-time diffusion model. The invention also relates to a modeling system for the space-time diffusion of the urban internal infectious diseases. The method can construct a fine-scale infectious disease space-time diffusion model in the city to realize prediction and early warning and prevention and control measure simulation of space precision.
Description
Technical Field
The invention relates to a method and a system for modeling space-time diffusion of urban internal infectious diseases.
Background
Thanks to the great improvement of the travel convenience, the urban internal population flow has extremely strong dynamic time-varying characteristics, and the spread of infectious diseases, particularly respiratory infectious diseases, in cities is accelerated. The history and practical experience of infectious diseases show that the consequences are particularly serious once the infectious diseases are outbreaked in a super-large city with dense population, developed traffic and high-speed social operation. The modeling of the space-time spread of the infectious diseases by mathematical and computer means is helpful for understanding the spreading process of the infectious diseases, predicting the spread situation and simulating and evaluating the effect of each prevention and control measure.
Wherein, the propagation dynamics model (referred to as a chamber model herein) is still the mainstream model for modeling the space-time diffusion of infectious diseases. The simulation and effect evaluation of various prevention and control measures such as travel limitation, social separation and the like are mainly carried out under the model system.
Typical representatives of the propagation dynamics model are the SIR and SIS models. On the basis, according to different pathogenesis and transmission modes of diseases, an SIRS model, an SEIR model, an SIS model with life and death migration, a model with an age structure and the like are developed. The model can be constructed for a single geographic unit, such as the whole of a country, a province and a city, and a population model (propagation model) based on propagation dynamics can also be constructed by regarding the population of a plurality of geographic units as different populations and combining population flow among the populations. Cao Shi Dong et al (2009) have constructed a kinetic model reflecting the SARS spreading process, have carried on the optimal estimation to important epidemiological parameter such as incubation period, infection phase, etc., and have simulated the effects and influences of four kinds of intervention measures such as assessing vaccination rate, etc.. Li et al (2020) incorporate into internet population migration data and build population models based on SEIR for 375 Chinese cities. Lai et al (2020) established a population model based on SEIR for 340 Chinese cities based on Internet migration data. Zhou et al (2020) build a population model based on SEIR for 10 administrative districts in Shenzhen city based on mobile phone mobile positioning data, and quantitatively evaluate the effectiveness of four types of travel restriction measures (namely, reducing the travel amount among all regions, blocking high-risk regions, restricting the travel inside regions and restricting the travel of infected persons).
Although the propagation dynamics model has the advantages of clear mechanism, high modeling and operating efficiency and the like, the current modeling and prevention and control research based on the propagation dynamics is mainly at the national/state/province/city level, and the modeling of the space-time diffusion of infectious diseases at a fine scale deep inside the city is not available.
Disclosure of Invention
In view of the above, there is a need to provide a modeling method and system for urban internal infectious disease spatio-temporal diffusion, which can construct population models based on propagation dynamics at the fine spatial granularity inside cities.
The invention provides a space-time diffusion modeling method for urban internal infectious diseases, which comprises the following steps: a. collecting infectious disease related data and preprocessing the collected data, wherein the data comprises: mobile phone positioning data; b. constructing a space-time spreading model of the infectious diseases in the city according to the preprocessed data and the pathogenesis and transmission mechanism of the infectious diseases; c. and determining and estimating parameters of the constructed urban internal infectious disease space-time diffusion model.
Further, the pre-processing comprises: and processing the collected mobile phone positioning data into grid data, wherein the grid data comprises grid resident population quantity, whole-day population flow quantity between grids and resident population quantity flowing out of the grid all day.
Further, the step b comprises:
considering the space-time influence of urban internal population flow on disease transmission, a population model based on susceptibility-latent-infection-removal is constructed according to the disease course characteristics and the transmission mechanism of infectious diseases.
Further, the constructed urban internal infectious disease space-time diffusion model is as follows:
wherein suffix symbols 1 and 0 represent dominant and recessive infections, respectively,
i.e. the total resident population in the grid remains unchanged.
new_infectious1＝σδLn
new_infectious0＝(1-σ)δLn
Wherein:
fnthe number of people who flow out of the grid all day among the resident population of the grid n is obtained from the data preprocessed in the step S1;
hmnthe number of persons representing the number of persons from grid m to n throughout the day is obtained from the data preprocessed in step S1.
