Animal monkey pox epidemic situation epidemic situation assessment method based on monkey pox virus detection data

Abstract

The invention discloses an animal monkey pox epidemic situation assessment method based on monkey pox virus detection data, and the method comprises the steps: obtaining multi-source monkey pox virus detection data, carrying out the data preprocessing, determining monkey pox epidemic situation points, extracting the time characteristics and spatial characteristics of each monkey pox epidemic situation point, carrying out the clustering of the monkey pox epidemic situation points, and obtaining a monkey pox epidemic situation assessment result. Extracting influence factors related to the animal monkey pox epidemic situation from the monkey pox epidemic situation points in the same cluster by utilizing correlation analysis according to a clustering result; performing space-time analysis on the influence factors by using a graph neural network, and analyzing and defining corresponding space-time factors according to the risk space-time characteristics of the animal monkey pox epidemic situation; and constructing a monkey pox risk prediction model according to the influence factors and the time-space factors, and obtaining the risk prediction level of the animal monkey pox epidemic situation at the target monkey pox epidemic situation point for epidemic situation assessment. The epidemic situation is predicted and evaluated based on the time-space factors and the influence factors of the monkey pox epidemic situation, and a scientific basis is provided for reasonably configuring and utilizing medical and health resources.

Description

Technical Field

[0001] The present invention relates to the field of deep learning and epidemiological technology, and more specifically, to a method for evaluating the epidemic situation of animal monkeypox based on monkeypox virus detection data. Background Art

[0002] Monkeypox is a rare zoonosis characterized by skin rash. It is an infectious disease caused by monkeypox virus, which belongs to the genus Orthopoxvirus of the Poxviridae family. Monkeypox virus can be transmitted from person to person and from animal to person. Genome sequencing analysis of viral DNA shows that monkeypox virus mainly includes two subtypes (monkeypox virus type I and monkeypox virus type II), and the currently prevalent one is monkeypox virus type I. For the prevention and control of monkeypox virus, WHO is developing or partially developing interim guidelines to support member states in raising awareness, surveillance, laboratory diagnosis and testing, case investigation and contact tracing, clinical management and infection prevention and control, vaccines and immunization, as well as risk communication and community engagement.

[0003] Traditional infectious disease prediction mainly relies on traditional epidemiological models and statistical methods, such as SEIR model and ARIMA model. However, these methods are based on assumptions and simplifications and cannot fully capture the complex factors and nonlinear associations in the spread of the epidemic. In recent years, with the development of artificial intelligence technology, deep learning models have shown great potential in epidemic prediction. Deep learning models can automatically learn and extract high-level features from epidemic data, so as to better understand and predict the patterns and trends of epidemic spread. Monkeypox epidemic data may be interfered with and noisy by various factors, such as reporting delays, missing data, and erroneous reporting. The stability and robustness of the model are also issues that need to be considered. Therefore, how to aggregate multi-source monkeypox virus detection data to evaluate the epidemic situation of monkeypox is an urgent problem to be solved. Summary of the invention

[0004] In order to solve the above technical problems, the present invention proposes a method for evaluating the epidemic situation of animal monkeypox epidemic based on monkeypox virus detection data. Based on the risk spatiotemporal transmission characteristics of the animal monkeypox epidemic, the spatiotemporal correlation of the animal monkeypox epidemic is analyzed and the corresponding spatiotemporal factors and influencing factors are defined, so as to predict and evaluate the epidemic situation, and provide a scientific basis for the rational allocation and utilization of medical and health resources.

[0005] The present invention provides a method for evaluating the epidemic situation of animal monkeypox based on monkeypox virus detection data, comprising the following steps:

[0006] Acquire multi-source monkeypox virus detection data and perform data preprocessing, determine monkeypox epidemic points based on the preprocessed multi-source monkeypox virus detection data, and extract the temporal and spatial characteristics of each monkeypox epidemic point;

[0007] Clustering the monkeypox epidemic sites based on the temporal and spatial characteristics, and extracting influencing factors related to the animal monkeypox epidemic using correlation analysis for the monkeypox epidemic sites in the same cluster according to the clustering results;

[0008] Use graph neural networks to conduct spatiotemporal analysis of the influencing factors, analyze the spatiotemporal correlation of the animal monkeypox epidemic and define corresponding spatiotemporal factors based on the spatiotemporal characteristics of the risk of the animal monkeypox epidemic;

[0009] A monkeypox risk prediction model is constructed based on the influencing factors and spatiotemporal factors to obtain the risk prediction level of the animal monkeypox epidemic at the target monkeypox epidemic point, and the epidemic situation is evaluated based on the changes between the risk prediction level and the current risk level.

