New infectious disease multi-stage strategy optimization method for balancing health and economy

Abstract

The invention relates to a new infectious disease multi-stage strategy optimization method for balancing health and economy, and the method comprises the steps: representing population flow on different land utilization through introducing noctilucence for land utilization; a land utilization perception infectious disease model is used for simulating and evaluating single or combined management and control measures of different land parcels based on a health dimension, and an input-output economic disaster influence model is used for simulating and evaluating single or combined management and control measures of different land parcels based on an economic dimension. And based on two dimensions of target optimization health and economy of different stages of the new infectious disease, obtaining multi-stage optimal countermeasures. Therefore, the problems that existing direct population flow data are missing, so that simulation is difficult, and health and economic loss are difficult to balance in different stages of infectious disease pandemic are solved.

Description

Technical Field

[0001] This application relates to the technical field of response strategies for emerging infectious diseases, and particularly to a method for optimizing multi-stage strategies for emerging infectious diseases that balances health and economy. Background Art

[0002] Regarding the response strategies for emerging infectious diseases, spatial targeted control measures can balance the comprehensive consideration of the two dimensions of economy and health. In the health dimension, individual infectious disease models can focus on fine spatial scales, but when simulating large-scale infection epidemics, they require a large amount of parallel computing, thus limiting the simulation scope and scale by computing power. In addition, introducing a social contact matrix that depicts the heterogeneous contact frequencies of infectious disease transmission between different age groups and locations into the infectious disease compartment model can also simulate and quantify the effects of spatial targeted control measures, but it relies on surveys that are costly and usually have statistical biases.

[0003] In addition to health impacts, optimal measures should also consider the risk assessment of economic shocks. Existing technologies have improved traditional compartment models by introducing additional variables such as the degree of epidemic mitigation and the level of economic development, and constructed hybrid epidemic-economic models, but they can only reveal the interactions between disease transmission, epidemic management, and economic growth, and cannot provide guiding suggestions for spatial targeted control measures; in addition, existing technologies can also use input-output economic models to evaluate the economic impacts of epidemic intervention measures, but they only focus on economic impacts, so they face challenges in seamless integration with epidemiological simulations.

[0004] In summary, the lack of existing direct population flow data makes simulation difficult, and it is difficult to balance health and economic losses in different stages of the pandemic, and it is impossible to maintain social and economic stability and people's lives and safety in the early control stage and the later liberalization stage of emerging infectious diseases, which urgently needs to be solved. Summary of the Invention

[0005] This application provides a method for optimizing multi-stage strategies for emerging infectious diseases that balances health and economy to solve problems such as the lack of existing direct population flow data, which makes simulation difficult, and it is difficult to balance health and economic losses in different stages of the pandemic.

[0006] An optimization method for a multi-stage strategy of emerging infectious diseases that balances health and economy according to an embodiment of the first aspect of the present application includes the following steps: constructing daily land use-based night light time series data and case report correction data for a target research area; constructing a mobile spatial network emerging infectious disease model based on the night light time series data, the case report correction data, and a preset target traditional emerging infectious disease model, and based on the control objectives of emerging infectious diseases at different stages, simulating and evaluating different types of preset control measures in terms of health through the mobile spatial network emerging infectious disease model to obtain the health benefits corresponding to the different types of control measures; constructing an input-output economic disaster assessment model for the emerging infectious diseases, and using the input-output economic disaster assessment model to simulate and evaluate the different types of control measures in terms of economy to obtain the economic benefits corresponding to the different types of control measures; and respectively determining the target control measures for the emerging infectious diseases at the different stages based on the health benefits, the economic benefits, and the control objectives at the different stages.

[0007] Optionally, in an embodiment of the present application, the constructing of the daily land use-based night light time series data and the case report correction data for the target research area includes: obtaining a remote sensing night light data set for the target research area, and separating monthly land use-based night light data from the remote sensing night light data set based on a preset endmember-oriented time linear unmixing strategy, and decomposing the monthly night light data into different land use types; subtracting the immigration and emigration indices of a preset Baidu migration scale index, normalizing the result of the subtraction of the immigration and emigration indices, and allocating the monthly land use-based night light data to the daily land use-based night light time series data; obtaining the case report data for the target research area, and performing confirmation number correction and recovery number correction operations on the case report data to obtain the case report correction data.

[0008] Optionally, in one embodiment of the present application, a mobile space network emerging infectious disease model is constructed based on the night light time series data, the case report correction data and the preset target traditional emerging infectious disease model, and based on the control objectives of emerging infectious diseases at different stages, the mobile space network emerging infectious disease model is used to perform a simulation evaluation of the health dimension of preset different types of control measures to obtain the health benefits corresponding to the different types of control measures, including: based on the night light time series data, determining the change in night light of land use, and converting the change in night light of land use into the mobile space network emerging infectious disease model; under the preset early spread trend of the epidemic, determining the model parameters corresponding to the mobile space network emerging infectious disease model by optimizing and fitting the real epidemic curve; based on the model parameters, adjusting the night light time series data on different land uses to simulate the changes in population mobility across land uses corresponding to multiple scenarios, and combining the changes in population mobility across land uses and the preset attenuation contact infection rate parameters to evaluate the impact of the different types of control measures, wherein the multiple scenarios Including scenarios of completely restricting population mobility across land uses, completely liberalizing population mobility across land uses, restricting population mobility across some land uses, and opening population mobility across some land uses; based on preset simulation time and simulation time interval, simulating the different types of control measures to obtain the shortest duration of effective epidemic control corresponding to each type of control measure; determining the epidemic endpoint according to the preset maximum curvature strategy, and using the epidemic endpoint and the shortest duration to compare the epidemic infection trends under different scenarios to obtain scenario comparison results; in the early control stage of emerging infectious diseases at the different stages, converting the epidemic infection trends under the different scenarios into monetized health benefits in the socioeconomic dimension; in the late liberalization stage of emerging infectious diseases at the different stages, by implementing any type of control measures among the different types of control measures or implementing comprehensive liberalization measures in each of the preset multiple experimental stages, the coverage information of the maximum medical resources in the target study area to the target medical resources demand is estimated, and the health benefits corresponding to the different types of control measures are obtained based on the coverage information.

[0009] Optionally, in an embodiment of the present application, building the input-output economic disaster assessment model for the newly emerging infectious disease, and using the input-output economic disaster assessment model to simulate and evaluate different types of control measures from an economic dimension to obtain the economic benefits corresponding to different types of control measures, including: adjusting the value-added loss rate and simulation interval of the target traditional input-output model, and reorganizing multiple preset land use type departments in the target traditional input-output model to build the input-output economic disaster assessment model; evaluating the control economic shock losses and control economic reconstruction losses corresponding to different types of control measures through the input-output economic disaster assessment model, and using the control economic shock losses and the control economic reconstruction losses to determine the economic benefits corresponding to different types of control measures.

[0010] Optionally, in an embodiment of the present application, determining the target control measures for the newly emerging infectious disease in different stages based on the health benefits, the economic benefits, and the control objectives in different stages, including: building an objective function corresponding to the early control stage of the newly emerging infectious disease based on the health benefits, the economic benefits, and the control objectives in different stages, so as to determine the target control measures in the early control stage of the newly emerging infectious disease through the objective function; obtaining at least one liberalization scenario that meets the requirement of sufficient medical resource supply in the later liberalization stage of the newly emerging infectious disease, and determining the economic losses corresponding to each liberalization scenario in the at least one liberalization scenario; comparing the economic losses of each liberalization scenario to obtain a target liberalization scenario with the smallest economic loss, and determining the target control measures in the later liberalization stage of the newly emerging infectious disease corresponding to the target liberalization scenario.