Further, the parameters of the urban internal infectious disease space-time diffusion model comprise:
beta is the effective infection rate of a dominant infected person and needs to be estimated;
epsilon is the ratio of the effective infection rate of a recessive infected person to an explicit infected person, and is taken according to literature;
1/delta is latent period, and is taken according to literature;
sigma is the proportion of dominant infected persons, and is taken according to literature;
1/γ1the infection period of the dominant infected person is taken according to the literature;
1/γ0the infection period of recessive infected people is determined according to literature.
And (3) taking different values for beta, simulating daily newly-increased curves corresponding to a given number of seed cases, estimating R0 from each daily newly-increased curve by using an exponential growth method through an R0 toolkit of R language, and further fitting the relation between beta and R0 to obtain the effective infection rate beta corresponding to the basic regeneration number of the infectious disease R0.
The invention provides a space-time diffusion modeling system for urban internal infectious diseases, which comprises a data acquisition and preprocessing unit, a model construction unit and a parameter determination and estimation unit, wherein the data acquisition and preprocessing unit comprises: the data acquisition and preprocessing unit is used for acquiring infectious disease related data and preprocessing the acquired data, and the data comprises: mobile phone positioning data; the model building unit is used for building a space-time diffusion model of the infectious disease in the city according to the preprocessed data and the pathogenesis and transmission mechanism of the infectious disease; the parameter determining and estimating unit is used for determining and estimating parameters of the constructed urban internal infectious disease space-time diffusion model.
Further, the pre-processing comprises: and processing the collected mobile phone positioning data into grid data, wherein the grid data comprises grid resident population quantity, whole-day population flow quantity between grids and resident population quantity flowing out of the grid all day.
Further, the model building unit is specifically configured to:
considering the space-time influence of urban internal population flow on disease transmission, a population model based on susceptibility-latent-infection-removal is constructed according to the disease course characteristics and the transmission mechanism of infectious diseases.
Further, the model building unit is specifically configured to:
further, the constructed urban internal infectious disease space-time diffusion model is as follows:
wherein suffix symbols 1 and 0 represent dominant and recessive infections, respectively,
i.e. the total resident population in the grid remains unchanged.
new_infectious1＝σδLn
new_infectious0＝(1-σ)δLn
Wherein:
fnthe number of people who flow out of the grid all day among the resident population of the grid n is the number preprocessed by the data acquisition and preprocessing unitObtaining the data;
hmnthe number of people from the grid m to n in the whole day is represented, and the number is obtained from the data preprocessed by the data acquisition and preprocessing unit.
Further, the parameters of the urban internal infectious disease space-time diffusion model comprise:
beta is the effective infection rate of a dominant infected person and needs to be estimated;
epsilon is the ratio of the effective infection rate of a recessive infected person to an explicit infected person, and is taken according to literature;
1/delta is latent period, and is taken according to literature;
sigma is the proportion of dominant infected persons, and is taken according to literature;
1/γ1the infection period of the dominant infected person is calculated according to literature;
1/γ0the infection period of recessive infected people is determined according to literature.
And (3) taking different values for beta, simulating daily newly-increased curves corresponding to a given number of seed cases, estimating R0 from each daily newly-increased curve by using an exponential growth method through an R0 toolkit of R language, and further fitting the relation between beta and R0 to obtain the effective infection rate beta corresponding to the basic regeneration number of the infectious disease R0.
According to the urban internal infectious disease space-time diffusion modeling method and system, population flow characteristics among urban internal areas are fully considered, and an urban internal fine-scale infectious disease space-time diffusion model is constructed so as to realize prediction early warning and space-precision prevention and control measure simulation.
Drawings
FIG. 1 is a flow chart of the modeling method for the space-time diffusion of the urban internal infectious diseases according to the invention;
FIG. 2 is a schematic diagram of the infection status and evolution process of COVID-19 in a single population according to an embodiment of the present invention;
FIG. 3 is a schematic illustration of the internal infection of the mesh and the infection between the meshes due to the flow of the population according to an embodiment of the present invention;
FIG. 4 is a diagram illustrating a fitting relationship between the effective infection rate β and the estimated value of R0 provided by an embodiment of the present invention;
FIG. 5 is a diagram of the hardware architecture of the modeling system for the spatiotemporal diffusion of the urban internal infectious diseases according to the present invention.
