Chikungunya thermal seasonal propagation risk assessment method based on coupling model

Abstract

The invention discloses a Chikungunya thermal seasonal propagation risk assessment method and system based on a coupling model. The method comprises the following steps: acquiring basic data and preprocessing the basic data; model parameters are divided into a fixed type and a dynamic type, the dynamic parameters are combined with environmental data to calculate and assign values, historical epidemic situation data in the past three years are used as target values, key parameters are adjusted through a least square method, and the error between a model simulation epidemic situation curve and an actual observation value is minimized. Based on the influence of a standardized meteorological data quantification environment on mosquitoes, generating a day-by-day key parameter sequence and inputting the day-by-day key parameter sequence into a medium module; the age structure model is used for simulating population dynamics of mosquitoes at all stages, and the SEI model is used for simulating transmission of viruses in the mosquitoes. The number of infectious mosquitoes output by the medium module is used as a drive, crowd state conversion is simulated through an SEIR model, and the number of infected people every day and the morbidity are obtained; and risk level assessment of the assessment area is completed, and quantitative data support is provided for risk assessment.

Description

Technical Field

[0001] This invention belongs to the field of infectious disease transmission dynamics prediction technology, and more specifically, relates to a method for assessing the seasonal transmission risk of Chikungunya fever based on a coupled model. Background Technology

[0002] Chikungunya fever, an acute infectious disease transmitted by mosquitoes, is closely linked to environmental conditions and exhibits significant seasonality, posing a persistent threat to human health and public health security. In recent years, influenced by global warming, the spatiotemporal fluctuations of meteorological factors such as temperature and rainfall have become more frequent, leading to an expansion of the mosquito's range and a shortening of its reproductive cycle, thereby exacerbating the risk of Chikungunya fever transmission and increasing the difficulty of its control.

[0003] Current assessment methods for the risk of Chikungunya fever transmission generally suffer from insufficient characterization of the interaction between the "environment-vector-host" relationship. Most assessment methods rely solely on historical epidemic data to extrapolate trends, failing to adequately consider the dynamic impact of meteorological factors on mosquito population dynamics (such as egg hatching rate and larval development rate) and viral replication (such as extravirtual incubation period). This results in assessment results that are difficult to accurately reflect the seasonal variation patterns of the epidemic and cannot predict the peak risk period and high-risk areas in advance.

[0004] Meanwhile, existing assessment models often suffer from fixed and simplistic parameter settings. Some models ignore the dynamic attributes of parameters, setting environment-related parameters such as mosquito bite rate and virus transmission efficiency to fixed values, which cannot adapt to environmental differences in different seasons and regions. Other models, although introducing dynamic parameters, lack a systematic calibration process based on historical epidemic data, resulting in a large deviation between simulation results and actual epidemic curves, making it difficult to meet the accuracy requirements of public health prevention and control assessments.

[0005] Furthermore, current risk assessment results are mostly presented in the form of abstract data, failing to be effectively transformed into tiered guidelines that align with prevention and control practices. This leaves prevention and control departments lacking clear basis for resource allocation and measure formulation, making it difficult to accurately respond to the risk of chikungunya transmission. In addition, existing models rarely use the SEIR-SEI coupling framework to simultaneously characterize the state transitions of both the vector and the host, resulting in insufficient accuracy in simulating the "environment-vector-host" chain transmission. Therefore, there is an urgent need to develop a method that can integrate multi-dimensional factors of the environment, vector, and host, possess dynamic parameter calibration capabilities, and output practical risk assessment results to improve the prediction and prevention of seasonal chikungunya transmission risks. Summary of the Invention

[0006] This invention aims to improve the accuracy of predicting seasonal epidemic transmission risks and the scientific nature of prevention and control decisions by integrating multiple dimensions of environmental, media, and host factors to dynamically depict the impact of meteorological conditions on mosquito populations and virus transmission, accurately simulate the human infection process, output quantitative risk indicators, and complete risk level assessments.

[0007] To address the aforementioned deficiencies or improvement needs of existing technologies, as a first aspect of this invention, the present invention provides a method for assessing the seasonal spread risk of Chikungunya heat based on a coupled model, comprising:

[0008] S1. Complete the collection of basic data and preprocess the collected data;

[0009] S2. Divide the model parameters into fixed parameters and dynamic parameters and assign values ​​to them respectively; among them, the dynamic parameters need to be calculated in conjunction with environmental data; then use the historical epidemic data of the past 3 years as the target value, and use the least squares method to adjust the key parameters to minimize the error between the epidemic curve simulated by the model and the actual observed values;

[0010] S3. Based on standardized meteorological data, the impact of temperature, rainfall, and humidity on mosquitoes is quantified, and a daily key parameter sequence is generated and input into the vector module. The population dynamics of mosquito eggs, larvae, and adults are simulated through an age structure model. At the same time, the transmission process of the virus in susceptible, exposed, and infectious adults is simulated according to the SEI model. Then, driven by the number of infectious mosquitoes output by the vector module, the SEIR model is used to simulate the state transition of four groups of people in the population: susceptible, exposed, infected, and recovered. Finally, the daily number of infected people and the core indicators of the incidence rate are obtained.

[0011] S4. Based on the daily number of infections predicted by the model, and combined with the preset risk level classification threshold, complete the risk level assessment of the assessment area.

[0012] Furthermore, the fixed parameters in S2 are parameters that do not change dynamically with environmental conditions or time, and are determined based on the biological characteristics of the disease, the inherent attributes of mosquito species, and epidemiological consensus; they include population-related fixed parameters, mosquito-related fixed parameters, and virus-related fixed parameters.

[0013] Furthermore, the dynamic parameters in S2 are parameters that need to be calculated in real time based on daily standardized meteorological data, change dynamically with environmental conditions, and directly support the operation of the medium module and the host module; they include temperature-driven dynamic parameters, rainfall-driven dynamic parameters, humidity-related dynamic parameters, and medium-host linkage dynamic parameters.

