A method and apparatus for analyzing the transmission dynamics of a communicable disease

Abstract

The application discloses a kind of media infectious disease transmission dynamics analysis method and device, it is related to infectious disease analysis field, comprising: S1, the mosquito vector transmission dynamics model of integration age, gender and infection state is constructed;S2, unknown parameter of mosquito vector transmission dynamics model is estimated based on historical infectious disease data, and the mosquito vector transmission dynamics model of parameter calibration is obtained;S3, intervention approach is quantified into control variable and is included in the mosquito vector transmission dynamics model of parameter calibration, constructs objective function, is solved using optimal control theory (optimal control theory), and the control variable of solution is obtained;S4, using the control variable of solution is carried out media infectious disease intervention.The application can quantitatively evaluate the heterogeneity contribution of different gender, age group and infection state to disease transmission;Optimal control theory is introduced to solve numerically, and the optimal implementation path of each intervention measure in real epidemic situation is clarified.

Description

Technical Field

[0001] This invention relates to the field of infectious disease analysis technology, and in particular to a method and apparatus for analyzing the transmission dynamics of vector-borne infectious diseases. Background Technology

[0002] Mosquito-borne infectious disease transmission dynamics modeling technology is mainly used to analyze the transmission mechanisms of mosquito-borne infectious diseases and identify key transmission factors, providing theoretical support and data reference for formulating precise prevention and control strategies. By constructing mathematical models to quantify the impact of factors such as demographic characteristics, transmission routes, and vector population dynamics on epidemic spread, the implementation effects of different intervention measures can be effectively evaluated, helping to improve the scientific and targeted nature of mosquito-borne infectious disease prevention and control. It has important application value in both emergency response to sudden outbreaks and routine prevention and control.

[0003] In the study of parameter fitting for propagation dynamics models, various statistical inference methods have been widely used. Among them, Markov Chain Monte Carlo (MCMC) is a fundamental method for Bayesian parameter inference. Its core idea is to sample from complex posterior distributions by constructing Markov chains.

[0004] At the modeling level, existing research still falls short in simultaneously integrating the heterogeneous structures of age, sex, and infection status into the dynamics of mosquito-borne infectious disease transmission models. This deficiency limits the ability to quantify the specific roles of different demographic groups in the transmission process, thus hindering the development of targeted prevention and control strategies. At the parameter fitting level, traditional Markov chain Monte Carlo methods often suffer from slow mixing speeds and low sampling efficiency when dealing with high-dimensional continuous parameter spaces due to the random walk characteristics; while particle Markov chain Monte Carlo methods can handle latent variable problems in state-space models, their computational cost is high, and they focus more on state inference rather than the rapid and efficient exploration of the parameters themselves. Summary of the Invention

[0005] To address the above problems, this invention proposes a method and apparatus for analyzing the transmission dynamics of vector-borne infectious diseases. It constructs a mosquito-borne transmission dynamics model integrating age, sex, and infection status; estimates unknown parameters of the mosquito-borne transmission dynamics model based on historical infectious disease data; and uses the parameter-calibrated mosquito-borne transmission dynamics model to analyze vector-borne infectious diseases. This divides the total population into multiple subgroups with different epidemiological characteristics, allowing for a quantitative assessment of the heterogeneous contribution of different sexes, age groups, and infection status (symptomatic / asymptomatic) to disease transmission.

[0006] The specific steps of the method for analyzing the transmission dynamics of vector-borne infectious diseases are as follows:

[0007] S1, Construct a mosquito-borne transmission dynamics model that integrates age, sex, and infection status;

[0008] S2, Based on historical infectious disease data, the unknown parameters of the mosquito vector transmission dynamics model are estimated to obtain a parameter-calibrated mosquito vector transmission dynamics model;

[0009] S3. The intervention pathway is quantified into control variables and incorporated into the parameter-calibrated mosquito vector transmission dynamics model. An objective function is constructed, and optimal control theory is used to solve the parameter-calibrated mosquito vector transmission dynamics model with control variables incorporated to obtain the solved control variables.

[0010] S4 uses the solved control variables to intervene in vector-borne infectious diseases.

[0011] Preferably, S2 further includes: using a parameter-calibrated mosquito vector transmission dynamics model to analyze vector-borne infectious diseases, specifically: using the next-generation matrix method to derive the basic reproduction number formula of the parameter-calibrated mosquito vector transmission dynamics model, thereby conducting vector-borne infectious disease analysis; the basic reproduction number formula can be decomposed according to the transmission paths between different species, and within the population subgroups that consider age, sex and infection status, the transmission contribution of each subgroup is summed.