Detailed Description
The present invention will be described in further detail with reference to the accompanying drawings and specific embodiments.
This example will be described with reference to an infectious disease, COVID-19, in a certain market:
referring to FIG. 1, it is a flow chart of the operation of the preferred embodiment of the modeling method for the spatio-temporal diffusion of the internal infectious diseases in the city according to the present invention.
Step S1, collecting infectious disease related data and preprocessing the collected data. The data includes: and (5) positioning data of the mobile phone. The pretreatment comprises the following steps: and processing the collected mobile phone positioning data into grid data, wherein the grid data comprises grid resident population quantity, whole-day population flow quantity between grids and resident population quantity flowing out of the grid all day.
Specifically, the method comprises the following steps:
(1) mobile phone positioning data: the mobile phone positioning data used in this embodiment is collected from china union network communication group limited company (china unicom for short), and entrusts smart footprint data technology limited company (smart footprint for short) to process into grid data based on 500 mx 500m, where the grid data includes the number of residential population of the grid, the daily population movement between grids, and the number of residential population flowing through the grid all day. Because the user of the connected mobile phone only covers part of the population, the population distribution and the population mobility data are population full sample data obtained by sampling the intelligent footprint on the basis of the data of the connected user.
Grid population number: in the embodiment, based on the observation records of 12 months and full months in 2019, the grid where the user is observed at night (21: 00-8: 00 the next day) for the longest residence time is taken as the residence place of the user, and the number of the resident population of each grid is aggregated on the basis.
Secondly, on the basis of the first step, the embodiment also counts the number of the resident population of each grid which continuously stays all day (namely, the resident population does not flow out of the grid all day).
③ the whole day population mobility between grids: and identifying the movement behavior of the user across the grid positions, wherein if the stay time of the grid before the displacement and the stay time of the grid after the displacement both exceed 30 minutes, the displacement is an effective trip. And aggregating all effective trips of all users on a typical day, such as 2019.12.18, to obtain the population flow between all-day grids, and further calculating the all-day population inflow and outflow of each grid according to the population flow between all-day grids. Wherein the population flow among the whole day grids is an OD matrix (Origin-Destination matrix).
It should be noted that the mobile phone positioning data is only one data source for acquiring higher quality of the population number of the living space units and the population flow quantity among the space units; it is within the scope of the present invention that other data sources may be used to obtain the population sizes of residences and the population flow volumes among the spatial units in the grid or other type of spatial unit.
And step S2, constructing a space-time diffusion model of the infectious diseases in the city according to the preprocessed data and the pathogenesis and transmission mechanism of the infectious diseases.
In the embodiment, a 500-meter grid is used as a population division unit, the space-time influence of urban internal population flow on disease propagation is fully considered, and a population model based on susceptibility-Latent-infection-removal (SLIR) is constructed according to the disease course characteristics and the propagation mechanism of COVID-19.
FIG. 2 shows the COVID-19 infection status and evolution process in a single population. The embodiment takes whether the grid is invaded by pathogens and whether the grid has infectivity as a division standard, and the residential population in the grid N is NnDivided into susceptible population SnGroup L in latent periodnAnd the population in the infection period (including the dominant infected person I)1And recessive infected person I0) And removing population Rn。
Wherein, the latent period (latent period) refers to the period from the invasion of a pathogen into the body to the occurrence of infectivity; the infectious phase (communicable period) indicates the period of now contagious to convalescent, isolated or dead (i.e. transmissible). The population in the infectious stage, i.e., the infected persons, are classified into dominant infected persons and recessive infected persons. Wherein the dominant infected person is attacked 1-2 days after entering the infection period, which is 1.5 days in the embodiment. If the infection is not found in advance by means of close contact person tracing and the like, the dominant infected person is admitted to the hospital and isolated about 6 days after the onset of the disease in the natural state (corresponding to R0 ≈ 2.3), so the infection period is about 1.5+6 days; the infection period in recessive infected persons is about 10 days.
As shown in fig. 3, due to the existence of population movement between grids, a susceptible population residing within each grid may be infected not only by infected persons of the grid, but also by infected persons of other grids who flow into the grid, and who are traveling outside. Wherein the infected persons of the other mesh include infected persons local to the mesh and infected persons who flow into the mesh from the other mesh.