[0011] Optionally, in an embodiment of the present application, the mathematical expression of the objective function is:

[0012]

[0013] TC(t) = EC(t) - HB(t)

[0014] where EC(t) represents the economic benefits, HB(t) represents the health benefits, R(t) represents the cost-benefit ratio, and TC(t) represents the total loss.

[0015] The second aspect of the embodiments of the present application provides a multi-stage strategy optimization device for new infectious diseases that weighs health and economy, including: a construction module for constructing daily land use-based nocturnal light time series data and case report correction data for the target research area; a health benefit evaluation module for constructing a mobile spatial network new infectious disease model based on the nocturnal light time series data, the case report correction data, and a preset target traditional new infectious disease model, and performing a health dimension simulation evaluation of different types of preset control measures through the mobile spatial network new infectious disease model based on the control objectives of new infectious diseases at different stages to obtain the health benefits corresponding to different types of control measures; an economic benefit evaluation module for constructing an input-output economic disaster evaluation model for the new infectious disease and performing an economic dimension simulation evaluation of different types of control measures using the input-output economic disaster evaluation model to obtain the economic benefits corresponding to different types of control measures; a trade-off optimization module for respectively determining the target control measures for the new infectious disease at different stages based on the health benefits, the economic benefits, and the control objectives at different stages.

[0016] Optionally, in an embodiment of the present application, the construction module includes: a first acquisition unit for acquiring the remote sensing nocturnal light data set of the target research area, separating the monthly nocturnal light data by land use based on a preset endmember-guided time linear unmixing strategy, and decomposing the monthly nocturnal light data into different land use types; a normalization unit for subtracting the immigration and emigration indices of the preset Baidu migration scale index, normalizing the result of the subtraction of the immigration and emigration indices, and allocating the monthly nocturnal light data by land use type to the daily land use-based nocturnal light time series data; a correction unit for acquiring the case report data of the target research area and performing an operation of correcting the number of confirmed cases and the number of recovered cases on the case report data to obtain the case report correction data.

[0017] Optionally, in an embodiment of the present application, the health benefit evaluation module includes: a first conversion unit, configured to determine the change amount of the land use night light based on the night light time series data, and convert the change amount of the land use night light into the emerging infectious disease model of the mobile space network; a first determination unit, configured to determine the model parameters corresponding to the emerging infectious disease model of the mobile space network by optimizing and fitting the real epidemic curve in the case of a preset early epidemic spread trend; an adjustment unit, configured to adjust the night light time series data on different land uses based on the model parameters to simulate the population flow changes across land uses corresponding to multiple scenarios, and combine the population flow changes across land uses and a preset decay contact infection rate parameter to evaluate the impact effects of different types of control measures, where the multiple scenarios include a scenario of completely restricting population flow across land uses, a scenario of completely liberalizing population flow across land uses, a scenario of restricting population flow in some land uses, and a scenario of opening up population flow in some land uses; a simulation unit, configured to simulate different types of control measures based on a preset simulation time and simulation time interval to obtain the shortest duration for effective epidemic control corresponding to each type of control measure; a comparison unit, configured to determine the epidemic end point according to a preset maximum curvature strategy, and use the epidemic end point and the shortest duration to compare the epidemic infection trends in different scenarios to obtain a scenario comparison result; a second conversion unit, configured to convert the epidemic infection trends in different scenarios into monetized health benefits in the social and economic dimension during the early control stage of emerging infectious diseases; an estimation unit, configured to estimate the coverage information of the maximum medical resources in the target research area for the required amount of target medical resources by implementing any one type of control measure among different types of control measures or implementing a full liberalization measure in each of a preset number of experimental stages during the later liberalization stage of emerging infectious diseases, and obtain the health benefits corresponding to different types of control measures according to the coverage information.

[0018] Optionally, in an embodiment of the present application, the economic benefit evaluation module includes: a modeling unit, configured to adjust the value-added loss rate and simulation interval of a target traditional input-output model, and reorganize a plurality of preset land use type departments in the target traditional input-output model to construct the input-output economic disaster evaluation model; an evaluation unit, configured to evaluate the control economic shock losses and control economic reconstruction losses corresponding to different types of control measures through the input-output economic disaster evaluation model, and use the control economic shock losses and the control economic reconstruction losses to determine the economic benefits corresponding to different types of control measures.

[0019] Optionally, in an embodiment of the present application, the trade-off optimization module includes: a second determination unit, configured to construct an objective function corresponding to the early control stage of the emerging infectious disease based on the health benefits, the economic benefits, and the control objectives at different stages, so as to determine the target control measures in the early control stage of the emerging infectious disease through the objective function; a second acquisition unit, configured to acquire at least one release scenario that meets the requirement of sufficient medical resource supply in the later release stage of the emerging infectious disease, and determine the economic loss corresponding to each release scenario in the at least one release scenario; a comparison unit, configured to compare the economic losses of each release scenario to obtain a target release scenario with the minimum economic loss, and determine the target control measures in the later release stage of the emerging infectious disease corresponding to the target release scenario.

[0020] Optionally, in an embodiment of the present application, the mathematical expression of the objective function is:

[0021]

[0022] TC(t) = EC(t) - HB(t)

[0023] where EC(t) represents the economic benefits, HB(t) represents the health benefits, R(t) represents the cost-benefit ratio, and TC(t) represents the total loss.

[0024] An embodiment of the third aspect of the present application provides an electronic device, including: a memory, a processor, and a computer program stored on the memory and executable on the processor, where the processor executes the program to implement the multi-stage strategy optimization method for emerging infectious diseases that weighs health and economy as described in the above embodiments.

[0025] An embodiment of the fourth aspect of the present application provides a computer-readable storage medium, where the computer-readable storage medium stores a computer program, and when the program is executed by a processor, it implements the multi-stage strategy optimization method for emerging infectious diseases that weighs health and economy as described above.

[0026] An embodiment of the fifth aspect of the present application provides a computer program product, including a computer program, where the computer program is executed to implement the multi-stage strategy optimization method for emerging infectious diseases that weighs health and economy as described above.

[0027] Therefore, the embodiments of the present application have the following beneficial effects:

[0028] Embodiments of the present application can construct daily land - use - specific night - light time - series data and case - report - corrected data for the target research area; construct a mobile spatial network new infectious disease model based on the night - light time - series data, the case - report - corrected data, and a preset target traditional new infectious disease model, and based on the control objectives at different stages of new infectious diseases, simulate and evaluate different types of preset control measures from the health dimension through the mobile spatial network new infectious disease model to obtain the health benefits corresponding to different types of control measures; construct an input - output economic disaster assessment model for new infectious diseases, and use the input - output economic disaster assessment model to simulate and evaluate different types of control measures from the economic dimension to obtain the economic benefits corresponding to different types of control measures; based on the health benefits, economic benefits, and control objectives at different stages, respectively determine the target control measures for new infectious diseases at different stages, thereby effectively assisting in the decision - making of response measures for new infectious diseases at different stages and providing reliable auxiliary information for the optimal control strategies at different stages. Thus, the problems of the lack of direct population flow data in the prior art, which makes simulation difficult, and the difficulty in balancing health and economic losses at different stages of the infectious disease pandemic are solved.

