Method and system for analyzing space-time aggregation of epidemic situation of infectious diseases

Abstract

The invention relates to the technical field of infectious disease prevention and control, in particular to an infectious disease epidemic situation space-time aggregation analysis method and system, and the method comprises the steps: constructing a case growth trajectory propagation model and an epidemic situation propagation and diffusion model through a data warehouse; predicting an epidemic situation diffusion path and a time-space coverage change graph by using the case growth trajectory propagation model and the epidemic situation propagation and diffusion model; and fusing the predicted epidemic situation diffusion path and the time-space coverage change map with the spatial data of the key public place, and calculating to obtain an epidemic situation influence index of the key public place. By constructing the case growth trajectory propagation model and the epidemic situation propagation and diffusion model, the diffusion path and the space-time coverage change of the epidemic situation can be predicted more accurately, a more scientific basis is provided for prevention and control work, and according to the predicted epidemic situation diffusion condition and the key public occasion epidemic situation influence index, the prediction of the epidemic situation is realized. Medical resources, manpower, materials and the like can be configured more reasonably, and the prevention and control efficiency is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of infectious disease prevention and control, and in particular to a method and system for analyzing the spatiotemporal clustering of infectious disease epidemics. Background Art

[0002] In the past 20 years, from SARS to the new coronavirus, serious damage has been caused to human society. Therefore, people have had a deep understanding of the theoretical research on the communication system and have proposed many models. At the same time, many model-based data processing and simulation methods have been proposed. We have a clearer understanding of the various parameter variables in the process of epidemic propagation. However, the spatiotemporal correlations of various variable data in the communication system still need to be analyzed and studied. In order to better and quickly find cases in the epidemic, timely prepare vaccines and detection systems, and better prevent and control the epidemic, it is necessary to quickly analyze the changing patterns of epidemic propagation.

[0003] Based on the existence of multivariate relationships among various variables in the propagation process, the multivariate relationships, the complex coupling between the variables, the fast data generation speed and large data volume, have caused certain difficulties in data processing and spatiotemporal analysis of propagation data, affecting the rapid and accurate analysis of propagation data. Summary of the invention

[0004] (I) Purpose of the invention

[0005] The purpose of the present invention is to provide a method and system for analyzing the spatiotemporal clustering of infectious disease epidemics, which can improve the accuracy of analysis.

[0006] (II) Technical solution

[0007] To solve the above problems, the present invention provides a method for analyzing the spatiotemporal clustering of infectious disease epidemics, comprising:

[0008] Collect multi-source data related to the spread of infectious diseases;

[0009] Classify the multi-source data using preset classification rules to obtain a data warehouse;

[0010] Using the data warehouse, construct a case growth trajectory transmission model and an epidemic spread model;

[0011] Utilize the case growth trajectory transmission model and epidemic spread model to predict the epidemic spread path and the time-space coverage change map;

[0012] The predicted epidemic spread path and spatiotemporal coverage change map are integrated with the spatial data of key public places to calculate the epidemic impact index of key public places.

[0013] In another aspect of the present invention, preferably, classifying the multi-source data using a preset classification rule includes:

[0014] Classifying the multi-source data according to different case types to obtain first classified data;

[0015] Classifying the first classified data by spatial position to obtain second classified data;

[0016] Classifying the first classified data according to the time dimension to obtain third classified data;

[0017] The second classified data and the third classified data are clustered to obtain a data warehouse, wherein the data in the data warehouse includes a correspondence between a time dimension and a spatial position.

[0018] In another aspect of the present invention, preferably, the analysis method further comprises: modifying the epidemic spread model;

[0019] The amendments include:

[0020] Obtaining in real time the time difference between two adjacent sampling time nodes and the number of case changes within the time difference;

[0021] According to the time difference and the corresponding number of case changes, a statistical trend of the time change of the epidemic is formed;

[0022] The time-varying statistical trend is used to revise the epidemic spread model.

[0023] In another aspect of the present invention, preferably,

[0024] The case growth trajectory propagation model is expressed using the following formula:

[0025]

[0026] Among them, i represents the spatial unit number, S i is the number of susceptible individuals in spatial unit i, E i is the number of exposed persons in spatial unit i, I i is the number of infected people in spatial unit i, R i is the number of recovered persons in space unit i, N i =S i +E i +I i +R i , N i is the total population of spatial unit i, β i is the infection rate of spatial unit i, σ is the conversion rate from latent to infected, and γ is the recovery rate of infected.