[0012] Preferably, the integration of population age, gender, and infection status specifically involves dividing the population into: susceptible males / females in each age group; latent infected males / females in each age group; symptomatic infected males / females in each age group; asymptomatic infected males / females in each age group; and recovered males / females in each age group.

[0013] Preferably, the mosquito vector transmission dynamics model is expressed as follows:

[0014] ;

[0015] in, Indicates the first susceptible males in the age group; Indicates the first Susceptible females in the age group; Indicates the first Male asymptomatic carriers in the age group; Indicates the first Latent infections in women within the same age group; Indicates the first Symptomatic infections were found in men within the age group; Indicates the first Symptomatic infections were found in women in the age group; Indicates the first Asymptomatic males in the age group; Indicates the first Asymptomatic infections in women within the age group; Indicates the first Male recoveries in the age group; Indicates the first Female recoveries in the age group; Indicates the first Effective transmission rate among men in the age group; Indicates the first Effective transmission rate among women in the age group; Indicates susceptible mosquito larvae; Indicates infection by mosquito larvae; Indicates susceptible adult mosquitoes; Indicates adult mosquitoes in the incubation period; Indicates infection by adult mosquitoes; This indicates the effective transmission rate of mosquitoes to humans; Indicates the effective transmission rate of mosquitoes to humans; This indicates the probability that an infected person will develop asymptomatic infection; This indicates the recovery rate of symptomatic individuals; Indicates the recovery rate of asymptomatic individuals; This indicates the conversion rate from latent to symptomatic individuals; This indicates the rate at which individuals in the incubation period develop into asymptomatic individuals; This indicates the rate at which mosquitoes transition from the incubation period to the infectious period. Represents the total population; Indicates the total number of mosquito vectors; Indicates the number of age groups; This indicates the average birth rate of the mosquito population. Indicates seasonal effect factors; Indicates the natural mortality rate of the mosquito population; This indicates the lowest infection rate from vertical transmission; This indicates the mosquito swarm's emergence rate.

[0016] Preferably, the parameter estimation is performed using the particle Markov chain Monte Carlo method.

[0017] Preferably, the intervention pathways include mosquito-to-human transmission control, human-to-mosquito transmission control, and mosquito population suppression; the quantification of the intervention pathways into control variables and their incorporation into the parameter-calibrated mosquito vector transmission dynamics model specifically involves:

[0018] In the parameter-calibrated mosquito vector transmission dynamics model, the effective transmission rate from mosquito to human is... Multiply by a coefficient Model calibration was performed to determine the effective transmission rate of human-to-human mosquitoes. Multiply by a coefficient Perform model calibration, including the total number of mosquito vectors. Multiply by a coefficient Model calibration was performed to obtain the intervention mosquito vector transmission dynamics model; among which, This indicates the effective transmission rate of mosquitoes to humans; Indicates the effective transmission rate of mosquitoes to humans; Indicates the total number of mosquito vectors; Control variables representing intervention pathways for controlling mosquito-to-human transmission; Control variables representing intervention pathways for human-mosquito transmission control; Control variables representing intervention pathways for mosquito population suppression.

[0019] Preferably, the optimized control objective function is a weighted sum of the number of monitorable cases and the control function, expressed as:

[0020] ;

[0021] in, Indicates intervention approach The objective function is a variable; Indicates the duration of a daily cycle; express Weighting factors; Indicates the first Symptomatic infections were found in men within the age group; express Weighting factors; Indicates the first Symptomatic infections were found in women in the age group; express Cost factors; express Cost factors; Indicate Cost factors; Control variables representing intervention pathways for controlling mosquito-to-human transmission; Control variables representing intervention pathways for human-mosquito transmission control; Control variables representing intervention pathways for mosquito population suppression; This indicates the number of age groups.

[0022] Preferably, the step of solving the mosquito vector transmission dynamics model with parameters calibrated and incorporating control variables using optimal control theory specifically involves:

[0023] Using Pontryagin's maximum principle, the optimal control problem of minimizing the objective function is transformed into a Hamiltonian system that can be solved numerically.

[0024] A numerical iterative algorithm is used to solve the Hamiltonian system numerically, and the control variables, i.e. the optimal control trajectory, are obtained.

[0025] Preferably, the numerical iteration algorithm is a forward-backward scan method.

[0026] On the other hand, a device for analyzing the transmission dynamics of vector-borne infectious diseases includes the following:

[0027] The mosquito vector transmission dynamics model building module is used to construct a mosquito vector transmission dynamics model that integrates age, sex, and infection status.

