A wild animal host-based influenza virus real-time monitoring system and method

Abstract

The application provides a kind of real-time monitoring system and method based on wild animal host influenza virus, it is related to animal influenza virus monitoring technical field, comprising the following steps: step S1, dynamic monitoring point is laid out;Step S2, sample collection and pretreatment;Step S3, real-time detection and typing virus;Step S4, data real-time transmission and sharing;Step S5, virus sequence alignment and variation analysis;Step S6, dynamic risk assessment and early warning.The real-time monitoring system and method based on wild animal host influenza virus, through portable real-time fluorescent RT-PCR detection equipment realizes field real-time detection, combines double mode communication and cloud real-time analysis, eliminates the time lag of sample transportation and laboratory detection, realizes the real-time feedback of virus information.

Description

Technical Field

[0001] This invention relates to the field of animal influenza virus monitoring technology, specifically to a real-time monitoring system and method for influenza viruses based on wild animal hosts. Background Technology

[0002] During peak influenza season, medical institutions organize viral nucleic acid testing (such as RT-PCR) and subtyping for suspected patients. They also set up regular sampling points in key units such as large poultry farms and pig farms to isolate and sequence the virus and upload the data to national or international influenza databases. Some research institutions or government departments conduct special surveys of wild birds in key areas such as migratory bird flyways and important wetland reserves from time to time. After capturing or sampling, the samples are transported back to the laboratory for molecular testing and strain identification.

[0003] In addition, influenza surveillance networks at all levels typically consist of primary healthcare stations, local disease control centers, provincial epidemiological laboratories, and the national influenza center. Primary stations are responsible for sample collection and preliminary testing, while positive samples are sent to higher-level laboratories for further nucleic acid sequencing or virus isolation. The test results are then uploaded to the national center or the World Health Organization's (WHO) Global Influenza Programme (GISRS). At the data level, many countries have established or participated in international influenza genome sharing platforms. Surveillance agencies upload identified influenza virus sequences to public databases for research institutions and public health departments to conduct large-scale bioinformatics comparisons and epidemiological analyses, thereby supporting research on virus origin tracing and evolutionary trends.

[0004] However, current technology still has the following shortcomings:

[0005] (1) Significant time lag in test results: The process of uploading test results from the laboratory to the public database involves multiple steps such as sample transportation, laboratory testing, and data processing, resulting in a time lag of several days to several weeks, which cannot meet the needs of real-time monitoring.

[0006] (2) Lack of real-time analysis and evaluation system: Existing technology relies on manual comparison of virus sequences and risk assessment, lacking an efficient automated comparison and real-time risk assessment system, making it difficult for relevant departments to obtain information on abnormal virus strains appearing in wild hosts in a timely manner;

[0007] (3) Poor adaptability of sampling methods: The migration behavior and habitat distribution of wild hosts fluctuate greatly with the seasons, climate and human activities. Traditional single-point short-term sampling methods are difficult to adapt to their dynamic changes, and often miss detection at critical moments when the virus mutates on a small scale and has the ability to spread across species.

[0008] (4) Delayed early warning of cross-species transmission: Due to the above-mentioned time lag, analysis efficiency and sampling suitability issues, existing technologies cannot capture early signals of cross-species transmission of viruses in a timely manner, and cannot meet the urgent need for early detection and dynamic assessment of cross-species transmission.

[0009] To address the aforementioned problems, a real-time monitoring system and method for influenza viruses based on wild animal hosts is proposed. Summary of the Invention

[0010] To address the shortcomings of existing technologies, this invention provides a real-time monitoring system and method for influenza viruses based on wild animal hosts. The technical problems to be solved by this invention are: significant time lag in detection results, lack of real-time analysis and evaluation system, poor adaptability of sampling methods, and delayed early warning of cross-species transmission.