[0014] Furthermore, the specific steps in S3, which simulate the virus transmission process among susceptible, exposed, and infectious adult worms based on the SEI model, are as follows:

[0015] Using daily as the time step, the SEI model is used to dynamically characterize the virus in susceptible S... v Exposure E v Infectious I v The core logic of the state transitions among the three types of adult mosquitoes is that "susceptible mosquitoes, after biting and infecting humans, become exposed, then, after an incubation period outside the virus, become infectious, and infectious mosquitoes continue to transmit the virus and also die naturally." Specific equations include: Susceptible adult S v Equation of population change, exposed adult E v Equation of population change and infection of adult I v Equation for quantitative change.

[0016] Furthermore, the specific process of simulating the state transitions of the four groups of people—susceptible, exposed, infected, and recovered—using the SEIR model in S3 is as follows:

[0017] The number of infectious mosquitoes output daily by the media module I v Using (t) as the core driving variable and combining the biological characteristics of Chikungunya fever transmission via mosquito bites, the SEIR model uses four sets of coupled differential equations to dynamically simulate the population in susceptible S... h Exposure E h , infection I h , restore R h Transitions between four types of states;

[0018] The media module outputs I daily v (t), where t is the daily number of infectious mosquitoes, which needs to be compared with the total number of mosquitoes N. v (t) combined to calculate the proportion of infectious mosquitoes. Where, N v (t)=S v (t)+E v (t)+I v (t), S v (t) represents the number of susceptible adult mosquitoes at time t; E v (t) represents the number of adult mosquitoes exposed at time t; I v (t) represents the number of adult mosquitoes in the infectious state at time t;

[0019] Then, using daily as the time step, differential equations were constructed to simulate the state transitions of four groups of people, including: susceptible S... h Population size change equation, exposure E h Equation of population size change, infection I h Equation of population size change and recovery R h The equation for population size change; the core logic is that "susceptible people are bitten by infectious mosquitoes and then become exposed, then become infected after the incubation period, and then develop immunity and enter the recovery state after infection";

[0020] Daily based on the previous day's S h E h I h R h The value, combined with the I output of the media module on that day v (t) and N v (t), calculate the number of people in each state on the day through the above four sets of equations, and complete the daily linkage of "the number of infectious mosquitoes → the transformation of the state of the population".

[0021] Furthermore, in S3, the susceptible S h The equation for population size change is:

[0022] The number of susceptible individuals decreased solely due to infection via infectious mosquito bites, with no new sources of infection; it is assumed that population movement during the assessment period has a negligible impact on the replenishment / loss of susceptible individuals, or that these individuals have already been included in the data through preprocessing. h Initial values ​​are used to construct the following equation:

[0023]

[0024] in, β represents the rate of change in the number of susceptible individuals over time. h The probability of infection in a population after being bitten by an infectious mosquito is based on the mosquito bite rate β. bite (T t The infection efficiency of the virus after it enters the human body is a fixed biological parameter; S h (t) represents the number of people in a susceptible state at time t.

[0025] Furthermore, E is exposed in S3. h The equation for population size change is:

[0026] The number of exposed individuals is determined by the increase in the number of people transitioning from a susceptible state to an infected state and the decrease in the number transitioning to an infectious state, which essentially reflects the incubation period of the virus in the human body:

[0027]

[0028] in, This indicates the rate of change in the number of exposed individuals over time. E represents the daily conversion rate of exposed individuals to an infectious state. h (t) represents the number of people exposed at time t.

[0029] Furthermore, the infection I in S3 h The equation for population size change is:

[0030] The number of infected individuals is determined by the "increase in the number of people transitioning from the exposed state" and the "decrease in the number of people transitioning to the recovery state," which is directly related to the transmission of the virus to the mosquito population. Assuming that the infected population is the only source of virus acquisition for mosquitoes, the following equation is constructed:

[0031]

[0032] in, This indicates the rate of change in the number of infected individuals over time. The daily conversion rate of infected individuals to recovery status; I h (t) represents the number of people infected at time t.

[0033] Furthermore, in S3, R is restored. h The equation for population size change is:

[0034] The number of recovered individuals increases solely due to the transition from an infectious state, with no source of decrease; assuming no secondary infection following Chikungunya fever and lifelong immunity in the recovered population, the following equation is constructed:

[0035]

[0036] in, This indicates the rate of change in the number of recovered individuals over time.

[0037] As a second aspect of the present invention, a seasonal transmission risk assessment system for Chikungunya heat based on a coupling model is also provided, comprising:

[0038] The basic data acquisition and preprocessing unit is used to complete the acquisition of basic data and preprocess the acquired data.

[0039] The model parameter classification, assignment, and calibration unit is used to classify model parameters into fixed parameters and dynamic parameters and assign values ​​to them separately. Among them, dynamic parameters need to be calculated in conjunction with environmental data. Then, using historical epidemic data from the past three years as target values, the key parameters are adjusted using the least squares method to minimize the error between the epidemic curve simulated by the model and the actual observed values.

[0040] The vector-host module simulation and index output unit is used to quantify the impact of temperature, rainfall, and humidity on mosquitoes based on standardized meteorological data, generate daily key parameter sequences, and input them into the vector module. It simulates the population dynamics of mosquito eggs, larvae, and adults through an age structure model, and simulates the transmission process of viruses among susceptible, exposed, and infectious adults according to the SEI model. Then, driven by the number of infectious mosquitoes output by the vector module, it uses the SEIR model to simulate the state transitions of four groups of people in the population: susceptible, exposed, infected, and recovered, and finally obtains the daily number of infected people and the core indicators of incidence rate.

[0041] The transmission risk level assessment unit is used to assess the risk level of an assessment area based on the daily number of infections predicted by the model and a preset risk level classification threshold.

