Brucellosis kinetic model construction method based on multi-host-environment coupling

Abstract

The invention relates to the field of brucellosis kinetic model construction, in particular to a brucellosis kinetic model construction method based on multi-host-environment coupling, and the method comprises the steps: collecting multi-host target parameters, and building an initial chamber value of a brucellosis kinetic model; performing parameter dynamic calibration processing according to the initial chamber value of the brucellosis kinetic model to obtain a calibration chamber value of the brucellosis kinetic model; the calibration chamber value of the brucellosis kinetic model is utilized to perform dynamic data fitting processing based on environmental coupling to establish the brucellosis kinetic model, and the model quantification discovers that the long-term environmental pollution risk can be reduced by treatment (improvement) of chronic patients; environment propagation contribution quantification: environment propagation paths have certain contribution to basic regeneration numbers, which indicates that neglecting environmental factors severely underestimates prevention and control resource demands.

Description

Technical Field

[0001] The present invention relates to the field of constructing a Brucella dynamics model, and particularly to a method for constructing a Brucella dynamics model based on multi-host-environment coupling. Background Art

[0002] Existing mechanism-driven models (such as traditional SEIR / SIR models) usually simplify the complex interactions of Brucella between animals-humans-environment. For example, most models only focus on the transmission dynamics of a single host (human or animal), and do not incorporate the environment as an independent transmission medium into the kinetic equations (such as the accumulation and decay of Brucella in the environment). This results in the model being unable to truly reflect the indirect transmission path of Brucella through contaminated environments (such as water sources, soil), limiting the accuracy of predictions and the pertinence of prevention and control strategies. Existing models usually only consider direct contact transmission between hosts (such as sheep-human contact) when calculating the basic reproduction number, ignoring the indirect transmission path of environmental media (such as the continuous release and infection of Brucella in the environment). This leads to a low R0 estimate, unable to accurately assess the true transmission potential of Brucella, and thus affecting the formulation of prevention and control thresholds (such as the target of vaccination coverage rate). Existing data-driven models (such as deep learning) can capture complex data features, but are difficult to explain key transmission mechanisms due to their "black box" nature; while traditional mechanism-driven models have high interpretability, but have insufficient representation ability for non-linear dynamics (such as changes in population behavior, timeliness of prevention and control measures). This contradiction limits the collaborative application of models in long-term policy formulation and short-term early warning. Existing models often simplify the infection status of the population into a single compartment (such as the infected state I ), without distinguishing the course differences between acute ( I ah ), and chronic ( I ch ) Brucella. This results in the model being unable to evaluate the impact of different clinical stages on the transmission risk (such as chronic patients may carry pathogens for a long time), and it is also difficult to quantify the effectiveness of targeted intervention measures (such as the treatment of chronic diseases). Summary of the Invention

[0003] Aiming at the deficiencies of the existing technology, the present invention provides a method for constructing a Brucella dynamics model based on multi-host-environment coupling. By constructing a dynamic differential equation system including animal populations, human populations, and environmental compartments, combined with data-driven parameter optimization technology, high-precision modeling of Brucella transmission mechanisms and quantitative evaluation of prevention and control strategies are achieved.

[0004] To achieve the above object, the present invention provides a method for constructing a Brucella dynamics model based on multi-host-environment coupling, including: Collecting multi-host target parameters to establish the initial compartment values of the Brucella dynamics model; Performing parameter dynamic calibration processing based on the initial compartment values of the Brucella dynamics model to obtain the calibrated compartment values of the Brucella dynamics model; Using the calibrated compartment values of the Brucella dynamics model to perform dynamic data fitting processing based on environmental coupling to establish a Brucella dynamics model.

[0005] Preferably, collecting multi-host target parameters to establish the initial compartment values of the Brucella dynamics model includes: Respectively obtaining the animal host target parameters, human host target parameters, and environmental target parameters as multi-host target parameters; Using the multi-host target parameters to obtain the initial compartment values of the Brucella dynamics model according to the corresponding fitting starting points.