[0027] In another aspect of the present invention, preferably,

[0028] The epidemic spread model is expressed by the following formula:

[0029]

[0030] Among them, M+1 represents the total number of spatial units, M ij represents the population migration rate from spatial unit j to spatial unit i, i, j represents the spatial unit number, S i is the number of susceptible individuals in spatial unit i, S j is the number of susceptible individuals in spatial unit j, E i is the number of exposed persons in spatial unit i, E j is the number of exposed persons in spatial unit j, I i is the number of infected people in spatial unit i, R j is the number of recovered persons in space unit j, N j is the total population of spatial unit j, β j is the infection rate of spatial unit j, σ is the conversion rate from latent to infected, and γ is the recovery rate of infected.

[0031] In another aspect of the present invention, preferably, using the case growth trajectory propagation model and the epidemic spread model to predict the epidemic spread path includes:

[0032] Calculate the sum of the infection probabilities of spatial unit i from all other spatial units j:

[0033]

[0034] Among them, InfectionRate i (t) represents the sum of the infection probabilities of spatial unit i from all other spatial units j at time t, I j (t) represents the number of infected persons in space unit j at time t; M ij represents the population migration rate from spatial unit j to spatial unit i, β j is the infection rate of spatial unit j, N j is the total population of spatial unit j; M+1 represents the total number of spatial units;

[0035] The number of susceptible people, exposed people, infected people, and recovered people in spatial unit i is updated using the following formula:

[0036]

[0037] Among them, S j is the number of susceptible individuals in spatial unit j, E j is the number of exposed persons in spatial unit j, I j is the number of infected people in spatial unit j, R jis the number of recovered persons in spatial unit j;

[0038] Based on the updated number of susceptible people, exposed people, infected people and recovered people in the spatial unit, the spatial spread path of the epidemic is predicted.

[0039] In another aspect of the present invention, preferably, using the case growth trajectory propagation model and the epidemic spread model, predicting the spatiotemporal coverage change map includes:

[0040] A spatiotemporal coverage change map is drawn based on the distribution of the predicted number of infected people in spatial units.

[0041] In another aspect of the present invention, preferably, the predicted epidemic spread path and the spatiotemporal coverage change map are integrated with the spatial data of key public places, and obtaining the epidemic impact index of key public places includes calculating the epidemic impact index of key public places using the following formula:

[0042]

[0043] Among them, II is the epidemic impact index, I is the predicted number of infected people, Flow is the average flow of people in key public places, ET is the average exposure time, Area is the area of ​​public places, and RC is the risk coefficient.

[0044] In another aspect of the present invention, preferably,

[0045] The analysis method also includes visualizing the predicted epidemic spread path, spatiotemporal coverage change map, and epidemic impact index of key public places according to the time axis;

[0046] The display of different data types is differentiated by color.

[0047] In another aspect of the present invention, preferably, a system for analyzing spatiotemporal clustering of infectious disease epidemics comprises:

[0048] Collection module: collects multi-source data related to the spread of infectious diseases;

[0049] Classification module: classifies the multi-source data using preset classification rules to obtain a data warehouse;

[0050] Construction module: using the data warehouse to construct a case growth trajectory propagation model and an epidemic spread model;

[0051] Prediction module: using the case growth trajectory propagation model and the epidemic spread model, predicting the epidemic spread path and the time-space coverage change map;

[0052] Calculation module: Integrate the predicted epidemic spread path and spatiotemporal coverage change map with the spatial data of key public places to calculate the epidemic impact index of key public places.

[0053] (III) Beneficial effects

[0054] The above technical solution of the present invention has the following beneficial technical effects:

[0055] The present invention can more accurately predict the spread path and spatiotemporal coverage changes of the epidemic by constructing a case growth trajectory propagation model and an epidemic spread diffusion model, providing a more scientific basis for prevention and control work. According to the predicted epidemic spread and the epidemic impact index of key public places, medical resources, manpower and materials can be more reasonably allocated to improve the efficiency of prevention and control. The present invention helps to establish and improve the infectious disease monitoring and early warning system, improve the accuracy of early warning, so as to take prevention and control measures in time to prevent the further spread of the epidemic. BRIEF DESCRIPTION OF THE DRAWINGS

[0056] Figure 1 It is a schematic diagram of the overall process of an embodiment of the present invention. DETAILED DESCRIPTION

[0057] In order to make the purpose, technical scheme and advantages of the present invention clearer, the present invention is further described in detail below in conjunction with specific embodiments and with reference to the accompanying drawings. It should be understood that these descriptions are only exemplary and are not intended to limit the scope of the present invention. In addition, in the following description, the description of well-known structures and technologies is omitted to avoid unnecessary confusion of the concept of the present invention.

