Method, device, equipment and medium for preventing and controlling insect-borne infectious diseases

Abstract

The present application relates to the technical field of infectious disease prevention and control, and provides a method, device, equipment and medium for preventing and controlling insect-borne infectious diseases, which can collect insect vector data in real time by using an air-ground integrated insect vector monitoring device, solve the problem of incomplete coverage, generate a risk level by using a failure mode and effects analysis method in the normal prevention and control stage, realize accurate profiling of the whole process of prevention and control, perform macro-micro linkage based on the risk level and a model in the emergency disposal stage to construct an insect-borne infectious disease dynamics model based on a meteorological environment coupling mechanism, solve the problem that prevention and control intensity cannot be configured differently according to the risk level difference of different communities, perform parallel simulation on each intervention measure combination by using the insect-borne infectious disease dynamics model, avoid the homogenization of one-size-fits-all prevention and control, and improve the prevention and control effect, and locate weak failure indicators according to the emergency disposal result to perform feedback correction, solve the defects of disconnection between peace and war, system solidification, and inability to adapt to scene changes.

Description

Technical Field

[0001] This invention relates to the field of infectious disease prevention and control technology, and in particular to a method, device, equipment and medium for the prevention and control of vector-borne infectious diseases. Background Technology

[0002] Acute vector-borne infectious diseases such as dengue fever, transmitted through bites, are characterized by rapid spread, wide distribution, and high insidiousness, making them a key public health concern globally. Taking dengue fever as an example, its global incidence has increased in recent years, putting nearly half the world's population at risk of infection. Furthermore, influenced by global warming and cross-border population movement, its epidemic area is expanding northward beyond traditional tropical and subtropical zones, continuously increasing the pressure on prevention and control. While existing dengue fever prevention and control technologies and academic research possess basic risk assessment capabilities, they still suffer from two major structural technical shortcomings: First, risk assessments in the public health field are mostly based on empirical qualitative analysis and simple indicator aggregation, lacking systematic quantitative methods. The industry has never systematically introduced FMEA (Failure Mode and Effects Analysis) from the field of quality management into dengue fever community prevention and control, making it impossible to achieve accurate risk profiling in communities, resulting in extensive allocation of prevention and control resources and lagging identification of hidden dangers.

[0003] Second, existing epidemic prediction models rely solely on time-series epidemic data for trend prediction, which generally suffers from fragmented modules and isolated data. They can only achieve post-event evaluation or simple trend prediction, without establishing data interaction, parameter-driven, and adaptive linkage between community baseline risk and dynamic models. Furthermore, they can only adopt a one-size-fits-all homogeneous prevention and control strategy, and cannot differentiate the prevention and control intensity according to the risk level of different communities. Therefore, they cannot fundamentally solve the industry pain points of disconnect between peacetime and wartime and poor adaptability of measures.

[0004] In summary, existing technologies lack both a solution for constructing a quantitative risk classification system for vector-borne infectious diseases across domains using FMEA and a linkage architecture that dynamically drives the dynamic model based on risk classification results for adaptive prevention and control. This makes it difficult to meet the dual needs of routine and refined prevention and control at the community level and intelligent emergency response to sudden outbreaks. Therefore, there is an urgent need for a peacetime-wartime integrated method for the prevention and control of vector-borne infectious diseases based on risk classification and model linkage. Summary of the Invention

[0005] In view of the above, it is necessary to provide a method, device, equipment and medium for the prevention and control of vector-borne infectious diseases, in order to solve the problem of the inability to effectively prevent and control vector-borne infectious diseases.

[0006] A method for preventing and controlling vector-borne infectious diseases, the method comprising: In response to the command to control vector-borne infectious diseases in the target control area, the ground-air integrated vector monitoring equipment deployed in the target control area is used to collect vector data in the target control area in real time; During the normalized prevention and control phase, a risk assessment indicator system is acquired and configured. Failure Mode and Effects Analysis (FMEA) is used to determine the measured score of each indicator in the risk assessment indicator system based on the vector data. The risk level of the target prevention and control area is then generated based on the measured score of each indicator. During the emergency response phase, an initial model constructed using a meteorological-environmental coupling mechanism is obtained. The risk level is input into the initial model to perform macro-level linkage to obtain risk-adaptive pulsed disinfection intervention items. The measured scores of each indicator are input into the initial model to perform micro-level linkage to obtain adaptively corrected intervention parameters. The initial model is then adjusted based on the risk-adaptive pulsed disinfection intervention items and intervention parameters to obtain a vector-borne infectious disease dynamic model. Multiple intervention combinations were obtained, and the vector-borne infectious disease dynamics model was used to perform parallel simulations of each intervention combination in order to select the optimal emergency measures for prevention and control. After the optimal emergency measures are completed, the emergency response results are obtained, and the weak failure indicators are located based on the emergency response results. Feedback corrections are then performed based on the weak failure indicators.

[0007] A vector-borne infectious disease control device, the vector-borne infectious disease control device comprising: The data acquisition unit is used to collect vector data in real time within the target prevention and control area in response to the vector infectious disease prevention and control instructions deployed in the target prevention and control area using ground-air integrated vector monitoring equipment. The generation unit is used to acquire the configured risk assessment indicator system during the normalized prevention and control phase, use the failure mode and effect analysis method to determine the measured score of each indicator in the risk assessment indicator system based on the vector data, and generate the risk level of the target prevention and control area based on the measured score of each indicator. The adjustment unit is used to, during the emergency response phase, acquire an initial model constructed using a meteorological environment coupling mechanism, input the risk level into the initial model to perform macro-level linkage to obtain risk-adaptive pulse disinfection intervention items, input the measured scores of each indicator into the initial model to perform micro-level linkage to obtain adaptively corrected intervention parameters, and adjust the initial model according to the risk-adaptive pulse disinfection intervention items and intervention parameters to obtain a vector-borne infectious disease dynamic model. The simulation unit is used to obtain multiple combinations of intervention measures and to perform parallel simulations of each combination of intervention measures using the vector-borne infectious disease dynamics model, so as to select the optimal emergency measures for prevention and control. The correction unit is used to obtain the emergency response results after the optimal emergency measures are implemented, locate the weak failure indicators based on the emergency response results, and perform feedback correction based on the weak failure indicators.

[0008] A computer device, the computer device comprising: A memory for storing at least one instruction; and a processor for executing the instructions stored in the memory to implement the method for preventing and controlling vector-borne infectious diseases.

[0009] A computer-readable storage medium storing at least one instruction, which is executed by a processor in a computer device to implement the method for preventing and controlling vector-borne infectious diseases.

[0010] As can be seen from the above technical solutions, this invention can utilize ground-to-air integrated vector monitoring equipment deployed within the target prevention and control area to collect vector data in real time, solving the pain points of monitoring lag and incomplete coverage. In the routine prevention and control phase, the failure mode and effect analysis method is adopted to generate risk levels based on the measured scores of each indicator in the risk assessment indicator system, enabling the identification, quantitative rating, and accurate profiling of failure risks throughout the entire prevention and control process. In the emergency response phase, the risk level is input into the initial model to execute macro- and micro-level linkages, constructing a vector-borne infectious disease dynamic model based on a meteorological environment coupling mechanism. This linkage between risk levels and the model solves the problem of not being able to differentiate the configuration of prevention and control intensity according to the risk levels of different communities. Parallel simulation of various intervention measures combinations is performed using the vector-borne infectious disease dynamic model to select the optimal emergency measures for prevention and control, avoiding the drawbacks of a one-size-fits-all approach and improving the effectiveness of prevention and control. Based on the emergency response results, weak failure indicators are located for feedback correction, addressing the shortcomings of peacetime-wartime disconnect, system rigidity, and inability to adapt to changing scenarios. Attached Figure Description

[0011] Figure 1 This is a flowchart of a preferred embodiment of the vector-borne infectious disease control method of the present invention; Figure 2 This is a functional block diagram of a preferred embodiment of the vector-borne infectious disease control device of the present invention; Figure 3 This is a schematic diagram of the structure of a computer device that implements a preferred embodiment of the method for preventing and controlling vector-borne infectious diseases according to the present invention. Detailed Implementation

[0012] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be described in detail below with reference to the accompanying drawings and specific embodiments.

[0013] like Figure 1The diagram shown is a flowchart of a preferred embodiment of the method for preventing and controlling vector-borne infectious diseases according to the present invention. The order of the steps in this flowchart can be changed, and some steps can be omitted, depending on different needs.

[0014] The method for preventing and controlling vector-borne infectious diseases is applied to one or more computer devices. The computer device is a device that can automatically perform numerical calculations and / or information processing according to pre-set or stored instructions. Its hardware includes, but is not limited to, microprocessors, application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), digital signal processors (DSPs), embedded devices, etc.

