Method for calculating the size of zika virus infection based on guillain-barre syndrome data

Abstract

This invention relates to the field of infectious disease control and provides a method for calculating the scale of Zika virus infection based on Guillain-Barré syndrome (GBS) data. The method includes: acquiring GBS incidence data; obtaining the excess GBS incidence based on the GBS incidence data; acquiring meteorological data; obtaining mosquito abundance data based on the meteorological data; obtaining mosquito transmission data based on the mosquito abundance data; determining the parameters to be predicted and population statistics; establishing a set of parameter prediction equations based on the population statistics, the parameters to be predicted, and the mosquito transmission data; fitting the parameter prediction equations and the excess GBS incidence to obtain the prediction parameters and concurrent conversion rate; calculating the basic reproduction number based on the prediction parameters; obtaining Zika virus infection data based on the concurrent conversion rate and the excess GBS incidence; and assessing the infection scale based on the Zika virus infection data and the basic reproduction number.

Description

Technical Field

[0001] This invention relates to the field of infectious disease prevention and control technology, and in particular to a method for calculating the scale of Zika virus infection based on Guillain-Barré syndrome data. Background Technology

[0002] Zika virus, an emerging arbovirus, is primarily transmitted by Aedes mosquitoes and can also be transmitted sexually. Its infection is highly correlated with serious complications such as Guillain-Barré syndrome (GBS). Zika virus-associated GBS is an autoimmune peripheral neuropathy characterized by ascending / symmetrical limb weakness, sensory disturbances, and diminished or absent deep reflexes. Zika virus infection is characterized by a high asymptomatic rate, easily confused mild symptoms, and severe underreporting in the early stages of an outbreak. Calculating the morbidity rate directly based on the number of reported Zika virus cases would result in a significant deviation from the actual scale of infection. Guillain-Barré syndrome, as a specific and serious complication of Zika virus infection, has a clear diagnostic profile, a high reporting rate, and a fixed temporal correlation and incidence rate with Zika virus infection, making it a reliable basis for calculating the actual scale of Zika virus infection.

[0003] In existing technologies, suspected, clinically diagnosed, and confirmed cases are collected through a statutory infectious disease reporting system, and the infection scale is calculated by combining this data with the total population of the monitored area; blood samples are collected from the target population to detect Zika virus-specific antibodies, and the actual number of infected people is inferred based on the antibody positivity rate, thereby calculating the infection scale; and the association between Zika virus and specific complications is utilized.

[0004] However, a high percentage of Zika virus infections are asymptomatic, and these asymptomatic infections cannot be detected through the case reporting system. Furthermore, primary healthcare institutions may miss cases due to insufficient diagnostic capabilities, and incomplete tracing of imported cases further affects data accuracy. Serological surveys are costly and time-consuming, and are prone to insufficient sampling representativeness. In addition, there is no fixed risk ratio between Zika virus infection and complications, leading to significant errors when extrapolating backwards.

[0005] Furthermore, based on classic infectious disease transmission dynamics models, a mosquito-borne transmission model for Zika virus was constructed. By fitting model parameters with reported case data, the infection scale was estimated, providing a highly adaptable foundation for constructing a mosquito-borne transmission model for Zika virus. However, the unique biological characteristics and transmission patterns of Zika virus lead to significant limitations in reusing or modifying traditional mosquito-borne virus transmission models. Consequently, these models struggle to adapt to real-world monitoring data, fail to reflect the impact of different community characteristics, and exhibit incomplete transmission route modeling, lack of meteorological dynamics in mosquito population modeling, overly simplified parameter assumptions, and a lack of accurate infection scale estimation capabilities. Summary of the Invention

[0006] This invention aims to at least solve one of the technical problems existing in related technologies. To this end, this invention provides a method for calculating the scale of Zika virus infection based on Guillain-Barré syndrome data, enabling accurate assessment and prediction of the scale of Zika virus infection.

