Vaccine anti-propagation protection effect evaluation method and device and electronic equipment

Abstract

The invention provides a vaccine anti-propagation protection effect evaluation method and device and electronic equipment. The method comprises the following steps: determining the probability # imgabs1 # that a # imgabs0 # primary case causes 0 filial generation case number due to the influence of enhanced control measures; determining the negative binomial probability distribution of the offspring case number # imgabs3 # generated by the # imgabs2 # primary case; determining a zero-expansion negative binomial distribution probability quality function based on the negative binomial probability distribution of the probability # imgabs5 # of the 0 offspring case number generated by the # imgabs4 # primary case due to the influence of the enhanced control measures and the negative binomial probability distribution of the offspring case number # imgabs7 # generated by the # imgabs6 # primary case; based on the zero-expansion negative binomial distribution probability quality function, determining the vaccine anti-propagation effectiveness; therefore, the anti-spreading capability of the vaccine can be estimated, indispensable scientific basis and technical support are provided for infectious disease prevention and treatment work, and public health and social safety are better guaranteed.

Description

Technical Field

[0001] The present disclosure relates to the technical field of medical research on infectious diseases, and in particular to a method, device and electronic equipment for evaluating the protective effect of vaccines against transmission. Background Art

[0002] In today's era of globalization, new infectious diseases frequently emerge, which poses a serious threat to public health and social security. The rapid spread of infectious diseases may lead to large-scale infection of the population, which in turn may cause a series of problems such as the run on medical resources and social disorder, seriously affecting human survival and development.

[0003] Vaccination, as an important means of preventing and controlling infectious diseases, plays an irreplaceable and key role in controlling the spread of the epidemic and reducing the burden of the disease on the population. It is one of the most effective methods currently. In the field of vaccine-related research, the concept of real-world effectiveness (VE) of vaccines is crucial. It mainly refers to the actual protective effect of vaccines in preventing specific infectious diseases in complex real-world situations. This real-world situation includes the interweaving of multiple complex factors such as different age groups, different health conditions, and diverse living environments.

[0004] After in-depth research on existing technologies, it was found that the current evaluation methods for the real-world effectiveness of vaccines mainly focus on evaluating the effectiveness of vaccines in preventing infection (or preventing susceptibility). Although this evaluation direction has certain value, it has obvious limitations and cannot fully reflect the role of vaccines in controlling the spread of the epidemic. Summary of the invention

[0005] The present disclosure provides a method, device and electronic device for evaluating the anti-transmission protection effect of a vaccine, so as to at least solve the above technical problems existing in the prior art.

[0006] According to a first aspect of the present disclosure, a method for evaluating the protective effect of a vaccine against transmission is provided, comprising: Based on The date of positive test of the primary case, The dummy variable of the input status of the primary case, the first regression coefficient to be estimated and the first intercept, determine the The probability that a primary case will result in zero offspring cases due to the impact of enhanced control measures ; Based on the Vaccination information of primary cases, Gender information of primary cases, The residential area information of the primary case, Age information of the primary cases and the The positive test date of the primary case, the second regression coefficient, the third regression coefficient, the target regression coefficient and the second intercept are used to determine the Number of offspring cases generated from primary cases The negative binomial probability distribution of ; Based on The probability that a primary case will result in zero offspring cases due to the impact of enhanced control measures and Number of offspring cases generated from primary cases The negative binomial probability distribution of , determines the probability mass function of the zero-inflated negative binomial distribution; Based on the zero-inflated negative binomial distribution probability mass function, the effectiveness of the vaccine in preventing transmission is determined.

[0007] In the above scheme, the The date of positive test of the primary case, The dummy variable of the input status of the primary case, the first regression coefficient to be estimated and the first intercept, determine the The probability that a primary case will result in zero offspring cases due to the impact of enhanced control measures ,include: Based on The natural cubic spline function value of the positive test date of the primary case, The dummy variable of the input status of the primary case, the first regression coefficient and the first intercept are used to determine the The probability that a primary case will result in zero offspring cases due to the impact of enhanced control measures The log odds ratio; Based on the The probability that a primary case will result in zero offspring cases due to the impact of enhanced control measures The logarithmic odds ratio is used to determine the The probability that a primary case will result in zero offspring cases due to the impact of enhanced control measures .

[0008] In the above scheme, the The natural cubic spline function value of the positive test date of the primary case, The dummy variable of the input status of the primary case, the first regression coefficient and the first intercept are used to determine the The probability that a primary case will result in zero offspring cases due to the impact of enhanced control measures The log odds ratios include:

[0009] in, for The log odds ratio, is the first intercept, For the Date of positive test of primary cases, is the natural cubic spline function, is the first regression coefficient, For the A dummy variable for the input status of the primary case.

[0010] In the above scheme, the Vaccination information of primary cases, Gender information of primary cases, The residential area information of the primary case, Age information of the primary cases and the The positive test date of the primary case, the second regression coefficient, the third regression coefficient, the target regression coefficient and the second intercept are used to determine the Number of offspring cases generated from primary cases The negative binomial probability distribution of , including: Based on the Vaccination information of primary cases, Gender information of primary cases, The residential area information of the primary case, Age information of the primary cases and the The positive test date of the primary case, the second regression coefficient, the third regression coefficient, the target regression coefficient and the second intercept are used to determine the first Number of offspring cases generated from primary cases The mean ; The said Number of offspring cases generated from primary cases The mean is , the discrete parameters are The negative binomial distribution of and discrete parameters Determine the Number of offspring cases generated from primary cases The negative binomial probability distribution of .

[0011] In the above scheme, the Vaccination information of primary cases, Gender information of primary cases, The residential area information of the primary case, Age information of the primary cases and the The positive test date of the primary case, the second regression coefficient, the third regression coefficient, the target regression coefficient and the second intercept are used to determine the first Number of offspring cases generated from primary cases The mean ,include: The mean is determined based on the following formula The log link function of :

[0012] in, For the Number of offspring cases generated from primary cases expectations, For the Vaccination information of primary cases, For the Gender information of primary cases, For the The residential area information of each primary case, For the Age information of primary cases, For the Date of positive test of primary cases, is the natural cubic spline function, is the second regression coefficient, is the third regression coefficient, is the target regression coefficient, Logarithm of the hazard ratio characterizing vaccination status.