[0010] In this scheme, the monkeypox epidemic points are determined based on the pre-processed multi-source monkeypox virus detection data, and the temporal and spatial characteristics of each monkeypox epidemic point are extracted, specifically:

[0011] Obtain the monkeypox virus detection data summarized by different detection methods within a preset time period, standardize and reduce the dimension of the collected multi-source monkeypox virus detection data, and screen positive samples from the pre-processed multi-source monkeypox virus detection data;

[0012] Extracting geographic location information from the screened positive samples, integrating the geographic location information after traversing all positive samples, using the integrated geographic location information to divide the positive samples into positive sample subsets, and determining the monkeypox epidemic point according to the geographic location labels of each positive sample subset;

[0013] Obtaining a time series sequence of positive samples from each monkeypox epidemic site, extracting a trend turning point in the time series sequence of the positive samples, dynamically segmenting the time series sequence of the positive samples using the trend turning point, extracting a trend feature identifier of each sub-segment after segmentation, characterizing the temporal aggregation of positive samples using the trend feature identifier, and generating a temporal feature of each monkeypox epidemic site;

[0014] Acquire the global spatial distribution according to the geographical location information of each monkeypox epidemic point, calculate the spatial weight of each monkeypox epidemic point by using the spatial distance, characterize the spatial relationship between the monkeypox epidemic points by using the spatial weight, and select the neighborhood epidemic points based on the spatial relationship;

[0015] The local spatial distribution is obtained based on each monkeypox epidemic point and its corresponding neighborhood epidemic point, and the global spatial distribution and the local spatial distribution are imported into the CNN network to mine the spatial clustering of positive samples and generate the spatial characteristics of each monkeypox epidemic point.

[0016] In this scheme, the monkeypox epidemic points are clustered based on the temporal and spatial characteristics, specifically:

[0017] Obtaining the temporal and spatial characteristics of each monkeypox epidemic point at a preset time step, sorting the monkeypox epidemic points according to the number of positive samples, and selecting a preset number of monkeypox epidemic points from the sorting results as initial clustering centers;

[0018] The metric function is constructed by Mahalanobis distance, and the Mahalanobis distance is calculated according to the temporal and spatial characteristics of the monkeypox epidemic points to characterize the similarity between the monkeypox epidemic points. Other monkeypox epidemic points are assigned to the initial clustering center with the closest distance to generate the corresponding clusters.

[0019] The cluster centers and clustering results are continuously updated through iterative calculations. After obtaining a new cluster center in each iteration, the distance between the monkeypox epidemic point and its cluster center is compared with the distance from the previous monkeypox epidemic point to the cluster center.

[0020] If it is less than or equal to, the monkeypox epidemic point will be retained in the cluster center to which it belongs. Otherwise, the distance will be recalculated and the monkeypox epidemic point will be assigned to the cluster center with the nearest distance. When the iteration number threshold is met, the clustering result of the last iteration will be selected as the clustering result of the monkeypox epidemic point.

[0021] In this scheme, the influencing factors related to the monkeypox epidemic in animals are extracted by correlation analysis based on the clustering results. Specifically:

[0022] According to the clustering results of monkeypox epidemic points, the clusters of monkeypox epidemic points with spatiotemporal aggregation within the preset time step are obtained. The monkeypox epidemic points in the same cluster indicate the existence of spatiotemporal aggregation;

[0023] Using big data and knowledge graphs to obtain relevant data sets of animal monkeypox epidemics, using maximum relevance and minimum redundancy to perform feature selection in the relevant data sets, and generating preliminary influencing factors of animal monkeypox epidemics at monkeypox epidemic points belonging to the same cluster;

[0024] Obtaining a set of preliminary influencing factors corresponding to all clusters in the clustering results, collecting parameter samples from the corresponding monkeypox epidemic points according to the set of preliminary influencing factors, generating a parameter sample data set for each cluster, performing data balancing processing, and generating participants;

[0025] The federation idea is introduced into the particle swarm algorithm to improve it. The particle swarm algorithm is initialized, the parameter sample data set of each participant is used as the algorithm input, the particle population is set and the particle position is decoded, and the particle fitness is calculated according to the classification accuracy of the combination of influencing factors.

[0026] The position is updated according to the comparison of particle fitness, and the best combination of influencing factors of each participant is obtained through iterative update, and the best combination of influencing factors of each participant is transmitted to the preset aggregation server;

[0027] After receiving the influencing factor combinations of all participants, the preset aggregation server shares the influencing factor combinations with all participants, re-updates the particle swarm, and performs iterative updates until the termination condition is met, and finally obtains the best influencing factor combination of each participant.