[0042] In summary, compared with the prior art, the above-described technical solutions conceived by this invention can achieve the following beneficial effects:

[0043] 1. The present invention provides a method for assessing the seasonal transmission risk of Chikungunya fever based on a coupled model. This method first completes basic data collection and preprocessing, then divides the model parameters into fixed and dynamic parameters and assigns values ​​to them respectively. Dynamic parameters are calculated in conjunction with environmental data. Historical epidemic data, such as the weekly incidence rate over the past three years, are used as target values. Key parameters are adjusted using the least squares method to minimize simulation errors. This technical feature ensures that the model parameters possess both biological rationality and data adaptability. Fixed parameters anchor the inherent characteristics of the disease and vector, while dynamic parameters respond to environmental changes. The parameter calibration process allows the model to reproduce historical epidemic patterns, providing an accurate parameter basis for subsequent simulations and avoiding assessment biases caused by unreasonable parameters.

[0044] 2. The present invention provides a method for assessing the seasonal transmission risk of Chikungunya fever based on a coupled model. This method quantifies the impact of temperature, rainfall, and humidity on mosquitoes using standardized meteorological data, generates daily key parameter sequences which are input into the vector module, simulates mosquito population dynamics using an age-dependent structural model, simulates virus transmission among mosquitoes using differential equations, and simulates population state transitions using a SEIR-SEI coupled model driven by the number of infectious mosquitoes. This technical feature, through the construction of an SEIR-SEI coupled model, achieves a chain-like simulation logic of "environment-vector-host," dynamically characterizing the impact of meteorological factors on mosquito survival and virus replication, as well as the transmission interaction between mosquitoes and the human population. It can output core indicators such as daily infection numbers and morbidity rates, accurately reconstructing the seasonal transmission process of the epidemic and providing quantitative data support for risk assessment.

[0045] 3. The chikungunya fever seasonal transmission risk assessment method based on a coupled model of the present invention uses the predicted incidence rate output by the model as the core basis, combined with the prevention and control threshold of the assessment area, to complete the risk level assessment. This technical feature combines the quantitative indicators derived from simulation with actual prevention and control needs, transforming abstract incidence rate data into directly applicable risk levels, clearly defining the epidemic risk level of the assessment area, providing clear guidance for prevention and control departments to formulate targeted measures, assisting in the rational allocation of prevention and control resources, and improving the accuracy and efficiency of chikungunya fever seasonal transmission prevention and control. Attached Figure Description

[0046] Figure 1 This is a flowchart of the seasonal propagation risk assessment method for Chikungunya heat based on a coupling model, according to an embodiment of the present invention.

[0047] Figure 2 This is a flowchart of a method according to an embodiment of the present invention;

[0048] Figure 3 This is a schematic diagram of the SEIR-SEI coupling model according to an embodiment of the present invention;

[0049] Figure 4 This is a schematic diagram of the system units in an embodiment of the present invention. Detailed Implementation

[0050] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention. Furthermore, the technical features involved in the various embodiments of this invention described below can be combined with each other as long as they do not conflict with each other.

[0051] Example 1

[0052] Please refer to Figure 1 This embodiment 1 provides a method for assessing the seasonal spread risk of Chikungunya subtropical heat based on a coupled model, including:

[0053] S1. Complete the collection of basic data and preprocess the collected data;

[0054] S2. Divide the model parameters into fixed parameters and dynamic parameters and assign values ​​to them respectively; among them, the dynamic parameters need to be calculated in conjunction with environmental data; then use the historical epidemic data of the past 3 years as the target value, and use the least squares method to adjust the key parameters to minimize the error between the epidemic curve simulated by the model and the actual observed values;

[0055] S3. Based on standardized meteorological data, the impact of temperature, rainfall, and humidity on mosquitoes is quantified, and a daily key parameter sequence is generated and input into the vector module. The population dynamics of mosquito eggs, larvae, and adults are simulated through an age structure model. At the same time, the transmission process of the virus in susceptible, exposed, and infectious adults is simulated according to the SEI model. Then, driven by the number of infectious mosquitoes output by the vector module, the SEIR model is used to simulate the state transition of four groups of people in the population: susceptible, exposed, infected, and recovered. Finally, the daily number of infected people and the core indicators of the incidence rate are obtained.

[0056] S4. Based on the daily number of infections predicted by the model, and combined with the preset risk level classification threshold, complete the risk level assessment of the assessment area.

[0057] Please refer to Figure 2 This embodiment 1 further elaborates on the above steps.

[0058] (1) Basic data collection and preprocessing

[0059] In the seasonal transmission risk assessment of Chikungunya fever, the completeness and standardization of basic data are the core prerequisites for subsequent model construction and risk prediction. Affected by global climate fluctuations, the meteorological conditions, mosquito population dynamics, and human activity characteristics of the assessment area are complexly correlated. Therefore, it is necessary to systematically collect three types of core data through multiple channels: First, obtain at least five years of daily temperature, rainfall, humidity, and other environmental data from meteorological stations, satellite remote sensing, or authoritative meteorological databases, while simultaneously collecting geographical data to accurately identify the distribution range of mosquito breeding grounds, providing a foundation for quantifying the environmental impact on mosquitoes. Second, record ecological parameters such as the developmental cycle, survival rate, and biting rate of dominant mosquito species in the field through fixed-point monitoring, combined with laboratory controlled experiments to determine the relationship between the viral incubation period and temperature changes, providing a biological basis for the vector module to simulate mosquito populations and virus transmission. Third, collect epidemiological data on the temporal and spatial distribution of confirmed Chikungunya fever cases, population susceptibility rates, and recovery periods after infection over the past five years, as well as information on population size, density, and mobility characteristics in the assessment area, to support the subsequent simulation of population state transitions in the host module.