[0011] To achieve the above objectives, the present invention provides the following technical solution:

[0012] A method for real-time monitoring of influenza viruses based on wild animal hosts includes the following steps:

[0013] Step S1: Deploy dynamic monitoring points;

[0014] Step S2: Sample collection and preprocessing;

[0015] Step S3: Real-time detection and virus typing;

[0016] Step S4: Real-time data transmission and sharing;

[0017] Step S5: Viral sequence alignment and mutation analysis;

[0018] Step S6: Dynamic risk assessment and early warning.

[0019] Preferably, step S1, setting up dynamic monitoring points, includes:

[0020] Based on historical data on wildlife migration routes and habitat distribution, combined with seasonal variation patterns, climate prediction information, and data on the distribution of human activity areas, an initial monitoring point network is constructed using a Geographic Information System (GIS). At the same time, real-time activity data transmitted by wildlife tracking devices is accessed. Combined with historical migration path models, aggregation areas are predicted using aggregation area probability prediction algorithms. The location and number of monitoring points are dynamically adjusted to form an adaptive monitoring network covering key areas of migratory bird passageways, wetland reserves, and wildlife aggregation areas.

[0021] Preferably, step S2, sample collection and preprocessing, includes:

[0022] At each dynamic monitoring point, non-invasive sampling methods were used to obtain wild animal samples. The sample identification module recorded the information of the sample collector, the collection time, the collection environment parameters and the host species to achieve full traceability of the sample. Then, portable sample pretreatment equipment was used to process the samples. Impurities were removed by centrifugation, and viral nucleic acid was extracted based on the magnetic bead method. During the extraction process, the quality control effect was verified by the nucleic acid concentration quality control formula.

[0023] Preferably, step S3, real-time detection and typing of the virus, includes:

[0024] The pretreated viral nucleic acid is introduced into a portable real-time fluorescent RT-PCR detection device. This device is pre-stored with a variety of influenza virus subtype-specific primers and probes. The virus positivity and subtype classification of the sample are determined by a fluorescence signal threshold determination algorithm, and an electronic report is automatically generated after the test is completed.

[0025] Preferably, step S4, real-time data transmission and sharing, includes:

[0026] Through satellite communication and dual-mode transmission modules, the electronic reports and metadata are transmitted end-to-end using the national cryptographic SM4 encryption algorithm. The encryption process follows the SM4 block cipher algorithm standard. Data is received through a cloud data center, and real-time connection is established with the national influenza database and the World Health Organization Global Influenza Program database to automatically synchronize detection data. At the same time, hierarchical access permissions are set to achieve secure data sharing.

[0027] The encryption formula is as follows:

[0028]

[0029] in,

[0030] : Encrypted ciphertext data;

[0031] Session key;

[0032] Plaintext data to be encrypted;

[0033] : SM4 encryption function.

[0034] Preferably, step S5, viral sequence alignment and mutation analysis, includes:

[0035] The cloud data center has a built-in bioinformatics analysis unit that uses the BLAST algorithm to compare newly acquired virus sequences with known influenza virus sequences in the database in real time. It calculates sequence homology using a sequence homology calculation model and then identifies gene mutation sites using a multiple sequence alignment algorithm to determine whether new variant strains have emerged. At the same time, it constructs and updates the virus evolutionary tree based on the neighbor-joining method. During the construction process, it calculates genetic distance using the Kimura two-parameter model to intuitively display the virus evolution path and kinship.

[0036] Preferably, step S6, dynamic risk assessment and early warning, includes:

[0037] A machine learning model trained and optimized with historical monitoring data was used, combined with virus mutation analysis results, wildlife activity trajectory data and human activity area distribution data, to assess the risk level of cross-species transmission of the virus through a cross-species transmission risk index model;

[0038] When the risk level reaches medium risk or above, early warning information will be pushed to disease control centers and public health management departments at all levels through multiple channels such as SMS, dedicated APP, and email. The information includes the scope of the risk area, the genetic characteristics of the risk strain, and targeted prevention and control recommendations.