[0007] This invention provides a method for calculating the scale of Zika virus infection based on Guillain-Barré syndrome data, including: S1: Obtain Guillain-Barré syndrome incidence data and calculate the excess incidence of Guillain-Barré syndrome based on the incidence data; S2: Obtain meteorological data, obtain mosquito abundance data based on the meteorological data, and obtain mosquito transmission data based on the mosquito abundance data; S3: Determine the parameters to be predicted and population statistics, and establish a set of parameter prediction equations based on the population statistics, the parameters to be predicted, and mosquito transmission data; S4: Data fitting was performed using the parametric prediction equation set and the excess incidence of Guillain-Barré syndrome to obtain prediction parameters and concurrent conversion rate, and the basic reproduction number was calculated based on the prediction parameters; S5: Viral infection data are obtained based on concurrent conversion rate and excess incidence of Guillain-Barré syndrome. The scale of viral infection is assessed based on viral infection data and basic reproduction number.

[0008] According to the Zika virus infection scale calculation method based on Guillain-Barré syndrome data provided by the present invention, in step S2, the meteorological data including rainfall and temperature data is obtained, the mosquito abundance data is obtained through the meteorological data, and mosquito transmission data including the instantaneous mosquito birth rate is estimated through the mosquito abundance data.

[0009] According to the Zika virus infection scale calculation method based on Guillain-Barré syndrome data provided by the present invention, in step S3, the population statistics include susceptible host data, exposed host data, asymptomatic host data, virus-infected host data, convalescent host data, and host recovery data.

[0010] According to the Zika virus infection scale calculation method based on Guillain-Barré syndrome data provided by the present invention, in step S4, the parameter prediction equation set is initially fitted to obtain the virtual excess incidence of Guillain-Barré syndrome, and the parameter prediction equation set is iterated based on the virtual excess incidence of Guillain-Barré syndrome and the excess incidence of Guillain-Barré syndrome to complete the data fitting.

[0011] According to the Zika virus infection scale calculation method based on Guillain-Barré syndrome data provided by the present invention, in step S4, infectious characteristic data is obtained from the prediction parameters, and human-vector transmission parameters and mosquito-vector transmission parameters are calculated using the infectious characteristic data; the basic reproduction number is calculated using the mosquito-vector transmission parameters and human-vector transmission parameters.

[0012] According to the Zika virus infection scale calculation method based on Guillain-Barré syndrome data provided by the present invention, in step S5, the virus infection data is obtained by dividing the excess number of Guillain-Barré syndrome cases by the concurrent conversion rate.

[0013] This invention also provides a Zika virus infection scale calculation system based on Guillain-Barré syndrome data, comprising: Excess incidence rate module: Used to obtain Guillain-Barré syndrome incidence data and obtain the excess incidence rate of Guillain-Barré syndrome based on the incidence data; Mosquito transmission data module: used to acquire meteorological data, obtain mosquito abundance data based on the meteorological data, and obtain mosquito transmission data based on the mosquito abundance data; The parameter prediction equation module is used to determine the parameters to be predicted and population statistics, and to establish a parameter prediction equation system based on the population statistics, the parameters to be predicted, and mosquito transmission data. Parameter calculation module: used to fit data through the parameter prediction equation set and the excess incidence of Guillain-Barré syndrome to obtain prediction parameters and concurrent conversion rate, and calculate the basic reproduction number based on the prediction parameters; Infection Scale Module: Used to obtain viral infection data based on concurrent conversion rate and excess incidence of Guillain-Barré syndrome, and to assess the viral infection scale based on viral infection data and basic reproduction number.

[0014] The present invention also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of the Zika virus infection scale calculation method based on Guillain-Barré syndrome data as described above.

[0015] The present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the Zika virus infection scale calculation method based on Guillain-Barré syndrome data as described above.

[0016] The present invention also provides a computer program product comprising a computer program stored on a non-transitory computer-readable storage medium, the computer program comprising program instructions, which, when executed by a computer, enable the computer to perform the steps of any of the Zika virus infection scale calculation methods based on Guillain-Barré syndrome data described above.