[0060] Because data from different sources have differences in spatiotemporal scales, and some data may be missing or anomaly-prone, standardized preprocessing is required to eliminate data interference and ensure that the data can be directly adapted to subsequent parameter calculations and model operation. For missing values ​​in meteorological data such as temperature and rainfall, interpolation using the average values ​​of neighboring meteorological stations or extrapolation of time series trends is employed to supplement the data. For sporadic missing case data, estimations are made by referencing the incidence trends of surrounding areas or the incidence patterns of similar populations during the same period, ensuring the continuity of the data sequence. The 3σ rule or box plot method is used to identify extreme outliers in the meteorological data, which are then corrected using historical climate characteristics. For epidemic data with contradictory temporal and spatial information, cross-validation is performed using hospital treatment records and community reporting information to correct errors or remove invalid samples, ensuring data accuracy. Hourly meteorological data is converted into daily averages, and weekly case data is broken down into daily values, with a unified time scale of "day". Using a 1km×1km grid precision, geographic registration and spatial interpolation are used to match data such as breeding ground distribution, population density, and case location to a unified grid unit, achieving "day-grid" scale alignment. Simultaneously, the units of data such as temperature, rainfall, population density, and incidence rate are standardized, and unstructured mosquito monitoring records are transformed into structured tables, laying a standardized data foundation for subsequent parameter classification and assignment, and vector-host module linkage simulation.

[0061] (2) Model parameter classification, assignment and calibration

[0062] In constructing the Chikungunya fever transmission risk assessment model, the classification and assignment of parameters are necessary to lay the foundation for model operation. Parameters are divided into two categories: fixed parameters and dynamic parameters. Fixed parameters are determined based on the inherent characteristics of the disease, vector, and virus, and do not change with the environment; they are the core logical anchors of the model. Population-related fixed parameters include the incubation period, infectious period, and the probability of infection after being bitten by an infected mosquito. These parameters are set based on clinical observations and epidemiological consensus on Chikungunya fever. Mosquito-related fixed parameters cover the probability of virus acquisition after biting an infected person, the upper limit of the basic lifespan of adult mosquitoes, the basic survival period of eggs, and the upper limit of larval carrying density per unit area of ​​water, determined with reference to field monitoring and laboratory rearing data of mosquito species. Virus-related fixed parameters are the basic threshold temperature and upper limit temperature for replication within mosquitoes, determined by viral biochemical characteristic experiments to ensure that the model conforms to biological transmission laws.

[0063] Dynamic parameters, calculated in real-time based on environmental data, are crucial for the model's response to seasonal changes and directly support the dynamic simulation of the vector and host modules. Temperature-driven dynamic parameters include mosquito hatching rate, larval development rate, adult daily mortality rate, biting rate, and viral incubation period. These parameters need to be calculated daily based on standardized temperature data to characterize the impact of temperature on mosquito survival and development, and viral replication. Rainfall-driven dynamic parameters include mosquito egg hatching rate correction factors and larval carrying capacity, adjusted based on daily rainfall data to reflect the changes in the mosquito breeding environment caused by precipitation. Humidity-related dynamic parameters include adult survival correction coefficients, determined based on daily humidity data, to help correct adult survival status. The vector-host linkage dynamic parameter is the proportion of infectious mosquitoes, calculated based on the number of infectious mosquitoes and the total number of adults output by the vector module, serving as the core variable connecting the vector and host modules.

[0064] After initial parameter assignment, the model accuracy needs to be calibrated and optimized using historical data. Using historical epidemic data, such as weekly incidence rates from the past three years, as target values, an error function is constructed between the model's simulated values ​​and actual observations. Key parameters are iteratively adjusted using the least squares method—focusing on optimizing dynamic parameters closely related to environmental response (such as the influence coefficient of temperature on the viral incubation period) and fixed parameters related to transmission efficiency (such as the probability of infection through bites), until the error between the model's simulated epidemic curve and actual observations is minimized. This calibration process enables the model to accurately reproduce the seasonal fluctuations of historical epidemics, ensuring reliable parameter support for subsequent simulations of vector population dynamics, virus transmission, and population state transitions based on environmental data, thus providing a scientific model foundation for risk assessment.

[0065] (3) Media-Host Module Simulation and Index Output

[0066] Please refer to Figure 3After completing the classification and assignment of model parameters and the calibration of key parameters, specific simulations need to be carried out based on standardized meteorological data and calibrated parameters. First, based on temperature, rainfall, and humidity data, corresponding models such as Gaussian function, exponential relationship, and hyperbolic tangent function are used to calculate daily key parameters such as mosquito hatching rate, larval development rate, adult survival rate, biting rate, viral incubation period, mosquito egg hatching rate correction factor, larval carrying capacity, and adult survival correction coefficient, and these parameters are then input into the media module.

[0067] Specifically, the calculation method for the key parameters is as follows:

[0068] Mosquito hatching rate μ hatch (T t Based on the mechanism by which temperature affects enzyme activity in mosquito eggs, a Gaussian function is used to characterize the nonlinear relationship between temperature and hatching rate, as shown in the following formula:

[0069]

[0070] Among them, T t Let μ be the average temperature of day t. hatch,max The maximum hatching rate is determined by the biological characteristics of mosquito species; T opt The optimal temperature for egg incubation was determined experimentally; σ T The temperature adaptation width parameter reflects the range of mosquitoes' tolerance to temperature fluctuations and is fitted using historical hatching data.