[0028] In this solution, graph neural network is used to perform spatiotemporal analysis on the influencing factors, specifically:

[0029] An undirected graph is constructed according to the spatial distribution of monkeypox epidemic points, and each monkeypox epidemic point is used as a node to obtain the best combination of influencing factors corresponding to the monkeypox epidemic point. Based on the best combination of influencing factors, additional features of the node are generated by obtaining parameter samples, and the edge structure between nodes is set according to whether there is spatiotemporal aggregation between the monkeypox epidemic points.

[0030] Neighborhood nodes are set according to the monkeypox epidemic points with spatiotemporal clustering, an adjacency matrix is ​​obtained to represent the undirected graph, and a graph neural network is used for learning representation. In the representation process, attention weights are introduced to weight the neighborhood nodes of the monkeypox epidemic points, and the node representation is updated using spatiotemporal convolution and neighbor aggregation to extract the spatiotemporal characteristics of the influencing factors corresponding to the monkeypox epidemic points.

[0031] In this scheme, according to the risk spatiotemporal characteristics of the animal monkeypox epidemic, the spatiotemporal correlation of the animal monkeypox epidemic is analyzed and the corresponding spatiotemporal factors are defined, specifically:

[0032] Match the temporal and spatial characteristics of each influencing factor of the monkeypox epidemic site with the temporal and spatial characteristics of the time series of positive samples at the monkeypox epidemic site to obtain the temporal correlation and spatial correlation respectively;

[0033] The time correlation and the space correlation are normalized to obtain the corresponding time factor and space factor, and the time factor and the space factor are added to obtain the time and space factors of each influencing factor.

[0034] In this scheme, a monkeypox risk prediction model is constructed based on the influencing factors and spatiotemporal factors to obtain the risk prediction level of the animal monkeypox epidemic at the target monkeypox epidemic site, specifically:

[0035] A monkeypox risk prediction model was constructed using a multi-layer perceptron based on the influencing factors of monkeypox epidemic sites and the spatiotemporal factors corresponding to the influencing factors. Training samples were constructed based on the influencing factor parameters with monkeypox epidemic risk level labels of monkeypox epidemic sites to train and test the monkeypox risk prediction model.

[0036] The spatiotemporal characteristics of the parameter samples corresponding to the current influencing factors of the target monkeypox epidemic point are obtained, and the spatiotemporal factors are combined with the monkeypox risk prediction model to output the risk prediction level of the animal monkeypox epidemic after a preset time using the fully connected layer.

[0037] In this scheme, the risk deviation between the predicted risk level of the target monkeypox epidemic site and the current risk level is obtained, and the ratio of the risk deviation to the preset time period is calculated as the risk change rate;

[0038] Based on the current risk level, a risk change benchmark value is obtained according to historical animal monkeypox epidemic examples, the risk change rate is compared with the risk change benchmark value, and the epidemic situation is evaluated based on the comparison deviation combined with the preset threshold interval difference.

[0039] Compared with the prior art, the present invention has the following beneficial effects:

[0040] Based on the risk spatiotemporal transmission characteristics of animal monkeypox epidemics, the present invention analyzes the spatiotemporal correlation of animal monkeypox epidemics and defines corresponding spatiotemporal factors and influencing factors, constructs a monkeypox risk prediction model to accurately predict the risk level of monkeypox epidemic points, and predicts and evaluates the epidemic situation, providing a scientific basis for the rational allocation and utilization of medical and health resources and the implementation of monkeypox epidemic prevention and control work. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] In order to more clearly illustrate the technical solutions in the embodiments or exemplary embodiments of the present invention, the drawings required for use in the embodiments or exemplary descriptions will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained according to the drawings without paying creative work.

[0042] Figure 1 A flow chart showing a method for assessing the epidemic situation of animal monkeypox based on monkeypox virus detection data;

[0043] Figure 2 A schematic diagram of the process of extracting influencing factors related to the epidemic of monkeypox in animals is shown;

[0044] Figure 3 A schematic diagram of the process of using graph neural network to perform spatiotemporal analysis of influencing factors is shown;

[0045] Figure 4 A block diagram of an animal monkeypox epidemic situation assessment system based on monkeypox virus detection data is shown. DETAILED DESCRIPTION

[0046] In order to more clearly understand the above-mentioned purpose, features and advantages of the present invention, the present invention is further described in detail below in conjunction with the accompanying drawings and specific embodiments. It should be noted that the embodiments of the present application and the features in the embodiments can be combined with each other without conflict.