[0006] Furthermore, the respectively obtaining the animal host target parameters, human host target parameters, and environmental target parameters as multi-host target parameters includes: Obtaining the birth rate of the animal host, the natural mortality rate of the animal host, the vaccination failure rate of the animal host, the effective vaccination rate, the infection rate between animal hosts, the infection rate of Brucella in the environment to sheep, the incubation period of Brucella in the animal host, the culling rate of infected animal hosts, and the quantity of Brucella excreted by infected sheep into the environment as the animal host target parameters; Obtaining the birth rate of the human host, the natural mortality rate of the human host, the proportion of acute infections turning into chronic infections, and the attack period of acute patients as the human host target parameters; Obtaining the attenuation rate of environmental Brucella, the disinfection frequency, the effective disinfection rate, the infection rate between the flock and humans, and the infection rate between environmental Brucella and humans as the environmental target parameters; Using the animal host target parameters, human host target parameters, and environmental target parameters as multi-host target parameters.

[0007] Furthermore, performing parameter dynamic calibration processing based on the initial compartment values of the Brucella dynamics model to obtain the calibrated compartment values of the Brucella dynamics model includes: Obtaining historical initial compartment values according to the initial compartment values of the Brucella dynamics model; Using the historical initial compartment values to obtain a historical initial compartment value combination based on the non-linear least squares method; Using the historical initial compartment value combination to perform parameter dynamic calibration processing to obtain the calibrated compartment values of the Brucella dynamics model.

[0008] Furthermore, using the calibrated compartment values of the Brucella dynamics model to perform dynamic data fitting processing based on environmental coupling to establish a Brucella dynamics model includes: Establish a disease - free equilibrium point equation system based on the Brucellosis transmission flow chart; Based on the disease - free equilibrium point equation system, perform dynamic data fitting processing based on environmental coupling to establish a Brucellosis dynamics model.

[0009] Furthermore, the establishment of the disease - free equilibrium point equation system based on the Brucellosis transmission flow chart includes: The calculation formula for establishing the ordinary differential equation system of Brucellosis based on the Brucellosis transmission flow chart is as follows: , where, S represents susceptible individuals, t represents time, A represents the population birth rate, represents the population vaccine inoculation failure rate, V represents individuals with immunity after receiving vaccine immunization, β represents the inter - population infection rate, E represents latent - infection individuals, I represents infective - stage individuals, W represents the quantity of Brucella excreted by the environment, represents the infection rate of environmental Brucella to individuals, d represents the population natural mortality rate, represents the effective vaccine inoculation rate, c represents the reciprocal of the latent period of Brucellosis in the population, α represents the culling rate of the infected population, k represents the quantity of Brucella excreted by infected individuals into the environment, µ represents the attenuation rate of environmental Brucella, n is the disinfection frequency, represents the effective disinfection rate, S h represents the susceptible population, A h represents the birth rate of the population, q represents the proportion of acute infections turning into chronic infections, represents the reciprocal of the onset time of acute patients, I ah represents acute Brucellosis patients, represents health education to reduce the infection risk, β h represents the infection rate between the population and humans, β wh represents the infection rate between Brucella in the environment and humans, d h represents the natural mortality rate of the population, I ch represents chronic Brucellosis patients, represents the population vaccine inoculation failure rate; According to the Brucellosis ordinary differential equations, the calculation formula of the disease-free equilibrium point equations is obtained from the disease-free equilibrium point as follows: , Furthermore, based on the disease-free equilibrium point equations, a Brucellosis dynamics model is established by performing dynamic data fitting processing based on environmental coupling, including: Using the disease-free equilibrium point equations to obtain the corresponding Brucellosis infection rate; According to the Brucellosis status, the calculation formula of the infection variable characteristic equations is obtained by sorting the corresponding infection variables using the Brucellosis infection rate as follows: , Using the disease-free equilibrium point equations to establish the calculation formula of the disease-free equilibrium characteristic matrix corresponding to the disease-free equilibrium point as follows: , , where, , According to the infection variable characteristic equations and the disease-free equilibrium characteristic matrix, the calculation formula of the Brucellosis dynamics model is as follows:

[0010] where, R 0 is the basic reproduction number, ρ is the calculated spectral radius, U -1 is the inverse matrix of U, F , U is the disease-free equilibrium characteristic matrix, is the average number of individuals infected by one exposed carrier among the completely susceptible population, , is the average number of individuals infected by one infected person among the completely susceptible population, , is the average number of Brucella excreted by an exposed carrier individual into the environment and infecting others, , is the average number of Brucella excreted by an infected individual into the environment and infecting others, .