[0071] Larval development rate γ develop (T t Based on the temperature-rate model of poikilothermic animal development, and considering the exponential relationship between larval metabolic rate and temperature, the formula is as follows:

[0072]

[0073] Wherein, γ0 is the larval development initiation temperature T. base The basal developmental rate at that time was determined experimentally; k T The metabolic temperature response coefficient reflects the increase in developmental rate for every 1°C increase in temperature, and is fitted using larval developmental cycle observation data; when T t <T base At that time, γ develop (T t When ) = 0, larval development stops;

[0074] Adult survival rate σ adult (T t Considering the effect of temperature on adult cellular respiration, the hyperbolic tangent function is used to describe the relationship between survival rate and temperature, as shown in the following formula:

[0075]

[0076] Among them, T adult,opt The optimal temperature for adult survival; T adult,max The upper limit of adult survival temperature; α T β T These are the survival response coefficients on both sides of the optimal temperature, which are fitted using adult insect survival experiment data;

[0077] Mosquito bite rate β bite (T t Based on the influence of temperature on mosquito activity frequency, and considering the nonlinear relationship between mosquito foraging behavior and ambient temperature, the formula is as follows:

[0078]

[0079] Where, β bite,max Maximum bite rate; T bite,min The lowest temperature at which mosquitoes begin to bite; The upper limit temperature for mosquito biting activity, when T t <T bite,min or T t >T bite,max At that time, β bite (T t ) = 0;

[0080] Extraviral incubation period; based on the temperature dependence of viral replication in mosquitoes, combined with the relationship between viral replication enzyme activity and temperature, the formula is as follows:

[0081]

[0082] Among them, EIP min The shortest extraviral incubation period at the optimal temperature was determined experimentally; T EIP,max T represents the upper limit temperature for viral replication. EIP,base The temperature at which viral replication begins, when T... t <T EIP,base or T t >T EIP,max At that time, EIP(T) t →+∞, meaning the virus stops replicating;

[0083] Mosquito egg hatching rate correction factor φ hatch (R t Based on the effect of rainfall on improving the microenvironment (humidity, dissolved oxygen) for egg incubation, the Logistic function is used to describe the relationship between the correction factor and rainfall, as shown in the following formula:

[0084]

[0085] Among them, R tR represents the rainfall on day t. hatch,th The rainfall threshold for egg hatching is based on experimental observations; λ R The rainfall response coefficient reflects the rate of increase of the correction factor with rainfall, and is fitted using historical incubation data;

[0086] Larval carrying capacity K(R) t Based on the linear relationship between rainfall and waterlogged area, and combined with the larval carrying capacity threshold per unit waterlogged area, the formula is as follows:

[0087] K(R t ) = A water (R t )·ρ larva

[0088] Among them, A water (R t )=α R ·R t +A water,base A water (R t Let α be the area of ​​water accumulation on day t. R The rainfall-water accumulation area conversion coefficient is obtained by fitting regional hydrological observation data; ρ larva The maximum carrying density of larvae per unit water accumulation area is determined experimentally.

[0089] Adult survival correction coefficient; based on the effect of humidity on the water retention capacity of the adult insect's epidermis, the normal distribution function is used to characterize the relationship between the correction coefficient and humidity, as shown in the following formula:

[0090]

[0091] Among them, H t H represents the relative humidity on day t. opt The optimal humidity for adult survival; σ H The humidity adaptation width parameter reflects the range of mosquitoes' tolerance to humidity fluctuations and is fitted using adult survival data.

[0092] Subsequently, the population dynamics of mosquito eggs, larvae, and adults were simulated using an instar structure model. The instar structure model uses a daily step size and is based on daily key parameters. It dynamically simulates the population changes of mosquito eggs, larvae, and adults by calculating the hatching and transformation of eggs, the development and emergence of larvae (limited by carrying capacity), and the replenishment, death, and state transition of adults.

[0093] Simultaneously, three sets of coupled differential equations were used to characterize the transmission of the virus among three types of adult mosquitoes: susceptible, exposed, and infectious, to obtain the number of infectious mosquitoes. Specifically, with a daily time step, the virus transmission among susceptible S mosquitoes was dynamically characterized using three sets of coupled differential equations. v Exposure E vInfectious I v The core logic of the state transitions among the three types of adult mosquitoes is that "susceptible mosquitoes, after biting and infecting humans, become exposed, then, after an incubation period outside the virus, become infectious, and infectious mosquitoes continue to transmit the virus and also die naturally." Specific equations include: Susceptible adult S v Equation of population change, exposed adult E v Equation of population change and infection of adult I v Equation for quantitative change.

[0094] Specifically, susceptible S v The daily variation in adult population is determined by three parts: "new additions," "decreases due to transition to the exposed state," and "decreases due to natural mortality." The susceptible adult population S... v The equation for the change in quantity is:

[0095]

[0096] Wherein, Λ(t) represents the number of newly added susceptible adults per day, which is determined by the rate at which larvae emerge as adults, and is related to the larval development rate γ output by the environment-driven module. develop (T t Larval carrying capacity K(R) t Positive correlation; β v It represents the probability of acquiring a virus after being infected by a mosquito bite, and is a fixed biological parameter based on the blood-sucking habits of mosquito species; The proportion of infected individuals in the population, I h (t) represents the number of infections in the population on day t, N h To assess the total population of the region; μ v (T t ( ) represents the daily mortality rate of adult mosquitoes;

[0097] Exposure E v The daily variation in the number of adult worms (infected with the virus but not infectious) is determined by three parts: the increase in the number of worms transitioning from the susceptible state, the decrease in the number transitioning to the infectious state, and the decrease in the number of worms dying naturally. Exposure to adult worms E v The equation for the change in quantity is:

[0098]

[0099] in, EIP(T) represents the daily conversion rate of exposed adults to the infectious state. t The incubation period outside the virus is t days, which shortens with temperature; the other parameters are consistent with the susceptible adult equation, which reflects the biological process that "exposed mosquitoes need to go through the viral replication cycle EIP before they become infectious".

[0100] Infectious I vThe daily variation in the number of adult insects (capable of transmitting viruses, i.e., infectious) is determined by two parts: "the increase from the exposed state" and "the decrease from natural mortality"; the equation for the change in the number of infectious adult insects (Iv) is...

[0101]

[0102] The equation directly correlates the transformation rate of exposed mosquitoes with temperature-driven EIP, dynamically reflecting the chain effect of "ambient temperature → virus replication rate → number of infected mosquitoes". For example, EIP shortens at high temperatures. Increase, I v (t) The growth is faster, and the corresponding population has an increased risk of infection.