[0013] In the above scheme, the mean value is based on and discrete parameters Determine the Number of offspring cases generated from primary cases The negative binomial probability distribution of Number of offspring cases generated from primary cases The negative binomial probability distribution of :

[0014] in, is the gamma function, For the Number of offspring cases generated from primary cases The coefficient of dispersion of the negative binomial distribution.

[0015] In the above scheme, the The probability that a primary case will result in zero offspring cases due to the impact of enhanced control measures and Number of offspring cases generated from primary cases The negative binomial probability distribution of , determines the zero-inflated negative binomial distribution probability mass function, including: Determine the zero-inflated negative binomial distribution probability mass function based on the following formula:

[0016] in, Characterization The number of offspring cases generated by the primary case, For the Number of offspring cases generated from primary cases The negative binomial probability distribution of .

[0017] In the above scheme, determining the effectiveness of the vaccine in preventing transmission based on the zero-inflated negative binomial distribution probability mass function includes: constructing a log-likelihood function based on the zero-inflated negative binomial distribution probability mass function; Based on the maximum likelihood estimation method, estimating the target regression coefficient, the second regression coefficient, the third regression coefficient, the second intercept and the discrete parameter in the log-likelihood function; determining an estimate of the target regression coefficient as the logarithm of the hazard ratio for vaccination status; The effectiveness of the vaccine in preventing transmission was determined based on the logarithm of the hazard ratio for the vaccination status.

[0018] According to a second aspect of the present disclosure, a device for evaluating the protective effect of a vaccine against transmission is provided, the device comprising: Zero-inflated building blocks for The date of positive test of the primary case, The dummy variable of the input status of the primary case, the first regression coefficient to be estimated and the first intercept, determine the The probability that a primary case will result in zero offspring cases due to the impact of enhanced control measures ; Negative binomial building block for Vaccination information of primary cases, Gender information of primary cases, The residential area information of the primary case, Age information of the primary case and the The positive test date of the primary case, the second regression coefficient, the third regression coefficient, the target regression coefficient and the second intercept are used to determine the Number of offspring cases generated from primary cases The negative binomial probability distribution of ; A probability mass function determination unit is used to determine the probability mass function based on the first The probability that a primary case will result in zero offspring cases due to the impact of enhanced control measures and Number of offspring cases generated from primary cases The negative binomial probability distribution of , determines the probability mass function of the zero-inflated negative binomial distribution; According to a third aspect of the present disclosure, there is provided an electronic device, including: At least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the method described in the present disclosure.

[0019] The present disclosure is based on The date of positive test of the primary case, The dummy variable of the input status of the primary case, the first regression coefficient to be estimated and the first intercept, determine the The probability that a primary case will result in zero offspring cases due to the impact of enhanced control measures Based on the Vaccination information of primary cases, Gender information of primary cases, The residential area information of the primary case, Age information of the primary cases and the The positive test date of the primary case, the second regression coefficient, the third regression coefficient, the target regression coefficient and the second intercept are used to determine the Number of offspring cases generated from primary cases The negative binomial probability distribution of The probability that a primary case will result in zero offspring cases due to the impact of enhanced control measures and Number of offspring cases generated from primary cases The negative binomial probability distribution is used to determine the zero-inflated negative binomial distribution probability mass function; based on the zero-inflated negative binomial distribution probability mass function, the effectiveness of the vaccine in preventing transmission is determined. In this way, the ability of the vaccine to prevent transmission can be estimated, providing an indispensable scientific basis and technical support for the prevention and control of infectious diseases, and better protecting public health and social security.

[0020] It should be understood that the content described in this section is not intended to identify the key or important features of the embodiments of the present disclosure, nor is it intended to limit the scope of the present disclosure. Other features of the present disclosure will become easily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] The above and other objects, features and advantages of the exemplary embodiments of the present disclosure will become readily understood by reading the detailed description below with reference to the accompanying drawings. In the accompanying drawings, several embodiments of the present disclosure are shown in an exemplary and non-limiting manner, in which: In the drawings, the same or corresponding reference numerals represent the same or corresponding parts.

[0022] Figure 1 A first optional flow chart of the method for evaluating the vaccine transmission protection effect provided by the embodiment of the present disclosure is shown; Figure 2 A second optional flow chart of the method for evaluating the vaccine transmission protection effect provided by the embodiment of the present disclosure is shown; Figure 3 shows the epidemic curve of real-world infectious disease surveillance data; Figure 4 A schematic diagram showing the mean number of offspring cases caused by real-world primary cases; Figure 5 An optional structural schematic diagram of a vaccine anti-transmission protection effect evaluation device provided in an embodiment of the present disclosure is shown; Figure 6 A schematic diagram of the structure of an electronic device according to an embodiment of the present disclosure is shown. DETAILED DESCRIPTION

[0023] In order to make the purpose, features, and advantages of the present disclosure more obvious and easy to understand, the technical solutions in the embodiments of the present disclosure will be clearly and completely described below in conjunction with the drawings in the embodiments of the present disclosure. Obviously, the described embodiments are only part of the embodiments of the present disclosure, not all of the embodiments. Based on the embodiments in the present disclosure, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of the present disclosure.

[0024] In the following description, reference is made to “some embodiments”, which describe a subset of all possible embodiments, but it can be understood that “some embodiments” may be the same subset or different subsets of all possible embodiments and may be combined with each other without conflict.

[0025] In the following description, the terms "first\second" involved are merely used to distinguish similar objects and do not represent a specific ordering of the objects. It can be understood that "first\second" can be interchanged with a specific order or sequence where permitted, so that the embodiments of the present disclosure described herein can be implemented in an order other than that illustrated or described herein.

[0026] Unless otherwise defined, all technical and scientific terms used in this disclosure have the same meaning as those commonly understood by those skilled in the art to which this disclosure belongs. The terms used in this disclosure are only for the purpose of describing the embodiments of this disclosure and are not intended to limit this disclosure.

