Evaluation and modeling of infectious disease spatiotemporal intervention based on mobile phone signaling data

Abstract

The application discloses a method for evaluating and modeling infectious disease space-time intervention based on mobile phone signaling data, comprising the following steps: obtaining an initial population flow matrix of each sub-region through mobile phone signaling data; constructing a SEIRQ model and simulating the propagation process of a pathogen; obtaining intervention measures, combining and optimizing the intervention measures to obtain a plurality of combined measures; obtaining an optimal combined measure based on the SEIRQ model and the plurality of combined measures, and obtaining a final population flow matrix after simulation implementation of the optimal combined measure; comparing the initial population flow matrix with the final population flow matrix after simulation implementation of the optimal combined measure, and evaluating the space-time intervention effect of the infectious disease based on the mobile phone signaling data. The model provided by the application can more accurately capture the transmission differences between different regions and the local outbreak of the epidemic disease, and can visualize the spatial evolution of the epidemic disease, thereby better guiding the formulation of epidemic disease control and intervention measures.

Description

Technical Field

[0001] This invention belongs to the field of infectious disease modeling, and in particular relates to a method for spatiotemporal intervention assessment and modeling of infectious diseases based on mobile phone signaling data. Background Technology

[0002] Emerging infectious disease pandemics have brought immense disasters to humanity, repeatedly impacting the course of human society. For example, the Black Death in the 14th century killed approximately 30%-60% of Europe's population; the Spanish flu of the early 20th century infected about a quarter of the world's population. Implementing various non-pharmaceutical interventions is an effective means of limiting the spread of epidemics. Although most countries generally respond proactively to the spread of infectious diseases, the degree of success in limiting infection and preventing pandemics varies greatly across regions, and interventions such as population movement restrictions impose considerable economic costs on society. Therefore, it is necessary to simulate and evaluate the characteristics of infectious diseases and the effectiveness of non-pharmaceutical interventions, which may provide insights and guidance for future decision-making in public health emergencies.

[0003] Simulation of infectious disease processes typically employs infectious disease dynamics models (compartmental models), which describe and predict the spread and evolution of infectious diseases within a population using mathematical equations and statistical methods. These models consider factors such as population structure, transmission modes, and transmission parameters. Therefore, they can effectively incorporate the intervention effects of measures such as public transportation restrictions and bans on public dining into the model. However, existing infectious disease dynamics models primarily focus on the overall trend of the study area, neglecting the variability in the effectiveness of interventions within that area. This results in relatively insufficient predictive power at spatial scales. Therefore, this invention urgently requires a spatiotemporal intervention assessment and modeling method for infectious diseases based on mobile phone signaling data to address the shortcomings of existing technologies. Summary of the Invention

[0004] To address the shortcomings of existing technologies, this invention proposes a method for assessing and modeling spatiotemporal interventions for infectious diseases based on mobile phone signaling data. This method utilizes mobile phone signaling data to accurately characterize the impact of intervention measures on high-resolution population travel networks, thereby improving the accuracy of simulation assessments of intervention measures.

[0005] To achieve the above objectives, this invention provides a method for spatiotemporal intervention assessment and modeling of infectious diseases based on mobile phone signaling data, comprising the following steps:

[0006] The initial population flow matrix for each sub-region is obtained using mobile signaling data;

[0007] Construct a SEIRQ model and simulate the transmission process of the pathogen;

[0008] Obtain intervention measures, combine and optimize the intervention measures to obtain several combined measures;

[0009] Based on the SEIRQ model and several of the combined measures, the optimal combined measures are obtained, and the final population flow matrix after the simulated implementation of the optimal combined measures is obtained.

[0010] The initial population flow matrix is ​​compared with the final population flow matrix after the simulation of the optimal combination of measures to evaluate the spatiotemporal intervention effect of infectious diseases based on mobile phone signaling data.