Based on the above pathogenesis and transmission mechanisms, the present example constructs a model as follows, where the suffix 1 and 0 indicate dominant and recessive infections, respectively:
wherein,
i.e. the total resident population in the grid remains unchanged.
new_infectious1＝σδLn
new_infectious0＝(1-σ)δLn
Wherein:
The number of overt infected persons encountered;
fnthe number of people who flow out of the grid all day among the resident population of the grid n is obtained from the data preprocessed in the step S1;
hmnthe number of persons representing the number of persons from grid m to n throughout the day is obtained from the data preprocessed in step S1.
And step S3, determining and estimating parameters of the constructed space-time diffusion model of the urban internal infectious diseases.
Specifically, the method comprises the following steps:
the main parameters related to the urban internal infectious disease space-time diffusion model comprise:
beta is the effective infection rate of the dominant infected person, namely the average number of infectious people per day of a dominant infected person to be estimated;
epsilon is the ratio of the effective infection rate of a recessive infected person to an explicit infected person, and the effective infection rate is 0.12 in the embodiment with reference to the national CDC report;
1/delta is latent period, and in this example, 5.2 days (latent period) -1.5 days are taken as 4.7 days;
σ is the proportion of dominant infected persons, and 75% is taken in the example;
1/γ1the infection period of the dominant infected person is 1.5 days +6 days in the natural state, namely 7.5 days according to the early epidemic report (the average of the onset to admission is about 6 days);
1/γ0the infection stage of recessive infected persons is 10 days in the natural state.
It is worth noting that: the parameters can be updated according to the development of COVID-19 and the latest research result.
The only parameter to be estimated by the urban internal infectious disease space-time diffusion model is the effective infection rate beta of a dominant infected person: a certain number of seed cases are given (the positions of the seed cases can be set according to population distribution), different values are taken for beta, the corresponding daily new curves are simulated, R0 is estimated from each daily new curve through an R0 toolkit of R language and by using an Exponential growth method (EG), and then the relation between beta and R0 is fitted to obtain the effective infection rate beta corresponding to the basic regeneration number R0 of COVID-19 approximately equal to 2.3. In the embodiment, given different effective infection rates beta, 500 initial seed cases are set, and daily new curves of different cities are obtained through the urban internal infectious disease space-time diffusion model simulation, so that the corresponding R0 is estimated. Figure 4 shows the relationship between the effective infection rate β and the estimated value of R0, which has a better linear fit relationship. Given R0 ═ 2.3, a value of 0.237 corresponding to an effective infection rate β was obtained.
And substituting the parameters into the model constructed in the step S2, namely completely constructing a COVID-19 space-time diffusion model with a grid size of 500 meters in a certain market. The model based on the parameters conforms to the basic propagation rule of COVID-19 in the natural state. And giving an initial case position, and performing space-time dynamic prediction on epidemic situation development. In addition, on the basis of the model, the effective infection rate beta of the dominant infected person and the time from onset to admission (corresponding to the infection period 1/gamma of the dominant infected person) are adjusted1) And the population flow among grids, and the like, namely the effectiveness of prevention and control measures such as wearing mask/sanitation sterilization, isolation treatment speed, population flow control and the like can be simulated and evaluated.
Referring to FIG. 5, a diagram of the hardware architecture of the modeling system 10 for the spatio-temporal diffusion of infectious diseases in cities according to the present invention is shown. The system comprises: a data acquisition and preprocessing unit 101, a model construction unit 102, and a parameter determination and estimation unit 103.
The data acquisition and preprocessing unit 101 is used for acquiring infectious disease related data and preprocessing the acquired data. The data includes: and (5) positioning data of the mobile phone. The pretreatment comprises the following steps: and processing the collected mobile phone positioning data into grid data, wherein the grid data comprises grid resident population quantity and grid population flow quantity. Specifically, the method comprises the following steps:
(1) mobile phone positioning data: the mobile phone positioning data used by the data collecting and preprocessing unit 101 is collected from china union network communication group ltd (china unicom for short), and entrusts the smart footprint data technology ltd (smart footprint for short) to process into grid data based on 500m × 500m, where the grid data includes the number of grid resident population and the population flow between grids. Because the user of the connected mobile phone only covers part of the population, the population distribution and the population mobility data are population full sample data obtained by sampling the intelligent footprint on the basis of the data of the connected user.