[0027] It should be understood that in the various embodiments of the present disclosure, the size of the serial number of each implementation process does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present disclosure.

[0028] Before further describing the embodiments of the present disclosure in detail, the nouns and terms involved in the embodiments of the present disclosure are described. The nouns and terms involved in the embodiments of the present disclosure are subject to the following interpretations.

[0029] 1) Vaccine real-world effectiveness (VE): the protective effect of a vaccine in preventing specific infectious diseases under real-world conditions.

[0030] 2) Individual reproduction number: Theoretically, the average number of secondary cases generated by one primary case.

[0031] 3) Contact tracing data: a type of infectious disease monitoring data that records contact information between cases and between cases and close contacts.

[0032] 4) Primary case-offspring (second-generation) case transmission pairs: The transmission chain of offspring cases caused by primary cases is inferred using the epidemiological information provided by contact tracing data.

[0033] 5) Probability mass function (PMF): A function that defines the probability distribution of a random variable, containing one or more parameters.

[0034] 6) Likelihood function: A function that defines the probability distribution of model parameter values ​​defined by a statistical or probability model under known observed data.

[0035] Emerging infectious diseases pose a serious threat to public health and social security. Vaccination is one of the most effective ways to control the epidemic and reduce the burden of disease. Vaccination can not only significantly reduce the risk of disease transmission, but also reduce the occurrence of severe cases and deaths. However, the immunity generated after vaccination may gradually weaken over time. In addition, the continuous mutation of pathogens, especially some emerging mutants with stronger transmissibility and immune escape ability, makes the real-world protection effect of existing vaccines on the population face challenges. The real-world effectiveness (VE) of vaccines refers to the actual protective effect of vaccines in preventing specific infectious diseases in real-world scenarios. Among them, the effectiveness of vaccines against transmission refers to the ability of vaccines to reduce the ability of infected people to spread pathogens to others. Specifically, the effectiveness of vaccines against transmission is the risk of transmission from primary cases to offspring cases that vaccines can reduce. Therefore, accurate evaluation of the effectiveness of vaccines against transmission can effectively guide vaccine allocation and vaccination priorities, lay the foundation for the optimization and adjustment of vaccination strategies, and provide a scientific basis for the formulation of public health policies.

[0036] Generally speaking, the evaluation methods of real-world effectiveness of vaccines almost always focus on evaluating the effectiveness of vaccines in preventing infection (or preventing susceptibility) rather than the effectiveness of vaccines in preventing transmission. The simple steps are to track groups of people with different vaccination status (or vaccine doses) in the real world, observe and compare the differences in incidence rates between two or more groups of people, and then calculate the risk ratio of infection to evaluate the effectiveness of vaccines in preventing infection.

[0037] The relevant technology proposes a research design by constructing a close contact population cohort, and then using a logistic regression model to analyze the association between the number of vaccination doses received by the primary case and the infection risk of its close contacts, and thereby estimate the effectiveness of the vaccine in preventing transmission.

[0038] Through systematic literature search, no invention patents related to key technologies for evaluating the real-world protective effect of vaccines in the field of infectious diseases were found; when evaluating the real-world protective effect of vaccines in preventing the spread of infectious diseases, there is currently no effective technical approach to using contact tracing data from epidemiological surveys, especially when negative close contact information is missing. Therefore, it is urgent to fill the relevant technical gaps in the accurate measurement of vaccine effectiveness in preventing transmission, so as to provide more scientific and accurate technical support for infectious disease prevention and control.

[0039] In response to the defects existing in the related technologies, the present disclosure provides a method for evaluating the anti-transmission protection effect of vaccines, which has solved some or all of the above-mentioned technical problems.

[0040] Figure 1A first optional flow chart of the method for evaluating the vaccine anti-transmission protection effect provided by the embodiment of the present disclosure is shown, and will be explained according to each step.

[0041] In the disclosed embodiment, the number of offspring cases generated by each primary case (outcome variable) is used as an indicator to measure the risk of disease transmission, which is consistent with the definition of the individual reproduction number and is one of the most commonly used indicators for quantifying transmission risk. Based on the random effect of disease transmission, the number of offspring cases caused by each primary case ( ) is a random variable and obeys a certain probability distribution. If there is no zero expansion phenomenon, the distribution obeys the negative binomial (NB or NegBin) distribution, denoted as ~NegBin(mean= , discrete parameter = ). In order to consider the situation where the frequency of zero-offspring cases is much higher than the theoretical expectation (zero inflation), an additional zero-inflated part is introduced into the traditional negative binomial distribution. Then, the distribution obeys the zero-inflated negative binomial (ZINB) distribution. Therefore, the zero-inflated negative binomial log-linear regression model is used to fit the distribution of offspring cases. The regression model consists of two parts, namely the zero-inflated part and the negative binomial part. In the embodiment of the present disclosure, the first The probability that a primary case will result in zero offspring cases due to the impact of enhanced control measures (i.e., the zero-expansion part), and Number of offspring cases generated from primary cases The negative binomial probability distribution (i.e., the negative binomial distribution part) is then used to determine the probability mass function of the zero-inflated negative binomial distribution based on the zero-inflated part and the negative binomial distribution part, and further based on the probability mass function of the zero-inflated negative binomial distribution, the effectiveness of the vaccine in preventing transmission is calculated. The specific steps include: Step S101, based on The date of positive test of the primary case, The dummy variable of the input status of the primary case, the first regression coefficient to be estimated and the first intercept, determine the The probability that a primary case will result in zero offspring cases due to the impact of enhanced control measures .

[0042] In some embodiments, the first The probability that a primary case will result in zero offspring cases due to the impact of enhanced control measures The log odds ratio (log odds) is used, and the natural cubic spline function is used to deal with nonlinear relationships. The log odds ratio can be based on The date of positive test of the primary case, The dummy variable of the input status of each primary case, the first regression coefficient to be estimated and the first intercept are determined, including:

[0043] in, for The log odds ratio, is the first intercept of the regression model (fixed constant), For the Date of positive test of primary case (expressed as calendar date), is the natural cubic spline function, is the first regression coefficient to be estimated, For the A dummy variable (or virtual variable) representing the input status of a primary case. The variable takes the value of 1 for an input case and 0 otherwise.