[0011] Optionally, obtaining the initial population flow matrix for each sub-region using the mobile signaling data includes:

[0012] Obtain the spatial unit scale, wherein the spatial unit scale is a street unit;

[0013] The study area is divided based on the street units to obtain several sub-regions;

[0014] The user trajectory is obtained by simulating population flow in each sub-region using the mobile phone signaling data;

[0015] Based on the user trajectory, the user stop points are extracted, and the total number of user trips between geographic grids is obtained. The geographic grids include departure grids and destination grids.

[0016] The total number of user trips between the geographic grids is aggregated into the total number of trips between each sub-region, and the initial population flow matrix of each sub-region is obtained.

[0017] Optionally, constructing the SEIRQ model and simulating the transmission process of the pathogen includes:

[0018] The SEIRQ model is used to generate population cells in each sub-region. The population cells include: susceptible cells, latent cells, infected cells, removal cells, and isolation cells.

[0019] Based on the susceptible compartment, when a susceptible person in the susceptible compartment is transferred to the latent compartment, pathogen detection is performed;

[0020] If the latent person in the latent chamber is positive, the latent person is transferred to the isolation chamber for isolation. If the latent person is not positive, the latent person is transferred to the infection chamber, and then to the removal chamber.

[0021] Optionally, the SEIRQ model includes: change information of the susceptible chambers, latent chambers, infected chambers, removal chambers, and isolation chambers in each sub-region;

[0022] The change information of the susceptible compartment is as follows:

[0023]

[0024] The changes in the hidden chamber are as follows:

[0025]

[0026] The changes in the infection chamber are as follows:

[0027]

[0028] The changes to the removed compartment are as follows:

[0029]

[0030] The changes to the isolation chamber are as follows:

[0031] Q i (t)=Q i (t-1)+E i (t-1)*ε (i，t)

[0032] Among them, S i (t) represents the population in the susceptible cell in region i at time t, E i (t) represents the number of people in the lurking cell in region i at time t. i (t) represents the number of infected individuals in the cell in region i at time t, R i Q(t) represents the number of people removed from the warehouse in region i at time t. i (t) represents the number of people in the isolated cells in region i at time t, N i Let i be the total population of region i. Let be the number of people leaving region i at time t. Let k be the number of people leaving region j at time k. (i，j) k (j，i) ε is the population mobility coefficient. (i，t) α is the unit isolation coefficient. i The infectivity β of infected individuals in region i i Let γ be the latency period in region i. i Let be the recovery rate in region i.

[0033] Optionally, the interventions include mobility control, pathogen detection, spatial unit isolation, and vaccine effectiveness.

[0034] Optionally, the intervention measures can be combined and optimized to obtain several combined measures, including:

[0035] The intervention measures are adjusted and optimized using a combined measures model, and implemented in the target sub-region according to a predetermined time to obtain several combined measures.

[0036] Optionally, the combined measures model includes quantitative information on viral infectivity, quantitative information on pathogen detection, quantitative information on vaccination effectiveness, and quantitative information on movement restrictions;

[0037] The viral infectivity quantitative information is as follows:

[0038]

[0039]

[0040]

[0041]

[0042] The pathogen detection quantification information is as follows:

[0043]

[0044]

[0045] The quantitative information on the effectiveness of the vaccination is as follows:

[0046] α_v i =α i *Eff

[0047] The quantitative information regarding the flow restriction is as follows:

[0048] k_control (i，j) =k (i，j) *C, C∈[0,1]

[0049] Where, α i The infectivity of infected individuals in region i. Let ε be the basic reproduction number of subregion i, D be the infectious disease cycle, Avec be the infectivity of each subregion, Ivec be the initial infection number of each subregion, Rvec be the number of recoveries in each subregion, M be the subregions of the study area, k be the number of subregions, and ε be the basic reproduction number of subregion i. (i，t) Where μ is the unit isolation coefficient and μ is the attenuation factor. α_v represents the decay days, C represents the flow restriction factor, and α_v represents the flow rate limitation factor. i k_control is used to measure the infectivity of infectious diseases after vaccination. (i，j) Let k be the flow coefficient between sub-regions i and j under control measures. (i，j) This represents the population mobility coefficient.

