A method for early prediction and early warning of large-scale outbreaks of pathogenic microorganisms

Abstract

The present invention relates to a method for early prediction and early warning of large-scale outbreaks of pathogenic microorganisms. The method monitors domestic sewage at fixed locations to obtain virus content data; performs filtering, cleaning and difference processing on the data; estimates the parameters of the virus dynamics transmission model in different patches; estimates the characteristics of virus transmission between patches; constructs a priority management method and a two-dimensional risk control map; integrates the parameters of the virus dynamics transmission model in different patches, the characteristics of virus transmission between patches and the priority management method, displays the dynamic growth and extinction process of the virus in a large set of patches, and predicts the transmission trend. By integrating the intra-plaque and inter-plaque transmission models, large-scale outbreaks can be predicted and warned 4-6 weeks in advance, and communities where sustained outbreaks may occur can be found. According to the transmission characteristic indicators of the virus in different regions, different regions are ranked for risk, and the allocation of social prevention and control resources is optimized.

Description

Technical Field

[0001] The present invention mainly relates to the fields of medical and computer technology, and in particular to a method for early prediction and early warning of large-scale outbreaks of pathogenic microorganisms. Background Art

[0002] At present, the new crown early warning system mainly relies on two types of monitoring methods. One is direct detection based on nucleic acid monitoring and rapid antigen detection, and the other is to detect the new coronavirus in domestic sewage, and indirectly reflect the number of infected people based on the amount of sewage virus. Due to the continuous mutation of the virus, it gradually develops in the direction of immune escape and rapid spread, and sewage monitoring can involve asymptomatic infections over a wide area. Usually, before people infected with the new coronavirus show symptoms, the virus's RNA ribonucleic acid will remain in their feces and be discharged into the sewer. The new coronavirus nucleic acid was detected in the sludge samples, and the detection results were consistent with the trend of local epidemiological data. The original copy data of sewage monitoring can provide early warning of the spread of the virus, 4 days earlier than the first local clinical case.

[0003] The above-mentioned record of background technical knowledge is intended to help ordinary technicians in this field understand the existing technologies that are relatively close to the present invention, and at the same time facilitate the understanding of the inventive concept and technical solution of this application. It should be clear that in the absence of clear evidence that the above-mentioned content has been disclosed before the filing date of this patent application, the above-mentioned background technology should not be used to evaluate the novelty of the technical solution of this application. Summary of the invention

[0004] In order to solve at least one of the technical problems mentioned in the above background technology, the purpose of the present invention is to provide a method for early prediction and warning of large-scale outbreaks of pathogenic microorganisms. The method integrates intra-plaque and inter-plaque transmission models, can predict and warn large-scale outbreaks 4-6 weeks in advance, and discover communities where sustained outbreaks may occur. According to the transmission characteristic indicators of the virus in different regions, different regions are ranked according to risk, and the allocation of social prevention and control resources is optimized.

[0005] A method for early prediction and early warning of large-scale outbreaks of pathogenic microorganisms, comprising:

[0006] S100, monitor domestic sewage at a fixed location and obtain virus content data copy / L;

[0007] S200, filtering and cleaning the data, and performing difference processing if there are missing values ​​after filtering and cleaning;

[0008] S300, estimation of parameters of the virus transmission model in different plaques;

[0009] S400, estimate the characteristics of virus transmission between plaques;

[0010] S500, build a priority management method, first determine the characteristic parameters Rt and e of the transmission within the patch, build a two-dimensional risk control map, and divide different patches into transmission damping areas, high-risk areas, and low-risk areas;

[0011] S600 integrates the virus dynamics transmission model parameters in different plaques, the virus transmission characteristics between plaques and the priority management methods to show the dynamic growth and extinction process of the virus in a large plaque collection and predict the transmission trend.

[0012] In some specific implementations, in step S100, the virus content data copy / L is a numerical value.