[0044] Step S102, based on the Vaccination information of primary cases, Gender information of primary cases, The residential area information of the primary case, Age information of the primary case and the The positive test date of the primary case, the second regression coefficient, the third regression coefficient, the target regression coefficient and the second intercept are used to determine the Number of offspring cases generated from primary cases The negative binomial probability distribution of .

[0045] In some embodiments, The number of offspring cases generated by a primary case is expressed as ,use express The mean (i.e. expected value) of Vaccination information of each primary case (such as the number of vaccinations and the time of each injection), Gender information of the primary case (male or female), The residential area information (residential area code or activity area code) of the primary case, the The positive test date of each primary case is represented by the log link function , and using the natural cubic spline function to deal with nonlinear relationships, we can get:

[0046] in, For the Number of offspring cases generated from primary cases expectations, For the Vaccination information of primary cases, For the Gender information of the primary case (1 if male, 0 if female), For the The residential area information of each primary case, For the Age information of primary cases, For the Date of positive test of primary cases, is the natural cubic spline function, is the second regression coefficient, is the third regression coefficient, is the target regression coefficient, representing the logarithm of the risk ratio of vaccination status, i.e. is the risk ratio (RR) by vaccination status.

[0047] In some embodiments, It follows the negative binomial distribution, denoted by ~NegBin(mean= , discrete parameter = ), which is often used to capture heterogeneity in the spread of infectious diseases. is the discrete parameter of the negative binomial distribution (ranging from 0 to positive infinity), which is used to control the discreteness of the offspring case count distribution so that The variance of Therefore, the probability mass function of the negative binomial distribution can be used express The negative binomial probability distribution of is as follows:

[0048] in, is the gamma function.

[0049] Step S103, based on The probability that a primary case will result in zero offspring cases due to the impact of enhanced control measures and Number of offspring cases generated from primary cases Determine the zero-inflated negative binomial probability mass function for the negative binomial distribution.

[0050] In some embodiments, the zero-inflation part and the negative binomial distribution part are combined to obtain The number of offspring cases generated by the primary case (i.e., the random variable ) is the zero-inflated negative binomial distribution probability mass function ,express Probability distribution, the specific formula is as follows:

[0051] It should be noted that in the embodiments of the present disclosure, In actual situations, The number of offspring cases generated by a primary case. This is the case without considering the number of cases in the next generation due to the impact of enhanced control measures. The number of offspring cases generated by a primary case.

[0052] Step S104, determining the effectiveness of the vaccine in preventing transmission based on the zero-inflated negative binomial distribution probability mass function.

[0053] In some embodiments, the risk ratio (RR, ie, exp( β 1)) is used to measure the risk of disease transmission in different vaccination statuses, with 0 doses as the reference level, indicating how many times the risk of transmission in different vaccination statuses (greater than 0 doses) is that of the unvaccinated status (0 doses). The vaccine's effectiveness against transmission is calculated based on the risk ratio, where when both sides of the RR or its 95% confidence interval are less than 1, the vaccine's effectiveness against transmission is calculated as (1-RR)×100%; when either side of the RR or its 95% confidence interval is greater than 1, the vaccine's effectiveness against transmission is calculated as -[1-(1 / RR)]×100%.

[0054] Specifically, the hazard ratio can be constructed based on the above zero-inflated negative binomial regression model to construct a likelihood function and perform parameter estimation. Zero-inflated negative binomial distribution probability mass function It can be further used to construct the log-likelihood function ,include:

[0055] Using the maximum likelihood estimation method, the log-likelihood function is estimated The target regression coefficient in , the second regression coefficient , the third regression coefficient , second intercept and discrete parameters Specifically, when applying the zero-inflated negative binomial regression model to fit the current real-world data set, the point estimate of each parameter is determined by maximizing the log-likelihood function of the model, and its 95% confidence interval is constructed by adding or subtracting 1.96 times the standard error of the point estimate. represents the natural logarithm of the hazard ratio for vaccination status. It is the likelihood function, which is a function of the probability distribution of the model parameter values ​​defined by the statistical or probability model under known observation data.

[0056] In some embodiments, the risk ratio RR of the vaccination status is determined based on the logarithm of the risk ratio of the vaccination status; in response to the risk ratio of the vaccination status being less than 1, or both sides of the 95% confidence interval of the risk ratio of the vaccination status being less than 1, the effectiveness of the vaccine in preventing transmission is the difference between 1 and the risk ratio of the vaccination status, that is, (1-RR)×100%; in response to the risk ratio of the vaccination status being greater than 1, or both sides of the 95% confidence interval of the risk ratio of the vaccination status being greater than 1, the effectiveness of the vaccine in preventing transmission is the difference between the inverse of the risk ratio of the vaccination status and 1, that is, -[1-(1 / RR)]×100%.

[0057] In this way, the method for evaluating the protective effect of vaccines against transmission provided by the embodiments of the present invention introduces a zero-inflated probability structure, which effectively addresses the observation bias caused by the changes in infectious disease epidemic trends and epidemic prevention measures over time in the real world, and allows direct correction of one or more influencing factors that change over time; the negative binomial distribution structure is introduced, which effectively addresses the problem of heterogeneity in infectious disease transmission between individuals in the real world, thereby improving the accuracy of the model's estimation of the effectiveness of vaccines against transmission; a multi-factor zero-inflated negative binomial linear regression model is used to evaluate the protective effect of vaccines against transmission, which can simultaneously incorporate multiple confounding factors and effectively correct the impact of confounding bias on the evaluation results; the number of offspring cases is used as the outcome variable in the epidemiological design, which effectively addresses the problem of missing information on negative close contacts in the real world, ensuring the feasibility of the research design and the integrity of data collection.

[0058] Figure 2 A second optional flow chart of the method for evaluating the anti-transmission protection effect of a vaccine provided in an embodiment of the present disclosure is shown and will be described according to each step.

[0059] Step S201, constructing the zero-inflated part of the zero-inflated negative binomial regression model.