[0050] Optionally, the combined measures include: a first combined measure, a second combined measure, and a third combined measure;

[0051] The first combination of measures includes high-frequency pathogen detection, efficient vaccination, and loose movement control;

[0052] The second combination of measures includes low-frequency pathogen detection, moderate-efficiency vaccination, and moderate mobility control;

[0053] The third set of measures includes low-frequency pathogen detection, inefficient vaccination, and strict movement control.

[0054] Optionally, based on the SEIRQ model and several of the combined measures, the optimal combined measures are obtained, and the final population flow matrix after the simulated implementation of the optimal combined measures is obtained, including:

[0055] Obtain the parameters of the SEIRQ model, which include the infectivity of the infected person, the incubation period, and the recovery rate;

[0056] Using the parameters of the SEIRQ model, several combined measures are adjusted to obtain the optimal combined measures;

[0057] Using the optimal combination of measures and the mobile phone signaling data, the final population flow matrix after the simulated implementation of the optimal combination of measures is obtained.

[0058] The present invention has the following beneficial effects:

[0059] Compared to traditional infectious disease dynamics models, the model proposed in this invention can more accurately capture the differences in transmission between different regions and the local outbreaks of epidemics, and can visualize the spatial evolution of epidemics, thereby better guiding the formulation of epidemic control and intervention measures. Traditional SEIR models usually treat the population as a whole, while the model proposed in this invention can model at different spatial scales and uses mobile signaling to integrate into the SEIRQ model, taking into account the spatial flow of population and incorporating geographical information such as population flow patterns into the model, which can better characterize the spatial spread and evolution of epidemics. This invention also integrates non-pharmaceutical interventions into the model, which will consider the degree, timing and effect of these measures, evaluate the effectiveness of non-pharmaceutical interventions, and thus provide more accurate prediction and decision support. Attached Figure Description

[0060] The accompanying drawings, which form part of this application, are used to provide a further understanding of this application. The illustrative embodiments and descriptions of this application are used to explain this application and do not constitute an undue limitation of this application. In the drawings:

[0061] Figure 1 This is a flowchart illustrating the spatiotemporal intervention assessment and modeling method for infectious diseases based on mobile phone signaling data, as described in an embodiment of the present invention.

[0062] Figure 2 This is a flowchart of mobile phone signaling data processing proposed in an embodiment of the present invention;

[0063] Figure 3 This is a schematic diagram of the SEIRQ model framework proposed in an embodiment of the present invention;

[0064] Figure 4 This is a framework diagram of the combined measures proposed in the embodiments of the present invention;

[0065] Figure 5 These are infectious disease curves under different combinations of measures proposed in the embodiments of the present invention;

[0066] Figure 6 This is an infectious disease curve diagram under normal scenarios and normal model simulation proposed in the embodiments of the present invention. Detailed Implementation

[0067] It should be noted that, unless otherwise specified, the embodiments and features described in the present invention can be combined with each other. The present application will now be described in detail with reference to the accompanying drawings and embodiments.

[0068] It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions, and although a logical order is shown in the flowchart, in some cases the steps shown or described may be executed in a different order than that shown here.

[0069] Existing infectious disease dynamics models typically treat the population as a whole, ignoring spatial details and differences. This means that the models cannot accurately capture the variations in transmission between different regions and the local outbreaks of epidemics. This invention presents a spatially fine-grained infectious disease dynamics model with a street-level spatial scale. Simulations using the SEIRQ (Susceptible Exposed Infected Removed-Quarantined) model are performed on each spatial unit. Figure 1 As shown, this embodiment provides a method for spatiotemporal intervention assessment and modeling of infectious diseases based on mobile phone signaling data, specifically including the following:

[0070] Step 1: Divide the study area into several sub-study areas, extract the starting location points of mobile phone signaling data and aggregate them into the sub-study areas to form a population flow matrix.

[0071] like Figure 2As shown, population flow in each sub-study area is simulated using mobile phone signaling data, specifically including the following:

[0072] (1) Simulate population flow in each sub-study area based on mobile phone signaling data to obtain user trajectories.