[0028] The parameter calibration module is used to estimate the unknown parameters of the mosquito vector transmission dynamics model based on historical infectious disease data, and obtain the parameter-calibrated mosquito vector transmission dynamics model.

[0029] The control variable solving module is used to quantify the intervention path into control variables and incorporate them into the parameter-calibrated mosquito vector transmission dynamics model. It constructs the objective function and uses optimal control theory to solve the parameter-calibrated mosquito vector transmission dynamics model with control variables in it, thereby obtaining the solved control variables.

[0030] The vector-borne infectious disease intervention module is used to intervene in vector-borne infectious diseases using the solved control variables.

[0031] Compared with the prior art, the present invention has the following beneficial effects:

[0032] (1) This invention constructs a mosquito vector transmission dynamics model by dividing the total population into multiple subgroups with different epidemiological characteristics. Based on this model, the basic reproduction number is analytically decomposed to quantitatively assess the heterogeneous contribution of different genders, age groups and infection status (symptomatic / asymptomatic) to disease transmission.

[0033] (2) This invention introduces optimal control theory to construct a Hamiltonian system with mosquito-to-human transmission control, mosquito vector control and human-to-mosquito transmission control as control variables. By numerically solving the Hamiltonian system, the optimal implementation path of various intervention measures in the real epidemic scenario is clarified, and the prevention and control effects of different intervention combinations on curbing the epidemic are comprehensively evaluated. Attached Figure Description

[0034] The present invention will now be described in further detail with reference to the accompanying drawings;

[0035] Figure 1 This is a flowchart of the vector-borne infectious disease transmission dynamics analysis method according to an embodiment of the present invention;

[0036] Figure 2 This is a chamber structure diagram of the vector-borne disease transmission model in the vector-borne disease transmission dynamics analysis method of this invention.

[0037] Figure 3This invention provides an optimal disease control strategy analysis framework for intervention-oriented methods in the analysis of vector-borne infectious disease transmission dynamics.

[0038] Figure 4 This is a structural block diagram of the vector-borne infectious disease transmission dynamics analysis device according to an embodiment of the present invention. Detailed Implementation

[0039] The present invention will be further described below through specific embodiments.

[0040] like Figure 1 As shown, a method for analyzing the transmission dynamics of vector-borne infectious diseases includes the following specific steps:

[0041] S1. Construct a mosquito-borne transmission dynamics model that integrates age, sex, and infection status.

[0042] This embodiment constructs a human-vector transmission dynamics model for age- and gender-stratified vector-borne infectious diseases. The model divides the population into age- and gender-stratified groups. There are several warehouses, among which... This indicates the number of age groups. For each age group... The model includes the following compartments:

[0043] : No. Susceptible males / females in the age group;

[0044] : No. Male / female individuals in the age group who are incubating for infection;

[0045] : No. Symptomatic infected individuals in the age group (male / female);

[0046] : No. Asymptomatic male / female individuals in the age group;

[0047] : No. Male / female recoveries in the age group.

[0048] The media section is divided into the following five compartments:

[0049] Susceptible mosquito larvae;

[0050] Infected mosquito larvae;

[0051] Susceptible to adult mosquitoes;

[0052] Adult mosquitoes during their incubation period;

[0053] Infected adult mosquitoes.

[0054] This model is based on the following assumptions:

[0055] (1) The transmission of mosquito-borne infectious diseases is achieved only through cross-species interaction, that is, transmission between vector and human (vector to human or human to vector), without considering direct human to human or vector to vector routes.

[0056] (2) Effective transmission rate varied with age and sex group. The first male age group... Effective transmission rate As a baseline, the effective transmission rate for all other groups was obtained by multiplying this baseline by the corresponding incidence rate ratio (IRR).

[0057] (3) After infection, susceptible individuals first enter the incubation period, during which they are not infectious. Subsequently, they are infected based on probability. Experience the average incubation period It later develops into an asymptomatic infection, and ultimately the recovery rate is high. Recovery; or by probability Experience the average incubation period Later, they developed into symptomatic infected individuals, and then recovered. Rehabilitation.

[0058] (4) The proportion of mosquitoes acquiring viruses through vertical transmission is This process is affected by the mosquito birth rate. Influenced by seasonal factors Adjustment, therefore the effective vertical propagation rate is Infected mosquito larvae have an average emergence period of... They develop into infectious adult mosquitoes capable of transmitting viruses.

[0059] (5) Newly hatched mosquito larvae (excluding those vertically infected) enter the susceptible mosquito larvae enclosure. After an average of [time period missing], After emerging from their molting stage, they develop into susceptible adult mosquitoes. These susceptible adult mosquitoes infect humans through bites, acquire the virus, and enter the incubation period mosquito enclosure, where they remain for an average of [time period missing]. After an incubation period, they transform into adult mosquitoes in the infectious stage, which then become capable of transmitting the virus.