[0060] In some embodiments, to take into account that disease control measures or self-protection behaviors that change over time may reduce the risk of transmission, resulting in no offspring cases (i.e., the number of offspring cases is 0), use Indicates The probability that a primary case will result in zero offspring cases due to enhanced control measures (e.g., contact tracing or timely isolation after case detection). Statistical adjustments are made for the positive test date and importation status of each primary case, because disease control measures and the emphasis of each measure vary at different stages of the outbreak and in different importation states. The logistic link function is used to represent The log odds ratio (log odds) is used, and the natural cubic spline function (ns) is used to handle nonlinear relationships. The specific formula is as follows:

[0061] in, Characterize natural cubic spline functions; Characterization Date of positive test for each primary case (expressed as a calendar date); Indicates A dummy variable (or virtual variable) for the input status of a primary case, which takes the value of 1 for an input case and 0 otherwise; represents the first regression coefficient to be estimated; represents the first intercept of the zero-inflated part of the regression model, which is a fixed constant.

[0062] Step S201, constructing the negative binomial distribution part of the zero-inflated negative binomial regression model.

[0063] In some embodiments, for the negative binomial distribution portion, The number of offspring cases generated by an index case (i.e., primary case) is expressed as a random variable ,use express The expected value of . Vaccination status (or vaccine doses) was introduced to estimate the vaccine effect, and important confounding variables such as sex, age, residential area, and positive test date were adjusted. The logarithmic link function is used to represent , and the natural cubic spline function is used to deal with nonlinear relationships. The specific formula is as follows:

[0064] in, Characterize natural cubic spline functions; for Expectations, that is, The expected number of offspring cases generated by primary cases ( ); Indicates Vaccination status (or number of doses) of each primary case; Indicates A dummy variable for the sex status of the primary case. If the case is male, the variable value is 1, otherwise the variable value is 0; Indicates The residential area of ​​the primary case; Indicates Age of primary case; Indicates Date of positive test of each primary case; , and Respectively represent the regression coefficients to be estimated (target regression coefficient, second regression coefficient and third regression coefficient). In particular, exp( ) is the risk ratio (RR) of vaccination status, that is, RR=exp( ); Represents the second intercept of the negative binomial distribution part of the regression model, which is a fixed constant.

[0065] at this time, It follows the negative binomial distribution, denoted by ~NegBin(mean= , discrete parameter = ), which is often used to capture heterogeneity in the spread of infectious diseases. is the discrete parameter of the negative binomial distribution (ranging from 0 to positive infinity), which is used to control the discreteness of the offspring case count distribution so that The variance of Therefore, the probability mass function of the negative binomial distribution can be used express The negative binomial probability distribution of is as follows:

[0066] in, is the gamma function.

[0067] Step S203, determining a zero-inflated negative binomial distribution regression model.

[0068] In some embodiments, the zero-expanded part and the negative binomial part are combined to obtain The number of offspring cases generated by the primary case (i.e., the random variable ) is the zero-inflated negative binomial distribution probability mass function ,express Probability distribution, the specific formula is as follows:

[0069] Step S204, evaluating the vaccine's protective effect against transmission.

[0070] In some embodiments, the effectiveness of a vaccine in preventing transmission is used to evaluate the protective effect of a vaccine in preventing transmission.

[0071] In some embodiments, the risk ratio (RR, ie, exp( β1)) is used to measure the risk of disease transmission in different vaccination statuses, with 0 doses as the reference level, indicating how many times the risk of transmission in different vaccination statuses (greater than 0 doses) is that of the unvaccinated status (0 doses). The vaccine's effectiveness against transmission is calculated based on the risk ratio, where when both sides of the RR or its 95% confidence interval are less than 1, the vaccine's effectiveness against transmission is calculated as (1-RR)×100%; when either side of the RR or its 95% confidence interval is greater than 1, the vaccine's effectiveness against transmission is calculated as -[1-(1 / RR)]×100%.

[0072] Based on the acquired analyzable real-world data, the zero-inflated negative binomial regression model provided by the embodiment of the present disclosure is applied to construct the likelihood function and perform parameter estimation. As the probability mass function (PMF) of the zero-inflated negative binomial distribution, It can be further used to construct the log-likelihood function , the specific formula is as follows:

[0073] Using the maximum likelihood estimation method, the log-likelihood function is estimated The target regression coefficient in , the second regression coefficient , the third regression coefficient , second intercept and discrete parameters Specifically, when applying the zero-inflated negative binomial regression model to fit the current real-world data set, the point estimate of each parameter is determined by maximizing the log-likelihood function of the model, and its 95% confidence interval is constructed by adding or subtracting 1.96 times the standard error of the point estimate. The natural logarithm of the hazard ratio representing vaccination status.

[0074] In this way, through the vaccine anti-transmission protection effect evaluation method provided by the embodiment of the present disclosure, the real-world protection effect of the vaccine against the spread of infectious diseases can be evaluated based on contact tracing data; the problem of inter-individual transmission heterogeneity in the actual transmission of infectious diseases and the changes in epidemic trends and prevention measures over time are taken into account, and the model can better reflect the complexity of infectious disease transmission in the real world; a zero-inflated negative binomial linear regression model that can incorporate multiple confounding factors is constructed, which can correct the confounding bias caused by these confounding factors in the evaluation process of the vaccine anti-transmission protection effect; the number of offspring cases is used as the outcome variable in the epidemiological design, which successfully overcomes the dilemma of missing information on negative close contacts in real-world data.

[0075] Figure 3 Shown is an epidemic curve of real-world infectious disease surveillance data.

[0076] For the outbreak of a sudden new infectious disease, contact tracing is usually the primary prevention and control measure to curb the spread of infectious diseases. Contact tracing data records the time and space information of contact between cases or between cases and close contacts. This information can be used to reconstruct the original case-offspring case transmission pair, and then the number of offspring cases generated by each original case can be extracted, that is, the distribution of the number of offspring cases.

[0077] like Figure 3 As shown in the figure, based on the contact tracing information provided by the real-world infectious disease surveillance data of an emerging infectious disease in a certain area, 17,535 cases with detailed contact history were identified, and the number of offspring cases generated by each primary case was extracted. The epidemic curve of local cases (excluding imported cases) was drawn by stratification based on vaccination status (0, 1, 2, or 3 doses) and the number of offspring cases generated (0 or >0 cases).