Grid population number: in the embodiment, based on the observation records of 12 months and full months in 2019, the grid where the user is observed at night (21: 00-8: 00 the next day) for the longest residence time is taken as the residence place of the user, and the number of the resident population of each grid is aggregated on the basis.
And secondly, the data acquisition and preprocessing unit 101 also counts the number of resident population of each grid which continuously stays all day (namely, the grid does not exist all day).
③ the flow of the population between grids: and identifying the movement behavior of the user across the grid position, wherein if the stay time of the grid before the displacement and the stay time of the grid after the displacement both exceed 30 minutes, the displacement is an effective trip. And aggregating all effective trips of all users on a typical day, such as 2019.12.18, to obtain the population flow between all-day grids, and further calculating the all-day population inflow and outflow of each grid according to the population flow between all-day grids. Wherein the population flow between the whole day grids is an OD matrix (origin-destination matrix).
It should be noted that the mobile phone positioning data is only one data source for acquiring higher quality of the population number of the living space units and the population flow quantity among the space units; it is within the scope of the present invention that other data sources may be used to obtain the population sizes of residences and the population flow volumes among the spatial units in the grid or other type of spatial unit.
The model construction unit 102 is configured to construct a space-time spread model of the infectious disease in the city according to the preprocessed data and the pathogenesis and transmission mechanism of the infectious disease. Specifically, the method comprises the following steps:
in the embodiment, a 500-meter grid is used as a population division unit, the space-time influence of urban internal population flow on disease propagation is fully considered, and a population model based on susceptibility-Latent-infection-removal (SLIR) is constructed according to the disease course characteristics and the propagation mechanism of COVID-19.
FIG. 2 shows the COVID-19 infection status in a single population andand (5) evolution process. The embodiment takes whether the grid is invaded by pathogens and whether the grid has infectivity as a division standard, and the residential population in the grid N is NnDivided into susceptible population SnGroup L in latent periodnAnd the infection stage population (including the dominant infected I)1And recessive infected person I0) And removing population Rn。
Wherein, the latent period (latent period) refers to the period from the invasion of a pathogen into the body to the occurrence of infectivity; the infectious phase (communicable period) indicates the period of now contagious to convalescent, isolated or dead (i.e. transmissible). The population in the infectious stage, i.e., the infected persons, are classified into dominant infected persons and recessive infected persons. Wherein the dominant infected person is attacked 1-2 days after entering the infection period, which is 1.5 days in the embodiment. If the infection is not found in advance by means of close contact person tracing and the like, the dominant infected person is admitted to the hospital and isolated about 6 days after the onset of the disease in the natural state (corresponding to R0 ≈ 2.3), so the infection period is about 1.5+6 days; the infection period in recessive infected persons is about 10 days.
As shown in fig. 3, due to the existence of population movement between grids, a susceptible population residing within each grid may be infected not only by infected persons of the grid, but also by infected persons of other grids who flow into the grid, and who are traveling outside. Wherein the infected persons of the other mesh include infected persons local to the mesh and infected persons who flow into the mesh from the other mesh.
Based on the above pathogenesis and transmission mechanisms, the present example constructs a model as follows, where the suffix 1 and 0 indicate dominant and recessive infections, respectively:
wherein,
i.e. the total resident population in the grid remains unchanged.
new_infectious1＝σδLn
new_infectious0＝(1-σ)δLn
Wherein:
fnthe number of people who flow out of the grid all day among the resident population of the grid n is obtained from the data preprocessed by the data acquisition and preprocessing unit 101;
hmnthe number of people who represent the whole day from grid m to n is obtained from the data preprocessed by the data acquisition and preprocessing unit 101.
The parameter determining and estimating unit 103 is used for determining and estimating parameters of the constructed urban internal infectious disease space-time diffusion model. Specifically, the method comprises the following steps:
the main parameters related to the urban internal infectious disease space-time diffusion model comprise:
beta is the effective infection rate of the dominant infected person, namely the average number of infectious people per day of a dominant infected person to be estimated;
epsilon is the ratio of the effective infection rate of a recessive infected person to an explicit infected person, and the effective infection rate is 0.12 in the embodiment with reference to the national CDC report;
1/delta is latent period, and in this example, 5.2 days (latent period) -1.5 days are taken as 4.7 days;
σ is the proportion of dominant infected persons, and 75% is taken in the example;
1/γ1the infection period of the dominant infected person is 1.5 days +6 days in the natural state, namely 7.5 days according to the early epidemic report (the average of the onset to admission is about 6 days);
1/γ0the infection stage of recessive infected persons is 10 days in the natural state.