[0060] The model defines the total population as:

[0061] ;

[0062] The total number of mosquito vectors is as follows:

[0063] ;

[0064] To simplify subsequent calculations, based on its demographic meaning, It is considered a constant. Furthermore, the seasonal effect function is defined as follows:

[0065] ;

[0066] The media communication model that satisfies the above assumptions is as follows: Figure 2 As shown, the corresponding system of equations is as follows:

[0067] (1);

[0068] The biological significance of the model parameters is shown in Table 1.

[0069] Table 1: Biological significance of model parameters.

[0070]

[0071] S2. Based on historical infectious disease data, the unknown parameters of the mosquito vector transmission dynamics model are estimated to obtain a parameter-calibrated mosquito vector transmission dynamics model.

[0072] This method employs the Particle Markov Chain Monte Carlo (PMCMC) method to address unknown parameters in dynamic models. Bayesian inference is performed. The PMCMC framework combines particle filtering (PF) with Markov chain Monte Carlo (MCMC) sampling, making it suitable for parameter estimation of state-space models with partial observation noise and complex nonlinear dynamics. This method effectively solves the problem of the inherent difficulty in computing the likelihood function in such models.

[0073] Its methodological basis lies in transforming the parameter estimation problem into an estimation of the posterior distribution. The exploration, among which This represents time-series observation data. According to Bayes' theorem, the posterior distribution and the likelihood function... and prior distribution The product is directly proportional:

[0074] ;

[0075] Within this framework, the goodness of fit of a parameter is defined by its probability density under the posterior distribution. Regions with higher probability density correspond to parameter values ​​that are more consistent with prior knowledge and have better compatibility with observed data.

[0076] The posterior distribution of the parameters was sampled using the constructive Metropolis-Hastings (MH) algorithm. In the... In this iteration, the first step is to start from the proposed distribution. (Usually set to Gaussian random walk) Generate candidate parameter values To evaluate the candidate, particle filtering is invoked to calculate its corresponding marginal likelihood estimate. Particle filtering works by maintaining a set of weighted particles. By performing a sequential approximation on the filtered distribution of the hidden states, an unbiased estimate of the marginal likelihood is finally given:

[0077] ;

[0078] This estimate is embedded in the MCMC sampler, which correlates the parameters with the observation data and drives the exploration of the parameter space.

[0079] Based on the output of the particle filter, the algorithm performs an acceptance-rejection step to update the parameters. Candidate points Acceptance probability Determined by the following formula:

[0080] ;

[0081] This mechanism ensures that the Markov chain visits regions with higher posterior probabilities at a frequency proportional to the probability density. After a predefined burn-in period, the Markov chain generates a sequence of samples. This can be viewed as an approximation of independent and identically distributed samples from the target posterior distribution. Finally, based on this sample set, the posterior mean is calculated. Alternatively, the posterior mode can be used as a point estimate, and quantiles can be used to construct a confidence interval, thus completing the statistical inference and uncertainty quantification of parameter values.

[0082] Vector-borne infectious disease analysis was conducted using a parameter-calibrated mosquito vector transmission dynamics model.

[0083] Basic reproduction number of sex-age structure ( Derivation.

[0084] The disease-free equilibrium point (DFE) is represented as:

[0085] ;

[0086] in, , The basic reproduction number is calculated using the next-generation matrix method. Nonlinear terms including new infections. With outflow items As shown in the following formula:

[0087] ;

[0088] ;

[0089] and At the disease-free equilibrium point The Jacobian matrix at point is:

[0090] ;

[0091] ;

[0092] These include:

[0093] , ,

[0094] , ;

[0095] The basic reproduction number of model (1) For the next generation matrix The spectral radius. It can be directly calculated as follows:

[0096] ,

[0097] The basic reproduction number formula can be decomposed according to the transmission pathways between different species (humans and mosquitoes), and the transmission contribution of each subgroup is summed within the population subgroups that consider age, sex, and infection status:

[0098] ;

[0099] ;

[0100] This represents the average number of secondary infections produced by a single mosquito vector per unit time in a fully susceptible population. This value can be broken down into four components: the number of symptomatic secondary infections in males. Number of asymptomatic infections in second-generation males Number of symptomatic second-generation female infections Number of asymptomatic infections in second-generation females All of the above parts are in accordance with The age groups were further stratified. The mosquito-borne pathogens were similar in some aspects. This represents the average number of secondary mosquitoes generated per unit of time when a susceptible vector (mosquito) infects a population through its bite.