[0029] Additional aspects and advantages of the present application will be given in part in the following description, become apparent in part from the following description, or be understood through the practice of the present application. Brief Description of the Drawings

[0030] The above - mentioned and / or additional aspects and advantages of the present application will become apparent and be easily understood from the following description of the embodiments in conjunction with the drawings, where:

[0031] Figure 1 It is a flowchart of a multi - stage strategy optimization method for new infectious diseases that balances health and economy according to an embodiment of the present application;

[0032] Figure 2 It is a schematic diagram of the overall framework of a land - use - aware infectious disease transmission model and its scenario simulation settings provided by an embodiment of the present application;

[0033] Figure 3 It is a flowchart of scenario simulation of an economic input - output disaster impact model in the control of epidemic intervention provided by an embodiment of the present application;

[0034] Figure 4 It is an example diagram of a multi - stage strategy optimization device for new infectious diseases that balances health and economy according to an embodiment of the present application;

[0035] Figure 5 It is a schematic diagram of the structure of an electronic device provided by an embodiment of the present application.

[0036] Among them, 10 - a multi - stage strategy optimization device for new emerging infectious diseases that weighs health and economy; 100 - a construction module, 200 - a health benefit evaluation module, 300 - an economic benefit evaluation module, 400 - a trade - off optimization module; 501 - a memory, 502 - a processor, 503 - a communication interface. Detailed implementation manners

[0037] The embodiments of the present application will be described in detail below. The examples of the embodiments are shown in the drawings, where the same or similar reference numerals represent the same or similar elements or elements with the same or similar functions throughout. The embodiments described below with reference to the drawings are exemplary and are intended to explain the present application, and should not be construed as a limitation of the present application.

[0038] The multi - stage strategy optimization method for new emerging infectious diseases that weighs health and economy according to the embodiments of the present application will be described below with reference to the drawings. In response to the problems mentioned in the above - mentioned background art, the present application provides a multi - stage strategy optimization method for new emerging infectious diseases that weighs health and economy. In this method, by constructing the daily land - use - segmented night - light time - series data and case - report corrected data of the target study area; constructing a mobile spatial network new emerging infectious disease model according to the night - light time - series data, the case - report corrected data, and a preset target traditional new emerging infectious disease model, and based on the control objectives at different stages of the new emerging infectious disease, simulating and evaluating the health dimensions of different types of preset control measures through the mobile spatial network new emerging infectious disease model to obtain the health benefits corresponding to different types of control measures; constructing an input - output economic disaster evaluation model for new emerging infectious diseases, and using the input - output economic disaster evaluation model to simulate and evaluate the economic dimensions of different types of control measures to obtain the economic benefits corresponding to different types of control measures; based on the health benefits, economic benefits, and control objectives at different stages, respectively determining the target control measures for new emerging infectious diseases at different stages, thereby effectively assisting the decision - making of response measures for new emerging infectious diseases at different stages and providing reliable auxiliary information for the most appropriate control strategies at different stages. Thus, the problems of the lack of direct population flow data in the prior art, which makes simulation difficult, and the difficulty in balancing health and economic losses at different stages of the infectious disease pandemic are solved.

[0039] Specifically, Figure 1 is a flowchart of a multi - stage strategy optimization method for new emerging infectious diseases that weighs health and economy provided by the embodiments of the present application.

[0040] As Figure 1 shown, the multi - stage strategy optimization method for new emerging infectious diseases that weighs health and economy includes the following steps:

[0041] In step S101, construct the daily land - use - segmented night - light time - series data and case - report corrected data of the target study area.

[0042] In the embodiments of the present application, first, the nightlight time series data and the corrected case report data of land use are separated day by day, so as to provide reliable data support for the implementation of the multi-stage strategy optimization of emerging infectious diseases for weighing health and economy.

[0043] Optionally, in an embodiment of the present application, the nightlight time series data and the case report correction data of land use are constructed day by day for the target research area, including: obtaining the remote sensing nightlight data set of the target research area, and separating the monthly nightlight data of land use from the remote sensing nightlight data set based on a preset endmember-guided time linear unmixing strategy, and decomposing the monthly nightlight data into different land use types; subtracting the immigration and emigration indices of the preset Baidu migration scale index, normalizing the result of subtracting the immigration and emigration indices, and allocating the monthly nightlight data of land use types to the nightlight time series data of land use day by day; obtaining the case report data of the target research area, and performing the correction operations of the number of confirmed cases and the number of recovered cases on the case report data to obtain the case report correction data.

[0044] Specifically, first, the production steps of the nightlight time series data of land use day by day in the embodiments of the present application are as follows:

[0045] Step 1: Spatial decomposition

[0046] In the embodiments of the present application, the remote sensing images of the research area with low cloud cover in the remote sensing nightlight data sets before and after the epidemic can be selected, and the time resolution is usually monthly; the nightlight data of land use is separated from the collection of the remote sensing nightlight data set by using the endmember-guided time linear unmixing method, and the endmember sample matrix is the average value extracted by visual inspection of the area, and the light source reflectance abundance of each pixel is estimated based on this; after decomposing the night lights into different land use sources, the embodiments of the present application can calculate the sum of the pixel values of each land use per month; in addition, in order to obtain more refined dynamic nightlight time series data, the embodiments of the present application also need to allocate the monthly nightlights day by day according to the population migration index and prepare to be integrated into the traditional epidemiological model;

[0047] Step 2: Time allocation

[0048] The original nightlight data after the above decomposition is monthly data, but the epidemiological simulation is carried out on a daily iteration basis; both the Baidu Migration Index in China and the Google Community Mobility Dataset abroad collect community mobility reports that are not the actual values of population mobility and do not distinguish land use types. Instead, they reflect the relative mobility scale of the overall population and can only play a role in the daily allocation of nightlights. Only by allocating monthly nightlights to each day can the epidemiological simulation capture the patterns hidden behind human activities in each sector; therefore, in the embodiments of this application, it is necessary to subtract the inbound and outbound indices of the Baidu migration scale index and perform normalization processing to allocate the monthly nightlight data by land use type to a daily basis; the daily allocation based on nightlights by land use type can unify the population flow scale index globally, thus laying a foundation for large-scale epidemiological simulations.

[0049] Secondly, those skilled in the art should understand that case report data needs to extract the daily summary data of infected and recovered cases from multiple public official data sources (the websites of the Municipal Health Commission and local government media websites); due to the lack of understanding of emerging infectious diseases in the early stage, the initial reports lacked an accurate monitoring system. Therefore, in order to make up for the data deficiency, it is crucial to calibrate the data input into the model and process the epidemic data to adapt to the actual situation and cut off the influence of the delayed response of the medical system and testing errors.

[0050] In the embodiments of this application, the generation of corrected case report data (i.e., case report correction data) is divided into the following two categories:

[0051] (1) Correction of the number of confirmed cases:

[0052] The numbers reported officially represent confirmed cases, while what is actually reflected is the onset cases. The reason why they do not match is that these two numbers do not express the same physical meaning. The daily new case numbers reported are not the dates of their onset, which may distort the model curve in parameter estimation and reduce the reliability of the model; however, the duration from the onset date to case confirmation can integrate limited diagnostic capabilities and limited medical resources (such as shortages of hospital beds). Before sufficient epidemic prevention warehouses are established, the lack of medical resources is the basic reality of large-scale disease transmission. For example, in Area A, the time delay from getting sick to being diagnosed and hospitalized is the time waiting for test kits, while in Area B, it is the time waiting for test kits and hospital beds; taking the doctor dynamics in Area B as the natural decline trend in the first-onset area, the number of onset cases can be multiplied by the difference in doctors between Area A and Area B to estimate the total number of waiting bed days and the sum of each onset day in areas like Area A that are not prepared.