[0015] The computer device can be any electronic product that can interact with the user, such as a personal computer, tablet computer, smartphone, personal digital assistant (PDA), interactive network television (IPTV), smart wearable device, etc.

[0016] The computer equipment may also include network equipment and / or user equipment. The network equipment includes, but is not limited to, a single network server, a server group consisting of multiple network servers, or a cloud based on cloud computing consisting of a large number of hosts or network servers.

[0017] The server can be a standalone server or a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communication, middleware services, domain name services, security services, content delivery networks (CDN), and big data and artificial intelligence platforms.

[0018] Artificial intelligence (AI) is the theory, method, technology and application system that uses digital computers or machines controlled by digital computers to simulate, extend and expand human intelligence, perceive the environment, acquire knowledge and use knowledge to obtain the best results.

[0019] Foundational technologies for artificial intelligence generally include sensors, dedicated AI chips, cloud computing, distributed storage, big data processing, operating / interactive systems, and mechatronics. AI software technologies mainly encompass computer vision, robotics, biometrics, speech processing, natural language processing, and machine learning / deep learning.

[0020] The network in which the computer device is located includes, but is not limited to, the Internet, wide area network, metropolitan area network, local area network, and virtual private network (VPN).

[0021] S10, in response to the command for the prevention and control of vector-borne infectious diseases in the target prevention and control area, the ground-air integrated vector monitoring equipment deployed in the target prevention and control area is used to collect vector data in the target prevention and control area in real time.

[0022] In this embodiment, the target prevention and control area can be a key area for the prevention and control of vector-borne infectious diseases, such as a community, park, or wetland.

[0023] In this embodiment, the vector-borne infectious disease prevention and control command can be triggered according to actual prevention and control needs.

[0024] In this embodiment, the integrated ground-air vector monitoring device may include, but is not limited to: ground-based AI intelligent mosquito vector monitoring device, aircraft aerial inspection unit, external auxiliary data acquisition unit, etc.

[0025] The ground-based AI-powered intelligent mosquito vector monitoring equipment can be deployed at fixed monitoring points in the target control area to automatically identify mosquito vector species, distinguish between males and females, count mosquito density, and collect basic data from positive sites in real time. The collected data can be automatically synchronized to a cloud platform.

[0026] Of course, mobile manual monitoring points can also be added to cover blind spots of equipment such as green belts, corridors, and vacant houses.

[0027] The aerial inspection unit of the aircraft is used to take low-altitude aerial photos of construction sites, rooftop platforms, vacant land, landscape water bodies and other blind spots in the target prevention and control area, and uses AI recognition technology to identify hidden water breeding grounds and sanitary dead corners, and automatically generate a list of hidden dangers, a list of responsibilities and a list of rectification to push to relevant management personnel.

[0028] The data collected by the external auxiliary data acquisition unit may include, but is not limited to, one or more of the following combinations of data: Meteorological and environmental data: such as weekly average temperature, weekly average relative humidity, and weekly cumulative rainfall; Laboratory test data: such as the mosquito resistance correction coefficient (which can be obtained through bioassays); Standard parameters in the literature: fixed parameters based on the model (such as the proportion of asymptomatic infected individuals, the rate of infection after exposure in the population, the recovery rate, the rate of infection after exposure to Aedes mosquitoes, the natural mortality rate of Aedes mosquitoes, and the effectiveness of mosquito control in the population). Community field survey data: risk indicator questionnaire scores, population mobility, residents' health education behavior formation rate, and protective behavior data of high-risk / general populations; Historical business data: number of past cases, duration of the epidemic, consumption of prevention and control materials, and service records of PCO (Pest Control Operation) service agencies.

[0029] When the density of insect vectors is detected to exceed the safe threshold for transmission, targeted early warning information is automatically pushed to the community and the disease control platform.

[0030] In this embodiment, the data format, spatiotemporal coordinates, and indicator caliber of the collected data can be standardized to eliminate the problem of data heterogeneity from monitoring equipment, surveys, and meteorological systems.

[0031] In this embodiment, a standardized data interface can also be established to achieve secure data transmission.

[0032] The above embodiments can solve the problems of lagging and incomplete coverage in traditional manual monitoring, provide real-world dynamic underlying parameters for risk classification and dynamic models, achieve data homogeneous calibration between the two modules, and reduce the deviation between simulation and reality.

[0033] S11. During the normalized prevention and control phase, the configured risk assessment indicator system is obtained. Using the Failure Mode and Effects Analysis (FMEA) method, the measured score of each indicator in the risk assessment indicator system is determined based on the vector data. The risk level of the target prevention and control area is generated based on the measured score of each indicator.

[0034] In this embodiment, the normalized prevention and control stage refers to the ordinary prevention and control stage in which there are only imported cases of mosquito-borne infectious diseases and no local transmission outbreaks are found.

[0035] In this embodiment, the risk assessment index system is used to identify potential failure points in the entire process of vector-borne infectious disease prevention and control in the target prevention and control area. It can be a customized, multi-dimensional risk assessment system including 5 primary indicators, 17 secondary indicators, and 46 tertiary indicators, covering five core scenarios: regional background environmental risk, organizational coordination and guarantee risk, resource reserve support risk, prevention and control intervention implementation risk, and supervision, optimization and iteration risk, so as to achieve risk identification without blind spots in the entire prevention and control chain.

[0036] The primary indicators may include: risks related to the community's baseline environment, risks related to organizational coordination and support, risks related to resource reserves and support, risks related to the implementation of prevention and control interventions, and risks related to supervision, optimization, and iteration.

[0037] The secondary indicators are subordinate indicators of the primary indicators and may include: community type and infrastructure, population structure and activity characteristics, residents' behavior habits, medical treatment conditions, and ecological and meteorological conditions, which belong to the primary indicator of community background environment risk; organizational mechanism construction, monitoring and early warning system, and normalized prevention and control mechanism, which belong to the primary indicator of organizational coordination and guarantee risk; funding and materials, personnel and capacity building, which belong to the primary indicator of resource reserve support risk; case management, mosquito density monitoring and control, and health education, which belong to the primary indicator of prevention and control intervention implementation risk; and problem rectification mechanism, service quality supervision, and summary and improvement, which belong to the primary indicator of supervision, optimization and iteration risk.

[0038] The tertiary indicators are subordinate indicators of the secondary indicators and may include: community types, distribution of blind spots in regional supervision, and number of transportation hubs and medical institutions within the region (all belonging to the secondary indicator of community types and infrastructure); population density, age structure, and population mobility (all belonging to the secondary indicator of population structure and activity characteristics); residents' gardening habits, health education behavior formation rate, and medical visit habits (all belonging to the secondary indicator of residents' behavioral habits); accessibility of fever clinics, dengue fever diagnosis and treatment knowledge awareness rate of medical personnel in secondary and higher-level medical institutions, and dengue fever diagnosis and treatment knowledge awareness rate of medical personnel in primary-level medical institutions (all belonging to the secondary indicator of medical treatment conditions); greening and water environment, rainfall and temperature and humidity, and mosquito resistance (all belonging to the secondary indicator of ecological and meteorological conditions); the establishment of prevention and control leading groups, prevention and control plans and emergency plans, performance evaluation and meeting mechanisms, implementation of grid management, and cross-departmental collaboration mechanisms (all belonging to the secondary indicator of organizational mechanism construction); the timeliness of case reporting and discovery and the hierarchical response mechanism (all belonging to the secondary indicator of monitoring and early warning system); and the normalized prevention and control mechanism (all belonging to the secondary indicator of routine prevention and control mechanism). The following are secondary indicators: Monthly Friday cleaning, enhanced seasonal prevention and control, removal of overwintering mosquito eggs, and spring mosquito larvae control; the budget amount for bidding on pest control service agencies, reserve and management of prevention and control materials, and emergency material allocation and replenishment mechanisms (all under the secondary indicator of funding and materials); the establishment and staffing of community departments, pest control service agencies, and mobilization of social forces (all under the secondary indicator of personnel); the dengue fever prevention and control knowledge awareness rate of social workers, service capacity of pest control service agencies, community theoretical training, and emergency drills (all under the secondary indicator of capacity building); case isolation and treatment and case retrieval (all under the secondary indicator of case management); larval and adult mosquito monitoring and control (all under the secondary indicator of mosquito vector density monitoring and control); health education content and channels, coverage of key populations and key locations, and evaluation of education effectiveness (all under the secondary indicator of health education); feedback on inspection issues and closed-loop management of rectification (all under the secondary indicator of problem rectification mechanisms); service supervision of pest control service agencies and resident participation (all under the secondary indicator of service quality supervision); and a summary of prevention and control work (all under the secondary indicator of summary and improvement).