[0078] Figure 3 Figure 2. Daily number of confirmed infections of an emerging infectious disease in a region, stratified by number of vaccine doses received. The top panel shows the epidemic curve of infections that did not generate progeny cases and the cumulative distribution of vaccination status (i.e., terminal and sporadic cases). The bottom panel shows the epidemic curve of infections that generated at least 1 progeny case and the cumulative distribution of vaccination status (i.e., primary cases that led to critical and supercritical transmission).

[0079] Figure 3 The number of infected persons changes over time (i.e., reporting date) is shown in Figure 3 The above figure shows the change in the number of infected people who did not produce offspring cases over time. Among the cumulative number of infected people with all reported dates, there are those who received 0 vaccines (28% of the total), 1 vaccine (8% of the total), 2 vaccines (54% of the total) and 3 vaccines (10% of the total).

[0080] Figure 3 The figure below shows the change in the number of infected people who produce one offspring case over time. Among the cumulative number of infected people with all reported dates, there are those who have received 0 vaccines (39% of the total), 1 vaccine (6% of the total), 2 vaccines (49% of the total) and 3 vaccines (6% of the total).

[0081] Depend on Figure 3 It can be concluded that the phenomenon of zero expansion exists in the real world. Therefore, the embodiment of the present disclosure introduces a zero expansion part when evaluating the anti-transmission protection effect of the vaccine, so that the final estimation result is more accurate.

[0082] Figure 4 A schematic diagram showing the mean number of offspring cases resulting from real-world primary cases.

[0083] Figure 4 In the figure, stratified by vaccination status, the dots represent the average number of secondary cases caused by each primary case, the bars represent 95% confidence intervals, and the vertical axis is on a logarithmic scale.

[0084] Furthermore, a likelihood function is constructed based on the zero-inflated negative binomial regression model obtained above, and parameter estimation is performed. Specifically, a log-likelihood function is constructed, and the target regression coefficient, the second regression coefficient, the third regression coefficient, and the dispersion coefficient in the model are estimated by the maximum likelihood estimation method. The point estimate of each parameter is determined by maximizing the log-likelihood function of the model, and its 95% confidence interval is constructed by adding or subtracting 1.96 times the standard error from the point estimate.

[0085] Based on the estimated risk ratios for different vaccination statuses, the effectiveness of the vaccine against transmission under each vaccination status was further calculated, and a two-sided Wald test was used to test the hypothesis of the effectiveness of the vaccine against transmission, and the p value was calculated. When the p value was less than 0.05, the result was considered statistically significant.

[0086] In order to explore the stability of the estimated effectiveness of the vaccine against transmission, a sensitivity analysis was conducted, and a stratified analysis was performed according to potential confounding factors (e.g., vaccination status of offspring cases). The results showed that even when close contacts of vaccinated primary cases were more likely to be vaccinated, the size and trend of the estimated effectiveness of the vaccine against transmission were basically consistent with the main results. Therefore, after verification by sensitivity analysis, the zero-inflated negative binomial regression model provided in the embodiment of the present disclosure has good stability and reliability.

[0087] Figure 5 An optional structural schematic diagram of a vaccine anti-transmission protection effect evaluation device provided in an embodiment of the present disclosure is shown, and will be described according to each part.

[0088] In some embodiments, the vaccine anti-transmission protection effect evaluation device 500 includes: a zero-inflation construction unit 501, a negative binomial construction unit 502, a probability mass function determination unit 503 and an evaluation unit 504.

[0089] The zero expansion construction unit 501 is used to The date of positive test of the primary case, The dummy variable of the input status of the primary case, the first regression coefficient to be estimated and the first intercept, determine the The probability that a primary case will result in zero offspring cases due to the impact of enhanced control measures ; The negative binomial construction unit 502 is used to construct the negative binomial Vaccination information of primary cases, Gender information of primary cases, The residential area information of the primary case, Age information of the primary cases and the The positive test date of the primary case, the second regression coefficient, the third regression coefficient, the target regression coefficient and the second intercept are used to determine the Number of offspring cases generated from primary cases The negative binomial probability distribution of ; The probability mass function determination unit 503 is used to determine the probability mass function based on the The probability that a primary case will result in zero offspring cases due to the impact of enhanced control measures and Number of offspring cases generated from primary cases The negative binomial probability distribution of , determines the probability mass function of the zero-inflated negative binomial distribution; The evaluation unit 504 is used to determine the effectiveness of the vaccine in preventing transmission based on the zero-inflated negative binomial distribution probability mass function.

[0090] The zero expansion construction unit 501 is specifically configured to: The natural cubic spline function value of the positive test date of the primary case, The dummy variable of the input status of the primary case, the first regression coefficient and the first intercept are used to determine the The probability that a primary case will result in zero offspring cases due to the impact of enhanced control measures The log odds ratio; Based on the The probability that a primary case will result in zero offspring cases due to the impact of enhanced control measures The logarithmic odds ratio is used to determine the The probability that a primary case will result in zero offspring cases due to the impact of enhanced control measures .

[0091] The zero expansion construction unit 501 is specifically used for:

[0092] in, for The log odds ratio, is the first intercept, For the Date of positive test of primary cases, is the natural cubic spline function, is the first regression coefficient, For the A dummy variable for the input status of the primary case.

[0093] The negative binomial construction unit 502 is specifically used to construct the negative binomial based on the Vaccination information of primary cases, Gender information of primary cases, The residential area information of the primary case, Age information of the primary case and the The positive test date of the primary case, the second regression coefficient, the third regression coefficient, the target regression coefficient and the second intercept are used to determine the first Number of offspring cases generated from primary cases The mean ; The said Number of offspring cases generated from primary cases The mean is , the discrete parameters are The negative binomial distribution of and discrete parameters Determine the Number of offspring cases generated from primary cases The negative binomial probability distribution of .