[0053] (2) Correction of the number of recovered cases:

[0054] There is a certain time difference between the daily number of recoveries reported officially and the actual situation. Therefore, in the estimation of model parameters, due to this time difference, the number of recoveries cannot be accurately fitted; the simulated number of recoveries in the model often represents the actual number of recoveries, which has a different physical meaning from the recovery data reported officially. For example, the number of cured people reported officially in a certain area must meet the corresponding standards, that is, it takes 7 days for a person's nucleic acid to be positive to be marked as cured, which is different from the real cure time in reality. Therefore, an artificial time lag needs to be added to the simulation of the number of recoveries in the infectious disease model.

[0055] In the embodiments of the present application, the parameter γ, which is the reciprocal of the number of recovery days, can be transformed into a time-varying parameter and follow the Weibull distribution to approximate the time lag caused by the treatment plan in reality, as shown in the following formula:

[0056]

[0057] where the time unit t is defined as 1 day, L γ and k γ jointly control the shape of the Weibull decay distribution.

[0058] Thus, after the above correction process in the embodiments of the present application, the 7-day smoothing process can effectively reduce random errors and noise, thereby obtaining more reliable data collection.

[0059] It can be understood that the embodiments of the present application simulate the changes in population mobility on different land use types by using classified nighttime light data, and introduce it into the infectious disease model to evaluate the protection effects of various control measures on people's lives and safety, thereby providing effective support for the evaluation of spatially targeted measures.

[0060] In step S102, a new infectious disease model of mobile spatial network is constructed according to the nighttime light time series data, the case report correction data and the preset target traditional new infectious disease model, and based on the control objectives of the new infectious disease in different stages, the new infectious disease model of mobile spatial network is used to simulate and evaluate different types of preset control measures in terms of health dimension to obtain the health benefits corresponding to different types of control measures.

[0061] Furthermore, the embodiments of the present application also need to introduce night lights classified by land use to characterize the population mobility on different land uses, and construct a remote sensing-guided infectious disease transmission model (i.e., a new infectious disease model of mobile spatial network) to simulate and evaluate different types of control measures in terms of health dimension based on the objectives in different stages.

[0062] Optionally, in one embodiment of the present application, a mobile space network emerging infectious disease model is constructed based on night light time series data, case report correction data and a preset target traditional emerging infectious disease model, and based on the control objectives of emerging infectious diseases at different stages, the mobile space network emerging infectious disease model is used to simulate and evaluate the health dimension of different types of preset control measures to obtain the health benefits corresponding to different types of control measures, including: based on the night light time series data, determining the change in night light of land use, and converting the change in night light of land use into a mobile space network emerging infectious disease model; under the preset early spread trend of the epidemic, determining the model parameters corresponding to the mobile space network emerging infectious disease model by optimizing and fitting the real epidemic curve; based on the model parameters, adjusting the night light time series data on different land uses to simulate the changes in population mobility across land uses corresponding to multiple scenarios, and combining the changes in population mobility across land uses and the preset attenuation contact infection rate parameters to evaluate the impact of different types of control measures, wherein the multiple scenarios include complete The scenario of fully restricting population mobility across land uses, the scenario of completely liberalizing population mobility across land uses, the scenario of restricting population mobility across some land uses, and the scenario of opening population mobility across some land uses; based on the preset simulation time and simulation time interval, simulate different types of control measures to obtain the shortest duration of effective epidemic control corresponding to each type of control measure; determine the epidemic endpoint according to the preset maximum curvature strategy, and use the epidemic endpoint and the shortest duration to compare the epidemic infection trends under different scenarios to obtain scenario comparison results; in the early control stage of emerging infectious diseases at different stages, convert the epidemic infection trends under different scenarios into monetized health benefits in the socioeconomic dimension; in the late relaxation stage of emerging infectious diseases at different stages, implement any type of control measures among different types of control measures or implement comprehensive relaxation measures in each of the preset multiple experimental stages to estimate the coverage information of the maximum medical resources in the target study area to the target medical resources demand, and obtain the health benefits corresponding to different types of control measures based on the coverage information.

[0063] It should be noted that if Figure 2 As shown ( Figure 2 The red and black arrows in the middle represent external and internal infections, respectively. The specific steps of the embodiment of the present application using the land use-aware infectious disease propagation model (i.e., the mobile space network emerging infectious disease model) to accurately simulate the multi-stage scenario of an infectious disease pandemic outbreak are as follows:

[0064] Step 1: Framework construction:

[0065] The infectious disease transmission model with land use perception can simulate the transmission process without the support of first-hand population mobility data. By introducing the change in night light of each land use as a linear approximation of population mobility between regions in a closed traditional infectious disease model, it is transformed into an infectious disease model with a mobile spatial network, which can be used to simulate the scenarios of land use control interventions. Assuming that the confirmed patients are admitted and cannot move, a new infectious disease model of the mobile spatial network can be constructed as follows:

[0066]

[0067] Among them, the time unit t is defined as 1 day, S, E, I, R, and D respectively represent the five states of susceptible, latent, infected, recovered, and dead, N is the total population; β represents the contact infection rate, indicating the average number of people who transmit the disease from infected people to susceptible people every day; p represents the proportion of vulnerable groups who can only be infected and cannot actively infect others; α is the reciprocal of the incubation period of symptomatic infection; μ is the reciprocal of the incubation period to the recovery period of asymptomatic infection; γ is the reciprocal of the incubation period to the recovery period of symptomatic infection, and is the reciprocal of the infection to clearance period; s is the proportion of symptomatic infection; c is the case fatality rate; only symptomatic infected people can be added to the simulation of I, so only the number of symptomatic cases recorded in the official report is used to fit the model; in addition to traditional parameters, the new parameters of the change in night light of each land use category introduced in the embodiments of the present application are also important for simulating transmission by approximating population changes; P s 、P e are the proportions of S and E in the population mobility between regions; τ is the linear coefficient of the change in night light and the change in population; dM is the total change in night light.

[0068] It should be noted that the above β, p, and γ change with time during different infection periods of the epidemic. For example, β varies due to different virus strains, control policies, and climate conditions. Assuming that infected patients are admitted to the hospital according to the actual situation and remain static, the population mobility approximated by the change in night light is distributed to S, E, and R in a certain proportion.

[0069] Step 2: Parameter estimation:

[0070] Due to different virus strains, population changes, medical efficiency, and policy implementation, the model parameters need to be adjusted in the simulation for each epidemic outbreak; in the case of knowing the early spread trend of the epidemic, the model parameters are determined by optimizing the fitting of the real epidemic curve; for the multi-objective optimization problem, the non-dominated sorting genetic algorithm II is used to minimize the objective functions of the infection term and the recovery term; in the specific implementation process, due to the reporting bias and attribution error of death cases, the embodiments of the present application do not include the death term and the total number of people term in the multi-objective optimization through repeated experiments.