[0058] Obviously, the described embodiments are only some embodiments of the present invention, but not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0059] In the description of the present invention, it should be noted that the terms “first”, “second” and “third” are only used for descriptive purposes and cannot be understood as indicating or implying relative importance.

[0060] In addition, the technical features involved in the different embodiments of the present invention described below can be combined with each other as long as they do not conflict with each other.

[0061] The present invention will be described in more detail below with reference to the accompanying drawings. In each of the accompanying drawings, the same elements are represented by similar reference numerals. For the sake of clarity, the various parts in the accompanying drawings are not drawn to scale.

[0062] Embodiment 1

[0063] A method for analyzing the spatiotemporal clustering of infectious disease outbreaks. Figure 1 FIG. 1 shows an overall flow chart of an embodiment of the present invention, as shown in FIG. Figure 1 As shown, including:

[0064] Collect multi-source data related to the spread of infectious diseases; multi-source data may include infectious disease case data, epidemic point data, epidemiological survey data, transmission path data, and key public space data;

[0065] The multi-source data is classified and processed using a preset classification rule to obtain a data warehouse; optionally, in this embodiment, the multi-source data is classified and processed using a preset classification rule includes:

[0066] Classifying the multi-source data according to different case types to obtain first classified data;

[0067] The first classified data is classified by spatial position to obtain second classified data; the spatial position can be classified according to monitoring points, such as setting four typical monitoring points, including: acute respiratory infectious disease multi-source monitoring sentinel hospital, diarrheal disease comprehensive monitoring sentinel hospital, domestic sewage monitoring point, sewage treatment plant monitoring point;

[0068] Classifying the first classified data according to the time dimension to obtain third classified data;

[0069] Clustering the second classified data and the third classified data to obtain a data warehouse, wherein the data in the data warehouse includes a correspondence between a time dimension and a spatial position;

[0070] Using the data warehouse, construct a case growth trajectory transmission model and an epidemic spread model;

[0071] The analysis method also includes: modifying the epidemic spread model;

[0072] The amendments include:

[0073] Obtaining in real time the time difference between two adjacent sampling time nodes and the number of case changes within the time difference;

[0074] According to the time difference and the corresponding number of case changes, a statistical trend of the time change of the epidemic is formed;

[0075] The time-varying statistical trend is used to revise the epidemic spread model.

[0076] Furthermore, in this embodiment, the case growth trajectory propagation model is expressed using the following formula:

[0077]

[0078] Among them, i represents the spatial unit number, S i is the number of susceptible individuals in spatial unit i, E i is the number of exposed persons in spatial unit i, I i is the number of infected people in spatial unit i, R i is the number of recovered persons in space unit i, N i =S i +E i +I i +R i , N i is the total population of spatial unit i, β i is the infection rate of spatial unit i, σ is the conversion rate from latent to infected, and γ is the recovery rate of infected.

[0079] Further, in this embodiment,

[0080] The epidemic spread model is expressed by the following formula:

[0081]

[0082] Among them, M+1 represents the total number of spatial units, M ij represents the population migration rate from spatial unit j to spatial unit i, i, j represents the spatial unit number, S i is the number of susceptible individuals in spatial unit i, S j is the number of susceptible individuals in spatial unit j, E i is the number of exposed persons in spatial unit i, E j is the number of exposed persons in spatial unit j, I i is the number of infected people in spatial unit i, R j is the number of recovered persons in space unit j, N j is the total population of spatial unit j, β j is the infection rate of spatial unit j, σ is the conversion rate from latent to infected, and γ is the recovery rate of infected.

[0083] Furthermore, in this embodiment, using the case growth trajectory transmission model and the epidemic spread model, predicting the epidemic spread path includes:

[0084] Calculate the sum of the infection probabilities of spatial unit i from all other spatial units j:

[0085]

[0086] Among them, InfectionRate i (t) represents the sum of the infection probabilities of spatial unit i from all other spatial units j at time t, I j(t) represents the number of infected persons in space unit j at time t; M ij represents the population migration rate from spatial unit j to spatial unit i, β j is the infection rate of spatial unit j, N j is the total population of spatial unit j; M+1 represents the total number of spatial units;

[0087] The number of susceptible people, exposed people, infected people, and recovered people in spatial unit i is updated using the following formula:

[0088]

[0089] Among them, S j is the number of susceptible individuals in spatial unit j, E j is the number of exposed persons in spatial unit j, I j is the number of infected people in spatial unit j, R j is the number of recovered persons in spatial unit j;

[0090] Based on the updated number of susceptible people, exposed people, infected people and recovered people in the spatial unit, the spatial spread path of the epidemic is predicted.