[0094] The negative binomial construction unit 502 is specifically used to determine the mean based on the following formula The log link function of :

[0095] in, For the Number of offspring cases generated from primary cases expectations, is the second intercept, For the Vaccination information of primary cases, For the Gender information of primary cases, For the The residential area information of each primary case, For the Age information of primary cases, For the Date of positive test of primary cases, is the natural cubic spline function, is the second regression coefficient, is the third regression coefficient, is the target regression coefficient, Logarithm of the hazard ratio characterizing vaccination status.

[0096] The negative binomial construction unit 502 is specifically used for

[0097] in, is the gamma function, For the Number of offspring cases generated from primary cases The coefficient of dispersion of the negative binomial distribution.

[0098] The probability mass function determination unit 503 is specifically configured to determine the zero-inflated negative binomial distribution probability mass function based on the following formula:

[0099] in, Characterization The number of offspring cases generated by the primary case, For the Number of offspring cases generated from primary cases The negative binomial probability distribution of .

[0100] The evaluation unit 504 is specifically configured to construct a log-likelihood function based on the zero-inflated negative binomial distribution probability mass function; Based on the maximum likelihood estimation method, estimating the target regression coefficient, the second regression coefficient, the third regression coefficient, the second intercept and the discrete parameter in the log-likelihood function; determining an estimate of the target regression coefficient as the logarithm of the hazard ratio for vaccination status; The effectiveness of the vaccine in preventing transmission was determined based on the logarithm of the hazard ratio for the vaccination status.

[0101] According to an embodiment of the present disclosure, the present disclosure also provides an electronic device and a readable storage medium.

[0102] Figure 6 A schematic block diagram of an example electronic device 800 that can be used to implement an embodiment of the present disclosure is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present disclosure described and / or required herein.

[0103] like Figure 6As shown, the electronic device 800 includes a computing unit 801, which can perform various appropriate actions and processes according to a computer program stored in a read-only memory (ROM) 802 or a computer program loaded from a storage unit 808 to a random access memory (RAM) 803. In the RAM 803, various programs and data required for the operation of the electronic device 800 can also be stored. The computing unit 801, the ROM 802, and the RAM 803 are connected to each other via a bus 804. An input / output (I / O) interface 805 is also connected to the bus 804.

[0104] Multiple components in the electronic device 800 are connected to the I / O interface 805, including: an input unit 806, such as a keyboard, a mouse, etc.; an output unit 807, such as various types of displays, speakers, etc.; a storage unit 808, such as a disk, an optical disk, etc.; and a communication unit 809, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 809 allows the electronic device 800 to exchange information / data with other devices through a computer network such as the Internet and / or various telecommunication networks.

[0105] The computing unit 801 may be a variety of general and / or special processing components with processing and computing capabilities. Some examples of the computing unit 801 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various dedicated artificial intelligence (AI) computing chips, various computing units running machine learning model algorithms, digital signal processors (DSPs), and any appropriate processors, controllers, microcontrollers, etc. The computing unit 801 performs the various methods and processes described above, such as a vaccine anti-transmission protection effect evaluation method. For example, in some embodiments, the vaccine anti-transmission protection effect evaluation method may be implemented as a computer software program, which is tangibly contained in a machine-readable medium, such as a storage unit 808. In some embodiments, part or all of the computer program may be loaded and / or installed on the electronic device 800 via the ROM 802 and / or the communication unit 809. When the computer program is loaded into the RAM 803 and executed by the computing unit 801, one or more steps of the vaccine anti-transmission protection effect evaluation method described above may be performed. Alternatively, in other embodiments, the computing unit 801 may be configured to execute the method for evaluating the vaccine's anti-transmission protection effect in any other appropriate manner (eg, by means of firmware).

[0106] Various implementations of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chips (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various implementations can include: being implemented in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0107] The program code for implementing the method of the present disclosure may be written in any combination of one or more programming languages. These program codes may be provided to a processor or controller of a general-purpose computer, a special-purpose computer, or other programmable data processing device, so that the program code, when executed by the processor or controller, enables the functions / operations specified in the flow chart and / or block diagram to be implemented. The program code may be executed entirely on the machine, partially on the machine, partially on the machine and partially on a remote machine as a stand-alone software package, or entirely on a remote machine or server.

[0108] In the context of the present disclosure, a machine-readable medium may be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, device, or equipment. A machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, device, or device, or any suitable combination of the foregoing. A more specific example of a machine-readable storage medium may include an electrical connection based on one or more lines, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0109] To provide interaction with a user, the systems and techniques described herein can be implemented on a computer having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user can provide input to the computer. Other types of devices can also be used to provide interaction with the user; for example, the feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including acoustic input, voice input, or tactile input).

[0110] The systems and techniques described herein may be implemented in a computing system that includes back-end components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes front-end components (e.g., a user computer with a graphical user interface or a web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such back-end components, middleware components, or front-end components. The components of the system may be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: a local area network (LAN), a wide area network (WAN), and the Internet.

[0111] A computer system may include a client and a server. The client and the server are generally remote from each other and usually interact through a communication network. The relationship of client and server is generated by computer programs running on respective computers and having a client-server relationship with each other. The server may be a cloud server, a server of a distributed system, or a server combined with a blockchain.

[0112] It should be understood that the various forms of processes shown above can be used to reorder, add or delete steps. For example, the steps recorded in this disclosure can be executed in parallel, sequentially or in different orders, as long as the desired results of the technical solutions disclosed in this disclosure can be achieved, and this document does not limit this.

[0113] In addition, the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one of the features. In the description of the present disclosure, the meaning of "plurality" is two or more, unless otherwise clearly and specifically defined.

[0114] The above is only a specific embodiment of the present disclosure, but the protection scope of the present disclosure is not limited thereto. Any person skilled in the art who is familiar with the technical field can easily think of changes or substitutions within the technical scope disclosed in the present disclosure, which should be included in the protection scope of the present disclosure. Therefore, the protection scope of the present disclosure should be based on the protection scope of the claims.