[0039] In the above embodiments, the unique risk assessment indicator system can break through the technical bottleneck of traditional experience-based and qualitative risk assessment in the field of public health.

[0040] In this embodiment, the determination of the measured score of each indicator in the risk assessment indicator system based on the insect vector data using the Failure Mode and Effects Analysis (FMEA) method includes: Obtain the prevention and control related data of the target prevention and control area, and obtain the risk occurrence probability, risk severity and risk detectability of each indicator from the vector data and the prevention and control related data; Calculate the product of the probability of risk occurrence, the severity of risk, and the detectability of risk for each indicator to obtain the Risk Priority Number (RPN) for each indicator. For each indicator, the weight of the indicator is obtained by calculating the quotient between the risk order number of the indicator and the sum of the risk order numbers of all indicators. Obtain the upper limit of the comprehensive risk score for the target prevention and control area; Calculate the product of the weight and the upper limit of the comprehensive risk score to obtain the full score of the indicator; The potential failure modes of the indicators are obtained from the risk assessment indicator system, and the scoring constraints of the indicators are generated based on the potential failure modes and the full score. The scoring constraints for each indicator are sent to the testing terminal, and the measured score for each indicator is obtained from the testing terminal.

[0041] Each indicator refers to each of the tertiary indicators mentioned above.

[0042] The RPN value can be used to objectively quantify the failure probability, severity, and detectability of each prevention and control link.

[0043] Specifically, when the maximum comprehensive risk score is 100 points and the weight of indicator A is 0.1, the maximum score for indicator A is 100 * 0.1 = 10 points. The maximum score of 10 points and the potential failure modes of indicator A are sent to the testing terminal as constraints, and multiple questions are set (these questions are used to obtain information about indicator A). Feedback indicates that the sum of these multiple questions is 10 points. If the first question scores 0 points, the second question scores 3 points, and the third question scores 2 points, then the measured score for indicator A is 0 + 3 + 2 = 5 points.

[0044] The score returned by the measured terminal can be the median of the expert scores to avoid subjective errors.

[0045] The prevention and control situation varies in different areas, so the overall score of each area is also different, which allows for the creation of risk profiles for different prevention and control areas.

[0046] In the above embodiments, the multi-level weighting algorithm based on RPN value normalization takes the actual degree of risk as the core basis, automatically assigns high weight to high-risk links and low weight to low-risk links, completely subverting the traditional extensive mode of equal weight and manual subjective weighting, and can ensure that the risk assessment results are objective and accurate.

[0047] In this embodiment, generating the risk level of the target prevention and control area based on the measured score of each indicator includes: Calculate the product of the weight of each indicator and the corresponding measured score to obtain the weighted score of each indicator; Calculate the sum of the weighted scores of all indicators to obtain the target comprehensive risk score of the target prevention and control area; Obtain the entire region to which the target prevention and control area belongs, and obtain the comprehensive risk scores of all regions within the entire region; Each tier threshold is generated based on the target quantile and all the comprehensive risk scores. The risk level is obtained by comparing the overall risk score of the target with each grading threshold.

[0048] The "whole area" includes multiple prevention and control zones.

[0049] For example, when the target prevention and control area is a community, it can be classified according to the percentile of the comprehensive risk score of the entire sample of communities: Target comprehensive risk score > P80: Red, high-risk community; P60 < Target comprehensive risk score ≤ P80: Orange, relatively high-risk community; P40 < Target comprehensive risk score ≤ P60: Yellow, medium-risk community; P20 < Target comprehensive risk score ≤ P40: Blue, relatively low-risk community; Comprehensive risk score ≤ P20: Green, low-risk community. This completes the standardized risk profile for each community, providing unique, accurate, and parameterizable control benchmark data for the dynamic model during acute epidemics, enabling precise risk-based management during peacetime.

[0050] In the above embodiments, the FMEA failure mode and effect analysis method was introduced into the vector-borne infectious disease prevention and control scenario for the first time in a cross-domain and systematic manner, realizing the full-process failure risk identification, quantitative rating and accurate profiling of community dengue fever and other prevention and control tasks.

[0051] To further match static risk profiles with dynamic environments and the epidemic situation, this embodiment configures a basic validity period, such as 7 days, for the standardized risk profiles generated for each community.

[0052] Furthermore, it monitors three types of failure triggering conditions in real time, and automatically triggers the failure of the current profile and immediately initiates a new round of full-indicator FMEA reassessment when any of the following conditions are met: 1) New local cases of vector-borne infectious diseases, such as new cases of dengue fever, appear in the target prevention and control area; 2) The insect vector density monitoring data exceeded the safety warning threshold; 3) Meteorological conditions that significantly alter the breeding environment, such as extreme high temperatures (>40℃), extreme low temperatures (<15℃), or continuous heavy rainfall, occurring for several consecutive days (e.g., 3 consecutive days).

[0053] During the normalized prevention and control phase, monitoring data is updated daily and a reassessment is triggered in advance when the basic validity period is approaching (e.g., the reassessment is automatically triggered 1 day in advance when the 7-day validity period is approaching), thereby ensuring that the risk level assessment results during the normalized prevention and control phase are consistent with the actual on-site prevention and control conditions in real time.

[0054] S12, In the emergency response phase, an initial model constructed using a meteorological environment coupling mechanism is obtained. The risk level is input into the initial model to perform macro-level linkage to obtain risk-adaptive pulse disinfection intervention items. The measured scores of each indicator are input into the initial model to perform micro-level linkage to obtain adaptively corrected intervention parameters. The initial model is then adjusted based on the risk-adaptive pulse disinfection intervention items and intervention parameters to obtain a vector-borne infectious disease dynamic model.

[0055] In this embodiment, the emergency response phase can be the prevention and control phase following a local outbreak of mosquito-borne infectious diseases.

[0056] In this embodiment, before obtaining the initial model constructed using the meteorological environment coupling mechanism, the method further includes: Based on the division of the population into storage chambers, the storage chambers for each population group and each insect vector population were obtained; Based on the SEIAR (Susceptible-Exposed-Infectious / Asymptomatic-Recovered) cell model, we established the first differential equation for the change in cell size over time for each population and the second differential equation for the change in cell size over time for each vector population. Using the lower limit of the critical temperature and humidity tolerance range of the insect vector as a constraint, the temperature and humidity correction coefficient is constructed by linearly attenuating the real-time temperature and humidity of the target control area relative to the optimal temperature and humidity of the insect vector. Based on the incremental effect of the cumulative rainfall in the target prevention and control area on the breeding grounds, a rainfall correction coefficient is constructed using a saturated growth model with a marginally decreasing upper limit constraint. A meteorological environment coupling factor is constructed based on the temperature and humidity correction coefficient and the rainfall correction coefficient. Based on the aforementioned meteorological and environmental coupling factors, a first propagation term for transmission from insect vectors to humans and a second propagation term for transmission from humans to insect vectors are constructed respectively. Write the first propagation term into the corresponding first quantity change differential equation and write the second propagation term into the corresponding second quantity change differential equation to obtain a set of dynamic bidirectional propagation differential equations under the non-intervention benchmark. The differential equation sets for the case isolation intervention model, the stratified health education intervention model, the continuous pest control intervention model, and the risk-adaptive pulse pest control intervention model were constructed. By combining the various differential equations, the initial model is obtained.

[0057] The different groups of people in the wards can include: susceptible individuals, those in the incubation period, asymptomatic carriers, symptomatic carriers, and recovered individuals.

[0058] For example, in the case of mosquito-borne diseases (such as dengue fever, Zika virus disease or chikungunya fever), each vector population can include: susceptible Aedes mosquitoes, latent Aedes mosquitoes, and infectious Aedes mosquitoes.

[0059] For example, taking mosquitoes as an example, the initial model can be represented as follows: (1) SEIAR-M (Susceptible-Exposed-Infectious / Asymptomatic-Recovered-Meteorology & Environment, Susceptible-Exposed-Symptomatic / Asymptomatic-Recovered-Meteorology & Environment Coupling) Dynamic Two-Way Propagation Differential Equations (including meteorological-environment coupling) without intervention baseline: ; ; ; ; ; ; ; ; ; .