[0017] The above-described one or more technical solutions in the embodiments of the present invention have at least one of the following technical effects: This invention provides a method for calculating the scale of Zika virus infection based on Guillain-Barré syndrome (Guillain-Barré syndrome) data. By integrating monitoring data of excess Guillain-Barré syndrome, meteorologically driven host data, and a mosquito-borne coupling model, it effectively overcomes the bottlenecks of traditional methods in monitoring and predicting the scale of Zika virus infection. Using clearly diagnosed and highly reported excess Guillain-Barré syndrome cases as the core alternative indicator, it avoids the problem of underreporting of high rates of asymptomatic Zika virus infection, accurately calculates the underreporting multiple, and dynamically corrects reporting bias. By integrating the transmission pathways of mosquito-borne and human-to-human transmission, as well as meteorologically driven mosquito population dynamics, it closely aligns with the transmission patterns of Zika virus. It requires no serological data, relies on routine monitoring data to quickly fit parameters, meets real-time assessment needs, and simultaneously outputs multi-dimensional parameters such as infection scale and basic reproduction number, providing a quantitative basis for prevention and control decisions.

[0018] Additional aspects and advantages of the invention will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. Attached Figure Description

[0019] To more clearly illustrate the technical solutions in this invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0020] Figure 1 This is a flowchart illustrating the Zika virus infection scale calculation method based on Guillain-Barré syndrome data provided by the present invention.

[0021] Figure 2 This is a schematic diagram of the test results of the Zika virus infection scale calculation method based on Guillain-Barré syndrome data provided by the present invention.

[0022] Figure 3 This is a schematic diagram of the Zika virus infection scale calculation system based on Guillain-Barré syndrome data provided by the present invention.

[0023] Figure 4This is a schematic diagram of the Zika virus infection scale calculation device based on Guillain-Barré syndrome data provided by the present invention.

[0024] Figure label: 100. Excess Case Count Module; 200. Mosquito Transmission Data Module; 300. Parameter Prediction Equation System Module; 400. Parameter Calculation Module; 500. Infection Scale Module; 810. Processor; 820. Communication Interface; 830. Memory; 840. Communication Bus. Detailed Implementation

[0025] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of this invention will be clearly and completely described below. Obviously, the described embodiments are only some, not all, of the embodiments of this invention. Based on the embodiments of this invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this invention. The following embodiments are used to illustrate this invention but cannot be used to limit the scope of this invention.

[0026] In the description of the embodiments of the present invention, it should be noted that the terms "first", "second" and "third" are used for descriptive purposes only and should not be construed as indicating or implying relative importance.

[0027] In the description of the embodiments of the present invention, it should be noted that, unless otherwise explicitly specified and limited, the terms "connected" and "linked" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium. Those skilled in the art can understand the specific meaning of the above terms in the embodiments of the present invention based on the specific circumstances.

[0028] In the description of this specification, the references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., refer to specific features, structures, materials, or characteristics described in connection with that embodiment or example, which are included in at least one embodiment or example of the present invention. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Moreover, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of different embodiments or examples.

[0029] The following is combined Figures 1 to 4 Specific embodiments of the present invention are described below. Figure 1A flowchart illustrating the Zika virus infection scale calculation method based on Guillain-Barré syndrome data provided by this invention includes: S1: Obtain Guillain-Barré syndrome incidence data and calculate the excess incidence of Guillain-Barré syndrome based on the incidence data; Furthermore, the objective of this stage is to obtain the excess incidence of Guillain-Barré syndrome.

[0030] The specific implementation method for the above steps in this embodiment is as follows; First, it's necessary to obtain local Guillain-Barré syndrome (Guillain-Barré syndrome) incidence data. Then, by analyzing local historical statistical data, we can obtain the normal number of Guillain-Barré syndrome cases in the region. Calculating the difference between the Guillain-Barré syndrome incidence data and the normal number of Guillain-Barré syndrome cases yields the excess incidence of Guillain-Barré syndrome. This excess incidence is usually caused by Zika virus infection.