[0101] S3. The intervention pathway is quantified into control variables and incorporated into the parameter-calibrated mosquito vector transmission dynamics model. An objective function is constructed, and optimal control theory is used to solve the parameter-calibrated mosquito vector transmission dynamics model with control variables incorporated to obtain the solved control variables.

[0102] This embodiment, based on the World Health Organization's "Global Vector Control Strategies 2017-2030," analyzes the key factors influencing disease transmission. Building upon this, it focuses on controllable human interventions and constructs an intervention-transmission dynamics model that integrates population and vector structural characteristics. This model targets three key pathways: blocking mosquito-to-human transmission, controlling human-to-mosquito transmission, and suppressing mosquito populations. Guided by Pontryagin's maximum principle, an optimal control theory model is established, forming an analytical framework for optimizing disease control strategies. Figure 3 ).

[0103] To validate this framework, this embodiment employs the Hamiltonian Monte Carlo (HMC) method to solve for the analytical and numerical solutions of the control strategy in the human-mosquito transmission system, thereby systematically evaluating the effects of different intervention combinations. The core theory of this method lies in constructing a Hamiltonian function, transforming the original optimal control problem into solving a coupled system composed of state equations and costate equations, and thus identifying the optimal control variables that minimize the preset objective function.

[0104] Construct an optimal control theory model.

[0105] To evaluate the effectiveness of vector-borne disease intervention strategies, three different control pathways—controlling mosquito-to-human transmission, controlling human-to-mosquito transmission, and suppressing mosquito populations through mosquito netting—were used as control functions. Model (1) was included (see Table 2). By constructing the Hamiltonian system, the optimal timing and intensity of each intervention measure in controlling the outbreak were quantitatively assessed.

[0106] Table 2: Parameterization settings for intervention measures.

[0107]

[0108] The system structure after incorporating the above control measures is as follows:

[0109] (2);

[0110] Specifically, mosquito-to-human transmission control measures include clothing protection, use of mosquito nets, installation of window and door screens, use of repellents, clothing treatment, on-site personnel protection, traveler protection, and health education; human-to-mosquito transmission control measures include hospital isolation, home isolation, hospital mosquito prevention, returnee protection, health notification, and monitoring of co-exposed individuals; and mosquito-borne population control measures include breeding ground management, adult mosquito eradication, and emergency monitoring and assessment.

[0111] The primary objective of this method is to minimize the number of new symptomatic cases and the costs associated with controlling the epidemic. Therefore, the objective functional is defined as:

[0112] (3);

[0113] Where, constant Asymptomatic infected persons The weighting factor, and and The corresponding cost factor. Optimal control is determined based on Pontryagin's maximum principle. , so that:

[0114] (4);

[0115] in, Let T be the control set and T be the final time step. To define in the interval The space of integrable functions on.

[0116] To solve this optimal control problem, this study first constructs the Lagrangian and Hamiltonian functions for equations (2) - (4). The Lagrangian function for this optimal problem is defined as follows:

[0117] (5);

[0118] The Hamiltonian function integrates the state equation, costate variables (Lagrange multipliers), and the integrand of the objective function as follows:

[0119] ;

[0120] in, Represents a vector of state variables. For the control variable to be determined, The integrand (running cost) of the corresponding objective function. Describe the system dynamics, These are costate variables (also known as adjoint variables), and .

[0121] To find the minimum value of equation (5), the Hamiltonian function is introduced. Its definition is as follows:

[0122] (6);

[0123] in, Let be the right-hand side term of the differential equation (2) corresponding to the i-th state variable. This is the adjoint vector.

[0124] According to Pontryagin's maximum principle, optimal control... The Hamiltonian function must be made constant throughout the entire time interval. Take the minimum (or maximum) value. For the Hamiltonian function... Regarding control variables Find the partial derivative and set it to zero, that is:

[0125] ;

[0126] At this point, the explicit expression for optimal control can be obtained, and it can be represented as a state variable. With costate variables The function, i.e. .

[0127] The optimal control solution method and theorem derivation are used.

[0128] Solving the above optimal control system requires simultaneously satisfying the state equations and co-state equations. Among them, the state equations...

[0129] ;

[0130] With initial conditions Integrating forward along time, the costate equations

[0131] ;

[0132] The costate equations are based on the terminal conditions. Integrating backwards along time. To numerically solve this coupled system, this study employs the forward-backward scan method, which iteratively executes the following steps: Based on the estimated values ​​of the current control variables, the state equation is integrated forward to obtain the state trajectory; using the current state trajectory, the co-state equation is integrated backwards to determine the co-state trajectory; based on the Hamiltonian function minimization condition... Update the control variables.