[0103] Through daily iterative calculations of the above three sets of equations, S can be output. v (t), E v (t), I v The daily dynamic change curve of (t), where the number of infected adult insects I v (t) is the key output connecting the media module and the host module—the host module will subsequently be based on I... v (t) Calculate the risk of mosquito bite infection to humans, and finally realize the quantitative simulation of the transmission chain of "environment → mosquito → human".

[0104] Driven by the number of infectious mosquitoes and combined with established population-related parameters, the SEIR model uses four sets of coupled differential equations to simulate the transitions between four states in the population: susceptible, exposed, infected, and recovered. This ultimately generates core indicators such as the daily number of infections and the incidence rate. In a preferred embodiment, the specific process of simulating the state transitions of the four population states (susceptible, exposed, infected, and recovered) using the SEIR model is as follows:

[0105] The number of infectious mosquitoes output daily by the media module I v Using (t) as the core driving variable and combining the biological characteristics of Chikungunya fever transmission via mosquito bites, the SEIR model uses four sets of coupled differential equations to dynamically simulate the population in susceptible S... h Exposure E h , infection I h , restore R h Transitions between four types of states;

[0106] The media module outputs I daily v (t), where t is the daily number of infectious mosquitoes, which needs to be compared with the total number of mosquitoes N. v (t) combined to calculate the proportion of infectious mosquitoes. Where, N v (t)=S v (t)+E v (t)+I v (t), Sv (t) represents the number of susceptible adult mosquitoes at time t; E v (t) represents the number of adult mosquitoes exposed at time t; I v (t) represents the number of adult mosquitoes in the infectious state at time t;

[0107] Then, using daily as the time step, differential equations were constructed to simulate the state transitions of four groups of people, including: susceptible S... h Population size change equation, exposure E h Equation of population size change, infection I h Equation of population size change and recovery R h The equation for population size change; the core logic is that "susceptible people are bitten by infectious mosquitoes and then become exposed, then become infected after the incubation period, and then develop immunity and enter the recovery state after infection";

[0108] Daily based on the previous day's S h E h I h R h The value, combined with the I output of the media module on that day v (t) and N v (t), calculate the number of people in each state on the day through the above four sets of equations, and complete the daily linkage of "the number of infectious mosquitoes → the transformation of the state of the population".

[0109] In a preferred embodiment, susceptible S h The equation for population size change is:

[0110] The number of susceptible individuals decreased solely due to infection via infectious mosquito bites, with no new sources of infection; it is assumed that population movement during the assessment period has a negligible impact on the replenishment / loss of susceptible individuals, or that these individuals have already been included in the data through preprocessing. h Initial values ​​are used to construct the following equation:

[0111]

[0112] in, β represents the rate of change in the number of susceptible individuals over time. h The probability of infection in a population after being bitten by an infectious mosquito is based on the mosquito bite rate β. bite (T t The infection efficiency of the virus after it enters the human body is a fixed biological parameter; S h (t) represents the number of people in a susceptible state at time t.

[0113] In a preferred embodiment, E is exposed. h The equation for population size change is:

[0114] The number of exposed individuals is determined by the increase in the number of people transitioning from a susceptible state to an infected state and the decrease in the number transitioning to an infectious state, which essentially reflects the incubation period of the virus in the human body:

[0115]

[0116] in, This indicates the rate of change in the number of exposed individuals over time. E represents the daily conversion rate of exposed individuals to an infectious state. h (t) represents the number of people exposed at time t.

[0117] In a preferred embodiment, infection I h The equation for population size change is:

[0118] The number of infected individuals is determined by the "increase in the number of people transitioning from the exposed state" and the "decrease in the number of people transitioning to the recovery state," which is directly related to the transmission of the virus to the mosquito population. Assuming that the infected population is the only source of virus acquisition for mosquitoes, the following equation is constructed:

[0119]

[0120] in, This indicates the rate of change in the number of infected individuals over time. The daily conversion rate of infected individuals to recovery status; I h (t) represents the number of people infected at time t.

[0121] In a preferred embodiment, R is restored. h The equation for population size change is:

[0122] The number of recovered individuals increases solely due to the transition from an infectious state, with no source of decrease; assuming no secondary infection following Chikungunya fever and lifelong immunity in the recovered population, the following equation is constructed:

[0123]

[0124] in, This indicates the rate of change in the number of recovered individuals over time.

[0125] Furthermore, the daily number of infections is directly taken from the number of people with infection status on that day output by the model. This value reflects the scale of newly infected individuals with the ability to transmit the virus in the assessment area on that day, and its trend can intuitively reflect the short-term transmission intensity of the epidemic.

[0126] The incidence rate needs to be further calculated in conjunction with the total population of the assessment area. This involves correlating the daily number of infections with the fixed total population of the area (or the average population over a specific period), and converting the data to standard units of "cases / 100,000 people" or "cases / 10,000 people." During the calculation, it is crucial to ensure that the spatiotemporal scale of the population data and the epidemic data are consistent. For example, if the number of infections is daily data for a specific grid unit, the population size must also correspond to the resident population size of that grid unit to ensure that the incidence rate accurately reflects the infection risk level of different regions and time periods.

[0127] The generation of these two core indicators is a quantitative presentation of the simulation results of the "environment-media-population" transmission chain. They include both the absolute scale of infection (daily number of infections) and the relative risk intensity (morbidity rate), providing standardized data basis for subsequent risk level classification and the formulation of targeted prevention and control measures in combination with prevention and control thresholds.