[0031] S2: Obtain meteorological data, obtain mosquito abundance data based on the meteorological data, and obtain mosquito transmission data based on the mosquito abundance data; Furthermore, the objective of this stage is to obtain mosquito abundance data based on meteorological data, and then to obtain mosquito transmission data based on the mosquito abundance data. Specifically, in step S2, the meteorological data, including rainfall and temperature data, is acquired; the mosquito abundance data is obtained from the meteorological data; and mosquito transmission data, including the instantaneous mosquito birth rate, is estimated from the mosquito abundance data.

[0032] The specific implementation method for the above steps in this embodiment is as follows: First, local meteorological data needs to be obtained, including rainfall. and temperature data Subsequently, it can be obtained through meteorological data. t The ratio of mosquito populations to human populations over time, that is... t Mosquito abundance data over time m ( t ): Where exp() represents the exponential function. Represents the rainfall effect coefficient. Represents the temperature effect coefficient. This represents the coefficient of the first mosquito control action. This indicates the coefficient of the second mosquito control action. This represents the total time lag between weather factors and changes in mosquito populations. This includes the lag time for changes in the mosquito life cycle after being affected by weather factors, the incubation period of cases, and the reporting time; in this example, it is 30 days. This indicates the timeframe during which local authorities have implemented plans to control mosquito breeding or have carried out mosquito eradication programs. This is an indicator function; it takes a value of 1 if the condition within the parentheses is met, and 0 otherwise. Historically, the average lifespan of a mosquito is 14 days. After obtaining mosquito abundance data, the instantaneous mosquito birth rate can be estimated. The instantaneous mosquito birth rate is related to... m ( t It is directly proportional to.

[0033] S3: Determine the parameters to be predicted and population statistics, and establish a set of parameter prediction equations based on the population statistics, the parameters to be predicted, and mosquito transmission data; Furthermore, the objective of this stage is to determine the parameters to be predicted and population statistics, thereby establishing a set of parameter prediction equations. Specifically, step S3 further includes: In step S3, the population statistics include susceptible host data, exposed host data, asymptomatic host data, virus-infected host data, convalescent host data, and host recovery data.

[0034] The specific implementation method for the above steps in this embodiment is as follows: First, the parameters to be predicted need to be determined. Specifically, these include the proportion of symptomatic Zika virus infections, the transmission rate of asymptomatic infections, and the conversion rate of infected individuals. Additionally, population statistics need to be constructed, including data on susceptible hosts, exposed hosts, asymptomatic hosts, virus-infected hosts, convalescent hosts, and host recovery. Both the population statistics and the parameters to be predicted are constructed and await prediction. Next, a set of parameter prediction equations is established based on the population statistics, the parameters to be predicted, and mosquito transmission data. in, The number of infected mosquito populations, The total number of people, For the number of susceptible host groups, The number of host species infected with the Zika virus. This refers to the number of host populations during the recovery period. The rate of change in the number of susceptible host populations. It is the reciprocal of the average incubation period in human hosts. To determine the number of host groups exposed, the exposed host group refers to the host during the incubation period of Zika virus infection. To expose the rate of change in the host population size, The number of asymptomatic host populations, The rate of change in the number of asymptomatic host populations. The rate of change in the number of host populations infected with Zika virus. The rate of change in the host population during the recovery period. The rate of change in the number of people in the host rehabilitation population. for t Mosquito instantaneous birth rate over time For the number of susceptible disease vectors, The rate of change in the population of susceptible disease-carrying mosquitoes. This refers to the number of mosquito vectors during the incubation period of Zika virus infection. This represents the rate of change in mosquito populations during the incubation period of Zika virus infection, as currently statistically analyzed. The rate of change in the number of infected mosquitoes. β For the contact transmission rate of Zika virus, The transmission rate of asymptomatic infected individuals, θ The proportion of symptomatic Zika virus infections. It is the reciprocal of the average infectious period for asymptomatic individuals. It is the reciprocal of the average infectious period. It is the reciprocal of the average infectious period for recovered patients. The infection rate during the recovery period, Mosquito bite rate b For the probability of host propagation, c For mosquito-borne transmission probability, It is the reciprocal of the average incubation period of mosquito vectors. It is the reciprocal of the average lifespan of a female mosquito. For concurrent conversion rate, For the first i The excess incidence of Guillain-Barré syndrome in a week. In this example, the time period for calculating the excess incidence of Guillain-Barré syndrome is 1 week.