[0073] (2) Based on the user trajectory, extract the user's dwell points (500-meter buffer zone and areas where the user has stayed for more than 30 minutes) using a threshold method. The specific formula is as follows:

[0074] L = {l1, l2, l3, ..., l n} (1)

[0075] l i =(Lng x Lat x , t i ), 1≤i≤m (2)

[0076]

[0077]

[0078] Where L represents the stopping path, l1, l2, l3, ..., l n For all stops along the stop path, l i l i+1 Represents the point of stay, Lng x Lat x For the accuracy of displacement at the dwell point, t i To record the stopping point l i The time, x is the time when the stop point position exceeds the threshold for movement, m is the number of stop points, ω d ω is the spatial threshold. t As a time threshold, Lng y Lat y For the accuracy of the dwell point without displacement, t i+1 To record the stopping point l i+1 The time y represents the time during which movement does not occur beyond the designated stopping point position. In this study, the spatial threshold ω... d Given a distance of 500m and a time threshold ω of 30 minutes, starting from the stop point l i To stop point l i+1 It was considered a trip.

[0079] Based on the original data format, the threshold method is used to define movement between two consecutive stops (departure point and destination point), every 30 minutes, including the total number of user trips between every two geographic grids (departure point grid and destination point grid).

[0080] (3) Aggregate the total number of user trips between geographic grids into the total number of trips between each subspace unit;

[0081] (4) The data is processed into a population flow matrix representing the inter-regional population movement, serving as the baseline scenario for the flow coefficient. The population flow matrix is ​​shown in Table 1, where S... 1，1 S represents the total number of population trips from sub-study area 1 to sub-study area 1. i，1 S represents the total number of population trips from sub-study area i to sub-study area 1. 1，i S represents the total number of trips made by the population from sub-study area 1 to sub-study area i. i，i This represents the total number of population trips from sub-study area i to sub-study area i.

[0082] Table 1

[0083]

[0084] Step 2: Construct the SEIRQ model. The SEIRQ model generates five population cells (susceptible cell, latent cell, infected cell, removal cell, and isolation cell) in each sub-study area. At this stage, the model summarizes model parameters such as the infectivity of infected individuals, incubation period, and recovery rate.

[0085] like Figure 3 As shown, the SEIRQ model process is first divided into the following three stages:

[0086] Initialization Phase: The study area is divided into sub-regions based on spatial unit scale (street). Population initialization is performed in each sub-region, dividing the population into five isolation zones: susceptible individuals (S), latent individuals (E), infected individuals (I), removed individuals (R), and isolated individuals (Q), while considering the impact of control measures.

[0087] The infection process simulation phase: Infection begins with the initial case in each sub-region, i.e., from a susceptible individual (S). Susceptible individuals (S) are transferred to latent individuals (E) and then tested for pathogens. Latent individuals (E) who test positive are transferred to the isolated individuals (Q) for isolation, while the remaining latent individuals are transferred to the infected individuals (I), and then to the removed individuals (R). Subsequently, the population within the sub-region moves between susceptible individuals (S) and latent individuals (E) according to a population mobility coefficient.

[0088] Cyclic Simulation Phase: Following the workflow of the above-described infectious disease simulation phase, the simulation is continuously repeated to simulate the spread and evolution of the epidemic. Each cyclical simulation allows for timely adjustments to intervention measures as needed, such as adjusting the population mobility coefficient to reduce population movement, thereby influencing the spatial and temporal spread of the infectious disease.

[0089] SEIRQ model formula:

[0090] The changes in the susceptible population cells are determined by the number of susceptible individuals, infected individuals, and the number of people flowing in and out of the area at the previous moment; the changes in the latent population cells are determined by the number of latent individuals, symptomatic individuals, infected individuals, and the isolation coefficient at the previous moment; the changes in the infected population cells are determined by the number of symptomatic individuals, latent individuals, and recovered individuals at the previous moment; the changes in the removed population cells are determined by the number of recovered individuals and symptomatic individuals at the previous moment; the changes in the isolated population cells are determined by the number of isolated individuals and infected individuals at the previous moment. The specific formulas for the changes in population cells in each sub-region are as follows:

[0091]

[0092]

[0093]

[0094]

[0095] Q i (t)=Q i (t-1)+E i (t-1)*ε (i，t) (9)

[0096] SEIRQ model parameters:

[0097] The meanings of the symbolic parameters describing the above dynamic model are shown in Table 2.