[0060] in, Indicates the susceptible individual; t represents time. R represents the daily average temperature and humidity correction factor for the target area; R represents the rainfall correction factor (calculated based on weekly cumulative rainfall); () represents the meteorological environment coupling factor; Indicates the first propagation term; This indicates the effective transmission probability from Aedes mosquitoes to humans; Indicates Aedes mosquitoes in their infectious period; Indicates the human cells belonging to each group of people; This indicates individuals infected during the incubation period; This indicates the rate at which susceptible individuals in a population become infected. Indicates an symptomatic carrier; This indicates the proportion of asymptomatic carriers in the total infected population; Indicates the recovery rate of infected individuals; This indicates an asymptomatic carrier; Indicates the restorer; Indicates susceptible Aedes mosquitoes; Indicates the natural growth rate of Aedes mosquitoes; The mosquito vector compartments to which each vector population belongs are used to characterize the mosquito vector population size; K represents the Aedes mosquito carrying capacity. Indicates the natural mortality rate of Aedes mosquitoes; This indicates the effective transmission probability from humans to Aedes mosquitoes. Indicates the second propagation term; Indicates the incubation period of Aedes mosquitoes; This indicates the rate at which Aedes mosquitoes are exposed to infection.

[0061] Among them, the initial number of susceptible Aedes mosquitoes (S m0 ), initial incubation period Aedes mosquito count (E) m0 ), the number of Aedes mosquitoes during the initial infectious period (I m0 ), the effective transmission probability from Aedes mosquitoes to humans ( h ), the effective transmission probability from humans to Aedes mosquitoes ( m ), Aedes mosquito natural growth rate ( The carrying capacity (K) of Aedes mosquitoes can be obtained through model fitting. , , , , This can be obtained by consulting literature, where the default... =0.75, =0.2, =0.2, =0.1, =1 / 21.

[0062] The temperature and humidity correction coefficient is a normalized correction factor for the combined effect of temperature and humidity on dengue fever transmission. Assuming the optimal temperature range for Aedes mosquito transmission is 25℃-30℃ and the optimal relative humidity range is 60%-80%, then the temperature and humidity correction coefficient... It can be represented as follows: .

[0063] in, It represents the relationship between the weekly average temperature T (in °C) and the weekly average relative humidity RH (in %). This indicates the optimal temperature, with a value of 27.5℃. This indicates a temperature tolerance margin of 15℃, meaning the suitable temperature range is 27.5±15℃. The optimal relative humidity is 70%. This indicates the humidity tolerance half-width, with a value of 40%, meaning the suitable humidity range is 70% ± 40%.

[0064] When T=27.5℃ and RH=70%, =1, the risk of transmission is the greatest; when T<15℃ or >40℃, or RH<30% or >90%, =0, the transmission risk is negligible. By using temperature and humidity correction coefficients, the model can dynamically adjust the transmission rate based on real-time meteorological data.

[0065] The rainfall correction coefficient is the positive contribution factor of rainfall to mosquito breeding grounds. The rainfall correction coefficient R can be expressed as follows: .

[0066] in, This represents the maximum correction factor, set to 1.5, to reflect the significant increase in breeding ground area after heavy rainfall; This represents the growth rate parameter, with a set value of 0.05. This indicates the cumulative rainfall, expressed in mm.

[0067] When RR=0, R=0 (no rain, no additional correction); as RR increases, R approaches zero in a saturated growth manner. ; It is a natural exponential decay function, ensuring that R increases with increasing rainfall, but the marginal effect decreases.

[0068] Specifically, the meteorological environment coupling factor allows the combined effects of temperature, humidity, and rainfall on the transmission rate to be directly applied to the human-mosquito transmission term. In practical engineering deployments, more complex nonlinear coupling functions can be constructed based on local historical data and expert experience, such as Gaussian response functions for temperature and humidity, and piecewise linear functions for rainfall.

[0069] (2) Differential equations of the case isolation intervention model: ; .

[0070] in, This indicates the standard isolation ratio for identifiable cases, and can be customized as needed.

[0071] By introducing the standard isolation ratio q for identifiable cases and incorporating measured data of FMEA community population mobility indicators into the model, the optimal isolation intensity can be inferred from the model simulation.

[0072] (3) Differential equations of the stratified health education intervention model: ; ; ; .

[0073] in, The coefficient of difference in protection among different groups is a dimensionless correction coefficient with a value range of [0,1]. It is used to represent the differences in protective behavior compliance and exposure risk among different groups in the community. It can be obtained through community surveys. It calculates the ratio of high-risk groups (such as the elderly, children, and outdoor workers) to the general population in terms of indicators such as "mosquito net / repellent usage rate" and "duration of outdoor activities". σ = 1 - (protective behavior formation rate of high-risk groups / protective behavior formation rate of the general population). The larger the value of σ, the more significant the difference in the level of protection among the groups. The model needs to cover more diverse strategies in health education intervention items. The health education coverage rate can be customized according to needs or calculated by model simulation based on preset prevention and control targets; E represents the health education behavior formation rate (such as the proportion of residents who have mastered the use of mosquito nets, mosquito repellent products and other protective measures), which can be obtained by conducting a current situation survey in the target community to obtain measured data; F represents the mosquito prevention effectiveness rate of the population (such as the proportion of people who can effectively prevent mosquitoes by using mosquito nets, mosquito repellent products and other protective measures), which can be obtained by consulting literature, such as F=0.888.

[0074] By introducing the population-stratified protection difference coefficient σ, and based on the measured data of the formation rate of health education behavior in FMEA and the literature search data of the population mosquito prevention effectiveness, and substituting them into the model, the optimal health education coverage rate can be deduced from the model simulation.

[0075] (4) Differential equations of the continuous pest control intervention model: ; ; .

[0076] in, This indicates the additional mortality rate of Aedes mosquitoes resulting from pest control, and can be customized according to needs; This represents the correction coefficient for mosquito resistance attenuation, with a value range of [0,1], and can be obtained through laboratory bioassays.

[0077] By combining measured data of FMEA mosquito density index, the additional mortality rate x of pest control can be adaptively adjusted.

[0078] (5) Differential equations of the risk-adaptive pulse insecticidal intervention model: ; in, Represents the instantaneous kill coefficient at time t; Indicates the timing of the disinfection pulse.

[0079] Based on the differential equations of the risk-adaptive pulse pest control intervention model, the pest control cycle can be adaptively matched to achieve a precise match between risk and intervention intensity.

[0080] In addition to the meteorological environment coupling factor, other dynamic correction parameters such as mosquito resistance coefficient can also be introduced.

[0081] Among these methods, we can rely on the long-term accumulated big data on community prevention and control, and use mathematical statistics and machine learning to fit and obtain the quantitative conversion coefficients between the sub-indicators and each intervention parameter.

[0082] The above (1)-(5) can be selected according to actual needs. For example, in an emergency, if you want to simulate the effect of case isolation and the trend of epidemic development, you can choose to use the differential equation system of the case isolation intervention model (2).

[0083] The different compartments within the initial model are associated and moved with configured parameters. The process of each compartment changing over time is represented by differential equations, thus forming a set of differential equations that can express the dengue fever transmission process.

[0084] In this embodiment, the step of inputting the risk level into the initial model to perform macro-level linkage and obtain risk-adaptive pulse disinfection intervention items includes: Obtain the configured mapping table, and use the risk level to traverse the mapping table to obtain the pulse elimination cycle constraint parameters; The pulse disinfection cycle constraint parameters are input into the differential equation set of the risk-adaptive pulse pest control intervention model to limit the trigger interval of the disinfection pulse time in the differential equation set of the risk-adaptive pulse pest control intervention model, thereby obtaining the risk-adaptive pulse disinfection intervention term.

[0085] The mapping table records the correspondence between risk levels and pulse disinfection cycles. For example, when the risk level is red (high risk), the pulse disinfection cycle is 1 day; when the risk level is orange (relatively high risk), the pulse disinfection cycle is 2 days; when the risk level is yellow (medium risk) or blue (low risk), the pulse disinfection cycle is 3 days; and when the risk level is green (low risk), a daily monitoring mode is triggered, with preventative disinfection every 7 days. The pulse disinfection cycle is uniquely determined by the FMEA risk rating result, achieving adaptive and coordinated control.

[0086] In this embodiment, the step of inputting the measured score of each indicator into the initial model to perform micro-level linkage and obtain the adaptively corrected intervention parameters includes: Based on the measured scores of each indicator, scores for control indicators, protection indicators, and disinfection indicators are generated. The proportion of explicit cases requiring standardized isolation is corrected based on the scores of the control-related indicators in the differential equation set of the case isolation intervention model. The population-stratified protection difference coefficient of the differential equation system of the stratified health education intervention model is corrected based on the scores of the protection-related indicators. The additional mortality rate from pest control is corrected based on the scores of the pest control indicators in the differential equation system of the continuous pest control intervention model. The instantaneous kill coefficient of the differential equation system of the risk adaptive pulse insecticidal intervention model is corrected based on the scores of the aforementioned pest control indicators. By integrating and correcting the standardized isolation ratio of the obvious cases, the population stratification protection difference coefficient, the additional mortality rate from disinfection, and the instantaneous kill coefficient, each intervention parameter is obtained.

