New avian influenza high-risk host species identification method based on virus sharing entropy

Abstract

The invention relates to the technical field of bird flu host species identification, in particular to a new bird flu high-risk host species identification method based on virus sharing entropy. According to the method, on the basis of spatial distribution prediction of bird flu host species, the bird flu virus sharing entropy of the bird flu host species is calculated species by species according to the overlapping areas of the target host species and all other host species by extracting the distribution area overlapping area of the target host species and any other host species, and the bird flu virus sharing entropy of the bird flu host species is obtained. And quantitatively analyzing the possibility that the target host species and other host species are subjected to avian influenza cross-species transmission infection. By comparing and analyzing the entropy value of the shared entropy of the avian influenza virus, the avian influenza host species with high virus reassortment risk are identified, and a scientific basis is provided for prevention and control of epidemic diseases.

Description

Technical Field

[0001] The present invention relates to the technical field of identifying avian influenza host species, and particularly relates to a method for identifying high-risk host species of emerging avian influenza based on virus sharing entropy. Background Art

[0002] Avian influenza is an infectious disease of poultry caused by influenza A virus, which occurs and spreads among a variety of different types of animals and humans, with a complete diversity of host groups. Traditional methods for identifying high-risk host species of avian influenza and assessing their transmission and spillover risks rely on long-term epidemiological investigations, that is, by collecting samples, virus isolation, virus detection, and virus pathogenicity analysis to determine the host species of avian influenza virus, and using virus detection rates, virus loads, clinical manifestations after virus infection, mortality rates, virus pathogenic subtypes, and pathological sections, etc. to judge whether the host species is a high-risk host species. However, such methods mostly focus on the identification of avian influenza risk host species at the local scale, and require a large amount of human and material costs, while greatly increasing the risk of investigators being infected with avian influenza. More importantly, the variability of avian influenza virus poses new challenges to the identification of high-risk host species and the assessment of transmission and spillover risks, and in the past, high-risk host identification based on epidemiological investigations often needed to form identification results after the host transmitted the virus and became ill, and it was impossible to achieve timely ultra-early prediction and prevention.

[0003] Meanwhile, in the context of global change, the spatio-temporal distribution ranges of species are undergoing significant changes. Such changes not only affect the habitats of species but also further reshape the spatio-temporal overlaps among species, thereby increasing the likelihood of the emergence and spread of new viruses. To address this challenge, methods based on large-scale prediction models for simulating the spatial risks and hotspots of virus spillover transmission have emerged. Existing studies have predicted the changes in the geographical distribution ranges of 3,139 mammalian species by 2070 under different climate change and land use scenarios through constructing species distribution models (SDMs). By comparing the current and future predicted geographical distribution ranges, species pairs and specific geographical regions that do not currently have geographical overlaps but may have them in the future are identified. Such overlap changes mean that species that were originally geographically isolated will encounter each other for the first time in new areas, and such first encounters may become opportunities for cross-species virus transmission, thus increasing the risk of virus spillover transmission. This method uses spatial units as the evaluation units and the number of first encounters of different host species within the evaluation space as the identification indicator for high-risk areas of emerging infectious diseases. Although significant progress has been made in evaluating the spatial risks and hotspots of virus spillover transmission by large-scale prediction models, existing methods mainly evaluate the virus spillover transmission risks within a specific space through the number of "first encounters" of host species within a unit space, and no accurate and effective evaluation indicators and methods for the risk levels of specific host species have been formed. At the same time, existing methods and indicators have not fully considered the special role and complexity of avian influenza virus reassortment in avian influenza spillover and pathogenicity when evaluating virus spillover transmission risks, making it difficult to accurately and effectively identify high-risk host species for emerging avian influenza. As a result, the existing evaluation methods based on the distribution of host species and their spatio-temporal overlapping contacts still have blind spots and defects in identifying high-risk host species for emerging avian influenza.