[0039] A real-time influenza virus monitoring system based on wild animal hosts implements the aforementioned real-time influenza virus monitoring method based on wild animal hosts, comprising:

[0040] The dynamic sampling module includes a mobile monitoring station, a non-invasive sampling tool, a portable sample pretreatment device, and a sample identification unit. The mobile monitoring station is equipped with a GIS positioning module and environmental monitoring sensors to provide real-time feedback of geographic coordinates and environmental parameters. The portable sample pretreatment device includes temperature control, nucleic acid extraction, and dual quality control functions. The sample identification unit enables sample traceability via QR code / RFID.

[0041] The real-time detection and analysis module includes a portable real-time fluorescent RT-PCR detection device, a viral subtype typing database, and a fault self-checking unit. The portable real-time fluorescent RT-PCR detection device is pre-stored with multi-subtype-specific primers and probes to enable rapid detection and typing. The fault self-checking unit monitors the device status and provides repair guidance.

[0042] The data transmission and sharing module includes a dual-mode communication unit, a data encryption unit, and a cloud data center. The dual-mode communication unit ensures real-time transmission between the field and the cloud. The data encryption unit uses the SM4 algorithm to provide data encryption. The cloud data center enables data synchronization, hierarchical sharing, and disaster recovery backup.

[0043] The bioinformatics analysis module includes an automated sequence alignment unit, a virus mutation identification unit, a phylogenetic tree construction unit, and a mutation site annotation unit, which are used for sequence alignment, mutation identification, dynamic updating of the phylogenetic tree, and functional annotation of mutation sites.

[0044] The risk assessment and early warning module includes a machine learning model library, a risk level determination unit, and an early warning push unit. The model library contains iteratively optimized risk assessment models. The risk level determination unit classifies risks into four levels. The early warning push unit pushes early warning information through multiple channels and tracks the reception status.

[0045] The host tracking linkage module establishes a connection with wildlife tracking equipment, receives real-time host activity data, provides a basis for the dynamic sampling module to adjust the sampling points, and links the host trajectory with the virus detection results to clarify the virus transmission path.

[0046] This invention provides a real-time monitoring system and method for influenza viruses based on wild animal hosts. It has the following beneficial effects:

[0047] This invention enables real-time field detection using a portable real-time fluorescence RT-PCR detection device. By combining dual-mode communication with real-time cloud analysis, it eliminates the time lag between sample transportation and laboratory testing, and achieves real-time feedback of virus information.

[0048] This invention uses GIS and a clustering area probability prediction algorithm to deploy dynamic monitoring points and adjust the points by combining host tracking data, which solves the problem of poor adaptability of traditional single-point sampling and reduces the risk of missed detection.

[0049] This invention automatically completes sequence alignment and variant identification through a bioinformatics analysis unit, combines a machine learning model to assess cross-species transmission risk in real time, and pushes early warning information through multiple channels to solve the problem of delayed early warning.

[0050] This invention uses the national cryptographic algorithm SM4 to encrypt data, sets hierarchical access permissions, realizes secure data transmission and sharing, and ensures that data is not lost through distributed disaster recovery backup;

[0051] The portable device of this invention supports solar power and backup battery power, the reagent cold storage compartment maintains the activity of the reagents, the fault self-test unit ensures the stability of the device, and it is suitable for complex monitoring environments in the field.

[0052] The risk areas, strain characteristics, and prevention and control recommendations output by this invention can directly provide decision support for disease control centers and public health departments, and help prevent the early spread of influenza viruses across species. Attached Figure Description

[0053] Figure 1 This is a flowchart illustrating a method for implementing an invention.

[0054] Figure 2 This is a schematic diagram of the system architecture for implementing an invention. Detailed Implementation

[0055] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0056] like Figure 1 As shown, a method for real-time monitoring of influenza viruses based on wild animal hosts includes the following steps:

[0057] Step S1: Deploy dynamic monitoring points;

[0058] Based on historical data on wildlife migration routes and habitat distribution, combined with seasonal variation patterns, climate prediction information, and data on the distribution of human activity areas, an initial monitoring point network is constructed using a Geographic Information System (GIS). At the same time, real-time activity data transmitted by wildlife tracking devices (satellite tracking collars and radio trackers) is accessed. Combined with historical migration path models, aggregation areas are predicted using aggregation area probability prediction algorithms. The location and number of monitoring points are dynamically adjusted to form an adaptive monitoring network covering key areas of migratory bird passageways, wetland reserves, and wildlife aggregation areas.