[0071] Step 3: Scenario setting:

[0072] Scenario analysis is based on the ability to simulate realistic scenarios with determined parameters for the spread of infectious diseases; on this basis, embodiments of the present application can adjust characteristic parameters and variables to cooperate with certain urban control measures. Therefore, changing the night light time series on different land uses can simulate the changes in population flow across land uses under different scenarios, combined with the decaying contact infection rate parameter, so as to evaluate the impact effects of different types of control measures. The relevant scenarios include completely restricting population flow across land uses, completely liberalizing population flow across land uses, restricting population flow in certain land uses, and opening population flow in certain land uses, as shown in Table 1:

[0073] Table 1

[0074]

[0075] It should be noted that in Table 1 refers to the real-time night light change during the epidemic outbreak period, while dM noepidemics refers to the substitution value of the night light change in the same period in other years when (almost) no such epidemic exists.

[0076] Step 4: Scenario comparison:

[0077] If the control measures fail to briefly control the outbreak of infectious diseases, widespread infections may still occur after the control measures are liberalized, resulting in insignificant control effects. To maximize the effect, simulations of 8 control measures are carried out at 2-week intervals for 2 to 24 weeks to find the shortest duration for each measure to control the epidemic; to make the simulation results comparable, embodiments of the present application can use the maximum curvature method to determine the end point of the epidemic and compare the epidemic infection trends under different scenarios.

[0078] To determine the end of the epidemic, embodiments of the present application can first perform least-squares fitting of a circle with a certain radius for each point K(t, t) (t = 1,..., T) on the infection curve, using n points for fitting, and the filtered curvature is calculated as follows:

[0079] FC t = C t I(270° ≤ θ t ≤ 360°)I(y t ≤ h)I(t > t p ) (14)

[0080] where I is the indicator function, C t is the original curvature of the fitted circle, FC t is the filtered curvature based on C t , t p is the peak time, θ tis the direction angle of the tangent vector from the vector starting from K to the center of the circle, and h is the upper threshold.

[0081] After that, the embodiments of the present application can be based on FC t The end of the epidemic is determined according to the point corresponding to the maximum value. The calculation of the health benefits is based on the number of infection cases avoided compared with the baseline scenario without control measures under different scenarios.

[0082] Step 5: Health assessment:

[0083] In the early control stage of infectious diseases, in order to make the health benefits comparable, the infection trend should be transformed into monetized health benefits in the socio-economic dimension; the health benefits brought by the control measures include three aspects: the first aspect considers the medical burden of critically ill / critically ill patients; the second aspect considers the pensions of the deceased population supported by the government; the third aspect considers the value of the loss of life of the deceased population. The sum of these three components constitutes the monetized health benefits.

[0084] In the later liberalization stage of infectious diseases, it is necessary to estimate whether the maximum medical resources can cover the demand for medical resources. The embodiments of the present application designed an experiment with six stages, aiming to explore the optimal liberalization strategy by implementing one of the eight control measures or full liberalization in each stage (each stage is four weeks).

[0085] Based on the scenario simulation of 531,441 strategies carried out within one year, the peak infection of each strategy was calculated, so as to estimate the peak of inpatients or critically ill / critically ill patients; the peak number of inpatients or critically ill / critically ill patients represents the demand for medical resources in terms of hospital beds or ICUs. When the simulated peak number of inpatients or critically ill / critically ill patients exceeds the maximum available number of beds or ICUs, the situation of medical resource shortage is likely to occur.

[0086] Thus, the embodiments of the present application use the infectious disease model with land use perception to simulate and evaluate the single or combined control measures of different plots in the health dimension.

[0087] In step S103, an input-output economic disaster assessment model for emerging infectious diseases is constructed, and the input-output economic disaster assessment model is used to simulate and evaluate different types of control measures in the economic dimension to obtain the economic benefits corresponding to different types of control measures.

[0088] After evaluating the health benefits of the control measures, further, the embodiments of the present application also need to construct an input-output economic disaster assessment model to simulate and evaluate the single or combined control measures of different plots in the economic dimension based on the input-output economic disaster impact model, so as to provide data and technical support for achieving the balance between health benefits and economic costs.

[0089] Optionally, in an embodiment of the present application, an input-output economic disaster assessment model for newly emerging infectious diseases is constructed, and the input-output economic disaster assessment model is used to simulate and evaluate different types of control measures from an economic dimension to obtain the economic benefits corresponding to different types of control measures, including: adjusting the added value loss rate and simulation interval of the target traditional input-output model, and reorganizing multiple land use type departments preset in the target traditional input-output model to construct an input-output economic disaster assessment model; evaluating the control economic shock loss and control economic reconstruction loss corresponding to different types of control measures through the input-output economic disaster assessment model, and determining the economic benefits corresponding to different types of control measures by using the control economic shock loss and control economic reconstruction loss.

[0090] In the actual implementation process, the disaster impact estimation tool of the embodiment of the present application adopts an input-output model, which can be used to evaluate the economic impact caused by infectious disease control measures and subsequent economic reconstruction. The economic losses caused by control measures include direct losses and indirect losses. This economic model assumes that when a department is curbed, the production of the corresponding local department will be affected by a certain proportion. Therefore, after the control causes direct losses, production bottlenecks in the supply chain between departments lead to a greater degree of productivity damage. The interdependence between departments within a region can be illustrated by both intermediate consumption demand and supply; considering the financial support of the country, the model calculates the production, supply and demand of each department every day, simulates the dynamic trend of the added value of each department after the shock until balance is reached, so as to restore the production capacity of each department to the level before the pandemic. The detailed information of the above economic simulation process is as Figure 3 shown Figure 3 where Y represents the production volume, D represents the total demand, F represents the final total demand, O represents the production demand, V represents the percentage of added value loss, and A represents the intermediate consumption coefficient in the regional input-output table. t represents the time length in units of one day, and i and j represent the i-th or j-th type of department.

[0091] To adapt to the current economic evaluation purpose, the embodiment of the present application can make the following three improvements to the traditional input-output model:

[0092] 1. In the economic evaluation of control, assume that the added value loss rate caused by the direct shock of the previous period department is the direct loss rate of actual production;

[0093] 2. Adjust the simulation interval from the conventional one month to one day to match the fine simulation of the infectious disease model;

[0094] 3. Reorganize the departments corresponding to land use, thus building a bridge for quantifying control measures between the infectious disease model and the economic model.

[0095] It should be noted that in all industries, the primary industry is not considered because its main production areas are not in the built-up areas covered by night lights, and the transportation industry is not considered because of its blind spots under the control scenarios.

[0096] In step S104, based on the health benefits, economic benefits, and control objectives at different stages, the target control measures for emerging infectious diseases at different stages are determined respectively.

[0097] Furthermore, the embodiments of the present application can optimize the two dimensions of health and economy based on the target at different stages of emerging infectious diseases to obtain the optimal response measures in multiple stages.

[0098] Optionally, in an embodiment of the present application, determining the target control measures for emerging infectious diseases at different stages based on the health benefits, economic benefits, and control objectives at different stages includes: constructing an objective function corresponding to the early control stage of emerging infectious diseases based on the health benefits, economic benefits, and control objectives at different stages, so as to determine the target control measures in the early control stage of emerging infectious diseases through the objective function; obtaining at least one release scenario that meets the requirement of sufficient medical resource supply in the later release stage of emerging infectious diseases, and determining the economic losses corresponding to each release scenario in the at least one release scenario; comparing the economic losses of each release scenario to obtain the target release scenario with the minimum economic loss, and determining the target control measures in the later release stage of emerging infectious diseases corresponding to the target release scenario.

[0099] It should be noted that the economic cost is the time-varying incremental loss of the direct impact and indirect supply chain effect caused by the epidemic intervention; the health benefit refers to the time-varying social and economic value avoided by controlling the epidemic intervention, which includes the medical expenses, pensions of each infected person, and the future potential of each deceased person.