[0004] Avian influenza virus is a segmented, negative-sense, single-stranded RNA virus, and its genome consists of 8 independent RNA segments. The replication and transcription of the virus rely on the virus's own RNA polymerase, which has a relatively high error rate, leading to easy variation of the virus during replication and transcription. Avian influenza virus can mutate through genetic reassortment. When different subtypes of influenza viruses co-infect the same host, gene segments can exchange with each other to form new virus subtypes. Genetic reassortment is a key factor in the large-scale epidemic of influenza virus. It can enable avian influenza virus to adapt to different species, evade immune surveillance, and may also lead to the emergence of new virus strains with stronger transmission ability and pathogenicity, promoting the continuous evolution of the virus during cross-species transmission, and then triggering new avian influenza outbreaks. Since there are often differences in the avian influenza virus typing stored and prevalent within different avian influenza host species, during the cross-species transmission of avian influenza, it often causes two different typed viruses to infect the same host individual, and dangerous virus reassortment often occurs during cross-species transmission. With the increasingly serious public health safety issues, there is an urgent need to accurately and efficiently identify high-risk host species of newly emerging avian influenza globally.

[0005] Based on the spatial distribution prediction of avian influenza host species, by quantifying the spatio-temporal overlap and contact possibility in space of host species carrying different typed avian influenza viruses, the possibility of virus reassortment and the risk of newly emerging avian influenza diseases are further evaluated, high-risk reassortment and host species prone to newly emerging avian influenza diseases are identified, providing a scientific reference and basis for public health decision-making and efficient implementation of epidemiological investigations. Summary of the Invention

[0006] Aiming at the above defects, the purpose of this application is to be based on the spatial distribution prediction of avian influenza host species. By extracting the overlapping area of the distribution areas of the target host species and any other host species, according to the overlapping areas of the target host species and all other host species, calculate the avian influenza virus sharing entropy of avian influenza host species species by species, and quantitatively analyze the possibility of cross-species transmission and infection of avian influenza between the target host species and other host species. By comparing and analyzing the entropy values of the avian influenza virus sharing entropy, identify avian influenza host species with high virus reassortment risk, providing a scientific basis for disease prevention and control. Specifically as follows:

[0007] A method for identifying high-risk host species of newly emerging avian influenza based on virus sharing entropy, comprising the following steps:

[0008] S1. Establish a distribution model of avian influenza host species;

[0009] S2. Use the avian influenza host species distribution model to predict the spatial distribution of avian influenza host species. According to the spatial distribution of avian influenza host species, conduct overlapping analysis of the distribution areas of the target host species and any other host species one by one;

[0010] S3. Extract the overlapping area of the distribution areas of the target host species and any other host species, construct the spatial overlapping area matrix of avian influenza host species, and calculate the avian influenza virus sharing entropy species by species;

[0011] S4. Evaluate the prediction reliability of the virus reassortment risk of host species based on the data of the richness of virus subtypes detected in host species;

[0012] S5. Conduct a statistical comparison of the entropy values of the avian influenza virus sharing entropy for all evaluated host species to identify host species with high virus reassortment risk.

[0013] Further, the establishment of the avian influenza host species distribution model described in S1 is specifically as follows:

[0014] (1) Obtain the list of avian influenza avian host species;

[0015] (2) Define the habitat preference functional groups of avian influenza avian host species and determine the land use percentage;

[0016] (3) Obtain and screen the distribution points of avian influenza avian host species;

[0017] (4) Obtain environmental data to determine environmental covariates;

[0018] (5) Obtain the species habitats according to the biogeographical boundaries, randomly select the same number of species non-occurrence points as the occurrence points of avian influenza avian host species in non-habitat areas to construct a point dataset, use the geographical information of the species occurrence points and non-occurrence points, combine with the environmental covariates, and add the land use percentage as a key driving factor to simulate the spatial distribution of avian influenza host species and obtain the avian influenza host species distribution model.