[0098] Table 2

[0099]

[0100] Step 3: The sub-region model, i.e., the SEIRQ model, begins its dynamic evolution and is adjusted in conjunction with control measures, including mobility control, pathogen detection, spatial unit isolation, and vaccine effectiveness. For example... Figure 6 As shown, the effectiveness of the model is evaluated by comparing real case data with model simulation data.

[0101] Step 4: Based on the baseline measures, adjust and optimize the control measures by implementing different control measures daily, weekly, monthly and yearly for specific sub-regions to form different combinations of measures, in order to assess the impact of control measures on the infectious disease curve.

[0102] This embodiment proposes a comprehensive framework for combining interventions to optimize the application of non-pharmaceutical interventions in disease control. Based on the infectious disease dynamics model of this invention, the framework aims to find the optimal combination of interventions to minimize disease transmission and impact.

[0103] Framework diagram of combined measures as follows Figure 4 As shown, different control measures can be implemented daily, weekly, monthly, and yearly for specific sub-regions, forming various combinations. This allows for the study and comparison of the impact of different combinations of control measures on the epidemic's spread trend. This flexible approach helps policymakers better formulate and adjust prevention and control strategies to address epidemics at different stages and in different regions. Figure 5 As shown, the curves of infectious diseases under different combinations of measures are illustrated, and the optimal combination of intervention measures can be found by comprehensively evaluating the intervention measures and the degree of harm of the infectious disease.

[0104] The combination of measures is formulated based on existing effective measures and strategies adopted by the government in infectious disease prevention and control, and takes into account relevant measures and strategies that can be quantified using data such as mobile phone data. These measures are quantified using model formulas and combined with this technical solution. The quantification formulas are as follows:

[0105] Viral infectivity:

[0106]

[0107]

[0108]

[0109]

[0110] Pathogen detection:

[0111]

[0112]

[0113] Vaccination efficacy rate:

[0114] α_v i =α i *Eff (16)

[0115] Movement restrictions:

[0116] k_control (i，j) =k (i，j) *C, C∈[0,1] (17)

[0117] in, Let ε be the basic reproduction number of subregion i, D be the infectious disease cycle, Avec be the infectivity of each sub-study region, Ivec be the initial infection number of each sub-study region, Rvec be the number of recoveries in each sub-study region, M be the sub-study regions of the study region, k be the number of sub-study regions, and ε be the basic reproduction number of subregion i. (i，t) Let μ be the unit isolation coefficient, set to 1, and μ be the attenuation factor. α_v represents the decay days, C represents the flow restriction factor, and α_v represents the flow restriction factor. i k_control is used to measure the infectivity of infectious diseases after vaccination. (i，j) Let be the flow coefficient between subregions i and j under control measures.

[0118] The parameter settings for the combined measures are shown in Table 3. The degree of implementation of the measures is set according to the actual situation and the spread of the epidemic, including pathogen detection, vaccination, movement control and spatial unit isolation.

[0119] Table 3

[0120]

[0121] The following are some examples of practical combinations of measures. In real-world situations, more combinations of measures can be listed to evaluate the effectiveness of the measures in light of the current characteristics of the epidemic, assess the economic costs, and identify the combinations that minimize risk and loss.

[0122] Combined measures 1: High-frequency pathogen detection (1 day) + Highly effective vaccination (80%) + Loose movement control (20%)

[0123] Under this measure, pathogen detection is frequent, allowing for timely identification and isolation of infected individuals in the event of an epidemic. Movement control is relatively relaxed, permitting a certain degree of population movement to maintain normal economic operations. This combination of measures is suitable when the epidemic is effectively controlled and vaccine resistance is good.