[0091] Furthermore, in this embodiment, the case growth trajectory propagation model and the epidemic spread model are used to predict the spatiotemporal coverage change graph, including:

[0092] A spatiotemporal coverage change map is drawn based on the distribution of the predicted number of infected people in spatial units. The spatiotemporal coverage change map not only shows the spread of the epidemic in different spatial units, but also reveals the dynamic process of the spread of the epidemic through the presentation of time series. This is crucial for evaluating the epidemic impact index of key public places at different time points. By comparing with the spatiotemporal coverage change map, it is possible to more accurately determine which occasions may face a higher epidemic risk at what time.

[0093] Furthermore, in this embodiment, the predicted epidemic spread path and the spatiotemporal coverage change map are integrated with the spatial data of key public places to obtain the epidemic impact index of key public places, including calculating the epidemic impact index of key public places using the following formula:

[0094]

[0095] Among them, II is the epidemic impact index, I is the predicted number of infected people, Flow is the average flow of people in key public places, ET is the average exposure time, Area is the area of ​​the public place, and RC is the risk coefficient. Among them, I represents the direct epidemic pressure that the public place may face. Flow is the average flow of people in key public places, which reflects the density of the crowd in the place. The larger the flow of people, the higher the potential risk of infection. ET is the average exposure time, that is, the average time people stay in the public place. The longer the exposure time, the higher the risk of infection. Area is the area of ​​the public place. The larger the area, the more likely it is to disperse the density of the crowd, but it also needs to consider the difficulty of management and monitoring. RC is the risk coefficient, which is a weighted coefficient that combines multiple factors (such as the ventilation conditions of the place, sanitary conditions, and personnel protection awareness, etc.) to adjust the final epidemic impact index. It can be confirmed based on the types of key public places such as hospitals, schools, shopping malls, transportation hubs, etc., the corresponding historical flow data, and special control requirements during the epidemic.

[0096] Utilize the case growth trajectory transmission model and epidemic spread model to predict the epidemic spread path and the time-space coverage change map;

[0097] The predicted epidemic spread path and spatiotemporal coverage change map are integrated with the spatial data of key public places to calculate the epidemic impact index of key public places.

[0098] The analysis method also includes visualizing the predicted epidemic spread path, spatiotemporal coverage change map, and epidemic impact index of key public places according to the time axis;

[0099] The display of different data types is differentiated by color.

[0100] The visualization method is to visualize the statistical information of infectious diseases through GIS technology. The visualization method includes the following steps: analyzing and displaying the geographical distribution patterns and spatial clustering of cases, including the analysis and data visualization display of the spatial location, spatial distribution, and diffusion path of the cases. Specifically, the longitude and latitude coordinate information corresponding to the case information and the corresponding disease area are obtained, and the disease area includes: communities, schools, etc.; a visualization method for geographic statistical data of disease areas and an algorithm for identifying spatial connectivity of diseases between regions are constructed. Different colors are used to distinguish and display the process of statistical visualization using three-dimensional spatial layers; an algorithm for identifying spatial connectivity of diseases between regions based on three-dimensional spatial layers is constructed; the visualization analysis results of the above steps in the data statistics process are constructed, and a data statistical graph is constructed, and then the data statistical graph is used for visualization.

[0101] The visualization method includes constructing a visualization model of the spatiotemporal distribution of the epidemic, wherein the construction of the visualization model of the spatiotemporal distribution of the epidemic includes: performing spatiotemporal aggregation on the data of each case, and establishing a data grid for the case according to the spatial position and time point.

[0102] The visualization method further includes: visually displaying data of different dimensions according to the query content input by the user, wherein the visualization display method is to display data models of different time dimensions.