[0087] For example, the measured scores of the population mobility indicator in the tertiary indicators can be classified as the control-related indicator scores, the measured scores of the resident health education behavior formation rate indicator in the tertiary indicators can be classified as the protection-related indicator scores, and the measured scores of the larval mosquito monitoring and control and adult mosquito monitoring and control indicators in the tertiary indicators can be classified as the disinfection-related indicator scores.

[0088] Furthermore, the measured scores of the sub-indicators are directly used as the initial fixed parameters input for the model.

[0089] If the rate of residents forming health education behaviors is low (e.g., only 20% in actual measurement), then substitute the actual behavior formation rate E=20% into SEIAR-M. Under the constraint of a predetermined disinfection cycle, the model will perform reverse simulation to solve for the optimal health education coverage rate required to achieve the preset prevention and control goals (e.g., ending the epidemic within 30 days, with a cumulative number of cases ≤300). When implementing the model, health education improvement work will be carried out according to the optimal health education coverage rate calculated by the model. If the rate of residents forming health education behaviors is high, the model simulation can reduce the health education coverage rate under the premise of meeting the epidemic prevention and control requirements, so as to save on prevention and control investment.

[0090] If the risk of population mobility indicators is high, the actual mobility data will be input into the model, and the model will calculate the minimum proportion of identifiable cases requiring standardized isolation to meet the prevention and control objectives. During the emergency response phase, isolation and control will be implemented according to the optimal proportion of identifiable cases required by the simulation.

[0091] In the above embodiments, the actual measured data of indicators can be combined with simulation and reverse optimization to achieve one model per community and one set of customized intervention parameters for each risk deficiency, thereby completing adaptive and differentiated prevention and control.

[0092] It should be noted that while routine prevention and control can rely on FMEA profiles with expiration dates for daily management, after an outbreak, data on vectors, populations, and cases will continuously change dynamically, and a single static profile will not be able to match the actual transmission risk on that day. Therefore, in the emergency response phase, a short-term dynamic reassessment mechanism (such as a 24-hour dynamic reassessment mechanism) can be set up: after the emergency response phase is initiated, instead of relying solely on the static risk profiles generated under normal circumstances, daily data on integrated ground-air vector monitoring, newly added cases, and real-time temperature, humidity, and rainfall data are retrieved synchronously. All three levels of indicators RPN values ​​and comprehensive risk scores are fully recalculated, and the five levels of real-time risk (red, orange, yellow, blue, and green) are updated. The daily pulse disinfection cycle, isolation, and public education intervention parameters are all recalibrated based on the latest risk data of the day, thereby achieving dynamic risk tracking during the emergency process and avoiding the disconnect between static profiles and the actual on-site prevention and control situation.

[0093] S13, obtain multiple combinations of intervention measures, and use the vector-borne infectious disease dynamics model to perform parallel simulations of each combination of intervention measures in order to select the optimal emergency measures for prevention and control.

[0094] The intervention combination may include one or more of the following measures: case isolation of varying intensities, tiered health education, continuous mosquito control, and pulsed mosquito control.

[0095] In this embodiment, the duration of the epidemic, the cumulative number of cases, and the decrease in mosquito density can be used as screening indicators to dynamically select the optimal emergency measures that are suitable for the current risk level and epidemic situation.

[0096] In the above embodiments, relying on the linkage mechanism to achieve differentiated and customized emergency prevention and control for different background risk areas can avoid the drawbacks of traditional one-size-fits-all homogeneous prevention and control, significantly reduce the error of epidemic simulation, improve the accuracy of prevention and control risk identification, and reduce prevention and control costs while ensuring the effectiveness of prevention and control.

[0097] S14. After the optimal emergency measures are completed, obtain the emergency response results, locate the weak failure indicators based on the emergency response results, and perform feedback correction based on the weak failure indicators.

[0098] In this embodiment, the emergency response results may include mosquito density decline curves, cumulative number of cases, duration of the outbreak, and resource consumption.

[0099] In this embodiment, the step of locating weak failure indicators based on the emergency response results and performing feedback corrections based on the weak failure indicators includes: Based on the emergency response results, the current measured scores of the corresponding indicators of the optimal emergency measures are traced back. Obtain the prevention and control threshold corresponding to the corresponding indicator, and take the indicator whose current measured score is lower than the corresponding prevention and control threshold as the weak failure indicator; The weights of the aforementioned weak failure indicators are increased to revise the risk assessment indicator system and the vector-borne infectious disease dynamics model.

[0100] The control threshold can be the optimal value selected based on experiments.

[0101] In the above embodiments, it is possible to reverse-optimize the priority of corresponding weak failure indicators in routine FMEA management in the community, and further revise the risk assessment indicator system and the vector-borne infectious disease dynamics model.

[0102] This embodiment, based on a three-tiered collaborative prevention and control mechanism that links peacetime and emergency response, and a two-way iterative mechanism, achieves seamless switching between peacetime and wartime modes and continuous optimization of the technical system. At the foundational layer, it focuses on strict case isolation, auxiliary screening of asymptomatic cases, and integrated ground-air intelligent early warning, relying on FMEA population risk data to build a solid basic barrier against epidemic transmission. At the core layer, in the early stages of an epidemic, based on FMEA mosquito-borne disease risk levels, it initiates a bound pulsed extermination cycle to quickly suppress the vector population. At the optimization layer, it couples multiple measures, including tiered health education and differentiated case management, and fine-tunes intervention strategies in real time based on dynamic simulation results, balancing prevention and control effectiveness with resource costs. A two-way iterative logic is established, using the peacetime FMEA risk profile as a calibration benchmark for the model during epidemic transmission, and using the actual prevention and control data after the model is implemented during epidemic transmission to guide the optimization of daily management priorities, forming a complete closed-loop prevention and control system.

[0103] Furthermore, unlike the independent and simply superimposed approach of risk assessment and epidemic modeling, this embodiment is the first to combine the core linkage mechanism of FMEA static risk profiles to dynamically drive the SEIAR-M dynamic model. It uses static risk profiles to dynamically correct model intervention parameters, achieving adaptive matching of prevention and control strategies. Through standardized data interfaces, it enables precise control of model intervention parameters by regional risk levels and risk scores of various risk indicators, solving the core problem that existing forward-looking prediction models can only predict but cannot adapt or differentiate policies.

[0104] Meanwhile, through a two-way closed loop of routine risk assessment, emergency simulation-based policy formulation, and real-world reverse optimization of key daily management points, it solves the defects of disconnect between peacetime and wartime, rigid systems, and inability to adapt to changing scenarios. It can adapt to the normalized and emergency prevention and control needs of different risk areas and different types of communities, and has strong scalability.

[0105] In addition, unlike existing models that can only make forward predictions and evaluate after the fact, the model in this embodiment relies on FMEA risk data to realize the full-process functions of risk profile calibration parameters, dynamic simulation prediction, and differentiated optimal decision-making in sequence, which solves the core pain points of existing technologies that can predict but cannot adapt or implement precise policies.

[0106] The following description will illustrate this embodiment in the context of a specific application scenario.

[0107] Example 1: Risk Quantitative Profiling and Routine Management of Risk-Prone Urban Communities using FMEA.

[0108] The urban area of ​​a certain city includes eight typical communities, covering four scenarios: urban villages, old residential areas, newly built residential areas, and suburban communities. This embodiment utilizes the unique FMEA cross-domain quantitative assessment system to conduct routine risk assessments. A multi-domain expert group was formed to score the probability of occurrence, severity, and detectability of each of the 46 risk indicators for failure of level-three prevention and control. The RPN value and normalized weight of each indicator were calculated. Combined with ground-air integrated drone inspections, AI mosquito vector monitoring real-time data (real-time collected monitoring data, corresponding to mosquito density monitoring results in the monitoring and early warning system and graded response mechanism within the 46 indicators), and the actual situation of the target communities, a quantitative score and a five-level risk profile for each community were completed.

[0109] Following assessment, two urban village communities were designated as red (high-risk); two older residential communities as orange (higher-risk); two ordinary communities as yellow (medium-risk); and two newly built communities as blue (lower-risk). Based on the risk profiles, differentiated control strategies were implemented. High-risk communities underwent high-intensity pulse disinfection once daily, while low-risk communities received routine control measures and preventative disinfection every seven days. After six months of control, mosquito density across the entire area decreased significantly, numerous hidden dangers were rectified in advance, and no local transmission outbreaks occurred due to imported dengue fever cases, validating the accuracy and practicality of the FMEA quantitative profiling system.