[0133] To ensure the stability and convergence of the algorithm, a relaxation update strategy is introduced:

[0134] ;

[0135] in, This is the relaxation (damping) factor. The updated control variables need to be projected onto the admissible control set. (For example This is essentially an implementation of the projected gradient method. Iteration continues until the relative change in the control variable falls below a preset tolerance or the maximum number of iterations is reached.

[0136] Based on the existence of the optimal solution to this control problem, we can obtain the following Theorem 1.

[0137] Theorem 1: Let For the optimal control system described in equations (2) and (3) with optimal control measures The corresponding state solution is given below. Then, the accompanying variable satisfying the following conditions can be derived. :

[0138] (7);

[0139] Satisfy boundary conditions or cross-sectional conditions:

[0140] ;

[0141] in,

[0142] ;

[0143] ;

[0144] ;

[0145] In addition, optimal control measures It is given by the following formula:

[0146] (8);

[0147] Proof: Based on Pontryagin's maximum principle and the Hamiltonian function (Equation (6)), the adjoint equation can be obtained as follows:

[0148] ;

[0149] ;

[0150] ;

[0151] ;

[0152] ;

[0153] ;

[0154] ;

[0155] ;

[0156] ;

[0157] ;

[0158] ;

[0159] ;

[0160] ;

[0161] ;

[0162] ;

[0163] The terminal boundary conditions (i.e., the final value conditions) are satisfied. To solve the target problem shown in equation (8), the following equation is used:

[0164] ;

[0165] ;

[0166] ;

[0167] Utilizing control space The properties located inside the control set yielded the expected results.

[0168] S4 uses the solved control variables to intervene in vector-borne infectious diseases.

[0169] In summary, the age-sex stratified human-mosquito transmission dynamics model constructed in this method stratifies the population according to both age and sex, and the mosquito population covers both larval and adult stages. Combining seasonal effects and vertical transmission, it characterizes the transmission mechanism. The PMCMC algorithm is used to estimate high-dimensional parameters, reducing the error control of epidemic prediction. The HMC algorithm is used to optimize the control path, providing a scientific tool for precise control. Compared with traditional models, this model shows improvements in targeting, economy, and practicality.

[0170] Specifically, this method addresses the heterogeneity of populations in vector-borne infectious diseases by stratifying the population according to age and sex, and combining this with the entire mosquito life cycle (larva-adult) to construct... Five high-dimensional compartmental systems accurately quantify the communication contributions of different groups in media interaction, making up for the technical limitations of traditional models in distinguishing the communication roles of different groups and clarifying the main body responsible for communication.

[0171] This method will use the basic reproduction number ( The virus can be broken down into multiple levels according to transmission route (mosquito to human / human to mosquito), age, gender, and infection status (asymptomatic / symptomatic), providing clear technical targets for targeted prevention and control, and breaking through the limitation of traditional breeding numbers that only reflect the overall transmission intensity.

[0172] This method focuses on three core intervention pathways: "mosquito-human transmission blocking, human-to-mosquito control, and mosquito swarm suppression." By constructing a Hamiltonian system and combining it with the Pontryagin maximum principle, it quantifies the optimal initiation timing, intensity trajectory, and combined effects of each intervention measure, thus forming a dynamically adapted intervention plan.

[0173] It should be noted that, based on the original scheme of dividing the population according to age and gender, the model can be further divided into dimensions such as occupational risk level, residential environment type, socioeconomic level, or immune background to more finely characterize the exposure risk and transmission contribution of different subgroups. In terms of vector classification, the larval stage can be simplified and merged to reduce model complexity, or the mosquito vector life cycle can be divided more finely (such as distinguishing between egg, larva, pupa, etc.), or the spatial heterogeneity of mosquito vector distribution can be further considered to construct a spatiotemporally coupled transmission model. The specific settings can be made according to the needs, and this embodiment does not impose any restrictions.

[0174] It should be noted that the model parameters can be set as follows: To enhance the model's responsiveness to environmental changes, key parameters (such as mosquito birth rate, bite rate, etc.) can be constructed as functions of external driving factors. For example, vector density indicators such as the Breteau index can be incorporated to dynamically calibrate the number of mosquitoes, or meteorological factors (such as temperature, precipitation, humidity) can be coupled to characterize the impact of seasonal fluctuations on mosquito population dynamics and virus transmission efficiency. The specific settings can be made as needed, and this embodiment does not impose any restrictions.