[0035] In the above predictive equations, the determined values ​​include mosquito bite rate, host transmission probability, mosquito vector transmission probability, Zika virus contact transmission rate, reciprocal of the average incubation period for mosquito vectors, reciprocal of the average incubation period for human hosts, reciprocal of the average infectious period for asymptomatic individuals, reciprocal of the average infectious period, reciprocal of the average infectious period for recovered patients, reciprocal of the average lifespan of female mosquitoes, and the infectious rate during the convalescent period. These values ​​can be determined based on historical statistical parameters of Zika virus transmission and can be adjusted within a certain range during data fitting. The remaining values ​​need to be solved through fitting, and we have: .

[0036] in, For the number of people in the host rehabilitation group, This represents the total number of mosquito vectors.

[0037] S4: Data fitting was performed using the parametric prediction equation set and the excess incidence of Guillain-Barré syndrome to obtain prediction parameters and concurrent conversion rate, and the basic reproduction number was calculated based on the prediction parameters; Furthermore, the objective of this stage is to establish a set of parametric prediction equations. Specifically, in step S4, the set of parametric prediction equations is initially fitted to obtain the virtual excess incidence of Guillain-Barré syndrome. Based on the virtual excess incidence of Guillain-Barré syndrome and the excess incidence of Guillain-Barré syndrome, the set of parametric prediction equations is iterated to complete the data fitting.

[0038] In step S4, infectious characteristic data is obtained from the predicted parameters, and human-vector transmission parameters and mosquito-vector transmission parameters are calculated using the infectious characteristic data; the basic reproduction number is calculated using the mosquito-vector transmission parameters and human-vector transmission parameters.

[0039] The specific implementation method for the above steps in this embodiment is as follows: Here, it is necessary to fit the parametric prediction equations to the excess incidence of Guillain-Barré syndrome (GBS) cases, thereby adjusting the values ​​of the predicted parameters to ensure the equations hold true, and ultimately obtaining the predicted parameters and the concurrent conversion rate. The predicted values ​​in the parametric prediction equations include the weekly excess incidence of GBS cases. During data fitting, an initial fit can be performed on the parametric prediction equations to ensure their validity. This initial fit yields a preliminary estimate of the weekly excess incidence of GBS cases, which is the virtual GBS excess incidence. In reality, since the statistical distribution of the weekly GBS excess incidence follows a negative binomial distribution, the quasi-virtual GBS excess incidence, which more closely approximates the actual GBS excess incidence during the data fitting process, also follows a negative binomial distribution. Specifically, the excess incidence of Guillain-Barré syndrome needs to be estimated using the log-likelihood function to obtain the overdispersion parameter of the negative binomial distribution, and then the size parameter of the negative binomial distribution of Guillain-Barré syndrome. Based on the size parameter, the excess incidence of virtual Guillain-Barré syndrome can be judged, and the excess incidence of virtual Guillain-Barré syndrome that conforms to the negative binomial distribution should be selected first.

[0040] Subsequently, the virtual excess incidence of Guillain-Barré syndrome (GBS) is compared with the actual GBS excess incidence. When the difference between the virtual and actual GBS excess incidence is significant, the parametric prediction equations need to be refitted to bring the virtual GBS excess incidence closer to the actual GBS excess incidence until a good match is achieved. The goodness of fit of the model is then quantified using the Bayesian information criterion to complete the data fitting. Furthermore, during data fitting, the rate of change of each variable parameter over time must conform to the rate of change obtained from the parametric prediction equations. The confidence interval of the data fit can also be obtained based on the goodness of fit between the virtual and actual GBS excess incidence. A higher goodness of fit and a smaller confidence interval indicate higher data reliability.