[0013] In some specific embodiments, in step S100, if the virus content data copy / L is obtained by interval sampling, the data has a null value problem, and the per capita virus content c on that day is calculated. mean , and fill in the empty values, Among them, c a is the non-empty data of the virus content in sewage on day a, p a is the regional population corresponding to the non-empty data on day a.

[0014] In some specific implementations, in step 200, filtering is used to clean the data noise peaks, effectively extract specific frequencies, and perform specific processing actions on the extracted parts. The filtering program is written in Python language, and the filtering program for raw data is developed based on the Scipy module, and the raw data is processed in the form of low-pass filtering, high-pass filtering, band-pass filtering, and band-stop filtering.

[0015] In some specific implementations, in step 200, the specific processing actions include gain, attenuation, and filtering.

[0016] In some specific implementations, in step 200, interpolation processing is performed on the missing data after filtering, and the Akima interpolation method is used to complete the row interpolation. Specifically, the interpolate method of the filter_inter class is used. Since the data needs to complete the fitting of the SIR model, the null values ​​after filtering need to be processed and interpolated. In this method, in order to better complete the model fitting, the curve needs to be as smooth as possible, and the effect of the derivative value of the elements is considered. This method uses the Akima interpolation method to complete the row interpolation, and the interpolate method of the filter_inter class is used.

[0017] In some specific implementations, in step S300, after the interpolation process is completed, when estimating the parameters of the virus dynamics transmission model in different plaques, further conversion is required from virus transmission to human transmission. Among them, N a is the number of infections in the region on day a; ci is the virus content of sewage at the i-th sewage sampling point; n is the total number of sewage sampling points; q i is the number of people covered by the sewage sampling point at position i; p is the water consumption; S a is the per capita viral emission.

[0018] In some specific implementations, in step S300, the dynamic process of virus transmission growth and extinction is simulated by establishing a dynamic process to complete the dynamic construction of the transmission process, and the SIR model is mainly used for parameter simulation. The solution of the differential equation group of the SIR model is completed based on the Scipy module of Python, and the curve fitting of the SIR model is completed by the nonlinear least squares method.

[0019] In some specific implementations, in step S300, if the model parameters cannot be simulated due to problems such as missing data, the following steps are performed: first, the number of infections is normalized and its cumulative distribution function is calculated; the cumulative distribution function of the normal distribution (the lognormal distribution can also be used) is used for fitting, and μ and σ in the equation are obtained through curve fitting; then μ and σ are substituted into the lognormal distribution function to calculate the number of infections on the corresponding date; finally, the obtained data set is refitted with the SIR model dynamic parameters to obtain the parameters of the model.

[0020] In some specific implementations, in step S400, it is determined whether the detection point has virus gene data, and the dynamic process of invasion and extinction is simulated by establishing a dynamic process. Step S400 mainly refers to the spread of the virus between different plaques (communities), and the unit of propagation is the plaque (community). It is determined whether the detection point has virus gene data, and the data is assigned "0" and "1". If there is, it is recorded as "1", indicating virus invasion, and if there is no, it is recorded as "0", and 0 means no invasion.

[0021] In some specific implementations, in step S400, the dynamic process of invasion and extinction is simulated by establishing a dynamic process, and the infection rate Intrusion Rate Where N is the total number of plaques that may be infected; n t is the number of infected plaques; c is the invasion rate; e is the virus transmission and extinction rate; t is the time of transmission or extinction.

[0022] In some specific implementations, in step S500,

[0023] Among them, R tR is the number of people that a positive infected person can spread within a specific period of time. This value represents the virus transmission characteristics of the patch (community). Due to differences in the social distance, immune capacity, control measures and medical facilities of hosts in different patches, the R in different patches (communities) is t There are large differences in the values; β and γ are parameters in the SIR transmission model, where β is the probability constant of actual contact and infection per unit time, called the infection rate, and γ is the probability constant of each infected patient recovering per unit time, called the recovery rate.

[0024] Among them, e is the virus transmission and extinction rate; M is the number of infected people; t is the time of transmission or extinction.