It is worth noting that: the parameters can be updated according to the development of COVID-19 and the latest research result.
The only parameter to be estimated by the urban internal infectious disease space-time diffusion model is the effective infection rate beta of a dominant infected person: a certain number of seed cases are given (the positions of the seed cases can be set according to population distribution), different values are taken for beta, the corresponding daily new curves are simulated, R0 is estimated from each daily new curve through an R0 toolkit of R language and by using an Exponential growth method (EG), and then the relation between beta and R0 is fitted to obtain the effective infection rate beta corresponding to the basic regeneration number R0 of COVID-19 approximately equal to 2.3.
In the embodiment, given different effective infection rates beta, 500 initial seed cases are set, and daily new curves of different cities are obtained through the urban internal infectious disease space-time diffusion model simulation, so that the corresponding R0 is estimated. Figure 4 shows the relationship between the effective infection rate β and the estimated value of R0, which has a better linear fit relationship. Given R0 ═ 2.3, a value of 0.237 corresponding to an effective infection rate β was obtained.
And substituting the parameters into the model constructed by the model construction unit 102, namely completely constructing a COVID-19 space-time diffusion model with the grid size of 500 meters in a certain market. The model based on the parameters conforms to the basic propagation rule of COVID-19 in the natural state. And giving an initial case position, and performing space-time dynamic prediction on epidemic situation development. In addition, on the basis of the model, the effective infection rate beta of the dominant infected person and the time from onset to admission (corresponding to the infection period 1/gamma of the dominant infected person) are adjusted1) And the population flow among grids, and the like, namely the effectiveness of prevention and control measures such as wearing mask/sanitation sterilization, isolation treatment speed, population flow control and the like can be simulated and evaluated.
The urban internal infectious disease space-time diffusion modeling method and system are integrated with population flow data, and a population model based on propagation dynamics is constructed on the fine spatial granularity (grid level of 500 meters) in the city, so that spatially refined infectious diseases, particularly COVID-19 space-time diffusion modeling is realized, and the method and system are used for infectious diseases, particularly COVID-19 space-time prediction early warning and prevention and control simulation.
Infectious disease transmission is a highly non-linear spatiotemporal process that is difficult to judge using intuition for future trends. The infectious disease model is not only constructed to approach a specific true phase, but also is used for systematically and scientifically researching the development trend of the complex process under different assumed intervention scenarios, and a vital planning tool is provided for policy makers, clinicians and public health practitioners. The urban internal infectious disease spatio-temporal diffusion model constructed by the invention can be effectively applied to epidemic situation spatio-temporal trend prediction and prevention and control measure simulation under the condition of ensuring the truth and reliability of data and the reasonable model expression form and parameters.
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 city internal infectious disease space-time diffusion modeling method is characterized by comprising the following steps:
a. collecting infectious disease related data and preprocessing the collected data, wherein the data comprises: positioning data of the mobile phone;
b. constructing a space-time spreading model of the infectious diseases in the city according to the preprocessed data and the pathogenesis and transmission mechanism of the infectious diseases;
c. and determining and estimating parameters of the constructed urban internal infectious disease space-time diffusion model.
2. The modeling method for urban internal infectious disease spatiotemporal diffusion according to claim 1, characterized in that:
the pretreatment comprises the following steps: and processing the collected mobile phone positioning data into grid data, wherein the grid data comprises grid resident population quantity, whole-day population flow quantity between grids and resident population quantity flowing out of the grid all day.
3. The modeling method of urban internal infectious disease spatiotemporal diffusion according to claim 2, wherein said step b comprises:
considering the space-time influence of urban internal population flow on disease transmission, a population model based on susceptibility-latent-infection-removal is constructed according to the disease course characteristics and the transmission mechanism of infectious diseases.