Claims

1. A method for evaluating the protective effect of a vaccine against transmission, characterized in that: The method comprises: Based on The date of positive test of the primary case, The dummy variable of the input status of the primary case, the first regression coefficient to be estimated and the first intercept, determine the The probability that a primary case will result in zero offspring cases due to the impact of enhanced control measures ; Based on the Vaccination information of primary cases, Gender information of primary cases, The residential area information of the primary case, Age information of the primary cases and the The positive test date of the primary case, the second regression coefficient, the third regression coefficient, the target regression coefficient and the second intercept are used to determine the Number of offspring cases generated from primary cases The negative binomial probability distribution of ; Based on The probability that a primary case will result in zero offspring cases due to the impact of enhanced control measures and Number of offspring cases generated from primary cases The negative binomial probability distribution of , determines the probability mass function of the zero-inflated negative binomial distribution; Based on the zero-inflated negative binomial distribution probability mass function, the effectiveness of the vaccine in preventing transmission is determined.

2. The method according to claim 1, characterized in that The The date of positive test of the primary case, The dummy variable of the input status of the primary case, the first regression coefficient to be estimated and the first intercept, determine the The probability that a primary case will result in zero offspring cases due to the impact of enhanced control measures ,include: Based on The natural cubic spline function value of the positive test date of the primary case, The dummy variable of the input status of the primary case, the first regression coefficient and the first intercept are used to determine the The probability that a primary case will result in zero offspring cases due to the impact of enhanced control measures The log odds ratio; Based on the The probability that a primary case will result in zero offspring cases due to the impact of enhanced control measures The logarithmic odds ratio is used to determine the The probability that a primary case will result in zero offspring cases due to the impact of enhanced control measures .

3. The method according to claim 2, characterized in that The The natural cubic spline function value of the positive test date of the primary case, The dummy variable of the input status of the primary case, the first regression coefficient and the first intercept are used to determine the The probability that a primary case will result in zero offspring cases due to the impact of enhanced control measures The log odds ratios include: in, for The log odds ratio, is the first intercept, For the The date of positive test of the primary case, is the natural cubic spline function, is the first regression coefficient, For the A dummy variable for the input status of the primary case.

4. The method according to claim 1, characterized in that: The Vaccination information of primary cases, Gender information of primary cases, The residential area information of the primary case, Age information of the primary cases and the The positive test date of the primary case, the second regression coefficient, the third regression coefficient, the target regression coefficient and the second intercept are used to determine the Number of offspring cases generated from primary cases The negative binomial probability distribution of , including: Based on the Vaccination information of primary cases, Gender information of primary cases, The residential area information of the primary case, Age information of the primary cases and the The positive test date of the primary case, the second regression coefficient, the third regression coefficient, the target regression coefficient and the second intercept are used to determine the first Number of offspring cases generated from primary cases The mean ; The said Number of offspring cases generated from primary cases The mean is , the discrete parameters are The negative binomial distribution of and discrete parameters Determine the Number of offspring cases generated from primary cases The negative binomial probability distribution of .

5. The method according to claim 4, characterized in that The Vaccination information of primary cases, Gender information of primary cases, The residential area information of the primary case, Age information of the primary cases and the The positive test date of the primary case, the second regression coefficient, the third regression coefficient, the target regression coefficient and the second intercept are used to determine the first Number of offspring cases generated from primary cases The mean ,include: The mean is determined based on the following formula The log link function of : in, For the Number of offspring cases generated from primary cases expectations, is the second intercept, For the Vaccination information of primary cases, For the Gender information of primary cases, For the The residential area information of each primary case, For the Age information of primary cases, For the The date of positive test of the primary case, is the natural cubic spline function, is the second regression coefficient, is the third regression coefficient, is the target regression coefficient, Logarithm of the hazard ratio characterizing vaccination status.

6. The method according to claim 4, characterized in that The mean value based on and discrete parameters Determine the Number of offspring cases generated from primary cases The negative binomial probability distribution of Number of offspring cases generated from primary cases The negative binomial probability distribution of : in, is the gamma function, For the Number of offspring cases generated from primary cases The coefficient of dispersion of the negative binomial distribution is For the Number of offspring cases generated from primary cases The negative binomial probability distribution of .

7. The method according to claim 1, characterized in that The The probability that a primary case will result in zero offspring cases due to the impact of enhanced control measures and Number of offspring cases generated from primary cases The negative binomial probability distribution of , determines the zero-inflated negative binomial distribution probability mass function, including: Determine the zero-inflated negative binomial distribution probability mass function based on the following formula: in, Characterization The number of offspring cases generated by the primary case, For the Number of offspring cases generated from primary cases Probability mass function of the zero-inflated negative binomial distribution.

8. The method according to claim 1, characterized in that Determining the effectiveness of the vaccine in preventing transmission based on the zero-inflated negative binomial distribution probability mass function includes: constructing a log-likelihood function based on the zero-inflated negative binomial distribution probability mass function; Based on the maximum likelihood estimation method, estimating the target regression coefficient, the second regression coefficient, the third regression coefficient, the second intercept and the discrete parameter in the log-likelihood function; determining an estimate of the target regression coefficient as the logarithm of the hazard ratio for vaccination status; The effectiveness of the vaccine in preventing transmission was determined based on the logarithm of the hazard ratio for the vaccination status.

9. A device for evaluating the protective effect of a vaccine against transmission, characterized in that: The device comprises: Zero-inflated building blocks for The date of positive test of the primary case, The dummy variable of the input status of the primary case, the first regression coefficient to be estimated and the first intercept, determine the The probability that a primary case will result in zero offspring cases due to the impact of enhanced control measures ; Negative binomial building block for Vaccination information of primary cases, Gender information of primary cases, The residential area information of the primary case, Age information of the primary cases and the The positive test date of the primary case, the second regression coefficient, the third regression coefficient, the target regression coefficient and the second intercept are used to determine the Number of offspring cases generated from primary cases The negative binomial probability distribution of ; A probability mass function determination unit is used to determine the probability mass function based on the first The probability that a primary case will result in zero offspring cases due to the impact of enhanced control measures and Number of offspring cases generated from primary cases The negative binomial probability distribution of , determines the probability mass function of the zero-inflated negative binomial distribution; An evaluation unit is used to determine the effectiveness of the vaccine in preventing transmission based on the zero-inflated negative binomial distribution probability mass function.

10. An electronic device, characterized in that: include: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the method according to any one of claims 1 to 8.

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