[0025] In some specific implementation methods, in step S600, the dynamic growth and extinction process of the virus in a large plaque set is displayed, and the transmission trend is predicted by executing the following steps: first, the actual number of infected people is used to perform random process constraints on the number of infected people simulated by the model, and a joint distribution diagram of the plaque invasion rate and the number of infected people under the same time series is plotted, and the plaque invasion and individual infection trends are observed to determine the early warning effect of cluster theory. Plaque invasion is divided into an expansion period and an extinction period. The infection coefficient and the extinction coefficient can be used to calculate the virus invasion ratio and the maximum invasion capacity in different regions, and the transmission trend can be predicted.

[0026] Traditional methods predict the infection of pathogenic microorganisms based on transmission dynamics or statistical models by monitoring the host infection situation. They often use individuals as infection units and predict the entire area as a uniform whole. The size of spatial patches at different observation scales is different. Individual infection does not have scale changes, but patches do. The smallest boundary of the scale is the individual, so the common scale division can be managed according to the spatial coverage unit of the actual monitoring area. Therefore, there are defects such as very high cost of monitoring the overall infection situation and large errors.

[0027] This study uses the entire area as a breeding habitat for pathogenic microorganisms. By monitoring the gene levels in sewage, the area is subdivided into different patches. The gene levels in the patches are monitored to characterize the infection. The virus is transmitted between different communities through the flow of infected people. Under the condition of full coverage of the entire area, dynamic monitoring and outbreak warning of population infections in different areas can be achieved, as well as regional hierarchical management of communities based on differences in the virus's transmission dynamics coefficients in different community patches. The method of the present invention can predict the large-scale transmission of the virus in the region in advance for a long period of time, and optimize resource allocation through regional hierarchical management.

[0028] A device for early prediction and warning of large-scale outbreaks of pathogenic microorganisms, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor. When the computer program is executed by the processor, each step of the aforementioned method for early prediction and warning of large-scale outbreaks of pathogenic microorganisms is implemented.

[0029] A computer-readable storage medium stores a computer program, which, when executed by a processor, implements the various steps of the above-mentioned method for early prediction and early warning of large-scale outbreaks of pathogenic microorganisms.

[0030] The beneficial effects of this application are:

[0031] The method of the present invention can predict the large-scale transmission of the virus in a region long in advance, and optimize resource allocation through regional hierarchical management. The method integrates the intra-plaque and inter-plaque transmission models, and can predict and warn large-scale outbreaks 4-6 weeks in advance, and discover communities where sustained outbreaks may occur. According to the transmission characteristic indicators of the virus in different regions, different regions are ranked for risk, and the allocation of social prevention and control resources is optimized. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] In order to make the above and / or other purposes, features, advantages and examples of the present invention more obvious and easy to understand, the drawings required for use in the specific implementation of the present invention will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without paying creative work.

[0033] Figure 1 It is a flow chart of the method for early prediction and early warning of large-scale outbreaks of pathogenic microorganisms;

[0034] Figure 2 is a schematic diagram of the reconstructed data set;

[0035] Figure 3 It is a schematic diagram of the calculation results of regional invasion ratio and block invasion and extinction rate;

[0036] Figure 4 It is a schematic diagram of the trend of the overall spread of infection in the entire region;

[0037] Figure 5 It is a two-dimensional plaque optimization map. DETAILED DESCRIPTION

[0038] Those skilled in the art can refer to the content of this article and appropriately replace and / or modify the process parameters to achieve the same. However, it should be particularly noted that all similar replacements and / or modifications are obvious to those skilled in the art and are considered to be included in the present invention. The products and preparation methods described in the present invention have been described through preferred examples. It is obvious that relevant personnel can modify or appropriately change and combine the products and preparation methods described herein without departing from the content, spirit and scope of the present invention to implement and apply the technology of the present invention.