[0103] The above method is used to display case data information, and at the same time, combined with the spatial location data of the case, visualization processing is performed to display the two-dimensional data layer and the three-dimensional data layer of the case, and finally the spatial location data of the case is visualized; after building the data warehouse, the collected data is optimized, and a data model is established for the above data, wherein the data processing specifically includes: according to the system database, by analyzing the transmission pathways of epidemic diseases, combining map analysis to obtain abnormal distribution data sets of spatial data of four typical monitoring points, combining the obtained system information to realize the visualization of data in different spatial location areas, for example: by analyzing the case distribution in the area where the four types of monitoring points are located, the different time stages of the area are displayed, the connectivity between the number of cases and patients in the area is analyzed, and the spatial connectivity of different areas is displayed through spatial grids.

[0104] Displaying case data includes obtaining case data information that needs to be displayed; constructing a case data information display model, wherein the case data information display model is a simulation model, and the case data constructed in the simulation model has an exponential calculation relationship with the epidemic point location of the case; based on the simulation model, constructing a spatiotemporal aggregation analysis layer for the spatial information of the epidemic point; specifically: analyzing and displaying the spatial location information of the patient case, obtaining the spatial information of the epidemic point corresponding to the epidemic transmission path information, and describing the data or the visualization information of the data status through different colors, wherein the above-mentioned visualization information of the data or the data status with different colors is visualized as a plurality of display layers, and the color distribution index of the spatial data of different monitoring points in the area is displayed; performing spatiotemporal aggregation analysis on the three-dimensional layer data of each case, and analyzing and displaying the data for each characteristic data according to the analysis results of the case number.

[0105] The present invention can more accurately predict the spread path and spatiotemporal coverage changes of the epidemic by constructing a case growth trajectory propagation model and an epidemic spread diffusion model, providing a more scientific basis for prevention and control work. According to the predicted epidemic spread and the epidemic impact index of key public places, medical resources, manpower and materials can be more reasonably allocated to improve the efficiency of prevention and control. The present invention helps to establish and improve the infectious disease monitoring and early warning system, improve the accuracy of early warning, so as to take prevention and control measures in time to prevent the further spread of the epidemic.

[0106] Embodiment 2

[0107] An analysis system for the spatiotemporal clustering of infectious disease epidemics, comprising:

[0108] Collection module: collects multi-source data related to the spread of infectious diseases;

[0109] Classification module: classifies the multi-source data using preset classification rules to obtain a data warehouse;

[0110] Construction module: using the data warehouse to construct a case growth trajectory propagation model and an epidemic spread model;

[0111] Prediction module: using the case growth trajectory propagation model and the epidemic spread model, predicting the epidemic spread path and the time-space coverage change map;

[0112] Calculation module: Integrate the predicted epidemic spread path and spatiotemporal coverage change map with the spatial data of key public places to calculate the epidemic impact index of key public places.

[0113] It should be understood that the above specific embodiments of the present invention are only used to illustrate or explain the principles of the present invention, and do not constitute a limitation of the present invention. Therefore, any modifications, equivalent substitutions, improvements, etc. made without departing from the spirit and scope of the present invention should be included in the protection scope of the present invention. In addition, the appended claims of the present invention are intended to cover all changes and modifications that fall within the scope and boundaries of the appended claims, or the equivalent forms of such scope and boundaries.

[0114] The present invention has been described above with reference to the embodiments of the present invention. However, these embodiments are for illustrative purposes only and are not intended to limit the scope of the present invention. The scope of the present invention is defined by the appended claims and their equivalents. Without departing from the scope of the present invention, a person skilled in the art may make various substitutions and modifications, which should all fall within the scope of the present invention.

[0115] Although the embodiments of the present invention have been described in detail, it should be understood that the various changes, substitutions, and alterations could be made hereto without departing from the spirit and scope of the invention.

[0116] Obviously, the above embodiments are merely examples for the purpose of clear explanation, and are not intended to limit the implementation methods. For those skilled in the art, other different forms of changes or modifications can be made based on the above description. It is not necessary and impossible to list all the implementation methods here. The obvious changes or modifications derived therefrom are still within the scope of protection of the invention.

Claims

1. A method for analyzing the spatiotemporal clustering of infectious disease epidemics, characterized in that: include: Collect multi-source data related to the spread of infectious diseases; Classify the multi-source data using preset classification rules to obtain a data warehouse; Using the data warehouse, construct a case growth trajectory transmission model and an epidemic spread model; Utilize the case growth trajectory transmission model and epidemic spread model to predict the epidemic spread path and the time-space coverage change map; The predicted epidemic spread path and spatiotemporal coverage change map are integrated with the spatial data of key public places to calculate the epidemic impact index of key public places.