[0175] It should be noted that the intervention measures and their parameters can be set as follows: On the basis of overall suppression of mosquito populations, refined control can be implemented for different life stages of the vector, that is, distinguishing between control measures for larvae and control measures for adult mosquitoes, so as to more accurately simulate the synergistic effect of different intervention methods; at the same time, the implementation effect of control measures can be dynamically adjusted according to actual survey data. For example, the impact of control variables on the transmission rate can be constructed as a nonlinear response function, or the time lag characteristics of the intervention effect can be considered to make the optimization results closer to the actual policy situation. The specific settings can be made as needed, and this embodiment does not impose any restrictions.

[0176] It should be noted that when fitting the basic parameters, the traditional Markov chain Monte Carlo method with lower computational cost can be used for Bayesian inference, or deterministic optimization algorithms such as nonlinear least squares can be used for parameter fitting in scenarios that require fast response. The specific settings can be made according to the needs, and this embodiment does not impose any restrictions.

[0177] like Figure 4 As shown, the present invention also discloses a device for analyzing the transmission dynamics of vector-borne infectious diseases, comprising:

[0178] The mosquito vector transmission dynamics model construction module 401 is used to construct a mosquito vector transmission dynamics model that integrates age, sex, and infection status.

[0179] The parameter calibration module 402 is used to estimate the unknown parameters of the mosquito vector transmission dynamics model based on historical infectious disease data, so as to obtain a parameter-calibrated mosquito vector transmission dynamics model.

[0180] The control variable solving module 403 is used to quantify the intervention path into control variables and incorporate them into the parameter-calibrated mosquito vector transmission dynamics model, construct the objective function, and use optimal control theory to solve the parameter-calibrated mosquito vector transmission dynamics model incorporating control variables to obtain the solved control variables.

[0181] Vector-borne infectious disease intervention module 404 is used to intervene in vector-borne infectious diseases using the solved control variables.

[0182] The specific implementation of the vector-borne infectious disease transmission dynamics analysis device is the same as the vector-borne infectious disease transmission dynamics analysis method, and will not be described again in this embodiment.

[0183] The above are merely specific embodiments of the present invention, but the design concept of the present invention is not limited thereto. Any non-substantial modifications made to the present invention using this concept shall be considered as infringing upon the protection scope of the present invention.

Claims

1. A method for analyzing the transmission dynamics of vector-borne infectious diseases, characterized in that, Includes the following steps: S1, Construct a mosquito-borne transmission dynamics model that integrates age, sex, and infection status; S2, Based on historical infectious disease data, the unknown parameters of the mosquito vector transmission dynamics model are estimated to obtain a parameter-calibrated mosquito vector transmission dynamics model; S3. The intervention pathway is quantified into control variables and incorporated into the parameter-calibrated mosquito vector transmission dynamics model. An objective function is constructed, and optimal control theory is used to solve the parameter-calibrated mosquito vector transmission dynamics model with control variables incorporated to obtain the solved control variables. S4 uses the solved control variables to intervene in vector-borne infectious diseases.

2. The method for analyzing the transmission dynamics of vector-borne infectious diseases according to claim 1, characterized in that, The S2 further includes: using a parameter-calibrated mosquito vector transmission dynamics model to analyze vector-borne infectious diseases, specifically: using the next-generation matrix method to derive the basic reproduction number formula of the parameter-calibrated mosquito vector transmission dynamics model, thereby conducting vector-borne infectious disease analysis; the basic reproduction number formula can be decomposed according to the transmission paths between different species, and within the population subgroups that consider age, sex and infection status, the transmission contribution of each subgroup is summed.

3. The method for analyzing the transmission dynamics of vector-borne infectious diseases according to claim 1, characterized in that, The integrated population's age, gender, and infection status are specifically divided into: susceptible males / females in each age group; latent infected males / females in each age group; symptomatic infected males / females in each age group; asymptomatic infected males / females in each age group; and recovered males / females in each age group.