[0100] In the early control stage of emerging infectious diseases, the embodiments of the present application can construct a corresponding objective function through economic losses and health benefits to optimize the trade-off between health and economy.

[0101] Optionally, in an embodiment of the present application, the mathematical expression of the objective function is:

[0102]

[0103] TC(t) = EC(t) - HB(t)

[0104] Wherein, EC(t) represents the economic benefit, HB(t) represents the health benefit, R(t) represents the cost-benefit ratio, and TC(t) represents the total loss.

[0105] As a feasible way, the objective function constructed by the embodiments of the present application is shown in the following formula:

[0106]

[0107] TC(t) = EC(t) - HB(t) (16)

[0108] Among them, EC(t) is the economic loss, HB(t) is the health benefit, R(t) is the cost-benefit ratio, and RC(t) is the total loss. The above parameters are all time-varying variable indicators. Therefore, the embodiments of the present application can select half a year, one year, or two years after the control measures as different time consideration ranges for auxiliary decision-making.

[0109] In the later stage of the release of a newly emerging infectious disease, the embodiments of the present application can screen out the scenarios with sufficient medical resource supply among all release scenarios, and then select the best scenario with the least economic loss from them.

[0110] Thus, the embodiments of the present application balance the economic and health losses in the response strategies of newly emerging infectious diseases in multiple stages by skillfully combining the infectious disease and economic models, provide auxiliary information for the optimal control strategies in different stages, effectively assist the decision-making of response measures in different stages of newly emerging infectious diseases, thereby providing power support for the economic stability and medical security threatened by disease outbreaks and physical coercive measures, and maintaining social and economic stability and people's lives and safety in the early control stage and the later release stage of newly emerging infectious diseases.

[0111] According to the multi-stage strategy optimization method for newly emerging infectious diseases that weighs health and economy proposed by the embodiments of the present application, by constructing the daily land use-based night light time series data and case report correction data of the target research area; constructing a mobile spatial network newly emerging infectious disease model according to the night light time series data, case report correction data, and a preset target traditional newly emerging infectious disease model, and based on the control objectives of newly emerging infectious diseases in different stages, simulating and evaluating the health dimensions of different types of control measures through the mobile spatial network newly emerging infectious disease model to obtain the health benefits corresponding to different types of control measures; constructing an input-output economic disaster assessment model for newly emerging infectious diseases, and using the input-output economic disaster assessment model to simulate and evaluate the economic dimensions of different types of control measures to obtain the economic benefits corresponding to different types of control measures; based on the health benefits, economic benefits, and control objectives in different stages, respectively determine the target control measures of newly emerging infectious diseases in different stages, thereby effectively assisting the decision-making of response measures in different stages of newly emerging infectious diseases and providing reliable auxiliary information for the optimal control strategies in different stages.

[0112] Secondly, describe the multi-stage strategy optimization device for newly emerging infectious diseases that weighs health and economy proposed by the embodiments of the present application with reference to the accompanying drawings.

[0113] Figure 4It is a block diagram of a multi-stage strategy optimization device for emerging infectious diseases that balances health and economy according to an embodiment of the present application.

[0114] As Figure 4 shown, the multi-stage strategy optimization device 10 for emerging infectious diseases that balances health and economy includes: a construction module 100, a health benefit evaluation module 200, an economic benefit evaluation module 300, and a trade-off optimization module 400.

[0115] Among them, the construction module 100 is used to construct daily land use-based nocturnal light time series data and case report correction data for the target study area.

[0116] The health benefit evaluation module 200 is used to construct a mobile spatial network emerging infectious disease model based on the nocturnal light time series data, case report correction data, and a preset target traditional emerging infectious disease model, and based on the control objectives of emerging infectious diseases at different stages, simulate and evaluate different types of preset control measures in terms of health through the mobile spatial network emerging infectious disease model to obtain the health benefits corresponding to different types of control measures.

[0117] The economic benefit evaluation module 300 is used to construct an input-output economic disaster evaluation model for emerging infectious diseases, and use the input-output economic disaster evaluation model to simulate and evaluate different types of control measures in terms of economy to obtain the economic benefits corresponding to different types of control measures.

[0118] The trade-off optimization module 400 is used to determine the target control measures for emerging infectious diseases at different stages based on health benefits, economic benefits, and control objectives at different stages.

[0119] Optionally, in an embodiment of the present application, the construction module 100 includes: a first acquisition unit, a normalization unit, and a correction unit.

[0120] Among them, the first acquisition unit is used to obtain the remote sensing nocturnal light data set of the target study area, and based on a preset endmember-guided temporal linear unmixing strategy, separate the monthly nocturnal light data by land use from the remote sensing nocturnal light data set, and decompose the monthly nocturnal light data into different land use types.

[0121] The normalization unit is used to subtract the immigration and emigration indices of the preset Baidu migration scale index, normalize the result of subtracting the immigration and emigration indices, and allocate the monthly nocturnal light data by land use type to the daily land use-based nocturnal light time series data.

[0122] The correction unit is used to obtain the case report data of the target study area, and perform operations on the case report data for correcting the number of confirmed cases and the number of recovered cases to obtain the case report correction data.

[0123] Optionally, in an embodiment of the present application, the health benefit assessment module 200 includes: a first conversion unit, a first determination unit, an adjustment unit, a simulation unit, a comparison unit, a second conversion unit, and an estimation unit.

[0124] Among them, the first conversion unit is used to determine the change amount of land use night light based on the night light time series data, and convert the change amount of land use night light into a new infectious disease model for the mobile space network.

[0125] The first determination unit is used to determine the model parameters corresponding to the new infectious disease model for the mobile space network by optimizing and fitting the real epidemic curve under the preset early epidemic spread trend.

[0126] The adjustment unit is used to adjust the night light time series data on different land uses based on the model parameters to simulate the population flow changes across land uses corresponding to multiple scenarios, and combine the population flow changes across land uses and the preset decay contact infection rate parameter to evaluate the impact effects of different types of control measures, where the multiple scenarios include the scenario of completely restricting population flow across land uses, the scenario of completely liberalizing population flow across land uses, the scenario of restricting population flow on some land uses, and the scenario of opening up population flow on some land uses.

[0127] The simulation unit is used to simulate different types of control measures based on the preset simulation time and simulation time interval to obtain the shortest duration for effective epidemic control corresponding to each type of control measure.

[0128] The comparison unit is used to determine the epidemic endpoint according to the preset maximum curvature strategy, and use the epidemic endpoint and the shortest duration to compare the epidemic infection trends under different scenarios to obtain the scenario comparison result.

[0129] The second conversion unit is used to convert the epidemic infection trends under different scenarios into monetized health benefits in the social and economic dimension during the early control stage of the new infectious disease in different stages.

[0130] The estimation unit is used to estimate the coverage information of the maximum medical resources in the target research area for the demand of the target medical resources during the late liberalization stage of the new infectious disease in different stages by implementing any type of control measure or implementing a full liberalization measure among different types of control measures in each experimental stage of the preset multiple experimental stages, and obtain the health benefits corresponding to different types of control measures according to the coverage information.

[0131] Optionally, in an embodiment of the present application, the economic benefit assessment module 300 includes: a modeling unit and an assessment unit.

[0132] Among them, the modeling unit is used to adjust the added value loss rate and simulation interval of the target traditional input-output model, and reorganize multiple preset land use type departments in the target traditional input-output model to construct an input-output economic disaster assessment model.