[0019] Further, the calculation formula of the avian influenza virus sharing entropy described in S3 is:

[0020]

[0021]

[0022] In the formula:

[0023] i is the number of other host species within the distribution range of the target host species,

[0024] is the spatial overlapping area of the distribution of the target host species and any other host species,

[0025] p i is the relative importance of the spatial overlap of the distribution of the target host species and any other host species.

[0026] Furthermore, the prediction reliability for assessing the reassortment risk of viruses in host species is specifically as follows: A linear regression model is constructed using the richness data of detected virus subtypes in host species and the shared entropy of avian influenza viruses. Through linear regression analysis, the prediction reliability of the reassortment risk of viruses in host species is evaluated based on R 2 and RMSE, where:

[0027]

[0028]

[0029]

[0030] In the formula: SSR is the sum of squared residuals, SST is the total sum of squares, y i is the actual value, is the predicted value, is the mean of the actual values;

[0031]

[0032] In the formula: y i is the actual value, is the predicted value.

[0033] Furthermore, identifying hosts with a high reassortment risk is as follows: The entropy values of the shared entropy of avian influenza viruses in all evaluated host species are statistically compared, and host species with a shared entropy value of avian influenza virus higher than the overall mean are identified as newly emerging avian influenza high-risk host species with a reassortment risk.

[0034] Beneficial effects: By collecting big data on species occurrence networks (including citizen observation data, literature reports, and field surveys, etc.), the present invention obtains the overlapping area of the distribution areas of the target host species and any other host species based on the avian influenza host species distribution model, and quantifies the diversity of other host species with overlapping distribution spaces within the distribution range of the target host species. Through the avian influenza virus sharing entropy calculated species by species, the possibility of cross-species transmission and infection of avian influenza between the target host species and other host species is effectively quantified, thereby identifying newly emerging highly risky hosts for avian influenza with a higher likelihood of virus reassortment. Compared with traditional methods for identifying highly risky host species for avian influenza, the present invention not only greatly reduces the required human and material costs, and reduces the risk of avian influenza infection for assessment and investigation personnel, but also can accurately and efficiently quantify and evaluate the risk degree of avian influenza virus reassortment in host species, and identify avian influenza host species with high virus reassortment risks, having significant advantages and innovation. In the context of the increasingly severe era of public health safety issues, the present invention has important public health significance, provides scientific support for the precise prevention and control and early warning of avian influenza viruses, can identify highly risky host species before and during the epidemic of avian influenza, so as to more centrally and efficiently invest investigation and management resources, or provide scientific guidance for avoiding exposure of highly risky personnel / livestock and poultry. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] Figure 1 is a schematic flow chart of the identification method of the present invention;

[0036] Figure 2 is a schematic diagram of the overlapping analysis of the distribution areas of the target host species and any other host species;

[0037] Figure 3 is a matrix of the spatial overlapping areas of host species of the genus Anas;

[0038] Figure 4 Linear regression analysis of avian influenza Anas host. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0039] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without making creative efforts fall within the scope of protection of the present invention.

[0040] Unless otherwise specifically stated, the relative arrangements, numerical expressions, and numerical values of the components and steps set forth in these embodiments do not limit the scope of the present application. At the same time, it should be understood that, for the sake of convenience in description, the sizes of the various parts shown in the drawings are not drawn in actual proportional relationships. Technologies, methods, and devices known to those of ordinary skill in the relevant art may not be discussed in detail, but where appropriate, the said technologies, methods, and devices should be regarded as part of the authorized specification. In all the examples shown and discussed here, any specific value should be construed as merely exemplary and not as a limitation. Therefore, other examples of the exemplary embodiments may have different values. It should be noted that like reference numerals and letters denote like items in the following drawings, and thus, once an item is defined in one drawing, it does not need to be further discussed in subsequent drawings.

[0041] Unless otherwise specified, the meanings of the scientific and technical terms in this specification are the same as those generally understood by those skilled in the art. However, in case of conflict, the definitions in this specification shall prevail.