[0039] Unless otherwise defined, technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which the invention belongs. The present invention uses the methods and materials described herein; however, other suitable methods and materials known in the art may also be used. The materials, methods, and examples described herein are illustrative only and are not intended to be limiting. All publications, patent applications, patents, provisional applications, database entries, and other references mentioned herein are incorporated herein by reference in their entirety. In the event of a conflict, the present specification, including definitions, shall prevail.

[0040] Unless specifically stated, the materials, methods, and examples described herein are illustrative only and not limiting. Although methods and materials similar or equivalent to those described herein can be used in the practice or testing of the present invention, suitable methods and materials are still described herein.

[0041] The present invention is described in detail below.

[0042] Embodiment 1:

[0043] Provide a method for early prediction and early warning of large-scale outbreaks of pathogenic microorganisms. The specific process is as follows: Figure 1 As shown, the specific steps include the following steps.

[0044] For domestic sewage monitoring at a fixed location, the virus content (copy / L) data is obtained, and the data is numerical. If it is interval sampling, the data has null value problems. Calculate the per capita virus content c on that day mean , and fill in the empty values, c a is the non-empty data of sewage virus content on day a, p a is the regional population corresponding to the non-empty data on day a.

[0045] After completing the null value filling, when estimating the parameters of the virus dynamics transmission model in different plaques, further conversion is required from virus transmission to human transmission. Among them, N a is the number of infections in the region on day a; c iis the virus content of sewage at the i-th sewage sampling point; n is the total number of sewage sampling points; q i is the number of people covered by the sewage sampling point at position i; p is the water consumption; S a is the per capita viral emission.

[0046] Due to the existence of systematic errors and random errors in the sampled data, data cleaning is required to facilitate the subsequent data analysis process. The methods adopted include the following two aspects: 1. For data noise peaks, filtering is used for cleaning. The filtering program is written in Python language, which effectively extracts specific frequencies and performs specific processing (gain, attenuation, filtering) on ​​the extracted part. A filtering program for raw data is developed based on the Scipy module, and the raw data is processed in the form of low-pass filtering, high-pass filtering, band-pass filtering, and band-stop filtering. The present invention can use the filter method of the filter_inter class. 2. For missing data after filtering, interpolation processing is performed. Since the data needs to complete the fitting of the SIR model, the null values ​​after filtering need to be processed and interpolation is required. In this method, in order to better complete the model fitting, the curve needs to be as smooth as possible, considering the effect of the element derivative value. The present invention uses the Akima interpolation method to complete the row interpolation and the interpolate method of the filter_inter class.

[0047] Estimation of virus transmission parameters in plaques. When estimating the parameters of the virus dynamics transmission model in different plaques, a conversion step is required, from virus transmission to human transmission. The dynamic process of its growth and extinction is simulated by establishing a dynamic process to complete the dynamic construction of the transmission process, mainly using the SIR model for parameter simulation. The solution of the differential equations of the SIR model is completed based on Python's Scipy module, and the curve fitting of the SIR model is completed by the nonlinear least squares method.

[0048] Since the model parameters cannot be simulated due to problems such as missing data, the present invention provides a solution for this. Perform the following steps: First, normalize the number of infected people and calculate its cumulative distribution function. The present invention uses the cumulative distribution function of the normal distribution (or the lognormal distribution) for fitting, and obtains μ and σ in the equation through curve fitting. Then, μ and σ are substituted into the lognormal distribution function to calculate the number of infected people on the corresponding date. Finally, the obtained data set is refitted with the SIR model kinetic parameters to obtain the parameters of the model.

[0049] Estimation method of virus transmission characteristics between plaques: This part mainly refers to the transmission of the virus between different plaques (communities), and the transmission unit is the plaque (community). Determine whether there is virus gene data at the detection point, and assign "0" and "1" to the data. If there is, it is recorded as "1", indicating virus invasion, and if there is no, it is recorded as "0", which means no invasion. The dynamic process of invasion and extinction is simulated by establishing a kinetic process. Infection rate Intrusion Rate Where N represents the total number of plaques that may be infected; n t represents the number of infected plaques; c represents the invasion rate; e represents the virus transmission and extinction rate; t is the time of transmission or extinction.