[0133] The evaluation unit is used to evaluate the control economic impact loss and control economic reconstruction loss corresponding to different types of control measures through the input-output economic disaster assessment model, and determine the economic benefits corresponding to different types of control measures by using the control economic impact loss and control economic reconstruction loss.

[0134] Optionally, in an embodiment of the present application, the trade-off optimization module 400 includes: a second determination unit, a second acquisition unit, and a comparison unit.

[0135] Among them, the second determination unit is used to construct an objective function corresponding to the early control stage of a newly emerging infectious disease based on health benefits, economic benefits, and control objectives at different stages, so as to determine the target control measures in the early control stage of the newly emerging infectious disease through the objective function.

[0136] The second acquisition unit is used to acquire at least one release scenario that meets the requirement of sufficient medical resource supply in the later release stage of the newly emerging infectious disease, and determine the economic loss corresponding to each release scenario in the at least one release scenario.

[0137] The comparison unit is used to compare the economic losses of each release scenario to obtain the target release scenario with the minimum economic loss, and determine the target control measures in the later release stage of the newly emerging infectious disease corresponding to the target release scenario.

[0138] Optionally, in an embodiment of the present application, the mathematical expression of the objective function is:

[0139]

[0140] TC(t) = EC(t) - HB(t)

[0141] Among them, EC(t) represents economic benefits, HB(t) represents health benefits, R(t) represents the cost-benefit ratio, and TC(t) represents the total loss.

[0142] It should be noted that the foregoing explanation of the embodiments of the optimization method for the multi-stage strategy of newly emerging infectious diseases that balances health and economy also applies to the device for optimizing the multi-stage strategy of newly emerging infectious diseases that balances health and economy in this embodiment, and will not be elaborated here.

[0143] The multi-stage strategy optimization device for emerging infectious diseases that balances health and economy proposed according to the embodiments of the present application includes a construction module for constructing the daily land use-based night light time series data and case report correction data of the target research area; a health benefit evaluation module for constructing a mobile spatial network emerging infectious disease model based on the night light time series data, case report correction data, and a preset target traditional emerging infectious disease model, and based on the control objectives of emerging infectious diseases at different stages, simulating and evaluating different types of control measures in terms of health through the mobile spatial network emerging infectious disease model to obtain the health benefits corresponding to different types of control measures; an economic benefit evaluation module for constructing an input-output economic disaster evaluation model for emerging infectious diseases and using the input-output economic disaster evaluation model to simulate and evaluate different types of control measures in terms of economy to obtain the economic benefits corresponding to different types of control measures; a trade-off and optimization module for determining the target control measures for emerging infectious diseases at different stages based on the health benefits, economic benefits, and control objectives at different stages, thereby effectively assisting the decision-making of response measures for emerging infectious diseases at different stages and providing reliable auxiliary information for the optimal control strategies at different stages.

[0144] Figure 5 The structural schematic diagram of the electronic device provided by the embodiment of the present application. The electronic device may include:

[0145] A memory 501, a processor 502, and a computer program stored on the memory 501 and executable on the processor 502.

[0146] When the processor 502 executes the program, it implements the multi-stage strategy optimization method for emerging infectious diseases that balances health and economy provided in the above embodiments.

[0147] Further, the electronic device further includes:

[0148] A communication interface 503 for communication between the memory 501 and the processor 502.

[0149] The memory 501 is used to store a computer program executable on the processor 502.

[0150] The memory 501 may include a high-speed RAM memory, and may also include a non-volatile memory, such as at least one disk memory.

[0151] If the memory 501, the processor 502, and the communication interface 503 are implemented independently, the communication interface 503, the memory 501, and the processor 502 can be interconnected via a bus and communicate with each other. The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, an Extended Industry Standard Architecture (EISA) bus, or the like. The bus can be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 5 only a thick line is used to represent it in Figure 5 , but it does not mean that there is only one bus or one type of bus.

[0152] Optionally, in a specific implementation, if the memory 501, the processor 502, and the communication interface 503 are integrated on a single chip, the memory 501, the processor 502, and the communication interface 503 can communicate with each other through an internal interface.

[0153] The processor 502 may be a Central Processing Unit (CPU), or an Application Specific Integrated Circuit (ASIC), or one or more integrated circuits configured to implement the embodiments of the present application.

[0154] The embodiments of the present application further provide a computer-readable storage medium, on which a computer program is stored, and when the program is executed by a processor, the above-mentioned multi-stage strategy optimization method for emerging infectious diseases that balances health and economy is implemented.

[0155] The embodiments of the present application further provide a computer program product, including a computer program, and when the computer program is executed, it is used to implement the above-mentioned multi-stage strategy optimization method for emerging infectious diseases that balances health and economy.

[0156] In the description of this specification, the descriptions referring to terms such as "one embodiment", "some embodiments", "examples", "specific examples", or "some examples", etc. mean that the specific features, structures, materials, or characteristics described in connection with the embodiment or example are included in at least one embodiment or example of this application. In this specification, the schematic expressions of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials, or characteristics described can be combined in any one or N embodiments or examples in a suitable manner. In addition, without contradiction, those skilled in the art can combine and combine the different embodiments or examples described in this specification and the features of different embodiments or examples.

[0157] In addition, the terms "first" and "second" are only used for descriptive purposes and cannot be understood as indicating or implying relative importance or implicitly specifying the quantity of the indicated technical features. Thus, the features defined with "first" and "second" may explicitly or implicitly include at least one of such features. In the description of this application, the meaning of "N" is at least two, such as two, three, etc., unless otherwise specifically defined.

[0158] Any process or method description depicted in a flowchart or described in other ways herein can be understood to represent a module, segment, or portion of code including one or N executable instructions for implementing a customized logical function or process, and the scope of the preferred embodiments of this application includes additional implementations, where the functions can be executed in a substantially simultaneous manner or in a reverse order according to the functions involved, rather than in the order shown or discussed, which should be understood by those skilled in the art to which the embodiments of this application pertain.

[0159] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a definable sequence list of executable instructions for implementing logical functions, and can be specifically implemented in any computer-readable medium for use by an instruction execution system, apparatus, or device (such as a computer-based system, a system including a processor, or other systems that can fetch and execute instructions from the instruction execution system, apparatus, or device), or used in conjunction with these instruction execution systems, apparatus, or devices. For the purposes of this specification, a "computer-readable medium" can be any device that can contain, store, communicate, propagate, or transport a program for use by or in conjunction with an instruction execution system, apparatus, or device. More specific examples (non-exhaustive list) of computer-readable media include the following: an electrical connection portion (electronic device) having one or N wirings, a portable computer disk cartridge (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disc read-only memory (CDROM). Additionally, the computer-readable medium can even be paper or other suitable media on which the program can be printed, because the program can be obtained electronically by optically scanning the paper or other media, followed by editing, interpretation, or otherwise processing as appropriate, and then stored in a computer memory.

[0160] It should be understood that various parts of the present application can be implemented by hardware, software, firmware, or a combination thereof. In the above-described embodiments, the N steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. If implemented in hardware, as in another embodiment, any one or a combination of the following techniques well known in the art can be used: discrete logic circuits having logic gate circuits for implementing logical functions on data signals, application-specific integrated circuits having appropriate combinational logic gate circuits, programmable gate arrays (PGAs), field programmable gate arrays (FPGAs), etc.

[0161] Those of ordinary skill in the art of this technology can understand that all or part of the steps carried by the method of implementing the above embodiments can be completed by instructing relevant hardware through a program, and the program can be stored in a computer-readable storage medium. When the program is executed, it includes one or a combination of the steps of the method embodiments.