[0042] The term "avian influenza" refers to an infectious disease caused by avian influenza viruses, which mainly affects birds, especially poultry. Avian influenza viruses can be transmitted through direct contact with birds and their secretions, or through contaminated environments, and can also be transmitted across species to mammals, including humans, causing severe respiratory diseases.

[0043] The term "high-risk host" refers to an organism that is susceptible to a specific pathogen and can efficiently transmit the pathogen.

[0044] The term "virus sharing" refers to the process by which viruses are transmitted and passed between different hosts.

[0045] The term "emerging infectious disease" refers to an infectious disease that has emerged for the first time in recent years, or has re-emerged in a specific region and poses a major threat to public health. Such diseases may originate from new pathogens or may be new variants of known pathogens.

[0046] The term "emerging zoonosis" refers to a pathogen that was originally transmitted only among animals but has crossed the species barrier due to various factors and spread to humans, causing diseases.

[0047] The term "citizen bird watching big data" refers to the data collected by the general public when observing and recording bird information. These data are mainly shared and recorded through bird watching activities, bird monitoring projects, or online network platforms (such as eBird).

[0048] The term "species distribution model", also known as the niche model, is an important tool in the field of ecology for predicting species distribution. The species distribution model predicts and simulates the species distribution of a species over a wider geographical range by analyzing the location data of the presence and absence points of the species and combining environmental factor covariates.

[0049] The term "host species distribution range" refers to the geographical area where a certain host species can survive and reproduce in a specific region or environment, which is usually affected by a combination of factors such as climate, terrain, and ecological conditions.

[0050] The term "interspecies transmission" refers to the process by which a pathogen spreads from one species to another species, which may trigger the emergence of a new infectious disease.

[0051] The term "avian influenza virus reassortment" refers to the exchange of gene segments between different avian influenza viruses in the same host, forming a new virus strain. Reassortment may lead to virus variation, producing new virus strains with stronger transmission ability or higher pathogenicity, increasing the risk of influenza pandemic.

[0052] The term "avian influenza high-risk host" refers to animals that are susceptible to infection and transmission of avian influenza virus. These animals are not only important carriers of avian influenza virus but also potential sources of human infection.

[0053] The first embodiment of the present application discloses a method for identifying a new avian influenza high-risk host species based on virus sharing entropy, including the following steps:

[0054] S1. Establish an avian influenza host species distribution model;

[0055] S2. Use the avian influenza host species distribution model to predict the spatial distribution of avian influenza host species. According to the spatial distribution of avian influenza host species, perform distribution area overlap analysis on the target host species and any other host species one by one;

[0056] S3. Extract the distribution area overlap area between the target host species and any other host species, construct an avian influenza host species spatial overlap area matrix, and calculate the avian influenza virus sharing entropy species by species;

[0057] S4. Evaluate the prediction reliability of the virus reassortment risk of the host species based on the data of the richness of the detected virus subtypes of the host species;

[0058] S5. Perform entropy value statistical comparison of the avian influenza virus sharing entropy of all evaluated host species to identify high virus reassortment risk hosts.

[0059] Among them, the establishment of the avian influenza host species distribution model in S1 is specifically as follows:

[0060] (1) Obtain a list of avian influenza bird host species;

[0061] (2) Define the habitat preference functional groups of avian influenza bird host species and determine the percentage of land use;

[0062] (3) Obtain and screen the distribution points of avian influenza bird host species;

[0063] (4) Obtain environmental data to determine environmental covariates;

[0064] (5) Obtain the species habitats according to biogeographical boundaries, randomly select the same number of species non-occurrence points as the occurrence points of avian influenza bird host species in non-habitat areas to construct a point dataset, use the geographical information of species occurrence points and non-occurrence points, combine with environmental covariates, add the percentage of land use as a key driving factor, simulate the spatial distribution of avian influenza host species, and obtain the avian influenza host species distribution model.

[0065] As Figure 1 shown, in this embodiment, the obtaining of the avian influenza bird host list includes three steps: defining the host range to be evaluated for risk level → confirming the host status of the species to be evaluated → obtaining the avian influenza bird host species list.