[0047] In the following description, many specific details are set forth to facilitate a full understanding of the present invention. However, the present invention may also be implemented in other ways different from those described herein. Therefore, the protection scope of the present invention is not limited to the specific embodiments disclosed below.

[0048] like Figure 1 As shown, an embodiment of the present invention provides a method for evaluating the epidemic situation of animal monkeypox based on monkeypox virus detection data, comprising:

[0049] S102, obtaining multi-source monkeypox virus detection data and performing data preprocessing, determining monkeypox epidemic points according to the preprocessed multi-source monkeypox virus detection data, and extracting temporal and spatial characteristics of each monkeypox epidemic point;

[0050] S104, clustering the monkeypox epidemic sites based on the temporal characteristics and spatial characteristics, and extracting influencing factors related to the animal monkeypox epidemic by using correlation analysis for the monkeypox epidemic sites in the same cluster according to the clustering results;

[0051] S106, using a graph neural network to perform spatiotemporal analysis on the influencing factors, analyzing the spatiotemporal correlation of the animal monkeypox epidemic and defining corresponding spatiotemporal factors according to the spatiotemporal characteristics of the risk of the animal monkeypox epidemic;

[0052] S108, constructing a monkeypox risk prediction model based on the influencing factors and spatiotemporal factors, obtaining the risk prediction level of the animal monkeypox epidemic at the target monkeypox epidemic point, and evaluating the epidemic situation based on the change between the risk prediction level and the current risk level.

[0053] It should be noted that common detection methods for monkeypox usually include nucleic acid testing, laboratory testing, clinical observation and imaging testing. The diagnosis of monkeypox mainly relies on the combination of clinical symptoms and laboratory testing. PCR testing is the most commonly used and accurate method. Early detection and timely treatment are crucial to controlling the disease.

[0054] Obtain monkeypox virus detection data summarized by different detection methods within a preset time period, standardize and reduce the dimension of the collected multi-source monkeypox virus detection data, and screen and determine positive samples from the pre-processed multi-source monkeypox virus detection data; extract geographic location information from the screened positive samples, integrate the geographic location information after traversing all positive samples, use the integrated geographic location information to divide the positive samples to generate positive sample subsets, and determine the monkeypox epidemic point according to the geographic location label of each positive sample subset;

[0055] The positive sample time series sequence of each monkeypox epidemic point is obtained, and the trend turning point in the positive sample time series sequence is extracted. The trend point is the turning point where the time series is converted from one form to another, and is the change point of the local change trend; the positive sample time series sequence is dynamically segmented using the trend turning point, and the trend feature identifier of each sub-segment after segmentation is extracted. The trend feature identifier includes trend distance and trend morphology, etc. The trend distance is the difference between the start and end points of the sequence segment to characterize the trend direction and amplitude of the sequence segment. The trend morphology characterizes sequence segments with the same (or similar) trends but different internal morphologies. Its essence is to measure the sequence segments through their trend points. The trend feature identifier is used to characterize the temporal clustering of positive samples, and the number of cases corresponding to the case sample sequence is read to have a trend of increasing, decreasing or remaining unchanged over time, so as to generate the temporal characteristics of each monkeypox epidemic point; the global spatial distribution is obtained according to the geographical location information of each monkeypox epidemic point, the spatial weight of each monkeypox epidemic point is calculated using the spatial distance, the spatial relationship between the monkeypox epidemic points is characterized by the spatial weight, and the neighborhood epidemic points are screened based on the spatial relationship; the local spatial distribution is obtained based on each monkeypox epidemic point and its corresponding neighborhood epidemic point, the global spatial distribution and the local spatial distribution are introduced into the CNN network to mine the spatial clustering of positive samples, and the spatial characteristics of each monkeypox epidemic point are generated.

[0056] It should be noted that the time characteristics and spatial characteristics of each monkeypox epidemic point at a preset time step are obtained, the monkeypox epidemic points are sorted according to the number of positive samples, and a preset number of monkeypox epidemic points are selected from the sorting results as initial clustering centers; a metric function is constructed through the Mahalanobis distance, and the Mahalanobis distance is calculated according to the time characteristics and spatial characteristics between the monkeypox epidemic points to characterize the similarity between the monkeypox epidemic points, and other monkeypox epidemic points are attributed to the initial clustering center with the nearest distance to generate corresponding clusters; the clustering center and clustering results are continuously updated through iterative calculation, and after obtaining a new clustering center in each iteration, the distance between the monkeypox epidemic point and its clustering center is compared with the distance from the previous monkeypox epidemic point to the clustering center; if it is less than or equal to, the monkeypox epidemic point is retained in the clustering center to which it belongs, otherwise the distance is recalculated, and the monkeypox epidemic point is attributed to the clustering center with the nearest distance. When the iteration number threshold is met, the clustering result of the last iteration is selected as the clustering result of the monkeypox epidemic point.