[0050] The construction of the plaque priority management method first requires the determination of the characteristic parameters Rt and e of the transmission within the plaque. Rt refers to how many people a positive infected person can spread within a specific period of time after taking various prevention and control measures. This value represents the virus transmission characteristics of the plaque (community). Due to differences in the social distance, immune ability, control measures and medical facilities of the hosts in different plaques, there are large differences in the Rt values ​​in different plaques (communities). Where β and γ are the infection rate and recovery rate in the SIR transmission model. Where M is the number of infected people, and t is the time of transmission or extinction. A two-dimensional risk control map is constructed through the characteristic parameters of infection within the plaque (Rt, cumulative transmission time, dynamic extinction time), and different plaques (communities) are divided into transmission damping areas, high-risk areas, low-risk areas, etc.

[0051] The parameters of the virus dynamics transmission model in different plaques, the characteristics of virus transmission between plaques and the priority management methods are integrated to demonstrate the dynamic growth and extinction process of the virus in a large plaque collection and predict the transmission trend. The following steps are performed: first, the actual number of infected people is used to constrain the number of infected people simulated by the model in a random process, and a joint distribution diagram of the plaque invasion rate and the number of infected people under the same time series is plotted. The trend of plaque invasion and individual infection is observed, and the early warning effect of cluster theory is judged. Plaque invasion is divided into an expansion period and an extinction period. The infection coefficient and the extinction coefficient can be used to calculate the virus invasion ratio and the maximum invasion capacity in different regions, and the transmission trend can be predicted.

[0052] Embodiment 2:

[0053] Based on the above embodiments, the present invention is described in detail by taking a case study of sewage monitoring data in a certain area as follows.

[0054] Domestic sewage samples at fixed locations are monitored at certain time intervals to obtain virus content (copy / L) data, which are numerical. Among them, domestic sewage at points that are not tested every day has a null value for the test data. The original data set should also include the location number (such as Site 2A), the population information covered by the monitoring point (such as 63000), and the sampling time (such as 2022-01-02), as shown in Table 1.

[0055] Table 1 - Original dataset

[0056]

[0057]

[0058] The virus gene data and infected population data were converted to each other, and the data of the days without sampling were filled with null values. The converted data are shown in Table 2.

[0059] Table 2 - Transformed infected people dataset

[0060]

[0061] Data cleaning, the original data is processed by low-pass filtering, high-pass filtering, band-pass filtering, and band-stop filtering. According to the results, low-pass filtering is more suitable for processing data sets, and the effect is best when the filter order is set to 8 and the normalized cutoff frequency is set to 0.4. The null values ​​after filtering are processed, and the Akima method is used for interpolation, and the interpolate method of the filter_inter class is used. The data after filtering and interpolation are shown in Table 3.

[0062] Table 3 - Dataset after filtering and interpolation

[0063]

[0064]

[0065] The preprocessed data set can be directly used for parameter simulation of the SIR model, where S represents the number of susceptible people, I represents the number of infected people, R represents the number of recovered people, b represents the number of people that the infectious individual has fixed contact with every day, and k represents the proportion of people who recover from the infection every day. The solution of the differential equations of the SIR model is completed based on the Python Scipy module, and the curve fitting of the SIR model is completed by the nonlinear least squares method.

[0066]

[0067]

[0068]

[0069] For the reorganization of the data that cannot be fitted, the cumulative function transformation is used to find the corresponding logarithmic normal distribution parameter values ​​(μ, σ). The reconstructed data set results are as follows: Figure 2 shown.

[0070] The regional invasion ratio and block invasion and extinction rate are calculated, such as Figure 3 shown.

[0071] By coupling intra-plaque transmission and inter-plaque invasion in the same time series, we can warn the trend of overall transmission and infection in the entire region from the perspective of plaque invasion. As can be seen from the figure, based on the data of the region, the method of the present invention can predict the occurrence of the virus 42 days in advance. The results are as follows Figure 4 shown.