[0162] In addition, each functional unit in various embodiments of the present application may be integrated into one processing module, or each unit may exist physically alone, or two or more units may be integrated into one module. The above-mentioned integrated module may be implemented in the form of hardware or in the form of a software functional module. When the integrated module is implemented in the form of a software functional module and sold or used as an independent product, it may also be stored in a computer-readable storage medium.

[0163] The above-mentioned storage medium may be a read-only memory, a magnetic disk, an optical disc, etc. Although the embodiments of the present application have been shown and described above, it can be understood that the above embodiments are exemplary and should not be construed as limiting the present application. Those of ordinary skill in the art can make changes, modifications, substitutions, and variations to the above embodiments within the scope of the present application.

Claims

1. A multi-stage strategy optimization method for emerging infectious diseases that balances health and economy, characterized in that: The following steps are involved: Construct the night light time series data and case report correction data of the target study area by land use on a daily basis; A mobile space network emerging infectious disease model is constructed according to the night light time series data, the case report correction data and the preset target traditional emerging infectious disease model, and based on the control objectives of emerging infectious diseases at different stages, a health dimension simulation evaluation is performed on preset different types of control measures through the mobile space network emerging infectious disease model to obtain the health benefits corresponding to the different types of control measures; Constructing an input-output economic disaster assessment model for the emerging infectious disease, and using the input-output economic disaster assessment model to simulate and evaluate the different types of control measures in the economic dimension, so as to obtain the economic benefits corresponding to the different types of control measures; Based on the health benefits, the economic benefits and the control objectives at different stages, target control measures for the emerging infectious diseases at different stages are determined respectively.

2. The method according to claim 1, characterized in that The construction of the night light time series data and case report correction data of the target study area by land use on a daily basis includes: Acquire a remote sensing night light data set of the target research area, and separate monthly night light data of different land use types from the remote sensing night light data set based on a preset end-member guided time linear unmixing strategy, and decompose the monthly night light data into different land use types; Subtract the migration in and out index of the preset Baidu migration scale index, normalize the result of the subtraction of the migration in and out index, and allocate the monthly night light data of different land use types to the daily night light time series data of different land use types; The case report data of the target research area is obtained, and the case report data is corrected for the number of confirmed cases and the number of recovered cases to obtain the case report corrected data.

3. The method according to claim 1, characterized in that The mobile space network emerging infectious disease model is constructed according to the night light time series data, the case report correction data and the preset target traditional emerging infectious disease model, and based on the control objectives of emerging infectious diseases at different stages, the mobile space network emerging infectious disease model is used to simulate and evaluate the health dimension of different types of preset control measures to obtain the health benefits corresponding to the different types of control measures, including: Based on the night light time series data, determining the change amount of land use night light, and converting the change amount of land use night light into the mobile space network emerging infectious disease model; Under the preset early epidemic spreading trend, the model parameters corresponding to the mobile space network emerging infectious disease model are determined by optimizing and fitting the real epidemic curve; Based on the model parameters, the night light time series data on different land uses are adjusted to simulate the changes in population mobility across land uses corresponding to various scenarios, and the impact of the different types of control measures are evaluated in combination with the changes in population mobility across land uses and the preset attenuation contact infection rate parameters, wherein the various scenarios include completely restricting the population mobility across land uses, completely opening up the population mobility across land uses, restricting the population mobility across some land uses, and opening up the population mobility across some land uses; Based on the preset simulation time and simulation time interval, the different types of control measures are simulated to obtain the shortest duration for effective control of the epidemic corresponding to each type of control measure; Determine the epidemic endpoint according to the preset maximum curvature strategy, and use the epidemic endpoint and the shortest duration to compare the epidemic infection trends under different scenarios to obtain scenario comparison results; In the early control phase of emerging infectious diseases in the above different stages, the epidemic infection trends under the above different scenarios are converted into monetary health benefits in the socio-economic dimension; In the later stage of relaxation of emerging infectious diseases in the different stages, by implementing any type of control measures among the different types of control measures or implementing comprehensive relaxation measures in each of the preset multiple experimental stages, the coverage information of the maximum medical resources in the target research area to the target medical resource demand is estimated, and the health benefits corresponding to the different types of control measures are obtained based on the coverage information.

4. The method according to claim 1, characterized in that: The constructing of the input-output economic disaster assessment model for the emerging infectious disease and using the input-output economic disaster assessment model to simulate and evaluate the different types of control measures in economic dimensions to obtain the economic benefits corresponding to the different types of control measures include: Adjusting the added value loss rate and simulation interval of the target traditional input-output model, and reorganizing a plurality of land use type sectors preset in the target traditional input-output model to construct the input-output economic disaster assessment model; The input-output economic disaster assessment model is used to evaluate the control economic impact losses and control economic reconstruction losses corresponding to the different types of control measures, and the control economic impact losses and control economic reconstruction losses are used to determine the economic benefits corresponding to the different types of control measures.

5. The method according to claim 1, characterized in that The target control measures for the emerging infectious diseases at different stages are determined based on the health benefits, the economic benefits and the control objectives at different stages, including: Based on the health benefits, the economic benefits and the control objectives at different stages, construct an objective function corresponding to the early control stage of the emerging infectious disease, so as to determine the target control measures in the early control stage of the emerging infectious disease through the objective function; Obtain at least one release scenario that meets the preset requirement of sufficient medical resource supply in the later release stage of the emerging infectious disease, and determine the economic loss corresponding to each release scenario in the at least one release scenario; The economic losses of each of the relaxation scenarios are compared to obtain a target relaxation scenario with the smallest economic losses, and the target control measures in the later relaxation stage of the emerging infectious disease corresponding to the target relaxation scenario are determined.

6. The method according to claim 5, characterized in that The mathematical expression of the objective function is: TC(t)=EC(t)-HB(t) Wherein, EC(t) represents the economic benefit, HB(t) represents the health benefit, R(t) represents the cost-effectiveness ratio, and TC(t) represents the total loss.

7. A multi-stage strategy optimization device for emerging infectious diseases that balances health and economy, characterized in that: include: A construction module is used to construct the night light time series data and case report correction data of the target study area by land use on a daily basis; A health benefit evaluation module is used to construct a mobile space network emerging infectious disease model based on the night light time series data, the case report correction data and a preset target traditional emerging infectious disease model, and based on the control objectives of emerging infectious diseases at different stages, perform a simulation evaluation of the health dimension of different types of preset control measures through the mobile space network emerging infectious disease model to obtain the health benefits corresponding to the different types of control measures; An economic benefit assessment module, used to construct an input-output economic disaster assessment model for the emerging infectious disease, and use the input-output economic disaster assessment model to simulate and evaluate the economic dimension of the different types of control measures to obtain the economic benefits corresponding to the different types of control measures; The trade-off optimization module is used to determine the target control measures for the emerging infectious disease at different stages based on the health benefits, the economic benefits and the control objectives at different stages.

8. An electronic device, characterized in that: include: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement a multi-stage strategy optimization method for emerging infectious diseases that balances health and economy as described in any one of claims 1 to 6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: The program is executed by a processor to implement a multi-stage strategy optimization method for emerging infectious diseases that balances health and economy as described in any one of claims 1-6.

10. A computer program product, comprising a computer program, characterized in that The computer program is executed to implement the multi-stage strategy optimization method for emerging infectious diseases that balances health and economy as described in any one of claims 1-6.

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