[0057] Figure 2 A schematic diagram of the process of extracting influencing factors related to the animal monkeypox epidemic is shown.

[0058] According to an embodiment of the present invention, the influencing factors related to the monkeypox epidemic in animals are extracted by correlation analysis of the monkeypox epidemic points in the same cluster according to the clustering results, specifically:

[0059] S202, obtaining a cluster of monkeypox epidemic points that are spatiotemporally clustered within a preset time step according to the clustering results of the monkeypox epidemic points, wherein the monkeypox epidemic points in the same cluster indicate the existence of spatiotemporal clustering;

[0060] S204, using big data and knowledge graphs to obtain relevant data sets of animal monkeypox epidemics, using maximum relevance and minimum redundancy to perform feature selection in the relevant data sets, and generating preliminary influencing factors of the occurrence of animal monkeypox epidemics at monkeypox epidemic points belonging to the same cluster;

[0061] S206, obtaining a set of preliminary influencing factors corresponding to all clusters in the clustering result, collecting parameter samples from the corresponding monkeypox epidemic points according to the set of preliminary influencing factors, generating a parameter sample data set for each cluster, performing data balancing processing, and generating a participant;

[0062] S208, introducing the federation idea into the particle swarm algorithm for improvement, initializing the particle swarm algorithm, taking the parameter sample data set of each participant as the algorithm input, setting the particle population and decoding the particle position, and calculating the particle fitness according to the classification accuracy of the combination of influencing factors;

[0063] S210, updating the position according to the comparison of the particle fitness, obtaining the best combination of influencing factors of each participant through iterative updating, and transmitting the best combination of influencing factors of each participant to the preset aggregation server;

[0064] S212, after receiving the influencing factor combination of all participants, the preset aggregation server shares the influencing factor combination with all participants, re-updates the particle swarm, and performs iterative update calculations until the termination condition is met, and finally obtains the best influencing factor combination of each participant.

[0065] It should be noted that according to big data and knowledge graphs, the influencing factors of animal monkeypox epidemics include infection routes, viral load, individual immunity and environmental factors. Due to the different influencing factors of different clusters, when applying standard feature selection algorithms in unbalanced data, the selected features usually focus on meeting the classification requirements of the majority class and ignore the minority class. Therefore, data balancing is performed to improve the applicability and comprehensiveness of influencing factor extraction. In the federated particle swarm algorithm, the commonly used KNN is selected as the classifier, and the classification accuracy of each particle is calculated as the fitness of the particle. Initialize the parameters of the particle swarm algorithm, set the size of the particle swarm, the maximum speed of the particles, the maximum number of iterations, the learning factor and other parameters, randomly generate the particle swarm, calculate the fitness of each particle according to the particle fitness function, update the optimal fitness of the particle, use the current optimal position of each particle as the initial position to find the global optimal position, use the particle itself and the global optimal value to update the particle speed and position, and calculate the fitness of the updated particle; during the iteration process, transmit the optimal influencing factor combination of each participant and its classification accuracy to the aggregation server; then, the aggregation server transmits M-1 optimal influencing factor combinations to each participant, where M is the total number of participants. The multi-party federated evolution feature selection based on particle swarm optimization improves the stability and robustness of the subsequent model, and improves the prediction performance of the influencing factors selected by each participant.

[0066] Figure 3 A schematic diagram of the process of using graph neural networks to perform spatiotemporal analysis of influencing factors is shown.

[0067] According to an embodiment of the present invention, a graph neural network is used to perform spatiotemporal analysis on the influencing factors, specifically:

[0068] S302, constructing an undirected graph according to the spatial distribution of monkeypox epidemic points, taking each monkeypox epidemic point as a node, obtaining the best combination of influencing factors corresponding to the monkeypox epidemic point, obtaining parameter samples based on the best combination of influencing factors to generate additional features of the node, and setting the edge structure between the nodes according to whether there is spatiotemporal aggregation between the monkeypox epidemic points;

[0069] S304, setting neighborhood nodes according to the monkeypox epidemic points with spatiotemporal clustering, obtaining an adjacency matrix to represent the undirected graph, using a graph neural network for learning representation, introducing attention weights in the representation process to weight the neighborhood nodes of the monkeypox epidemic points, using spatiotemporal convolution and neighbor aggregation to update the node representation, and extracting the spatiotemporal characteristics of the influencing factors corresponding to the monkeypox epidemic points.