[0072] According to the block transmission dynamics characteristic parameters, the transmission coefficient (Rt) and the virus extinction time (t), a two-dimensional plaque optimal control map is constructed, such as Figure 5 .

[0073] The entire area is used as a breeding habitat for pathogenic microorganisms. The area is subdivided into different patches by monitoring the gene levels in the sewage. The gene levels in the patches are monitored to characterize the infection. The virus is transmitted between different communities through the infected population. Under the condition of full coverage of the entire area, dynamic monitoring and outbreak warning of population infection in different areas can be achieved, as well as regional hierarchical management of communities based on the differences in the virus's transmission dynamics coefficients in different community patches. The method of the present invention can predict and warn large-scale outbreaks 4-6 weeks in advance, and find communities where sustained outbreaks may occur. According to the transmission characteristic indicators of the virus in different areas, different areas are ranked for risk, and the allocation of social prevention and control resources is optimized.

[0074] Embodiment 3:

[0075] On the basis of the above-mentioned embodiments, a device for early prediction and early warning of large-scale outbreaks of pathogenic microorganisms is provided, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor. When the computer program is executed by the processor, each step of the above-mentioned method for early prediction and early warning of large-scale outbreaks of pathogenic microorganisms is implemented, and the same technical effect can be achieved. To avoid repetition, it will not be repeated here.

[0076] Embodiment 4:

[0077] A computer-readable storage medium is also provided, on which a computer program is stored. When the computer program is executed by a processor, the various steps of the aforementioned method for early prediction and early warning of large-scale outbreaks of pathogenic microorganisms are implemented, and the same technical effect can be achieved. To avoid repetition, it will not be repeated here.

[0078] Computer readable media include permanent and non-permanent, removable and non-removable media, and can be implemented by any method or technology to store information. Information can be computer readable instructions, data structures, program modules or other data. Examples of computer storage media include, but are not limited to, phase change memory (PR AM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, read-only compact disk read-only memory (CD-ROM), digital versatile disk (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined herein, computer readable media does not include temporary computer readable media (transitory media), such as modulated data signals and carrier waves.

[0079] The conventional techniques in the above embodiments are prior arts known to those skilled in the art, and thus will not be described in detail here.

[0080] The specific embodiments described herein are merely examples of the spirit of the present invention. Those skilled in the art may make various modifications or additions to the specific embodiments described or replace them in similar ways, but they will not deviate from the spirit of the present invention or exceed the scope defined by the appended claims.

[0081] Although the present invention has been described in detail and some specific embodiments have been cited, it is obvious to those skilled in the art that various changes and modifications can be made without departing from the spirit and scope of the present invention.

[0082] Although the above-mentioned specific embodiments have been shown, described and pointed out the novel features applied to various embodiments, it should be understood that various omissions, substitutions and changes can be made to the form and details of the described device or method without departing from the spirit of the present disclosure. In addition, the above-mentioned various features and methods can be used independently of each other, or can be combined in various ways. All possible combinations and sub-combinations are intended to fall within the scope of the present disclosure. Many of the above-mentioned embodiments include similar components, and therefore, these similar components are interchangeable in different embodiments. Although the present invention has been disclosed in the context of certain embodiments and examples, it should be understood by those skilled in the art that the present invention can extend beyond the specifically disclosed embodiments to other alternative embodiments and / or applications and obvious modifications and equivalents thereof. Therefore, the present invention is not intended to be limited by the specific disclosure of the preferred embodiments herein.

[0083] Matters not covered in the present invention are all known technologies.