4. The modeling method for urban internal infectious disease spatiotemporal diffusion according to claim 3, characterized in that the constructed urban internal infectious disease spatiotemporal diffusion model is:
wherein suffix symbols 1 and 0 represent dominant and recessive infections, respectively,
i.e. the total resident population in the grid remains unchanged;
new_infectious1＝σδLn
new_infectious0＝(1-σ)δLn
wherein:
fnthe number of people who flow out of the grid all day among the resident population of the grid n is obtained from the data preprocessed in the step S1;
hmnthe number of persons representing the number of persons from grid m to n throughout the day is obtained from the data preprocessed in step S1.
5. The modeling method for urban internal infectious disease spatiotemporal diffusion according to claim 4, wherein the parameters of the urban internal infectious disease spatiotemporal diffusion model comprise:
beta is the effective infection rate of a dominant infected person and needs to be estimated;
epsilon is the ratio of the effective infection rate of a recessive infected person to an explicit infected person, and is taken according to literature;
1/delta is latent period, and is taken according to literature;
sigma is the proportion of dominant infected persons, and is taken according to literature;
1/γ1the infection period of the dominant infected person is taken according to the literature;
1/γ0the infection period of recessive infectors is taken according to literature;
taking different values for beta, simulating daily newly-increased curves corresponding to a given number of seed cases, estimating R0 from each daily newly-increased curve by using an exponential growth method through an R0 toolkit of R language, and further fitting the relation between beta and R0 to obtain the effective infection rate beta corresponding to the basic regeneration number of the infectious disease R0; wherein, the seed cases are arranged according to the population distribution position and the number.
6. The urban internal infectious disease space-time diffusion modeling system is characterized by comprising a data acquisition and preprocessing unit, a model construction unit and a parameter determination and estimation unit, wherein:
the data acquisition and preprocessing unit is used for acquiring infectious disease related data and preprocessing the acquired data, and the data comprises: mobile phone positioning data;
the model building unit is used for building a space-time diffusion model of the infectious disease in the city according to the preprocessed data and the pathogenesis and transmission mechanism of the infectious disease;
the parameter determining and estimating unit is used for determining and estimating parameters of the constructed urban internal infectious disease space-time diffusion model.
7. The system of claim 6, wherein:
the pretreatment comprises the following steps: and processing the collected mobile phone positioning data into grid data, wherein the grid data comprises grid resident population quantity, whole-day population flow quantity between grids and resident population quantity flowing out of the grid all day.
8. The system of modeling urban internal infectious disease spatiotemporal diffusion according to claim 7, wherein said model building unit is specifically configured to:
considering the space-time influence of urban internal population flow on disease transmission, a population model based on susceptibility-latent-infection-removal is constructed according to the disease course characteristics and the transmission mechanism of infectious diseases.
9. The system of modeling urban internal infectious disease spatiotemporal diffusion according to claim 8, wherein said model building unit is specifically configured to:
the constructed space-time diffusion model of the urban internal infectious disease is as follows:
wherein suffix symbols 1 and 0 represent dominant and recessive infections, respectively,
i.e. the total resident population in the grid remains unchanged;
new_infectious1＝σδLn
new_infectious0＝(1-σ)δLn
wherein:
fnthe number of people who flow out of the grid all day among the resident population of the grid n is obtained from the data preprocessed by the data acquisition and preprocessing unit;
hmnthe number of people from the grid m to n in the whole day is represented, and the number is obtained from the data preprocessed by the data acquisition and preprocessing unit.
10. The system of modeling urban internal infectious disease spatiotemporal diffusion according to claim 9, wherein said network model comprises:
the parameters of the urban internal infectious disease space-time diffusion model comprise:
beta is the effective infection rate of a dominant infected person and needs to be estimated;
epsilon is the ratio of the effective infection rate of a recessive infected person to an explicit infected person, and is taken according to literature;
1/delta is latent period, and is taken according to literature;
sigma is the proportion of dominant infected persons, and is taken according to literature;
1/γ1the infection period of the dominant infected person is taken according to the literature;
1/γ0the infection period of recessive infected persons is taken according to literature;
taking different values for beta, simulating daily newly-increased curves corresponding to a given number of seed cases, estimating R0 from each daily newly-increased curve by using an exponential growth method through an R0 toolkit of R language, and further fitting the relation between beta and R0 to obtain the effective infection rate beta corresponding to the basic regeneration number of the infectious disease R0; wherein, the seed cases are arranged according to the population distribution position and the number.
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