[0066] 1. Define the host range to be evaluated for risk level

[0067] Before evaluating the host risk level, it is necessary to clearly define the range of the host to be evaluated, which can be defined from the taxonomic dimension or the geographical range dimension, etc. In a specific evaluation, from the taxonomic perspective, such as determining the taxonomic group range of the host to be evaluated according to the levels of order, family, genus, etc. in the biological classification system, to evaluate which species within a certain order, family or genus are high-risk host species. From the geographical range perspective, the species within a specific spatial range can be defined as the host to be evaluated according to spatial boundaries, such as specific natural geographical regions, administrative regions such as protected area boundaries or provincial boundaries, to evaluate which species within a certain spatial range such as a protected area or an administrative region are high-risk host species.

[0068] 2. Confirm the host status of the species to be evaluated

[0069] The host status refers to whether a specific host species is infected with avian influenza and the richness of the detected avian influenza virus subtypes. Through the influenza virus database of the National Center for Biotechnology Information (NCBI), obtain the data on the richness of the detected virus subtypes of the host species to confirm the host status of the species to be evaluated.

[0070] 3. Obtain the avian influenza bird host species list

[0071] According to the host status of the species to be evaluated, the species with the richness of avian influenza virus subtypes detected greater than zero are identified as avian influenza bird host species. On this basis, a complete list containing the Chinese names and Latin names of avian influenza bird host species is further compiled.

[0072] In this embodiment, the establishment of the avian influenza host species distribution model incorporates environmental factors as covariates and land use percentages as key driving factors to simulate the spatial distribution of avian influenza host species. Among them, the land use percentage is obtained by delimiting the habitat preference functional groups of avian influenza bird host species, and the environmental covariates are obtained by acquiring environmental data and performing Pearson correlation analysis and importance value evaluation. Specifically:

[0073] 4. Delimit the habitat preference functional groups of avian influenza bird host species

[0074] According to the habitat preferences of avian influenza bird host species, the avian influenza bird host species are divided into seven host habitat preference functional groups: water body, forest land, shrubbery, grassland, farmland, bare land, and urban area.

[0075] Table 1 Habitat preference functional groups of avian influenza bird host species

[0076]

[0077] It should be noted that the land use percentage has different driving parameters according to the different habitat preferences of avian influenza bird host species. As shown in Table 1, the driving parameter of the land use percentage for bird host species with water bodies as the main habitat is the water body percentage or water area, and the driving parameter of the land use percentage for bird host species with forest land as the main habitat is the forest percentage, etc.

[0078] 5. Obtain the distribution point data of avian influenza bird host species

[0079] The distribution point data of avian influenza bird host species are obtained through field surveys, literature reports, and public databases such as GBIF and eBird. The required data need to meet: (1) including observation records such as human observations and machine observations; (2) being able to obtain species names, species classifications, observation times, and longitude and latitude coordinate information.

[0080] 6. Screen the distribution points of avian influenza bird host species

[0081] Screen the distribution points of avian influenza bird host species. The required distribution points need to meet the following conditions: (1) Remove the observation records with empty or duplicate longitude and latitude; (2) Remove the outliers according to biogeographical boundaries such as Wallace's Line and the living range of birds to avoid observation errors; (3) Remove the redundant points according to the grid resolution so that only one distribution point is retained in one grid cell; (4) Remove the species with less than 10 observation records.

[0082] 7. Obtain environmental data, conduct Pearson correlation analysis and importance value evaluation, and resample

[0083] Environmental data are environmental factors related to the survival and distribution of avian influenza bird host species, including climate variables, altitude, and land use types, etc. Among them, climate variables and altitude are important components of the natural environment, while land use types reflect the impact of human activities on the environment. Climate variables and altitude are obtained through the WorldClim database, where the climate data are taken as the average value from 1970 to 2000 and contain 19 climate variables (as shown in Table 2). Remove the highly collinear environmental data according to the Pearson correlation matrix as the environmental covariates for subsequent species spatial distribution simulation to avoid model overfitting. Resample to an appropriate spatial resolution.