[0110] Example 2: Adaptive emergency response using the FMEA risk-driven SEIAR-M model.

[0111] This example uses a high-risk (red) community as an example. A scenario simulating an imported dengue fever case triggering a local outbreak in this community is used to activate the coordinated prevention and control mechanism of this embodiment. First, the community's FMEA risk rating and risk scores for each risk indicator are retrieved: It is assumed that, based on current surveys, the community's population mobility is 0.75 and the rate of health education behavior formation is 20%. The prevention and control target is set as "ending the epidemic within 30 days, with a cumulative number of cases not exceeding 300." The community's FMEA risk level (red, with a pulse disinfection cycle of 1 day) and the above-mentioned assumed measured data are substituted into the SEIAR-M model. Through reverse simulation optimization, the model outputs an optimal health education coverage rate of 90% and an optimal case isolation rate of 85%. The intervention is implemented according to these simulation results: pulse disinfection once a day, an 85% case isolation rate, and a 90% health education coverage rate. Simultaneously, the SEIAR-M model is localized and calibrated based on local meteorological and drug resistance parameters.

[0112] The simulation process synchronously simulates the dynamic changes in risk during the emergency phase: Assuming that on the third day of the treatment of the red high-risk community, there have been no new cases for several consecutive days, all breeding grounds inspected by drones have been cleared, the real-time mosquito density has dropped significantly, and after triggering the daily FMEA reassessment, the regional risk is downgraded from red to yellow, and the pulse disinfection cycle is adjusted from 1 day to 3 days, thereby saving manpower and pesticides without increasing the risk of epidemic rebound; Assuming that on the fifth day of the treatment, a sudden rainstorm occurs, and many new waterlogged breeding grounds are added, the risk rises back to orange after the reassessment on the same day, and the disinfection cycle automatically switches to 2 days, so as to dynamically match the actual vector transmission risk on site and solve the shortcomings of static profile fixed disinfection strategies.

[0113] Simulation results show that existing traditional fixed models lack risk-based linkage control, have large simulation errors, and can only make general predictions of epidemic trends without providing differentiated strategies. In contrast, the FMEA-driven adaptive model in this embodiment accurately matches vulnerable risk points in the community, specifically strengthening mosquito control and public health education interventions. This allows for rapid containment of the epidemic in a shorter time, minimizing the number of infections and control costs. Subsequent comparisons of actual epidemic data and simulation results can verify the control value of increased health education coverage, and residents' health education work can be incorporated into the community's routine key control measures.

[0114] The specific values ​​in this embodiment (such as population mobility 0.75, education behavior formation rate 20%, optimal education coverage rate 90%, optimal isolation ratio 85%, etc.) are only set to illustrate the linkage logic of this embodiment. In actual applications, it is necessary to re-simulate the model based on the actual situation survey data and prevention and control objectives of the corresponding prevention and control area. This fully verifies that this embodiment is not a simple superposition of two technologies, but rather achieves a precise, low-cost, and high-efficiency prevention and control effect that cannot be achieved by existing single technologies through unique linkage logic.

[0115] Example 3: Comparative example.

[0116] Simulating the same scenario using existing publicly available forward-looking prediction models relies solely on epidemic time-series data to predict trends. These models lack FMEA community risk profiling and risk parameter linkage control logic, failing to differentiate the underlying risk levels of different communities. They can only employ a uniform, homogenized prevention and control strategy, resulting in insufficient control measures for high-risk communities and excessive control measures for low-risk communities. This leads to poor adaptability and an inability to specifically address weaknesses in community prevention and control. In contrast, the cross-domain integration and linkage control logic of this embodiment effectively overcomes these problems. It solves the industry pain points of traditional prevention and control assessment being crude, measures being blind, models being disconnected from reality, and unable to be differentiated and adapted. It can accurately block the local transmission of imported epidemics, significantly improving the intelligent, refined, and adaptive prevention and control level of vector-borne infectious diseases such as dengue fever in communities. It is applicable to various urban and rural community routine prevention and control and emergency response scenarios for sudden outbreaks.

[0117] This embodiment can be automated through computer software, intelligent monitoring terminals, and cloud data platforms. It can be directly deployed in urban public health prevention and control systems, community smart prevention and control platforms, and disease control emergency decision-making systems. It can be applied in batches to the routine prevention and control of vector-borne infectious diseases and the emergency response to sudden outbreaks in various regions and communities. Moreover, the model is reproducible, the parameters are calibrable, and the linkage logic can be programmed, which has strong engineering practicality and industrial promotion value.

[0118] As can be seen from the above technical solutions, this invention can utilize ground-to-air integrated vector monitoring equipment deployed within the target prevention and control area to collect vector data in real time, solving the pain points of monitoring lag and incomplete coverage. In the routine prevention and control phase, the failure mode and effect analysis method is adopted to generate risk levels based on the measured scores of each indicator in the risk assessment indicator system, enabling the identification, quantitative rating, and accurate profiling of failure risks throughout the entire prevention and control process. In the emergency response phase, the risk level is input into the initial model to execute macro- and micro-level linkages, constructing a vector-borne infectious disease dynamic model based on a meteorological environment coupling mechanism. This linkage between risk levels and the model solves the problem of not being able to differentiate the configuration of prevention and control intensity according to the risk levels of different communities. Parallel simulation of various intervention measures combinations is performed using the vector-borne infectious disease dynamic model to select the optimal emergency measures for prevention and control, avoiding the drawbacks of a one-size-fits-all approach and improving the effectiveness of prevention and control. Based on the emergency response results, weak failure indicators are located for feedback correction, addressing the shortcomings of peacetime-wartime disconnect, system rigidity, and inability to adapt to changing scenarios.

[0119] like Figure 2 The diagram shown is a functional block diagram of a preferred embodiment of the vector-borne infectious disease control device of the present invention. The vector-borne infectious disease control device 11 includes a data acquisition unit 110, a generation unit 111, an adjustment unit 112, a simulation unit 113, and a correction unit 114. The module / unit referred to in this invention is a series of computer program segments that can be executed by a processor and perform a fixed function, and are stored in a memory. In this embodiment, the functions of each module / unit will be described in detail in subsequent embodiments.

[0120] The acquisition unit 110 is used to collect vector data in real time within the target prevention and control area in response to the vector infectious disease prevention and control command for the target prevention and control area using the ground-air integrated vector monitoring equipment deployed within the target prevention and control area. The generation unit 111 is used to acquire the configured risk assessment index system during the normalized prevention and control phase, use the failure mode and effect analysis method to determine the measured score of each index in the risk assessment index system based on the vector data, and generate the risk level of the target prevention and control area based on the measured score of each index. The adjustment unit 112 is used to, during the emergency response phase, acquire an initial model constructed using a meteorological environment coupling mechanism, input the risk level into the initial model to perform macro-level linkage to obtain risk-adaptive pulse disinfection intervention items, input the measured scores of each indicator into the initial model to perform micro-level linkage to obtain adaptively corrected intervention parameters, and adjust the initial model according to the risk-adaptive pulse disinfection intervention items and intervention parameters to obtain a vector-borne infectious disease dynamic model. The simulation unit 113 is used to acquire multiple combinations of intervention measures and to perform parallel simulations of each combination of intervention measures using the vector-borne infectious disease dynamics model in order to select the optimal emergency measures for prevention and control. The correction unit 114 is used to obtain the emergency response result after the optimal emergency measures are executed, locate the weak failure indicators based on the emergency response result, and perform feedback correction based on the weak failure indicators.

[0121] As can be seen from the above technical solutions, this invention can utilize ground-to-air integrated vector monitoring equipment deployed within the target prevention and control area to collect vector data in real time, solving the pain points of monitoring lag and incomplete coverage. In the routine prevention and control phase, the failure mode and effect analysis method is adopted to generate risk levels based on the measured scores of each indicator in the risk assessment indicator system, enabling the identification, quantitative rating, and accurate profiling of failure risks throughout the entire prevention and control process. In the emergency response phase, the risk level is input into the initial model to execute macro- and micro-level linkages, constructing a vector-borne infectious disease dynamic model based on a meteorological environment coupling mechanism. This linkage between risk levels and the model solves the problem of not being able to differentiate the configuration of prevention and control intensity according to the risk levels of different communities. Parallel simulation of various intervention measures combinations is performed using the vector-borne infectious disease dynamic model to select the optimal emergency measures for prevention and control, avoiding the drawbacks of a one-size-fits-all approach and improving the effectiveness of prevention and control. Based on the emergency response results, weak failure indicators are located for feedback correction, addressing the shortcomings of peacetime-wartime disconnect, system rigidity, and inability to adapt to changing scenarios.