[0124] Combination Measure 2: Low-frequency pathogen detection (7 days) + moderately effective vaccination (54.3%) + moderate mobility control (40-60%)

[0125] Under this measure, pathogen testing is conducted weekly to ensure screening for infected individuals at a low frequency. Vaccination efficacy is moderate, providing some level of immune protection. Mobility control measures are moderate, restricting population movement and reducing potential transmission risks. This combination of measures is suitable for situations where the epidemic is at a moderate level of control and vaccine resistance is moderate.

[0126] Combination Measure 3: Low-frequency pathogen testing (3 days) + Inefficient vaccination (30%) + Strict movement control (80%)

[0127] Under this measure, pathogen testing is conducted every three days to screen for potential infections. Vaccination efficacy is low, providing limited immune protection. Strict movement control measures restrict population movement to minimize transmission opportunities. This combination of measures is suitable for situations with severe epidemics and poor vaccine resistance.

[0128] Step 5: Evaluate the effectiveness of various control measures combinations and output the control combination algorithm and decision.

[0129] Using the parameters of the SEIRQ model, multiple combined measures are adjusted to select the optimal combination. Based on the optimal combination and mobile phone signaling data, the final population flow matrix after the simulated implementation of the optimal combination is obtained, and then compared with the initial population flow matrix to evaluate the spatiotemporal intervention effect of infectious diseases based on mobile phone signaling data.

[0130] The evaluation of interventions using the SEIRQ model includes the following:

[0131] The average population flow matrix for weekdays / weekends in a week before the intervention measures were implemented was used as the baseline for population flow.

[0132]

[0133] Among them, C i (t) represents the baseline population of the sub-region. This represents the amount of population movement between sub-regions when no intervention measures were implemented.

[0134] The intervention measures were evaluated by comparing the weekday / weekend population mobility matrix after the intervention measures were implemented with the population mobility baseline.

[0135]

[0136] The parameters for the dissemination process are set in conjunction with the policy promulgation time, including the following:

[0137] like Figure 6 As shown, taking Shenzhen as an example, the actual number of infections is simulated, based on the flow of mobile phone signaling data, to test different control measures scenarios. The parameters of non-pharmaceutical intervention measures are adjusted at different time points, including starting intervention measures 5 days in advance to test the current trend of the infectious disease curve; increasing / decreasing the intensity of intervention measures to test the current trend of the infectious disease curve.

[0138] This study comprehensively adjusts different non-pharmaceutical interventions to investigate their impact on the spread curve of infectious diseases. Different combinations of interventions will affect the trend of the infectious disease curve. Finally, vector data of sub-units (districts / streets / grids) of the study area are used to map the risk of infectious diseases in the study area at various time points during the spread process. In other words, by combining vector data, the spatial spread of infectious diseases is visualized, and the impact of combinations of non-pharmaceutical interventions on the spatial spread of infectious diseases is studied.

[0139] The above are merely preferred embodiments of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A method for assessing and modeling spatiotemporal interventions in infectious diseases based on mobile phone signaling data, characterized in that, Includes the following steps: The initial population flow matrix for each sub-region is obtained using mobile signaling data; Construct a SEIRQ model and simulate the transmission process of the pathogen; Obtain intervention measures, combine and optimize the intervention measures to obtain several combined measures; The intervention measures include mobility control, pathogen detection, spatial unit isolation, and vaccine effectiveness; The intervention measures are combined and optimized to obtain several combined measures, including: The intervention measures are adjusted and optimized using a combined measures model, and the intervention measures are implemented in the target sub-region according to a predetermined time to obtain several combined measures; The combined measures model includes quantitative information on viral infectivity, quantitative information on pathogen detection, quantitative information on vaccination effectiveness, and quantitative information on movement restrictions. The viral infectivity quantitative information is as follows: The pathogen detection quantification information is as follows: The quantitative information on the effectiveness of the vaccination is as follows: The quantitative information regarding the flow restriction is as follows: in, For the region The infectivity of infected individuals in China sub-region The basic reproduction number, For the infection cycle of infectious diseases, Indicates the infectivity of each sub-study area. This indicates the initial infection number in each sub-study region. This indicates the number of recoveries in each sub-study area. Indicates a sub-study area of ​​the study area. Indicates the number of sub-study areas. The unit isolation coefficient, As the attenuation factor, For decay days, As the flow limiting factor, To assess the transmissibility of infectious diseases after vaccination, Sub-regions under control measures , The flow coefficient between them This is the population mobility coefficient; The combined measures include: a first combined measure, a second combined measure, and a third combined measure; The first set of measures includes high-frequency nucleic acid testing, efficient vaccination, and loose movement control. The second combination of measures includes low-frequency nucleic acid testing, moderate-efficiency vaccination, and moderate movement control. The third set of measures includes lower-frequency nucleic acid testing, inefficient vaccination, and strict movement control. Based on the SEIRQ model and several of the combined measures, the optimal combined measures are obtained, and the final population flow matrix after the simulated implementation of the optimal combined measures is obtained. The initial population flow matrix is ​​compared with the final population flow matrix after the simulation of the optimal combination of measures to evaluate the spatiotemporal intervention effect of infectious diseases based on mobile phone signaling data.