[0059] The formula for the probability prediction algorithm for clustered areas is as follows:

[0060]

[0061] in,

[0062] Geographic coordinates The probability of wild animals gathering at a location (value range: [0,1], the closer to 1, the higher the probability of gathering).

[0063] , , Weighting coefficients (satisfying) (The values ​​are obtained by training a migration model with historical monitoring data, corresponding to the contribution of historical data, real-time data, and environmental data, respectively).

[0064] :coordinate Historical migration frequency of wild animals in the area (standardized values ​​[0,1], based on migration trajectory statistics of the same season in the past 5 years);

[0065] :coordinate Real-time activity density of wild animals at the location (standardized value [0,1], calculated from real-time data of the tracking device, i.e., number of tracked individuals within 1km of the coordinates / total number of tracked individuals in the area);

[0066] :coordinate The environmental suitability index (standardized value [0,1], calculated by combining climate prediction (temperature, precipitation) and habitat type (wetland, woodland, etc.)) is in line with the requirements. ,in For climate suitability, (for habitat suitability).

[0067] when When the coordinates are identified as a "high-aggregation potential area", monitoring points are prioritized for deployment.

[0068] when When an area is identified as having "medium-level agglomeration potential," monitoring points are dynamically added.

[0069] when If the area is deemed a "low-aggregation potential area", no sites will be deployed or the number of sites will be reduced.

[0070] When extreme weather (heavy rain, heavy snow, high temperature) or human activity disturbance (temporary construction, large-scale tourism activities) occurs at the monitoring site, the temporary relocation process is automatically triggered. Based on the surrounding suitable habitat data of the GIS system, 3-5 alternative sites are recommended. After confirmation by the monitoring personnel, the site adjustment is completed to ensure the continuity of monitoring.

[0071] Step S2: Sample collection and preprocessing;

[0072] At each dynamic monitoring point, non-invasive sampling methods (fecal sample collection, feather-attached saliva sample collection, habitat saliva residue sample collection) were used to obtain wildlife samples. Then, the sample identification module (QR code / RFID tag) recorded the sample collection personnel information, collection time, collection environmental parameters (temperature, humidity, altitude) and host species to achieve full-process traceability of the samples. Next, the portable sample pretreatment equipment (with built-in constant temperature control module and nucleic acid extraction reagent chamber) was used to process the samples. Impurities were removed by centrifugation, and viral nucleic acid was extracted based on magnetic bead method. During the extraction process, the quality control effect was verified by nucleic acid concentration quality control formula.

[0073] The formula for nucleic acid concentration quality control is as follows:

[0074]

[0075] in,

[0076] Readings from a portable nucleic acid testing instrument;

[0077] ,Require Only then can subsequent testing proceed.

[0078] The portable sample pretreatment device is equipped with a dual quality control module: it has built-in positive control samples and negative control samples. The control experiment is performed simultaneously during each sample pretreatment. If the control experiment results are abnormal (positive control not detected, negative control detected), the device will immediately issue an audible and visual alarm and indicate the cause of the fault (reagent contamination, operation error), ensuring the accuracy of nucleic acid extraction results.

[0079] Step S3: Real-time detection and virus typing;

[0080] The pretreated viral nucleic acid is introduced into a portable real-time fluorescent RT-PCR detection device. This device is pre-stored with a variety of influenza virus subtype-specific primers and probes. The virus positivity and subtype classification of the sample are determined by the fluorescence signal threshold determination algorithm, and an electronic report is automatically generated after the test is completed.

[0081] The formula for determining a positive result is as follows:

[0082]

[0083] in,

[0084] Cycle threshold (i.e., the number of PCR cycles at which the fluorescence signal reaches a set threshold, used to determine viral load).