[0041] After data fitting, the predicted parameters and concurrent conversion rate can be obtained. The predicted parameters are those in the parametric prediction equation system other than the known parameters and concurrent conversion rate. Subsequently, based on the infectious disease characteristic data, including the proportion of symptomatic Zika virus infections and the transmission rate of asymptomatic infections, the human-to-human transmission parameters can be calculated. Mosquito vector transmission parameters : Finally, the basic reproduction number is calculated based on mosquito vector transmission parameters and human vector transmission parameters. : Here, the basic reproduction number is the average number of new infections caused by a single infected case in a fully susceptible population.

[0042] S5: Viral infection data are obtained based on concurrent conversion rate and excess incidence of Guillain-Barré syndrome. The scale of viral infection is assessed based on viral infection data and basic reproduction number.

[0043] Furthermore, the objective of this stage is to obtain viral infection data and subsequently assess the scale of viral infection. Specifically, in step S5, the viral infection data is obtained by dividing the excess number of Guillain-Barré syndrome cases by the complication conversion rate.

[0044] The specific implementation method for the above steps in this embodiment is as follows: After obtaining the concurrent conversion rate, since it represents the probability of Zika virus infection causing Guillain-Barré syndrome, the Zika virus infection data can be obtained by dividing the excess number of Guillain-Barré syndrome cases by the concurrent conversion rate. Combining this infection data with the basic reproduction number allows for an assessment of the virus transmission situation in the region. Other predictive parameters can also assist in assessing the Zika virus transmission situation in the region.

[0045] The effectiveness of the Zika virus infection scale calculation method based on Guillain-Barré syndrome data was also evaluated here. Epidemic surveillance data from a specific region was selected for validation, and the results are as follows: Figure 2 As shown, the simulated fitting curve predicted by the method provided by this invention has a high degree of overlap with the curve composed of actual reported cases, and it lies within the 95% confidence interval of the curve composed of reported cases, i.e., the gray area. Furthermore... Figure 2 The report also includes predictions for the basic reproduction number, with the green area representing the 95% confidence interval for the basic reproduction number.

[0046] The Zika virus infection scale calculation device based on Guillain-Barré syndrome data provided by the present invention is described below. The Zika virus infection scale calculation device based on Guillain-Barré syndrome data described below can be referred to in correspondence with the Zika virus infection scale calculation method based on Guillain-Barré syndrome data described above.

[0047] Figure 3 A schematic diagram of a Zika virus infection scale calculation system based on Guillain-Barré syndrome data is shown, such as... Figure 3 As shown, the method for calculating the Zika virus infection scale based on Guillain-Barré syndrome data as described above includes: Excess incidence rate module 100: Used to obtain Guillain-Barré syndrome incidence data and obtain the excess incidence rate of Guillain-Barré syndrome based on the incidence data; Mosquito transmission data module 200: used to acquire meteorological data, obtain mosquito abundance data based on the meteorological data, and obtain mosquito transmission data based on the mosquito abundance data; Parameter prediction equation module 300: used to determine the parameters to be predicted and population statistics, and to establish a parameter prediction equation system based on population statistics, parameters to be predicted and mosquito transmission data; The parameter calculation module 400 is used to fit data through the parameter prediction equation set and the excess incidence of Guillain-Barré syndrome to obtain the prediction parameters and concurrent conversion rate, and to calculate the basic reproduction number based on the prediction parameters. Infection Scale Module 500: Used to obtain viral infection data based on concurrent conversion rate and excess incidence of Guillain-Barré syndrome, and to assess the viral infection scale based on viral infection data and basic reproduction number.