[0070] It should be noted that since the aggregation of monkeypox in the time dimension, space dimension and time-space dimension is usually caused by the time correlation and space correlation of the epidemic, there is a certain time-space correlation in the spread and diffusion of the monkeypox epidemic. Match the time-space characteristics of each influencing factor of the monkeypox epidemic point with the time characteristics and space characteristics of the positive sample time series sequence of the monkeypox epidemic point, and obtain the time correlation and space correlation respectively; judge whether the change of the influencing factor is consistent with the development and change of the number of positive samples or the epidemic risk level. If the time series change of the influencing factor within the preset time is not correlated with the time series change of the number of positive samples, then the time correlation of the influencing factor is 0 at this time. The time correlation and space correlation are normalized to obtain the corresponding time factor and space factor, and the time factor and space factor are added to obtain the time-space factor of each influencing factor.

[0071] According to the influencing factors of the monkeypox epidemic point and the spatiotemporal factors corresponding to the influencing factors, a monkeypox risk prediction model is constructed using a multi-layer perceptron. The monkeypox risk prediction model incorporates the spatiotemporal factors of the risk of the epidemic, thereby taking into account the possible impact of the influencing factors and the spatiotemporal autocorrelation of the epidemic on the occurrence of the animal monkeypox epidemic. According to the influencing factor parameters with monkeypox epidemic risk level labels at the monkeypox epidemic point, a training sample is constructed to train and test the monkeypox risk prediction model; preferably, risk clustering is performed on the historical animal monkeypox epidemic, and three results of high-risk level clustering, medium-risk level clustering, and low-risk level clustering with statistical significance are distinguished by clustering, and data samples are constructed according to the indicator parameters of the influencing factor indicators of the risk level label, and the data samples are matched with the risk level labels to generate training samples for training the multi-layer perceptron. The spatiotemporal characteristics of the parameter samples corresponding to the current influencing factors of the target monkeypox epidemic point are obtained, and the monkeypox risk prediction model is introduced in combination with the spatiotemporal factors, and the risk prediction level of the animal monkeypox epidemic after the preset time is output using the fully connected layer.

[0072] Obtain the risk deviation between the predicted risk level of the target monkeypox epidemic point and the current risk level, calculate the ratio of the risk deviation to the preset time period as the risk change rate; obtain the historical risk change situation according to historical animal monkeypox epidemic examples based on the current risk level, calculate the risk change benchmark value according to the historical risk change situation, compare the risk change rate with the risk change benchmark value, when the risk change rate is greater than the risk change benchmark value, it proves that a high epidemic situation exists, obtain the preset threshold interval where the comparison deviation falls, and obtain the level of the high epidemic situation according to the level corresponding to the preset threshold interval.

[0073] Figure 4 A block diagram of the animal monkeypox epidemic situation assessment system based on monkeypox virus detection data of the present invention is shown.

[0074] The second aspect of the present invention also provides an animal monkeypox epidemic situation assessment system 4 based on monkeypox virus detection data, the system comprising: a memory 41 and a processor 42, wherein the memory and the processor store and execute an animal monkeypox epidemic situation assessment method program based on monkeypox virus detection data.

[0075] In the several embodiments provided in the present application, it should be understood that the disclosed methods and systems can be implemented in other ways. The system embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation, such as: multiple units or components can be combined, or can be integrated into another system, or some features can be ignored, or not executed. In addition, the coupling, direct coupling, or communication connection between the components shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms. In addition, the functional units in the various embodiments of the present invention can be all integrated into one processing unit, or each unit can be separately used as a unit, or two or more units can be integrated into one unit; the above-mentioned integrated unit can be implemented in the form of hardware or in the form of hardware plus software functional units.

[0076] It can be understood by those skilled in the art that all or part of the steps of the above-mentioned method embodiment can be completed by hardware related to program instructions, and the aforementioned program can be stored in a computer-readable storage medium. When the program is executed, the steps of the above-mentioned method embodiment are executed; and the aforementioned storage medium includes: various media that can store program codes, such as mobile storage devices, read-only memory (ROM), random access memory (RAM), magnetic disks or optical disks. Alternatively, if the above-mentioned integrated unit of the present invention is implemented in the form of a software function module and sold or used as an independent product, it can also be stored in a computer-readable storage medium. Based on such an understanding, the technical solution of the embodiment of the present invention can be essentially or partly reflected in the form of a software product, which is stored in a storage medium and includes several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the methods described in various embodiments of the present invention. And the aforementioned storage medium includes: various media that can store program codes, such as mobile storage devices, ROM, RAM, magnetic disks or optical disks.