[0011] Compared with the closest prior art, the beneficial effects of the present invention are: Compared with the traditional infectious disease transmission dynamics model (SEIR), the goodness of fit of the patent model for the cases in Inner Mongolia from 2001 to 2021 has been improved to, with the Mean Absolute Percentage Error (MAPE) MAPE = 15.08%, R 2 = 0.846, mainly attributed to the representation of the environmental compartment for the indirect transmission path; dynamic parameter optimization: simulation shows that increasing the vaccination rate from 40% to 80% can reduce the basic reproduction number from 2.5 to 1.5 and shorten the epidemic fading time by 42%; chronic infection management: by distinguishing between acute brucellosis patients ( I ah ) and chronic brucellosis patients ( I ch ), the model quantification finds that the treatment for chronic patients can reduce the long-term environmental pollution risk by 28%; quantification of the contribution of environmental transmission: the environmental transmission route has a certain contribution to the basic reproduction number, indicating that ignoring environmental factors will seriously underestimate the demand for prevention and control resources. Description of the Drawings

[0012] Figure 1 is a flowchart of a method for constructing a brucellosis dynamics model based on multi-host-environment coupling provided by the present invention; Figure 2 is a schematic diagram of the model of a method for constructing a brucellosis dynamics model based on multi-host-environment coupling provided by the present invention. Detailed Embodiments

[0013] The following further elaborates on the detailed embodiments of the present invention with reference to the drawings.

[0014] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention. Obviously, the described embodiments are some, but not all, of the embodiments of the present invention. All other embodiments obtained by those of ordinary skill in the art without creative efforts based on the embodiments of the present invention fall within the scope of protection of the present invention.

[0015] Embodiment 1: The present invention provides a method for constructing a brucellosis dynamics model based on multi-host-environment coupling, as Figure 1 shown, including: S1. Collect multi-host target parameters to establish the initial compartment values of the brucellosis dynamics model; S2. Perform parameter dynamic calibration processing according to the initial compartment values of the brucellosis dynamics model to obtain the calibrated compartment values of the brucellosis dynamics model; S3. Based on the calibrated compartment values of the brucellosis kinetics model, perform dynamic data fitting processing based on environmental coupling to establish a brucellosis kinetics model.

[0016] S1 specifically includes: S1-1. Obtain the animal host target parameters, human host target parameters, and environmental target parameters respectively as multi-host target parameters; S1-2. Use the multi-host target parameters to obtain the initial compartment values of the brucellosis kinetics model according to the corresponding fitting starting points.

[0017] S1-1 specifically includes: S1-1-1. Obtain the birth rate of the animal host, the natural mortality rate of the animal host, the vaccination failure rate of the animal host, the effective vaccination rate, the infection rate between animal hosts, the infection rate of Brucella in the environment to sheep, the incubation period of brucellosis in the animal host, the culling rate of infected animal hosts, and the amount of Brucella excreted by infected sheep into the environment as animal host target parameters; S1-1-2. Obtain the birth rate of the human host, the natural mortality rate of the human host, the proportion of acute infections turning into chronic infections, and the attack period of acute patients as human host target parameters; S1-1-3. Obtain the attenuation rate of environmental Brucella, the disinfection frequency, the effective disinfection rate, the infection rate between the sheep flock and humans, and the infection rate between environmental Brucella and humans as environmental target parameters; S1-1-4. Use the animal host target parameters, human host target parameters, and environmental target parameters as multi-host target parameters.

[0018] S2 specifically includes: S2-1. Obtain the historical initial compartment values according to the initial compartment values of the brucellosis kinetics model; S2-2. Use the historical initial compartment values to obtain a historical initial compartment value combination based on the nonlinear least squares method; S2-3. Use the historical initial compartment value combination to perform parameter dynamic calibration processing to obtain the calibrated compartment values of the brucellosis kinetics model.

[0019] S3 specifically includes: S3-1. Establish a disease-free equilibrium point equation system based on the brucellosis transmission flow chart; S3-2. Based on the disease-free equilibrium point equation system, perform dynamic data fitting processing based on environmental coupling to establish a brucellosis kinetics model.