[0048] Figure 4 An example is a schematic diagram of the physical structure of an electronic device, such as... Figure 4 As shown, the electronic device may include: a processor 810, a communication interface 820, a memory 830, and a communication bus 840, wherein the processor 810, the communication interface 820, and the memory 830 communicate with each other via the communication bus 840. The processor 810 can call a computer program in the memory 830 to execute a method for calculating the scale of Zika virus infection based on Guillain-Barré syndrome data. This method includes: S1: Obtain Guillain-Barré syndrome incidence data and calculate the excess incidence of Guillain-Barré syndrome based on the incidence data; S2: Obtain meteorological data, obtain mosquito abundance data based on the meteorological data, and obtain mosquito transmission data based on the mosquito abundance data; S3: Determine the parameters to be predicted and population statistics, and establish a set of parameter prediction equations based on the population statistics, the parameters to be predicted, and mosquito transmission data; S4: Data fitting was performed using the parametric prediction equation set and the excess incidence of Guillain-Barré syndrome to obtain prediction parameters and concurrent conversion rate, and the basic reproduction number was calculated based on the prediction parameters; S5: Viral infection data are obtained based on concurrent conversion rate and excess incidence of Guillain-Barré syndrome. The scale of viral infection is assessed based on viral infection data and basic reproduction number.

[0049] Furthermore, when the computer program in the aforementioned memory 830 can be implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes 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.

[0050] On the other hand, the present invention also provides a computer program product, the computer program product comprising a computer program stored on a non-transitory computer-readable storage medium, the computer program comprising program instructions, wherein when the program instructions are executed by a computer, the computer is able to execute the Zika virus infection scale calculation method based on Guillain-Barré syndrome data provided by the above methods, the method comprising: S1: Obtain Guillain-Barré syndrome incidence data and calculate the excess incidence of Guillain-Barré syndrome based on the incidence data; S2: Obtain meteorological data, obtain mosquito abundance data based on the meteorological data, and obtain mosquito transmission data based on the mosquito abundance data; S3: Determine the parameters to be predicted and population statistics, and establish a set of parameter prediction equations based on the population statistics, the parameters to be predicted, and mosquito transmission data; S4: Data fitting was performed using the parametric prediction equation set and the excess incidence of Guillain-Barré syndrome to obtain prediction parameters and concurrent conversion rate, and the basic reproduction number was calculated based on the prediction parameters; S5: Viral infection data are obtained based on concurrent conversion rate and excess incidence of Guillain-Barré syndrome. The scale of viral infection is assessed based on viral infection data and basic reproduction number.

[0051] In another aspect, the present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the Zika virus infection scale calculation methods based on Guillain-Barré syndrome data provided above, the method comprising: S1: Obtain Guillain-Barré syndrome incidence data and calculate the excess incidence of Guillain-Barré syndrome based on the incidence data; S2: Obtain meteorological data, obtain mosquito abundance data based on the meteorological data, and obtain mosquito transmission data based on the mosquito abundance data; S3: Determine the parameters to be predicted and population statistics, and establish a set of parameter prediction equations based on the population statistics, the parameters to be predicted, and mosquito transmission data; S4: Data fitting was performed using the parametric prediction equation set and the excess incidence of Guillain-Barré syndrome to obtain prediction parameters and concurrent conversion rate, and the basic reproduction number was calculated based on the prediction parameters; S5: Viral infection data are obtained based on concurrent conversion rate and excess incidence of Guillain-Barré syndrome. The scale of viral infection is assessed based on viral infection data and basic reproduction number.

[0052] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Those skilled in the art can understand and implement this without any creative effort.

[0053] Through the above description of the embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus necessary general-purpose hardware platforms, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solutions, in essence or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods described in the various embodiments or some parts of the embodiments.