[0122] like Figure 3 The diagram shown is a schematic representation of the computer device used in a preferred embodiment of the method for preventing and controlling vector-borne infectious diseases according to the present invention.

[0123] The computer device 1 may include a memory 12, a processor 13, and a bus (the arrow in the figure represents the bus), and may also include a computer program stored in the memory 12 and executable on the processor 13, such as a vector-borne infectious disease control program.

[0124] Those skilled in the art will understand that the schematic diagram is merely an example of computer device 1 and does not constitute a limitation on computer device 1. Computer device 1 can be either a bus topology or a star topology. Computer device 1 may also include more or fewer other hardware or software than shown in the diagram, or different component arrangements. For example, computer device 1 may also include input / output devices, network access devices, etc.

[0125] It should be noted that the computer device 1 described is merely an example. Other existing or future electronic products that are adaptable to this invention should also be included within the scope of protection of this invention and are incorporated herein by reference.

[0126] The memory 12 includes at least one type of readable storage medium, such as flash memory, portable hard drive, multimedia card, card-type memory (e.g., SD or DX memory), magnetic memory, magnetic disk, optical disk, etc. In some embodiments, the memory 12 can be an internal storage unit of the computer device 1, such as a portable hard drive of the computer device 1. In other embodiments, the memory 12 can be an external storage device of the computer device 1, such as a plug-in portable hard drive, smart media card (SMC), secure digital card (SD), flash card, etc., equipped on the computer device 1. Furthermore, the memory 12 can include both internal and external storage units of the computer device 1. The memory 12 can be used not only to store application software and various types of data installed on the computer device 1, such as code for vector-borne infectious disease control programs, but also to temporarily store data that has been output or will be output.

[0127] In some embodiments, the processor 13 may be composed of integrated circuits, such as a single packaged integrated circuit or multiple integrated circuits packaged with the same or different functions, including combinations of one or more central processing units (CPUs), microprocessors, digital processing chips, graphics processors, and various control chips. The processor 13 is the control unit of the computer device 1, connecting various components of the computer device 1 via various interfaces and lines. It executes programs or modules stored in the memory 12 (e.g., programs for controlling vector-borne infectious diseases) and calls data stored in the memory 12 to perform various functions of the computer device 1 and process data.

[0128] The processor 13 executes the operating system of the computer device 1 and various installed applications. The processor 13 executes these applications to implement the steps in the various embodiments of the vector-borne infectious disease prevention and control methods described above, for example... Figure 1 The steps are shown.

[0129] For example, the computer program may be divided into one or more modules / units, which are stored in the memory 12 and executed by the processor 13 to complete the present invention. The one or more modules / units may be a series of computer-readable instruction segments capable of performing specific functions, which describe the execution process of the computer program in the computer device 1. For example, the computer program may be divided into an acquisition unit 110, a generation unit 111, an adjustment unit 112, a simulation unit 113, and a correction unit 114.

[0130] The integrated unit implemented as a software functional module described above can be stored in a computer-readable storage medium. This software functional module, stored in a storage medium, includes several instructions to cause a computer device (which may be a personal computer, computer equipment, or network device, etc.) or processor to execute portions of the vector-borne infectious disease prevention and control methods described in the various embodiments of this invention.

[0131] If the modules / units integrated in the computer device 1 are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, all or part of the processes in the methods of the above embodiments of the present invention can also be implemented by a computer program instructing related hardware devices. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above.

[0132] The computer program includes computer program code, which may be in the form of source code, object code, executable file, or some intermediate form. The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording media, USB flash drive, portable hard drive, magnetic disk, optical disk, computer memory, read-only memory (ROM), random access memory, etc.

[0133] Furthermore, the computer-readable storage medium may primarily include a stored program area and a stored data area, wherein the stored program area may store the operating system, an application program required for at least one function, etc.; and the stored data area may store data created based on the use of blockchain nodes, etc.

[0134] The blockchain referred to in this invention is a novel application model of computer technologies such as distributed data storage, peer-to-peer transmission, consensus mechanisms, and encryption algorithms. Essentially, a blockchain is a decentralized database, a chain of data blocks linked together using cryptographic methods. Each data block contains information about a batch of network transactions, used to verify the validity of the information (anti-counterfeiting) and generate the next block. A blockchain can include an underlying blockchain platform, a platform product service layer, and an application service layer.

[0135] The bus can be a Peripheral Component Interconnect (PCI) bus or an Extended Industry Standard Architecture (EISA) bus, etc. This bus can be divided into address bus, data bus, control bus, etc. For ease of representation, in... Figure 3 The bus is represented by only one straight line, but this does not mean that there is only one bus or one type of bus. The bus is configured to enable communication between the memory 12 and at least one processor 13, etc.

[0136] Although not shown, the computer device 1 may also include a power supply (such as a battery) to power various components. Preferably, the power supply can be logically connected to the at least one processor 13 through a power management device, thereby enabling functions such as charging management, discharging management, and power consumption management. The power supply may also include one or more DC or AC power supplies, recharging devices, power fault detection circuits, power converters or inverters, power status indicators, and other arbitrary components. The computer device 1 may also include various sensors, Bluetooth modules, Wi-Fi modules, etc., which will not be described in detail here.

[0137] Furthermore, the computer device 1 may also include a network interface. Optionally, the network interface may include a wired interface and / or a wireless interface (such as a Wi-Fi interface, a Bluetooth interface, etc.), which is typically used to establish communication connections between the computer device 1 and other computer devices.

[0138] Optionally, the computer device 1 may further include a user interface, which may be a display, an input unit (such as a keyboard), and optionally, a standard wired interface or a wireless interface. Optionally, in some embodiments, the display may be an LED display, a liquid crystal display, a touch-sensitive liquid crystal display, or an OLED (Organic Light-Emitting Diode) touchscreen, etc. The display may also be appropriately referred to as a screen or display unit, used to display information processed in the computer device 1 and to display a visual user interface.

[0139] It should be understood that the embodiments described are for illustrative purposes only and are not limited to this structure in the scope of the patent application.

[0140] It will be understood by those skilled in the art that Figure 3 The structure shown does not constitute a limitation on the computer device 1, and may include fewer or more components than shown, or combine certain components, or have different component arrangements.

[0141] Combination Figure 1 The memory 12 in the computer device 1 stores multiple instructions to implement a method for preventing and controlling vector-borne infectious diseases, and the processor 13 can execute the multiple instructions to achieve the following: In response to the command to control vector-borne infectious diseases in the target control area, the ground-air integrated vector monitoring equipment deployed in the target control area is used to collect vector data in the target control area in real time; During the normalized prevention and control phase, a risk assessment indicator system is acquired and configured. Failure Mode and Effects Analysis (FMEA) is used to determine the measured score of each indicator in the risk assessment indicator system based on the vector data. The risk level of the target prevention and control area is then generated based on the measured score of each indicator. During the emergency response phase, an initial model constructed using a meteorological-environmental coupling mechanism is obtained. The risk level is input into the initial model to perform macro-level linkage to obtain risk-adaptive pulsed disinfection intervention items. The measured scores of each indicator are input into the initial model to perform micro-level linkage to obtain adaptively corrected intervention parameters. The initial model is then adjusted based on the risk-adaptive pulsed disinfection intervention items and intervention parameters to obtain a vector-borne infectious disease dynamic model. Multiple intervention combinations were obtained, and the vector-borne infectious disease dynamics model was used to perform parallel simulations of each intervention combination in order to select the optimal emergency measures for prevention and control. After the optimal emergency measures are completed, the emergency response results are obtained, and the weak failure indicators are located based on the emergency response results. Feedback corrections are then performed based on the weak failure indicators.

[0142] Specifically, the processor 13's implementation method for the above instructions can be found in [reference needed]. Figure 1 The descriptions of the relevant steps in the corresponding embodiments are not repeated here.

[0143] It should be noted that all the data involved in this case was legally obtained.

[0144] If any AI models, software tools, or components not belonging to this company appear in the embodiments of this invention, they are merely illustrative examples and do not represent actual use. All user personal information involved in the embodiments of this invention has been obtained by an entity authorized (with the knowledge and consent) or fully authorized by all parties through various legal and compliant means. The collection, storage, use, processing, transmission, provision, and disclosure of the information, data, and signals involved all comply with relevant laws and regulations and do not violate public order and good morals.

[0145] In the several embodiments provided by this invention, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of modules is only a logical functional division, and other division methods may be used in actual implementation.