[0020] S3-1 specifically includes: S3-1-1. The calculation formula for establishing the ordinary differential equations of brucellosis based on the brucellosis transmission flow chart is as follows: , where, S is the susceptible individual, t is the time, A is the population birth rate, is the population vaccine inoculation failure rate, V is the individual with immunity after receiving vaccine immunization, β is the inter-group infection rate, E is the latent infection individual, I is the infected individual during the infection period, W is the quantity of Brucella expelled into the environment, is the infection rate of environmental Brucella to individuals, d is the population natural mortality rate, is the effective vaccine inoculation rate, c is the reciprocal of the latent time of population brucellosis, α is the culling rate of the infected population, k is the quantity of Brucella expelled into the environment by the infected individual, µ is the attenuation rate of environmental Brucella, and n is the disinfection frequency, is the effective disinfection rate, S h is the susceptible population, A h is the birth rate of the population, q is the proportion of acute infection turning into chronic infection, is the reciprocal of the onset time of acute patients, I ah is the acute brucellosis patient, is the health education to reduce the infection risk, β h is the infection rate between the population and humans, β wh is the infection rate between the Brucella in the environment and humans, d h is the natural mortality rate of the population, I ch is the chronic brucellosis patient, is the population vaccine inoculation failure rate.

[0021] S3-1-2. The calculation formula for obtaining the disease-free equilibrium point equations according to the disease-free equilibrium point using the ordinary differential equations of brucellosis is as follows: , S3-2 specifically includes: S3-2-1. Obtain the corresponding Brucella infection rate by using the disease-free equilibrium equations; S3-2-2. According to the Brucella status, sort the infection variables by using the Brucella infection rate, and the calculation formula of the infection variable characteristic equations is as follows: , S3-2-3. Use the disease-free equilibrium equations to establish the disease-free equilibrium characteristic matrix corresponding to the disease-free equilibrium, and the calculation formula is as follows: , , where, , S3-2-4. Establish the Brucella dynamics model according to the infection variable characteristic equations and the disease-free equilibrium characteristic matrix, and the calculation formula is as follows:

[0022] where, R 0 is the basic reproduction number, ρ is the calculated spectral radius, U -1 is the inverse matrix of U, F , U is the disease-free equilibrium characteristic matrix, is the average number of individuals infected by an exposed carrier among the completely susceptible population, , is the average number of individuals infected by an infected person among the completely susceptible population, , is the average number of Brucella excreted by an exposed carrier individual infecting others, , is the average number of Brucella excreted by an infected individual infecting others, .

[0023] In this embodiment, the method for constructing the Brucella dynamics model based on multi-host-environment coupling is as Figure 2 shown, and the specific implementation methods include: Sheep transmission chain (upper side): S (Susceptible sheep): Receive a constant input ( A ), and through environmental contact ( W ) and contact with exposed and infected sheep ( ( E + I )) is converted to E (Exposed sheep).

[0024] E (Exposed sheep): After the incubation period (1 / c ), it turns into I (Infected sheep), releasing Brucella into the environment ( k ).

[0025] I (Infected sheep): Releasing Brucella into the environment ( k ), and may be culled ( ).

[0026] V (Immunized sheep): After the immunity decays over time ( ), it returns to the S compartment.

[0027] Human transmission chain (lower side): S h (Susceptible population): By contacting infected sheep ( ( E + I )), or environmental pathogens ( W ), it turns into I ah (Acute patients).

[0028] I ah (Acute patients): Some ( qλ ) turn into I ch (Chronic patients), and the rest recover ((1 - q ) λ ).

[0029] I ch (Chronic patients): Carry pathogens for a long time.

[0030] Environmental compartment ( W )(middle): Receive pathogens released by infected sheep ( k ( E + I ), and reduce through natural decay ( ) and disinfection intervention (n ).

[0031] Step 1: Model initialization Preset the parameter range based on the literature and expert experience as shown in Table 1 below, and set the initial compartment values as shown in Table 2 below;

[0032] Table 1 Set the initial values of the compartments according to the fitting starting point situation, as shown in the following table:

[0033] Table 2 Step 2: Parameter dynamic calibration Use the deSolve package in R software and adopt the fourth-order Runge-Kutta (RK4) algorithm to solve the differential equation system.

[0034] Fit the historical case data in Inner Mongolia region through nonlinear least squares method to generate 10,000 groups of parameter combinations and screen the optimal parameter set.

[0035] Step 3: Prevention and control strategy simulation: Modify the parameters (such as increasing), and simulate the impact of different prevention and control measures (vaccination, disinfection frequency) on the epidemic trend.

[0036] Improvement effect: Through dynamic data fitting, the model can optimize the parameters in real time, solve the problem that the existing technology relies on static assumptions, and improve the regional adaptability.