[0084] Land use data are simulated through the GLOBIO model (v3.5) at a resolution of 0.5°, including seven land classes: water body, forest land, shrub, grassland, farmland, bare land, and urban area. On this basis, a Generalized Linear Model (GLM) is established, and the importance values of each variable of the climate variables, altitude, and land classes after removing the highly collinear ones are evaluated through AIC (Akaike information criterion), and the top five variables with importance values are selected as the final environmental covariates.

[0085] Table 2 Climate variables

[0086]

[0087] Establish the avian influenza host species distribution model according to the above method. Then, conduct an analysis of the overlap area of the distribution areas between the target host species and any other host species - extract the overlap area of the distribution areas between the target host species and any other host species - construct the spatial overlap area matrix of avian influenza host species, calculate the avian influenza virus sharing entropy species by species - evaluate the prediction reliability - so as to identify the hosts with high virus reassortment risk, specifically:

[0088] 9. According to the spatial distribution of avian influenza host species, conduct an analysis of the overlap area of the distribution areas between the target host species and any other host species one by one

[0089] By overlaying the distribution ranges of avian influenza host species, the distribution area overlap analysis is carried out for the target host species and any other host species one by one. The schematic diagram of the distribution area overlap analysis between the target host species and any other host species is as Figure 2 shown.

[0090] 10. Extract the distribution area overlap area between the target host species and any other host species, construct the spatial overlap area matrix of avian influenza host species, and calculate the avian influenza virus sharing entropy for each species

[0091] Extract the distribution area overlap area between the target host species and any other host species, construct the spatial overlap area matrix of avian influenza host species, improve the calculation method of the Shannon index, and construct the avian influenza virus sharing entropy (ASE) algorithm. The formula is as follows:

[0092]

[0093] In the formula:

[0094] i is the number of other host species within the distribution range of the target host species,

[0095] is the distribution space overlap area between the target host species and any other host species,

[0096] p i is the relative importance of the distribution space overlap between the target host species and any other host species

[0097] 11. Evaluate the prediction reliability of the virus reassortment risk of host species based on the data of the richness of detected virus subtypes in host species

[0098] Construct a linear regression model by using the data of the richness of detected virus subtypes in host species and the avian influenza virus sharing entropy. Through linear regression analysis, evaluate the prediction reliability of the virus reassortment risk of host species based on R 2 and RMSE. The formula is as follows:

[0099]

[0100]

[0101]

[0102] In the formula:

[0103] SSR is the sum of squared residuals,

[0104] SST is the total sum of squares,

[0105] y i is the actual value,

[0106] is the predicted value,

[0107] is the mean of the actual values

[0108] R 2 is the proportion of the variance explained by the model in the total variance and is usually used to evaluate the goodness of fit of the model. R 2 ranges from 0 to 1, and the higher the value, the better the goodness of fit of the model.

[0109]

[0110] In the formula:

[0111] y i is the actual value,

[0112] is the predicted value

[0113] RMSE is used to evaluate the difference between the predicted value of the model and the actual observed value. The smaller the RMSE, the closer the predicted value of the model is to the actual value, and the higher the accuracy of the model.

[0114] 12. Statistically compare the entropy values of the avian influenza virus sharing entropy for all evaluated host species to identify hosts at high risk of virus reassortment

[0115] Based on the characteristics that species with higher avian influenza virus sharing entropy have a higher likelihood of carrying and infecting different subtypes of avian influenza viruses and a higher probability of generating new avian influenza reassortant viruses compared to species with lower avian influenza virus sharing entropy, host species with avian influenza virus sharing entropy values higher than the overall mean are identified as high-risk host species for emerging avian influenza with reassortment risk.