[0128] (4) Risk level assessment of transmission

[0129] After completing model predictions and obtaining core indicators such as daily infection numbers and incidence rates in the assessment area, a risk level assessment is conducted based on the model prediction results. This requires first establishing risk level classification standards by considering the public health prevention and control needs of the assessment area, the historical severity of epidemics, and population health protection goals. These standards typically use the predicted incidence rate as the core basis, while also referencing the daily infection growth trend, classifying the risk into four levels: low, medium, high, and extremely high. For example, a predicted incidence rate below 0.1 cases / 100,000 people with no growth trend is classified as low risk; an incidence rate between 0.1 and 1 cases / 100,000 people or a slow increase in infections is classified as medium risk; an incidence rate between 1 and 5 cases / 100,000 people or a rapid increase in infections is classified as high risk; and an incidence rate exceeding 5 cases / 100,000 people or an explosive increase in infections is classified as extremely high risk. The classification thresholds need to be adjusted based on actual conditions such as regional population density and medical resource reserves.

[0130] Subsequently, the daily forecast indicators output by the model are matched with the set risk level standards to determine the daily risk level of the assessment area. At the same time, combined with geospatial data, the risk levels of different grid units are visualized to clarify the spatial distribution of high-risk areas. For example, grid units with a continuous incidence rate above the threshold and a concentration of infected people are marked to form a spatial distribution map of risk levels.

[0131] Finally, a comprehensive assessment of the risk level trends over the assessment period (e.g., the next 14 days or 1 month) is conducted to determine whether the epidemic may escalate from low to high risk. For example, if a sustained upward trend in the incidence rate is predicted, an early warning of risk escalation should be issued. This process transforms the quantitative indicators predicted by the model into intuitive and actionable risk level results, providing a basis for prevention and control departments to formulate differentiated measures. For instance, mosquito control and population health monitoring can be implemented in high-risk areas, while routine prevention and control measures can be maintained in low-risk areas, thus facilitating precise allocation of prevention and control resources.

[0132] Example 2

[0133] Please refer to Figure 4 This embodiment 2 provides a risk assessment system for the seasonal spread of subtropical heat in Chikungunya based on a coupled model, including:

[0134] The basic data acquisition and preprocessing unit is used to complete the acquisition of basic data and preprocess the acquired data.

[0135] The model parameter classification, assignment, and calibration unit is used to classify model parameters into fixed parameters and dynamic parameters and assign values ​​to them separately. Among them, dynamic parameters need to be calculated in conjunction with environmental data. Then, using historical epidemic data from the past three years as target values, the key parameters are adjusted using the least squares method to minimize the error between the epidemic curve simulated by the model and the actual observed values.

[0136] The vector-host module simulation and index output unit is used to quantify the impact of temperature, rainfall, and humidity on mosquitoes based on standardized meteorological data, generate daily key parameter sequences, and input them into the vector module. It simulates the population dynamics of mosquito eggs, larvae, and adults through an age structure model, and simulates the transmission process of viruses among susceptible, exposed, and infectious adults according to the SEI model. Then, driven by the number of infectious mosquitoes output by the vector module, it uses the SEIR model to simulate the state transitions of four groups of people in the population: susceptible, exposed, infected, and recovered, and finally obtains the daily number of infected people and the core indicators of incidence rate.

[0137] The transmission risk level assessment unit is used to assess the risk level of an assessment area based on the daily number of infections predicted by the model and a preset risk level classification threshold.

[0138] Example 3

[0139] This embodiment 3 also provides a computer-readable storage medium storing a computer program that, when executed by a processor, can implement any step of a method for assessing the seasonal spread of Chikungunya heat based on a coupled model.

[0140] The computer-readable storage medium may include various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0141] For a description of the computer-readable storage medium provided in this application, please refer to the above method embodiments; further details will not be repeated here.

[0142] Those skilled in the art will readily understand that the above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for assessing the seasonal spread risk of Chikungunya subtropical heat based on a coupled model, characterized in that, include: S1. Complete the collection of basic data and preprocess the collected data; S2. Divide the model parameters into fixed parameters and dynamic parameters and assign values ​​to them separately; among them, the dynamic parameters need to be calculated in conjunction with environmental data; Then, using historical epidemic data from the past three years as the target value, the key parameters are adjusted using the least squares method to minimize the error between the epidemic curve simulated by the model and the actual observed values. S3. Based on standardized meteorological data, the impact of temperature, rainfall, and humidity on mosquitoes is quantified, and a daily key parameter sequence is generated and input into the vector module. The population dynamics of mosquito eggs, larvae, and adults are simulated through an age structure model. At the same time, the transmission process of the virus in susceptible, exposed, and infectious adults is simulated according to the SEI model. Then, driven by the number of infectious mosquitoes output by the vector module, the SEIR model is used to simulate the state transition of four groups of people in the population: susceptible, exposed, infected, and recovered. Finally, the daily number of infected people and the core indicators of the incidence rate are obtained. S4. Based on the daily number of infections predicted by the model, and combined with the preset risk level classification threshold, complete the risk level assessment of the assessment area.

2. The method for assessing the seasonal spread risk of subtropical heat in Chikunguna based on a coupled model according to claim 1, characterized in that, The fixed parameters in S2 are parameters that do not change dynamically with environmental conditions or time, and are determined based on the biological characteristics of the disease, the inherent attributes of mosquito species, and epidemiological consensus; they include population-related fixed parameters, mosquito-related fixed parameters, and virus-related fixed parameters.

3. The method for assessing the seasonal spread risk of subtropical heat in Chikunguna based on a coupled model according to claim 1, characterized in that, The dynamic parameters in S2 are those that need to be calculated in real time based on daily standardized meteorological data, change dynamically with environmental conditions, and directly support the operation of the media module and the host module. These include temperature-driven dynamic parameters, rainfall-driven dynamic parameters, humidity-related dynamic parameters, and media-host linkage dynamic parameters.