[0037] This patent uses the next-generation matrix method to calculate the basic reproduction number, and the specific steps are as follows: Based on the flow chart of Brucella transmission, the following ordinary differential equation system can be established: , It can be observed from the above equation system that , , Thus, it can be seen that , , Among them, sup is the "supremum", that is, the least upper bound of the following limit calculation, After that, , , Therefore, it can be obtained that , , Among them, Ω is the positive invariant set of the system, and R8 + is the eight-dimensional non-negative real number space. Let the right ends of all equations in the system be 0, and the disease-free equilibrium point of the system can be obtained , satisfying: , Among them, , , .

[0038] In the epidemic model, the basic reproduction number R 0 is crucial for determining the transmissibility. Generally speaking, R 0 is defined as the expected number of secondary infections produced by an infected individual in a completely susceptible population. If R 0 > 1, the number of infected individuals will increase, potentially leading to an epidemic. If R 0 < 1, the number of infected individuals will gradually decline, indicating the end of the epidemic.

[0039] Since the last three equations are independent of the first four equations in the system, only the following system needs to be considered: , Then, use the basic reproduction number R 0 to evaluate the infection rate within the system. Sort the infection variables by disease state and consider the vector . The system is: , Obtained according to the literature of van den Driessche and Watmough , , and are vectors. By calculating the and derivatives with respect to and generating the disease-free equilibrium point , we get: , , The basic reproduction number is defined as the spectral radius of the non-negative matrix F U -1 , and we can obtain .

[0040] Those skilled in the art should understand that the embodiments of the present invention can be provided as a method, a system, or a computer program product. Therefore, the present invention can take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present invention can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk memory, CD-ROM, optical memory, etc.) that contain computer-usable program code.

[0041] The present invention is described with reference to the flowcharts and / or block diagrams of methods, apparatuses (systems), and computer program products according to the embodiments of the present application. It should be understood that each flow and / or block in the flowchart and / or block diagram, as well as the combination of flows and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing devices to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing devices generate means for implementing the functions specified in one Figure 1 one flow or multiple flows and / or blocks Figure 1 one block or multiple blocks.

[0042] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing devices to work in a specific manner, so that the instructions stored in the computer-readable memory generate a manufactured article including instruction means, and the instruction means implement the functions specified in one Figure 1 one flow or multiple flows and / or blocks Figure 1 one block or multiple blocks.

[0043] These computer program instructions can also be loaded onto a computer or other programmable data processing devices, so that a series of operation steps are executed on the computer or other programmable devices to generate a computer-implemented process. Thus, the instructions executed on the computer or other programmable devices provide steps for implementing the functions specified in one Figure 1 one flow or multiple flows and / or blocks Figure 1 one block or multiple blocks.

[0044] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit them. Although the present invention has been described in detail with reference to the above embodiments, those of ordinary skill in the art should understand that: still can modify the specific implementation manners of the present invention or make equivalent replacements, and any modification or equivalent replacement without departing from the spirit and scope of the present invention should be covered by the protection scope of the claims of the present invention.

Claims

1. A method for constructing a Brucella dynamics model based on multi-host-environment coupling, characterized in that, Including: Collecting multi-host target parameters to establish the initial compartment values of the Brucella dynamics model; Performing parameter dynamic calibration processing according to the initial compartment values of the Brucella dynamics model to obtain the calibrated compartment values of the Brucella dynamics model; Using the calibrated compartment values of the Brucella dynamics model to perform dynamic data fitting processing based on environmental coupling to establish the Brucella dynamics model.

2. The method for constructing a Brucella dynamics model based on multi-host-environment coupling according to claim 1, wherein Collecting multi-host target parameters to establish the initial compartment values of the Brucella dynamics model includes: Respectively obtaining animal host target parameters, human host target parameters, and environmental target parameters as multi-host target parameters; Using the multi-host target parameters to obtain the initial compartment values of the Brucella dynamics model according to the corresponding fitting starting points.