[0054] 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 foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for calculating the scale of Zika virus infection based on Guillain-Barré syndrome data, characterized in that, include: S1: Obtain Guillain-Barré syndrome incidence data and calculate the excess incidence of Guillain-Barré syndrome based on the incidence data; S2: Obtain meteorological data, obtain mosquito abundance data based on the meteorological data, and obtain mosquito transmission data based on the mosquito abundance data; S3: Determine the parameters to be predicted and population statistics, and establish a set of parameter prediction equations based on the population statistics, the parameters to be predicted, and mosquito transmission data; S4: Data fitting was performed using the parametric prediction equation set and the excess incidence of Guillain-Barré syndrome to obtain prediction parameters and concurrent conversion rate, and the basic reproduction number was calculated based on the prediction parameters; S5: Viral infection data are obtained based on concurrent conversion rate and excess incidence of Guillain-Barré syndrome. The scale of viral infection is assessed based on viral infection data and basic reproduction number.

2. The method for calculating the scale of Zika virus infection based on Guillain-Barré syndrome data according to claim 1, characterized in that, In step S2, the meteorological data including rainfall and temperature data is acquired, the mosquito abundance data is obtained from the meteorological data, and the mosquito transmission data including the instantaneous birth rate of mosquito vectors is estimated from the mosquito abundance data.

3. The method for calculating the scale of Zika virus infection based on Guillain-Barré syndrome data according to claim 1, characterized in that, In step S3, the population statistics include susceptible host data, exposed host data, asymptomatic host data, virus-infected host data, convalescent host data, and host recovery data.

4. The method for calculating the scale of Zika virus infection based on Guillain-Barré syndrome data according to claim 1, characterized in that, In step S4, the parameter prediction equations are initially fitted to obtain the virtual excess incidence of Guillain-Barré syndrome. The parameter prediction equations are then iterated based on the virtual excess incidence of Guillain-Barré syndrome and the excess incidence of Guillain-Barré syndrome to complete the data fitting.

5. The method for calculating the scale of Zika virus infection based on Guillain-Barré syndrome data according to claim 1, characterized in that, In step S4, infectious characteristic data is obtained from the predicted parameters, and human-vector transmission parameters and mosquito-vector transmission parameters are calculated using the infectious characteristic data; the basic reproduction number is calculated using the mosquito-vector transmission parameters and human-vector transmission parameters.

6. The method for calculating the scale of Zika virus infection based on Guillain-Barré syndrome data according to claim 1, characterized in that, In step S5, the viral infection data is obtained by dividing the excess incidence of Guillain-Barré syndrome by the complication conversion rate.

7. A Zika virus infection scale calculation system based on Guillain-Barré syndrome data, used to execute the Zika virus infection scale calculation method based on Guillain-Barré syndrome data as described in any one of claims 1 to 6, characterized in that, include: Excess incidence rate module: Used to obtain Guillain-Barré syndrome incidence data and obtain the excess incidence rate of Guillain-Barré syndrome based on the incidence data; Mosquito transmission data module: used to acquire meteorological data, obtain mosquito abundance data based on the meteorological data, and obtain mosquito transmission data based on the mosquito abundance data; The parameter prediction equation module is used to determine the parameters to be predicted and population statistics, and to establish a parameter prediction equation system based on the population statistics, the parameters to be predicted, and mosquito transmission data. Parameter calculation module: used to fit data through the parameter prediction equation set and the excess incidence of Guillain-Barré syndrome to obtain prediction parameters and concurrent conversion rate, and calculate the basic reproduction number based on the prediction parameters; Infection Scale Module: Used to obtain viral infection data based on concurrent conversion rate and excess incidence of Guillain-Barré syndrome, and to assess the viral infection scale based on viral infection data and basic reproduction number.

8. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the steps of the Zika virus infection scale calculation method based on Guillain-Barré syndrome data as described in any one of claims 1 to 6.

9. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the Zika virus infection scale calculation method based on Guillain-Barré syndrome data as described in any one of claims 1 to 6.

10. A computer program product comprising a computer program stored on a non-transitory computer-readable storage medium, the computer program comprising program instructions, characterized in that, When the program instructions are executed by a computer, the computer is able to perform the steps of the Zika virus infection scale calculation method based on Guillain-Barré syndrome data as described in any one of claims 1 to 6.

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