2. The analysis method according to claim 1, characterized in that Classifying the multi-source data using preset classification rules includes: Classifying the multi-source data according to different case types to obtain first classified data; Classifying the first classified data by spatial position to obtain second classified data; Classifying the first classified data according to the time dimension to obtain third classified data; The second classified data and the third classified data are clustered to obtain a data warehouse, wherein the data in the data warehouse includes a correspondence between a time dimension and a spatial position.

3. The analysis method according to claim 1, characterized in that The analysis method also includes: modifying the epidemic spread model; The amendments include: Obtaining in real time the time difference between two adjacent sampling time nodes and the number of case changes within the time difference; Based on the time difference and the corresponding number of case changes, a statistical trend of the time change of the epidemic is formed; The time-varying statistical trend is used to revise the epidemic spread model.

4. The analysis method according to claim 1, characterized in that The case growth trajectory propagation model is expressed using the following formula: Among them, i represents the spatial unit number, S i is the number of susceptible individuals in spatial unit i, E i is the number of exposed persons in spatial unit i, I i is the number of infected people in spatial unit i, R i is the number of recovered persons in space unit i, N i =S i +E i +I i +R i , N i is the total population of spatial unit i, β i is the infection rate of spatial unit i, σ is the conversion rate from latent to infected, and γ is the recovery rate of infected.

5. The analysis method according to claim 4, characterized in that The epidemic spread model is expressed by the following formula: Among them, M+1 represents the total number of spatial units, M ij represents the population migration rate from spatial unit j to spatial unit i, i, j represents the spatial unit number, S i is the number of susceptible individuals in spatial unit i, S j is the number of susceptible individuals in spatial unit j, E i is the number of exposed persons in spatial unit i, E j is the number of exposed persons in spatial unit j, I i is the number of infected people in spatial unit i, R j is the number of recovered persons in space unit j, N j is the total population of spatial unit j, β j is the infection rate of spatial unit j, σ is the conversion rate from latent to infected, and γ is the recovery rate of infected.

6. The analysis method according to claim 4, characterized in that Using the case growth trajectory transmission model and the epidemic spread model, the epidemic spread paths are predicted to include: Calculate the sum of the infection probabilities of spatial unit i from all other spatial units j: Among them, InfectionRate i (t) represents the sum of the infection probabilities of spatial unit i from all other spatial units j at time t, I j (t) represents the number of infected persons in space unit j at time t; M ij represents the population migration rate from spatial unit j to spatial unit i, β j is the infection rate of spatial unit j, N j is the total population of spatial unit j; M+1 represents the total number of spatial units; The number of susceptible people, exposed people, infected people, and recovered people in spatial unit i is updated using the following formula: Among them, S j is the number of susceptible individuals in spatial unit j, E j is the number of exposed persons in spatial unit j, I j is the number of infected people in spatial unit j, R j is the number of recovered persons in spatial unit j; Based on the updated number of susceptible people, exposed people, infected people and recovered people in the spatial unit, the spatial spread path of the epidemic is predicted.

7. The analysis method according to claim 6, characterized in that Using the case growth trajectory transmission model and the epidemic spread model, the predicted spatiotemporal coverage change graph includes: A spatiotemporal coverage change map is drawn based on the distribution of the predicted number of infected people in spatial units.

8. The analysis method according to claim 7, characterized in that The predicted epidemic spread path and the time-space coverage change map are integrated with the spatial data of key public places to obtain the epidemic impact index of key public places, including using the following formula to calculate the epidemic impact index of key public places: Among them, II is the epidemic impact index, I is the predicted number of infected people, Flow is the average flow of people in key public places, ET is the average exposure time, Area is the area of ​​public places, and RC is the risk coefficient.

9. The analysis method according to claim 1, characterized in that The analysis method also includes visualizing the predicted epidemic spread path, spatiotemporal coverage change map, and epidemic impact index of key public places according to the time axis; The display of different data types is differentiated by color.

10. An analysis system for the spatiotemporal clustering of infectious disease epidemics, characterized in that: include: Collection module: collects multi-source data related to the spread of infectious diseases; Classification module: classifies the multi-source data using preset classification rules to obtain a data warehouse; Construction module: using the data warehouse to construct a case growth trajectory propagation model and an epidemic spread model; Prediction module: using the case growth trajectory propagation model and the epidemic spread model, predicting the epidemic spread path and the time-space coverage change map; Calculation module: Integrate the predicted epidemic spread path and spatiotemporal coverage change map with the spatial data of key public places to calculate the epidemic impact index of key public places.

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