3. The method for constructing a Brucella dynamics model based on multi-host-environment coupling according to claim 2, wherein, The respectively obtaining animal host target parameters, human host target parameters, and environmental target parameters as multi-host target parameters includes: Obtaining the birth rate of animal hosts, the natural mortality rate of animal hosts, the vaccine inoculation failure rate of animal hosts, the effective vaccine inoculation rate, the infection rate between animal hosts, the infection rate of Brucella in the environment to sheep, the incubation period of Brucella in animal hosts, the culling rate of infected animal hosts, and the quantity of Brucella excreted by infected sheep into the environment as animal host target parameters; Obtaining the birth rate of human hosts, the natural mortality rate of human hosts, the proportion of acute infections turning into chronic infections, and the attack period of acute patients as human host target parameters; Obtaining the attenuation rate of environmental Brucella, the disinfection frequency, the effective disinfection rate, the infection rate between flocks and humans, and the infection rate between Brucella in the environment and humans as environmental target parameters; Using the animal host target parameters, human host target parameters, and environmental target parameters as multi-host target parameters.

4. The method for constructing a Brucella dynamics model based on multi-host-environment coupling according to claim 2, wherein Performing parameter dynamic calibration processing according to the initial compartment values of the Brucella dynamics model to obtain the calibrated compartment values of the Brucella dynamics model includes: Obtaining historical initial compartment values according to the initial compartment values of the Brucella dynamics model; Using the historical initial compartment values to obtain a historical initial compartment value combination based on the nonlinear least squares method; Performing parameter dynamic calibration processing using the historical initial compartment value combination to obtain the calibrated compartment values of the Brucella dynamics model.

5. The method for constructing a Brucella dynamics model based on multi-host-environment coupling according to claim 4, characterized in that, Using the calibrated compartment values of the Brucella dynamics model to perform dynamic data fitting processing based on environmental coupling to establish the Brucella dynamics model includes: Establishing a disease-free equilibrium point equation system based on the Brucella transmission flow chart; According to the disease-free equilibrium point equation system, performing dynamic data fitting processing based on environmental coupling to establish the Brucella dynamics model.

6. The method for constructing a Brucella dynamics model based on multi-host-environment coupling according to claim 5, characterized in that The establishing a disease-free equilibrium point equation system based on the Brucella transmission flow chart includes: Based on the Brucella transmission flow chart, the calculation formula for establishing the ordinary differential equation system of Brucella is as follows: ， Among them, S is a susceptible individual, t is time, A is the population birth rate, is the population vaccine vaccination failure rate, V is an individual with immunity after receiving vaccine immunization, β is the inter-group infection rate, E is a latently infected individual, I is an infected individual during the infection period, W is the quantity of Brucella excreted into the environment, is the infection rate of environmental Brucella to individuals, d is the population natural mortality rate, is the effective vaccination rate, c is the reciprocal of the latent time of population brucellosis, α is the culling rate of the infected population, k is the quantity of Brucella excreted by an infected individual into the environment, µ is the attenuation rate of environmental Brucella, and n is the disinfection frequency, is the effective disinfection rate, S h is the susceptible population, A h is the birth rate of the population, q is the proportion of acute infections turning into chronic infections, is the reciprocal of the onset time of acute patients, I ah is a patient with acute brucellosis, is health education to reduce the infection risk, β h is the infection rate between the population and humans, β wh is the infection rate between Brucella in the environment and humans, d h is the natural mortality rate of the population, I ch is a patient with chronic brucellosis, is the population vaccine vaccination failure rate; Using the ordinary differential equation system of Brucella to obtain the calculation formula for the disease-free equilibrium point equation system according to the disease-free equilibrium point as follows: 。 7. The method for constructing a Brucella dynamics model based on multi-host-environment coupling according to claim 6, characterized in that According to the disease-free equilibrium point equation system, performing dynamic data fitting processing based on environmental coupling to establish the Brucella dynamics model includes: Using the disease-free equilibrium point equation system to obtain the corresponding Brucella infection rate; The calculation formula of the infection variable characteristic equation system is obtained by sorting the corresponding infection variables using the Brucella infection rate according to the Brucella status, as follows: ， The calculation formula of the disease-free equilibrium characteristic matrix is established for the disease-free equilibrium using the disease-free equilibrium equation system, as follows: ， ， Among them, , The calculation formula of the Brucella dynamics model is established based on the infection variable characteristic equation system and the disease-free equilibrium characteristic matrix, as follows: ， wherein, R 0 is the basic reproduction number, ρ is the calculated spectral radius, U -1 is the inverse matrix of U, F , U is the disease-free equilibrium eigenmatrix, is the average number of individuals infected by an exposed carrier in the completely susceptible population, , is the average number of individuals infected by an infected person in the completely susceptible population, , is the average number of Brucella excreted by an exposed carrier individual infecting others, , is the average number of Brucella excreted by an infected individual infecting others, .

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