[0116] The present invention will be described in detail below in conjunction with the embodiments and the accompanying drawings. However, it should be understood that the embodiments and the drawings are only used for exemplary description of the present invention and do not constitute any limitation to the protection scope of the present invention. All reasonable transformations and combinations within the scope of the inventive concept of the present invention fall within the protection scope of the present invention.

[0117] The host range for determining the risk level to be evaluated is birds of the genus Anas. Through the influenza virus database of the National Center for Biotechnology Information (NCBI), data on the richness of virus subtypes detected in host species of the genus Anas were obtained to confirm the host status of the species to be evaluated, as shown in Table 3. Based on the host status of the species to be evaluated, Anas species with a richness of detected avian influenza virus subtypes greater than zero were identified as avian influenza Anas host species. On this basis, a complete list containing the Chinese and Latin names of avian influenza Anas host species was further compiled. Considering that the habitat preference of avian influenza Anas host species is water bodies, the habitat preference functional group of avian influenza Anas host species was defined. Data on the distribution points of avian influenza Anas host species were obtained, and the distribution points of avian influenza Anas host species were screened to obtain the final simulated list of Anas hosts, as shown in Table 4.

[0118] Table 3 Host status of the taxon Anas to be evaluated

[0119]

[0120]

[0121] Table 4 Anas hosts

[0122]

[0123] To reduce the collinearity among climate variables, Pearson correlation analysis was performed on 19 climate variables using "ENMTools" in the R software. Four environmental data, namely the variance of temperature change (BIO4), the mean temperature of the warmest quarter (BIO10), the annual mean precipitation (BIO12), and the mean precipitation of the coldest month (BIO19), were selected to avoid overfitting of the model. A Generalized Linear Model (GLM) was established, and the importance values of the climate variables, altitude, and land use types after removing high collinearity were evaluated through AIC (Akaike information criterion). The top five variables with the highest importance values were selected as the final environmental covariates. A distribution model for avian influenza Anas host species was established. Using the geographical information of species occurrence points and non-occurrence points, combined with environmental covariates, and adding the percentage of water bodies as a key driving factor, the spatial distribution of avian influenza Anas host species was predicted. Based on the spatial distribution of avian influenza Anas host species, an analysis of the overlap of the distribution areas between the target host species and any other host species was carried out, the overlapping area of the distribution areas between the target host species and any other host species was extracted, and a spatial overlap area matrix of avian influenza host species was constructed, as Figure 3As shown in the (Spatial overlap area matrix of host species in the genus Anas). Calculate the shared entropy of avian influenza viruses species by species, as shown in Table 5. Construct a linear regression model with the data of the richness of detected virus subtypes in host species and the shared entropy of avian influenza viruses. The constructed model is as Figure 4 shown to evaluate the prediction reliability of the risk of virus reassortment in host species within the Anas genus.

[0124] Table 5 Richness of detected virus subtypes in host species of the genus Anas and shared entropy of avian influenza viruses

[0125]

[0126] There is a significant positive correlation between "richness of detected virus subtypes in host species" and "shared entropy of avian influenza viruses" (P < 0.01). Among them, the variation explained by the model accounts for 49% of the total variation, and the RMSE is 24.05. The prediction reliability of the risk of virus reassortment in the target host species is relatively high.

[0127] Based on the shared entropy of avian influenza viruses, identify the host species of the genus Anas with a shared entropy value higher than the overall mean as high-risk host species for virus reassortment. The mean shared entropy of avian influenza viruses in host species of the genus Anas is 4.107. Among the host species of the genus Anas with avian influenza, Anas platyrhynchos, Anas acuta, Anas crecca, Anas carolinensis, Anas rubripes, Anas zonorhyncha, Anas fulvigula, and Anas poecilorhyncha are high-risk host species for virus reassortment, as shown in Table 6.