[0146] This invention can be used in a wide variety of general-purpose or special-purpose computer system environments or configurations. Examples include: personal computers, server computers, handheld or portable devices, tablet devices, multiprocessor systems, microprocessor-based systems, set-top boxes, programmable consumer electronics, network PCs, minicomputers, mainframe computers, and distributed computing environments including any of the above systems or devices. This invention can be described in the general context of computer-executable instructions, such as program modules, that are executed by a computer. Generally, program modules include routines, programs, objects, components, data structures, etc., that perform specific tasks or implement specific abstract data types. This invention can also be practiced in distributed computing environments where tasks are performed by remote processing devices connected via a communication network. In distributed computing environments, program modules can reside in local and remote computer storage media, including storage devices.

[0147] The modules described as separate components may or may not be physically separate. The components shown as modules may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs.

[0148] Furthermore, the functional modules in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or in the form of hardware plus software functional modules.

[0149] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the present invention can be implemented in other specific forms without departing from the spirit or essential characteristics of the present invention.

[0150] Therefore, the embodiments should be considered exemplary and non-limiting in all respects, and the scope of the invention is defined by the appended claims rather than the foregoing description. Thus, all variations falling within the meaning and scope of equivalents of the claims are intended to be embraced within the invention. No appended diagram markings in the claims should be construed as limiting the scope of the claims.

[0151] Furthermore, it is clear that the word "comprising" does not exclude other units or steps, and the singular does not exclude the plural. Multiple units or devices described in this invention can also be implemented by a single unit or device through software or hardware. Terms such as "first," "second," etc., are used to indicate names and do not indicate any specific order.

[0152] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention.

Claims

1. A method for controlling vector-borne infectious diseases, characterized in that, The methods for preventing and controlling vector-borne infectious diseases include: In response to the command to control vector-borne infectious diseases in the target control area, the ground-air integrated vector monitoring equipment deployed in the target control area is used to collect vector data in the target control area in real time; During the normalized prevention and control phase, a risk assessment indicator system is acquired and configured. Failure Mode and Effects Analysis (FMEA) is used to determine the measured score of each indicator in the risk assessment indicator system based on the vector data. The risk level of the target prevention and control area is then generated based on the measured score of each indicator. During the emergency response phase, an initial model constructed using a meteorological-environmental coupling mechanism is obtained. The risk level is input into the initial model to perform macro-level linkage to obtain risk-adaptive pulsed disinfection intervention items. The measured scores of each indicator are input into the initial model to perform micro-level linkage to obtain adaptively corrected intervention parameters. The initial model is then adjusted based on the risk-adaptive pulsed disinfection intervention items and intervention parameters to obtain a vector-borne infectious disease dynamic model. Multiple intervention combinations were obtained, and the vector-borne infectious disease dynamics model was used to perform parallel simulations of each intervention combination in order to select the optimal emergency measures for prevention and control. After the optimal emergency measures are completed, the emergency response results are obtained, and the weak failure indicators are located based on the emergency response results. Feedback corrections are then performed based on the weak failure indicators. The method of using Failure Mode and Effects Analysis (FMEA) to determine the measured score of each indicator in the risk assessment indicator system based on the vector data includes: acquiring prevention and control related data of the target prevention and control area, and obtaining the risk occurrence probability, risk severity, and risk detectability of each indicator from the vector data and the prevention and control related data; calculating the product of the risk occurrence probability, risk severity, and risk detectability of each indicator to obtain the risk order number of each indicator; for each indicator, calculating the quotient between the risk order number of the indicator and the sum of the risk order numbers of all indicators to obtain the weight of the indicator; obtaining the upper limit of the comprehensive risk score of the target prevention and control area; calculating the product of the weight and the upper limit of the comprehensive risk score to obtain the full score of the indicator; acquiring the potential failure modes of the indicator from the risk assessment indicator system, and generating the scoring constraints of the indicator based on the potential failure modes and the full score; sending the scoring constraints of each indicator to the testing terminal, and obtaining the measured score of each indicator fed back by the testing terminal; Before acquiring the initial model constructed using the meteorological environment coupling mechanism, the population is divided into cells according to the population execution cell division, resulting in cells for each population and each vector population. Based on the SEIAR cell model, a first quantitative variation differential equation for each population cell over time and a second quantitative variation differential equation for each vector population cell over time are established. Using the lower limit of the vector's tolerance temperature and humidity critical range as a constraint, a temperature and humidity correction coefficient is constructed by linearly attenuating the real-time temperature and humidity of the target control area relative to the optimal temperature and humidity for the vector. Based on the incremental effect of the window cumulative rainfall in the target control area on the breeding ground, a saturated growth model with a marginally decreasing upper limit constraint is used to construct the rainfall correction coefficient. Quantity correction coefficients; meteorological environment coupling factors are constructed based on the temperature and humidity correction coefficients and the rainfall correction coefficients; a first propagation term from insect vectors to humans and a second propagation term from humans to insect vectors are constructed based on the meteorological environment coupling factors; the first propagation term is written into the corresponding first quantity change differential equation, and the second propagation term is written into the corresponding second quantity change differential equation, to obtain a set of dynamic bidirectional propagation differential equations under the no-intervention baseline; a set of differential equations for case isolation intervention model, a set of differential equations for stratified health education intervention model, a set of differential equations for continuous insect control intervention model, and a set of differential equations for risk-adaptive pulse insect control intervention model are constructed; the initial model is obtained by combining the sets of differential equations.

2. The method for controlling vector-borne infectious diseases as described in claim 1, characterized in that, The process of generating the risk level of the target prevention and control area based on the measured score of each indicator includes: Calculate the product of the weight of each indicator and the corresponding measured score to obtain the weighted score of each indicator; Calculate the sum of the weighted scores of all indicators to obtain the target comprehensive risk score of the target prevention and control area; Obtain the entire region to which the target prevention and control area belongs, and obtain the comprehensive risk scores of all regions within the entire region; Each tier threshold is generated based on the target quantile and all the comprehensive risk scores. The risk level is obtained by comparing the overall risk score of the target with each grading threshold.

3. The method for controlling vector-borne infectious diseases as described in claim 1, characterized in that, The step of inputting the risk level into the initial model to perform macro-level linkage and obtain risk-adaptive pulse disinfection intervention items includes: Obtain the configured mapping table, and use the risk level to traverse the mapping table to obtain the pulse elimination cycle constraint parameters; The pulse disinfection cycle constraint parameters are input into the differential equation set of the risk-adaptive pulse pest control intervention model to limit the trigger interval of the disinfection pulse time in the differential equation set of the risk-adaptive pulse pest control intervention model, thereby obtaining the risk-adaptive pulse disinfection intervention term.

4. The method for controlling vector-borne infectious diseases as described in claim 1, characterized in that, The process of inputting the measured scores of each indicator into the initial model to perform micro-level linkage and obtain the adaptively corrected intervention parameters includes: Based on the measured scores of each indicator, scores for control indicators, protection indicators, and disinfection indicators are generated. The proportion of explicit cases requiring standardized isolation is corrected based on the scores of the control-related indicators in the differential equation set of the case isolation intervention model. The population-stratified protection difference coefficient of the differential equation system of the stratified health education intervention model is corrected based on the scores of the protection-related indicators. The additional mortality rate from pest control is corrected based on the scores of the pest control indicators in the differential equation system of the continuous pest control intervention model. The instantaneous kill coefficient of the differential equation system of the risk adaptive pulse insecticidal intervention model is corrected based on the scores of the aforementioned pest control indicators. By integrating and correcting the standardized isolation ratio of the obvious cases, the population stratification protection difference coefficient, the additional mortality rate from disinfection, and the instantaneous kill coefficient, each intervention parameter is obtained.

5. The method for controlling vector-borne infectious diseases as described in claim 1, characterized in that, The step of locating weak failure indicators based on the emergency response results and performing feedback corrections based on the weak failure indicators includes: Based on the emergency response results, the current measured scores of the corresponding indicators of the optimal emergency measures are traced back. Obtain the prevention and control threshold corresponding to the corresponding indicator, and take the indicator whose current measured score is lower than the corresponding prevention and control threshold as the weak failure indicator; The weights of the aforementioned weak failure indicators are increased to revise the risk assessment indicator system and the vector-borne infectious disease dynamics model.

6. A device for controlling vector-borne infectious diseases, characterized in that, Applied to the method for preventing and controlling vector-borne infectious diseases as described in any one of claims 1 to 5.

7. A computer device, characterized in that, The computer device includes: A memory that stores at least one instruction; and a processor that executes the instructions stored in the memory to implement the method for controlling vector-borne infectious diseases as described in any one of claims 1 to 5.

8. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores at least one instruction, which is executed by a processor in a computer device to implement the method for preventing and controlling vector-borne infectious diseases as described in any one of claims 1 to 5.

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