4. The method for analyzing the transmission dynamics of vector-borne infectious diseases according to claim 3, characterized in that, The mosquito vector transmission dynamics model is expressed as follows: ； in, Indicates the first susceptible males in the age group; Indicates the first Susceptible females in the age group; Indicates the first Male asymptomatic carriers in the age group; Indicates the first Latent infections in women within the same age group; Indicates the first Symptomatic infections were found in men within the age group; Indicates the first Symptomatic infections were found in women in the age group; Indicates the first Asymptomatic males in the age group; Indicates the first Asymptomatic infections in women within the age group; Indicates the first Male recoveries in the age group; Indicates the first Female recoveries in the age group; Indicates the first Effective transmission rate among men in the age group; Indicates the first Effective transmission rate among women in the age group; Indicates susceptible mosquito larvae; Indicates infection by mosquito larvae; Indicates susceptible adult mosquitoes; Indicates adult mosquitoes in the incubation period; Indicates infection by adult mosquitoes; This indicates the effective transmission rate of mosquitoes to humans; Indicates the effective transmission rate of mosquitoes to humans; This indicates the probability that an infected person will develop asymptomatic infection; This indicates the recovery rate of symptomatic individuals; Indicates the recovery rate of asymptomatic individuals; This indicates the conversion rate from latent to symptomatic individuals; This indicates the rate at which individuals in the incubation period develop into asymptomatic individuals; This indicates the rate at which mosquitoes transition from the incubation period to the infectious period. Represents the total population; Indicates the total number of mosquito vectors; Indicates the number of age groups; This indicates the average birth rate of the mosquito population. Indicates seasonal effect factors; Indicates the natural mortality rate of the mosquito population; This indicates the lowest infection rate from vertical transmission; This indicates the mosquito swarm's emergence rate.

5. The method for analyzing the transmission dynamics of vector-borne infectious diseases according to claim 1, characterized in that, The parameter estimation was performed using the particle Markov chain Monte Carlo method.

6. The method for analyzing the transmission dynamics of vector-borne infectious diseases according to claim 1, characterized in that, The intervention pathways include intervention pathways for controlling mosquito-to-human transmission, intervention pathways for controlling human-to-mosquito transmission, and intervention pathways for suppressing mosquito populations. Specifically, quantifying these intervention pathways as control variables and incorporating them into a parameter-calibrated mosquito vector transmission dynamics model involves: in the parameter-calibrated mosquito vector transmission dynamics model, the effective transmission rate from mosquito to human is... Multiply by a coefficient Model calibration was performed to determine the effective transmission rate of human-to-human mosquitoes. Multiply by a coefficient Perform model calibration, including the total number of mosquito vectors. Multiply by a coefficient Model calibration was performed to obtain the intervention mosquito vector transmission dynamics model; among which, This indicates the effective transmission rate of mosquitoes to humans; Indicates the effective transmission rate of mosquitoes to humans; Indicates the total number of mosquito vectors; Control variables representing intervention pathways for controlling mosquito-to-human transmission; Control variables representing intervention pathways for human-mosquito transmission control; Control variables representing intervention pathways for mosquito population suppression.

7. The method for analyzing the transmission dynamics of vector-borne infectious diseases according to claim 1, characterized in that, The objective function of the optimization function is a weighted sum of the number of monitorable cases and the control function, expressed as: ； in, Indicates intervention approach The objective function is a variable; Indicates the duration of a daily cycle; express Weighting factors; Indicates the first Symptomatic infections were found in men within the age group; express Weighting factors; Indicates the first Symptomatic infections were found in women in the age group; express Cost factors; express Cost factors; Indicate Cost factors; Control variables representing intervention pathways for controlling mosquito-to-human transmission; Control variables representing intervention pathways for human-mosquito transmission control; Control variables representing intervention pathways for mosquito population suppression; This indicates the number of age groups.

8. The method for analyzing the transmission dynamics of vector-borne infectious diseases according to claim 1, characterized in that, The process of solving the mosquito vector transmission dynamics model with parameters calibrated and incorporating control variables using optimal control theory is as follows: Using Pontryagin's maximum principle, the optimal control problem of minimizing the objective function is transformed into a Hamiltonian system that can be solved numerically. A numerical iterative algorithm is used to solve the Hamiltonian system numerically, and the control variables, i.e. the optimal control trajectory, are obtained.

9. The method for analyzing the transmission dynamics of vector-borne infectious diseases according to claim 8, characterized in that, The numerical iteration algorithm is the forward-backward scanning method.

10. A device for analyzing the transmission dynamics of vector-borne infectious diseases, characterized in that, Including the following: The mosquito vector transmission dynamics model building module is used to construct a mosquito vector transmission dynamics model that integrates age, sex, and infection status. The parameter calibration module is used to estimate the unknown parameters of the mosquito vector transmission dynamics model based on historical infectious disease data, and obtain the parameter-calibrated mosquito vector transmission dynamics model. The control variable solving module is used to quantify the intervention path into control variables and incorporate them into the parameter-calibrated mosquito vector transmission dynamics model. It constructs the objective function and uses optimal control theory to solve the parameter-calibrated mosquito vector transmission dynamics model with control variables in it, thereby obtaining the solved control variables. The vector-borne infectious disease intervention module is used to intervene in vector-borne infectious diseases using the solved control variables.

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