[0128] Table 6 High-risk host species for virus reassortment within the genus Anas

[0129]

[0130] As the main natural host of avian influenza virus among waterfowl, the mallard has a relatively high virus carriage rate. In surveillance surveys in North America and Europe, the detection rate of avian influenza virus in mallards during the migration season can reach 10%-30%. The detection rate of avian influenza virus in green-winged teals is similar to that of mallards, about 10%-25%, and it is a potential carrier of highly pathogenic avian influenza (HPAI) H5N1. In addition, the surveillance survey data of other duck species (such as spot-billed ducks and pintails) show that in Asia and North America, the detection rate of avian influenza virus in these duck species during the migration season is about 5%-15%, which is similar to the results of the examples. Moreover, compared with the traditional method for identifying high-risk host species of avian influenza, the example not only greatly reduces the human and material costs required, reduces the risk of avian influenza infection for evaluation and investigation personnel, but also can accurately and efficiently quantify and evaluate the risk degree of avian influenza virus reassortment in host species, and identify avian influenza host species with high virus reassortment risk.

[0131] As described above, this is only the specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any changes or substitutions that can be easily thought of by those skilled in the art within the technical scope disclosed by the present invention should be covered within the protection scope of the present invention. Therefore, the protection scope of the present invention should be subject to the protection scope of the claims.

Claims

1. A method for identifying high-risk host species of newly emerging avian influenza based on virus sharing entropy, characterized in that, It includes the following steps: S1. Establish an avian influenza host species distribution model; S2. Use the avian influenza host species distribution model to predict the spatial distribution of avian influenza host species. According to the spatial distribution of avian influenza host species, conduct an analysis of the overlap of the distribution areas of the target host species and any other host species one by one; S3. Extract the overlapping area of the distribution areas of the target host species and any other host species, construct a matrix of the spatial overlapping areas of avian influenza host species, and calculate the avian influenza virus sharing entropy species by species; S4. Evaluate the prediction reliability of the risk of viral reassortment in host species based on the data of the richness of detected virus subtypes in host species; S5. Conduct a statistical comparison of the entropy values of the avian influenza virus sharing entropy for all evaluated host species to identify host species with a high risk of viral reassortment.

2. The recognition method according to claim 1, wherein The establishment of the avian influenza host species distribution model described in S1 is specifically as follows: (1) Obtain a list of avian influenza avian host species; (2) Define the habitat preference functional groups of avian influenza avian host species and determine the percentage of land use; (3) Obtain and screen the distribution points of avian influenza avian host species; (4) Obtain environmental data to determine environmental covariates; (5) Obtain the species habitats according to the biogeographical boundaries. Randomly select an equal number of species non-occurrence points in non-habitat areas to construct a point dataset. Using the geographical information of species occurrence points and non-occurrence points, combined with environmental covariates, and adding the percentage of land use as a key driving factor, simulate the spatial distribution of avian influenza host species to obtain the avian influenza host species distribution model.

3. The recognition method according to claim 1, characterized in that The calculation formula of the avian influenza virus sharing entropy described in S3 is: In the formula: i is the number of other host species within the distribution range of the target host species, is the overlapping area of the distribution space between the target host species and any other host species, p i is the relative importance of the spatial overlap between the target host species and any other host species.

4. The recognition method according to claim 1, wherein The prediction reliability for assessing the reassortment risk of viruses in host species specifically is: constructing a linear regression model with the data of the richness of virus subtypes detected in host species and the shared entropy of avian influenza viruses, and through linear regression analysis, assessing the prediction reliability of the reassortment risk of viruses in host species based on R 2 and RMSE, where: Where: SSR is the sum of squared residuals, SST is the total sum of squares, y i is the actual value, is the predicted value, is the mean of the actual values; where: y i is the actual value, is the predicted value.

5. According to the recognition method as described in claim 1, characterized in that Identifying host species with a high risk of viral reassortment is: Conduct a statistical comparison of the entropy values of the avian influenza virus sharing entropy for all evaluated host species, and identify host species with an avian influenza virus sharing entropy value higher than the overall average as newly emerging avian influenza high-risk host species with a risk of reassortment.

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