4. The method for assessing the seasonal spread risk of Chikungunya subtropical heat based on a coupled model according to claim 1, characterized in that, The specific process of virus transmission among susceptible, exposed, and infectious adult worms, as simulated using the SEI model in S3, is as follows: Using daily as the time step, the SEI model is used to dynamically characterize the virus in susceptible S... v Exposure E v Infectious I v The core logic of the state transitions among the three types of adult mosquitoes is that "susceptible mosquitoes, after biting and infecting humans, become exposed, then, after an incubation period outside the virus, become infectious, and infectious mosquitoes continue to transmit the virus and also die naturally." The specific equations include: Susceptible adult S v Equation of population change, exposed adult E v Equation of population change and infection of adult I v Equation for quantitative change.

5. The method for assessing the seasonal spread risk of subtropical heat in Chikunguna based on a coupled model according to claim 1, characterized in that, The specific process of simulating the state transition of four groups of people—susceptible, exposed, infected, and recovered—using the SEIR model in S3 is as follows: The number of infectious mosquitoes output daily by the media module I v Using (t) as the core driving variable and combining the biological characteristics of Chikungunya fever transmission via mosquito bites, the SEIR model uses four sets of coupled differential equations to dynamically simulate the population in susceptible S... h Exposure E h , infection I h , restore R h Transitions between four types of states; The media module outputs I daily v (t), where t is the daily number of infectious mosquitoes, which needs to be compared with the total number of mosquitoes N. v (t) combined to calculate the proportion of infectious mosquitoes. Where, N v (t)=S v (t)+E v (t)+I v (t), S v (t) represents the number of susceptible adult mosquitoes at time t; E v (t) represents the number of adult mosquitoes exposed at time t; I v (t) represents the number of adult mosquitoes in the infectious state at time t; Then, using daily as the time step, differential equations were constructed to simulate the state transitions of four groups of people, including: susceptible S... h Population size change equation, exposure E h Equation of population size change, infection I h Equation of population size change and recovery R h The equation for population size change; the core logic is "susceptible people are bitten by infectious mosquitoes and then become exposed, then become infected after an incubation period, and finally develop immunity and enter the recovery state after infection"; Daily based on the previous day's S h E h I h R h The value, combined with the I output of the media module on that day v (t) and N v (t), calculate the number of people in each state on the day through the above four sets of equations, and complete the daily linkage of "the number of infectious mosquitoes → the transformation of the state of the population".

6. The method for assessing the seasonal spread risk of Chikungunya subtropical heat based on a coupled model according to claim 5, characterized in that, S3 is susceptible to S h The equation for population size change is: The number of susceptible individuals decreased solely due to infection via infectious mosquito bites, with no new sources of infection; it is assumed that population movement during the assessment period had a negligible impact on the replenishment / loss of susceptible individuals, or that these individuals had already been included in the data preprocessing. h Initial values ​​are used to construct the following equation: in, β represents the rate of change in the number of susceptible individuals over time. h The probability of infection in a population after being bitten by an infectious mosquito is based on the mosquito bite rate β. bite (T t The infection efficiency of the virus after it enters the human body is a fixed biological parameter; S H (t) represents the number of people in a susceptible state at time t.

7. The method for assessing the seasonal spread risk of Chikungunya subtropical heat based on a coupled model according to claim 6, characterized in that, E exposed in S3 h The equation for population size change is: The number of exposed individuals is determined by the increase in the number of people transitioning from a susceptible state to an infected state and the decrease in the number transitioning to an infectious state, which essentially reflects the incubation period of the virus in the human body: in, This indicates the rate of change in the number of exposed individuals over time. E represents the daily conversion rate of exposed individuals to an infectious state. h (t) represents the number of people exposed at time t.

8. The method for assessing the seasonal spread risk of Chikungunya subtropical heat based on a coupled model according to claim 7, characterized in that, In S3, infection I h The equation for population size change is: The number of infected individuals is determined by the "increase in the number of people transitioning from the exposed state" and the "decrease in the number of people transitioning to the recovery state," which is directly related to the transmission of the virus to the mosquito population. Assuming that the infected population is the only source of virus acquisition for mosquitoes, the following equation is constructed: in, This indicates the rate of change in the number of infected individuals over time. The daily conversion rate of infected individuals to recovery status; I h (t) represents the number of people infected at time t.

9. A method for assessing the seasonal spread risk of subtropical heat in Chikunguna based on a coupled model, as described in claim 8, is characterized in that... In S3, R is restored h The equation for population size change is: The number of recovered individuals increases solely due to the transition from an infectious state, with no source of decrease; assuming no secondary infection following Chikungunya fever and lifelong immunity in the recovered population, the following equation is constructed: in, This indicates the rate of change in the number of recovered individuals over time.

10. A risk assessment system for the seasonal spread of subtropical heat in Chikunguna based on a coupled model, characterized in that, include: The basic data acquisition and preprocessing unit is used to complete the acquisition of basic data and preprocess the acquired data. The model parameter classification, assignment, and calibration unit is used to classify model parameters into fixed parameters and dynamic parameters and assign values ​​to them separately; among them, dynamic parameters need to be calculated in conjunction with environmental data; Then, using historical epidemic data from the past three years as the target value, the key parameters are adjusted using the least squares method to minimize the error between the epidemic curve simulated by the model and the actual observed values. The vector-host module simulation and index output unit is used to quantify the impact of temperature, rainfall, and humidity on mosquitoes based on standardized meteorological data, generate daily key parameter sequences, and input them into the vector module. It simulates the population dynamics of mosquito eggs, larvae, and adults through an age structure model, and simulates the transmission process of viruses among susceptible, exposed, and infectious adults according to the SEI model. Then, driven by the number of infectious mosquitoes output by the vector module, it uses the SEIR model to simulate the state transitions of four groups of people in the population: susceptible, exposed, infected, and recovered, and finally obtains the daily number of infected people and the core indicators of incidence rate. The transmission risk level assessment unit is used to assess the risk level of an assessment area based on the daily number of infections predicted by the model and a preset risk level classification threshold.

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