Claims

1. A method for early prediction and early warning of large-scale outbreaks of pathogenic microorganisms. Features include: S100, monitor domestic sewage at a fixed location and obtain virus content data copy / L; S200, filtering and cleaning the data, and performing difference processing if there are missing values ​​after filtering and cleaning; S300, estimating the parameters of the virus dynamics transmission model in different plaques; after completing the interpolation process, when estimating the parameters of the virus dynamics transmission model in different plaques, further conversion is required, from virus transmission to human transmission, , where N a is the number of infections in the region on day a; c i is the virus content of sewage at the i-th sewage sampling point; n is the total number of sewage sampling points; q i is the number of people covered by the sewage sampling point at position i; p is the water consumption; S a is the viral emission per capita; S400, estimate the characteristics of virus transmission between plaques; determine whether there is virus gene data at the detection point, and establish a dynamic process to simulate the dynamic process of invasion and extinction: infection rate , invasion rate ; Where N is the total number of plaques that may be infected; n t is the number of infected plaques; c is the invasion rate; e is the virus transmission and extinction rate; t is the time of transmission or extinction; S500, build a priority management method, first determine the characteristic parameters Rt and e of the transmission within the patch, build a two-dimensional risk control map, and divide different patches into transmission damping areas, high-risk areas, and low-risk areas; , where R t is the number of people a positive infected person can infect within a specific period of time; β is the probability constant of actual contact and infection per unit time, which is called the infection rate; γ is the probability constant of each infected patient recovering per unit time, which is called the recovery rate; , where e is the virus transmission and extinction rate; M is the number of infected people; t is the time of transmission or extinction; S600 integrates the virus dynamics transmission model parameters in different plaques, the virus transmission characteristics between plaques and the priority management methods to show the dynamic growth and extinction process of the virus in a large plaque collection and predict the transmission trend.

2. The method for early prediction and early warning of large-scale outbreaks of pathogenic microorganisms according to claim 1, Features: In step S100, if the virus content data copy / L is obtained by interval sampling, the data has a null value problem, and the per capita virus content c on that day is calculated. mean , and fill in the empty values, , where c a is the non-empty data of the virus content in sewage on day a; p a is the regional population corresponding to the non-empty data on day a.

3. The method for early prediction and early warning of large-scale outbreaks of pathogenic microorganisms according to claim 2, Features: In step 200, the data noise peaks are cleaned by filtering, specific frequencies are effectively extracted, and specific processing actions are performed on the extracted parts, including gain, attenuation, and filtering.

4. The method for early prediction and early warning of large-scale outbreaks of pathogenic microorganisms according to claim 1, Features: In step S300, if the model parameters cannot be simulated due to problems such as missing data, the following steps are performed: first, the number of infections is normalized and its cumulative distribution function is calculated; the cumulative distribution function of the normal distribution is used for fitting, and μ and σ in the equation are obtained through curve fitting; then μ and σ are substituted into the log-normal distribution function to calculate the number of infections on the corresponding date; finally, the obtained data set is refitted with the SIR model dynamic parameters to obtain the parameters of the model.

5. The method for early prediction and early warning of large-scale outbreaks of pathogenic microorganisms according to claim 1, Features: In step S600, the dynamic growth and extinction process of the virus in a large plaque set is displayed, and the transmission trend is predicted by executing the following steps: first, the actual number of infected people is used to constrain the number of infected people simulated by the model in a random process, and a joint distribution diagram of the plaque invasion rate and the number of infected people under the same time series is drawn. The plaque invasion and individual infection trends are observed, and the early warning effect of cluster theory is judged. The plaque invasion is divided into an expansion period and an extinction period. The infection coefficient and the extinction coefficient can be used to calculate the virus invasion ratio and the maximum invasion capacity in different regions, and the transmission trend can be predicted.

6. A device for predicting and warning of large-scale outbreaks of pathogenic microorganisms in advance, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, Features: When the computer program is executed by the processor, each step of the method for early prediction and early warning of large-scale outbreaks of pathogenic microorganisms according to any one of claims 1 to 5 is implemented.

7. A computer-readable storage medium having a computer program stored thereon, Features: When the computer program is executed by a processor, each step of the method for early prediction and early warning of large-scale outbreaks of pathogenic microorganisms according to any one of claims 1 to 5 is implemented.

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