[0077] The above description is only a specific implementation mode of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can easily think of changes or substitutions within the technical scope disclosed by the present invention, which should be covered by the protection scope of the present invention.

Claims

1. A method for evaluating the epidemic situation of animal monkeypox based on monkeypox virus detection data, characterized in that: The following steps are involved: Acquire multi-source monkeypox virus detection data and perform data preprocessing, determine monkeypox epidemic points based on the preprocessed multi-source monkeypox virus detection data, and extract the temporal and spatial characteristics of each monkeypox epidemic point; Clustering the monkeypox epidemic sites based on the temporal and spatial characteristics, and extracting influencing factors related to the animal monkeypox epidemic using correlation analysis for the monkeypox epidemic sites in the same cluster according to the clustering results; Use graph neural networks to conduct spatiotemporal analysis of the influencing factors, analyze the spatiotemporal correlation of the animal monkeypox epidemic and define corresponding spatiotemporal factors based on the spatiotemporal characteristics of the risk of the animal monkeypox epidemic; A monkeypox risk prediction model is constructed based on the influencing factors and spatiotemporal factors to obtain the risk prediction level of the animal monkeypox epidemic at the target monkeypox epidemic point, and the epidemic situation is evaluated based on the changes between the risk prediction level and the current risk level.

2. The method for evaluating the epidemic situation of animal monkeypox based on monkeypox virus detection data according to claim 1, characterized in that: The monkeypox epidemic points were determined based on the pre-processed multi-source monkeypox virus detection data, and the temporal and spatial characteristics of each monkeypox epidemic point were extracted, specifically: Obtain the monkeypox virus detection data summarized by different detection methods within a preset time period, standardize and reduce the dimension of the collected multi-source monkeypox virus detection data, and screen positive samples from the pre-processed multi-source monkeypox virus detection data; Extracting geographic location information from the screened positive samples, integrating the geographic location information after traversing all positive samples, using the integrated geographic location information to divide the positive samples into positive sample subsets, and determining the monkeypox epidemic point according to the geographic location labels of each positive sample subset; Obtaining a time series sequence of positive samples from each monkeypox epidemic site, extracting a trend turning point in the time series sequence of the positive samples, dynamically segmenting the time series sequence of the positive samples using the trend turning point, extracting a trend feature identifier of each sub-segment after segmentation, characterizing the temporal aggregation of positive samples using the trend feature identifier, and generating a temporal feature of each monkeypox epidemic site; Acquire the global spatial distribution according to the geographical location information of each monkeypox epidemic point, calculate the spatial weight of each monkeypox epidemic point by using the spatial distance, characterize the spatial relationship between the monkeypox epidemic points by using the spatial weight, and select the neighborhood epidemic points based on the spatial relationship; The local spatial distribution is obtained based on each monkeypox epidemic point and its corresponding neighborhood epidemic point, and the global spatial distribution and the local spatial distribution are imported into the CNN network to mine the spatial clustering of positive samples and generate the spatial characteristics of each monkeypox epidemic point.

3. The method for evaluating the epidemic situation of animal monkeypox based on monkeypox virus detection data according to claim 1, characterized in that: The monkeypox epidemic points are clustered based on the temporal and spatial characteristics, specifically: Obtaining the temporal and spatial characteristics of each monkeypox epidemic point at a preset time step, sorting the monkeypox epidemic points according to the number of positive samples, and selecting a preset number of monkeypox epidemic points from the sorting results as initial clustering centers; The metric function is constructed by Mahalanobis distance, and the Mahalanobis distance is calculated according to the temporal and spatial characteristics of the monkeypox epidemic points to characterize the similarity between the monkeypox epidemic points. Other monkeypox epidemic points are assigned to the initial clustering center with the closest distance to generate the corresponding clusters. The cluster centers and clustering results are continuously updated through iterative calculations. After obtaining a new cluster center in each iteration, the distance between the monkeypox epidemic point and its cluster center is compared with the distance from the previous monkeypox epidemic point to the cluster center. If it is less than or equal to, the monkeypox epidemic point will be retained in the cluster center to which it belongs. Otherwise, the distance will be recalculated and the monkeypox epidemic point will be assigned to the cluster center with the nearest distance. When the iteration number threshold is met, the clustering result of the last iteration will be selected as the clustering result of the monkeypox epidemic point.