2. The method for spatiotemporal intervention assessment and modeling of infectious diseases based on mobile phone signaling data as described in claim 1, characterized in that, Obtaining the initial population flow matrix of each sub-region using the mobile phone signaling data includes: Obtain the spatial unit scale, wherein the spatial unit scale is a street unit; The study area is divided based on the street units to obtain several sub-regions; The user trajectory is obtained by simulating population flow in each sub-region using the mobile phone signaling data; Based on the user trajectory, the user stop points are extracted, and the total number of user trips between geographic grids is obtained. The geographic grids include departure grids and destination grids. The total number of user trips between the geographic grids is aggregated into the total number of trips between each sub-region, and the initial population flow matrix of each sub-region is obtained.

3. The method for spatiotemporal intervention assessment and modeling of infectious diseases based on mobile phone signaling data as described in claim 1, characterized in that, Constructing the SEIRQ model and simulating the transmission process of the pathogen includes: The SEIRQ model is used to generate population cells in each sub-region. The population cells include: susceptible cells, latent cells, infected cells, removal cells, and isolation cells. Based on the susceptible compartment, when a susceptible person in the susceptible compartment is transferred to the latent compartment, pathogen detection is performed; If the latent person in the latent chamber is positive, the latent person is transferred to the isolation chamber for isolation. If the latent person is not positive, the latent person is transferred to the infection chamber, and then to the removal chamber.

4. The method for assessing and modeling spatiotemporal interventions in infectious diseases based on mobile phone signaling data as described in claim 3, characterized in that, The SEIRQ model includes: change information of the susceptible chambers, latent chambers, infected chambers, removal chambers, and isolation chambers in each sub-region; The change information of the susceptible compartment is as follows: The changes in the hidden chamber are as follows: The changes in the infection chamber are as follows: The changes to the removed compartment are as follows: The changes to the isolation chamber are as follows: in, for Always in the area The number of people in the vulnerable population cells. for Always in the area The number of people in the infiltrator's cell. for Always in the area The number of people in the infected wards. for Always in the area The number of people removed from the cell block. for Always in the area The number of people in the quarantine cells. For the region Total population for Time from the region The number of people leaving, For time from region The number of people leaving, , This is the population mobility coefficient. The unit isolation coefficient, For the region Infectivity of infected individuals in China For the region The incubation period in the middle, For the region The recovery rate.

5. The method for spatiotemporal intervention assessment and modeling of infectious diseases based on mobile phone signaling data as described in claim 1, characterized in that, Based on the SEIRQ model and several combined measures, the optimal combined measures are obtained, and the final population flow matrix after the simulated implementation of the optimal combined measures is obtained, including: Obtain the parameters of the SEIRQ model, which include the infectivity of the infected person, the incubation period, and the recovery rate; Using the parameters of the SEIRQ model, several combined measures are adjusted to obtain the optimal combined measures; Using the optimal combination of measures and the mobile phone signaling data, the final population flow matrix after the simulated implementation of the optimal combination of measures is obtained.

Images