4. The method for evaluating the epidemic situation of animal monkeypox based on monkeypox virus detection data according to claim 1, characterized in that: According to the clustering results, correlation analysis was used to extract the influencing factors related to the monkeypox epidemic in animals from the monkeypox epidemic points in the same cluster, specifically: According to the clustering results of monkeypox epidemic points, the clusters of monkeypox epidemic points with spatiotemporal aggregation within the preset time step are obtained. The monkeypox epidemic points in the same cluster indicate the existence of spatiotemporal aggregation; Using big data and knowledge graphs to obtain relevant data sets of animal monkeypox epidemics, using maximum relevance and minimum redundancy to perform feature selection in the relevant data sets, and generating preliminary influencing factors of animal monkeypox epidemics at monkeypox epidemic points belonging to the same cluster; Obtaining a set of preliminary influencing factors corresponding to all clusters in the clustering results, collecting parameter samples from the corresponding monkeypox epidemic points according to the set of preliminary influencing factors, generating a parameter sample data set for each cluster, performing data balancing processing, and generating participants; The federation idea is introduced into the particle swarm algorithm to improve it. The particle swarm algorithm is initialized, the parameter sample data set of each participant is used as the algorithm input, the particle population is set and the particle position is decoded, and the particle fitness is calculated according to the classification accuracy of the combination of influencing factors. The position is updated according to the comparison of particle fitness, and the best combination of influencing factors of each participant is obtained through iterative update, and the best combination of influencing factors of each participant is transmitted to the preset aggregation server; After receiving the influencing factor combinations of all participants, the preset aggregation server shares the influencing factor combinations with all participants, re-updates the particle swarm, and performs iterative updates until the termination condition is met, and finally obtains the best influencing factor combination of each participant.

5. The method for evaluating the epidemic situation of animal monkeypox based on monkeypox virus detection data according to claim 1, characterized in that: The graph neural network is used to perform spatiotemporal analysis on the influencing factors, specifically: An undirected graph is constructed according to the spatial distribution of monkeypox epidemic points, and each monkeypox epidemic point is used as a node to obtain the best combination of influencing factors corresponding to the monkeypox epidemic point. Based on the best combination of influencing factors, additional features of the node are generated by obtaining parameter samples, and the edge structure between nodes is set according to whether there is spatiotemporal aggregation between the monkeypox epidemic points. Neighborhood nodes are set according to the monkeypox epidemic points with spatiotemporal clustering, an adjacency matrix is ​​obtained to represent the undirected graph, and a graph neural network is used for learning representation. In the representation process, attention weights are introduced to weight the neighborhood nodes of the monkeypox epidemic points, and the node representation is updated using spatiotemporal convolution and neighbor aggregation to extract the spatiotemporal characteristics of the influencing factors corresponding to the monkeypox epidemic points.

6. The method for evaluating the epidemic situation of animal monkeypox based on monkeypox virus detection data according to claim 1, characterized in that: According to the risk spatiotemporal characteristics of the animal monkeypox epidemic, the spatiotemporal correlation of the animal monkeypox epidemic was analyzed and the corresponding spatiotemporal factors were defined, specifically: Match the temporal and spatial characteristics of each influencing factor of the monkeypox epidemic site with the temporal and spatial characteristics of the time series of positive samples at the monkeypox epidemic site to obtain the temporal correlation and spatial correlation respectively; The time correlation and the space correlation are normalized to obtain the corresponding time factor and space factor, and the time factor and the space factor are added to obtain the time and space factors of each influencing factor.

7. The method for evaluating the epidemic situation of animal monkeypox based on monkeypox virus detection data according to claim 1, characterized in that: A monkeypox risk prediction model is constructed based on the influencing factors and spatiotemporal factors to obtain the risk prediction level of the animal monkeypox epidemic at the target monkeypox epidemic site, specifically: A monkeypox risk prediction model was constructed using a multi-layer perceptron based on the influencing factors of monkeypox epidemic sites and the spatiotemporal factors corresponding to the influencing factors. Training samples were constructed based on the influencing factor parameters with monkeypox epidemic risk level labels of monkeypox epidemic sites to train and test the monkeypox risk prediction model. The spatiotemporal characteristics of the parameter samples corresponding to the current influencing factors of the target monkeypox epidemic point are obtained, and the spatiotemporal factors are combined with the monkeypox risk prediction model to output the risk prediction level of the animal monkeypox epidemic after a preset time using the fully connected layer.

8. The method for evaluating the epidemic situation of animal monkeypox based on monkeypox virus detection data according to claim 7, characterized in that: Obtaining the risk deviation between the predicted risk level of the target monkeypox epidemic site and the current risk level, and calculating the ratio of the risk deviation to a preset time period as the risk change rate; Based on the current risk level, a risk change benchmark value is obtained according to historical animal monkeypox epidemic examples, the risk change rate is compared with the risk change benchmark value, and the epidemic situation is evaluated based on the comparison deviation combined with the preset threshold interval difference.

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