[0085] PCR cycle number (range: 1~40);

[0086] : No. The fluorescence signal intensity during the next cycle (detected in real time by the device);

[0087] : Fluorescence signal determination threshold (set to 10 times the baseline fluorescence intensity, which is the average fluorescence value of the first 10 cycles).

[0088] Judgment rules:

[0089] when At that time, it was determined to be "virus positive", and the subtype was matched according to the fluorescence signal of the specific probe (such as FAM fluorescence labeled by H5 subtype probe and HEX fluorescence labeled by H9 subtype probe);

[0090] when If the result is "suspected positive," a repeat test is required.

[0091] when At that time, it was determined to be "virus negative".

[0092] The portable real-time fluorescence RT-PCR detection device features field endurance and emergency backup capabilities: it supports solar charging (enough for 30 sample tests on a single charge) and backup battery power (enough for 20 sample tests on a single backup battery charge), and has a built-in reagent refrigeration compartment (maintaining a constant temperature of 2-8℃ to ensure reagent activity); the fault self-diagnosis module can monitor the device's temperature control, optical detection, reagent levels, and other statuses in real time, automatically generating repair instructions (such as replacing the optical module or replenishing reagents) when abnormalities occur, and supports data transfer between devices to ensure uninterrupted testing.

[0093] Step S4: Real-time data transmission and sharing;

[0094] Through satellite communication and dual-mode transmission modules, the electronic reports and metadata are transmitted end-to-end using the national cryptographic SM4 encryption algorithm. The encryption process follows the SM4 block cipher algorithm standard. Data is received through a cloud data center, and real-time connection is established with the national influenza database and the World Health Organization Global Influenza Program database to automatically synchronize detection data. At the same time, hierarchical access permissions are set to achieve secure data sharing.

[0095] The encryption formula is as follows:

[0096]

[0097] in,

[0098] : Encrypted ciphertext data (block length 128 bits);

[0099] Session key (generated by the cloud data center and portable device through a key negotiation protocol, 128 bits in length).

[0100] Plaintext data to be encrypted (electronic reports and metadata, processed in 128-bit blocks, padded with zeros if necessary);

[0101] SM4 encryption function (including round function, S-box substitution, linear transformation and other operations, a total of 32 rounds of iteration);

[0102] The encrypted data is sent to the cloud data center via a dual-mode transmission module, and the cloud then transmits the data. After decryption, the data is received; simultaneously, a real-time connection is established between the national influenza database and the World Health Organization Global Influenza Program database, automatically synchronizing detection data, and verifying data consistency through a data consistency check formula during the synchronization process. Ensure data integrity Use the SHA-256 hash function and set tiered access permissions (administrators, disease control personnel, researchers) to achieve secure data sharing.

[0103] The cloud data center adopts a distributed storage and disaster recovery backup architecture: data is synchronized in real time to three backup nodes in different regions. When the master node fails, it automatically switches to the backup node within 10 minutes to ensure that the data is not lost. At the same time, a data retention mechanism is set up, and the monitoring data is stored in categories of "raw data - analysis results - early warning records" with a retention period of no less than 10 years to meet the needs of subsequent traceability and scientific research.

[0104] Step S5: Viral sequence alignment and mutation analysis;

[0105] The cloud data center has a built-in bioinformatics analysis unit that uses the BLAST algorithm to compare newly acquired virus sequences with known influenza virus sequences in the database in real time. It calculates sequence homology using a sequence homology calculation model and then identifies gene mutation sites (HA protein gene, NA protein gene mutation sites) using a multiple sequence alignment algorithm to determine whether new variant strains have emerged. At the same time, it dynamically constructs and updates the virus phylogenetic tree based on the neighbor-joining method (NJ method) or the maximum likelihood method (ML method). During the construction process, the genetic distance is calculated using the Kimura two-parameter model to intuitively display the virus evolution path and phylogenetic relationships.

[0106] The formula for calculating sequence homology is as follows:

[0107]

[0108] in,

[0109] S: Homology between the new viral sequence and known sequences (value range: [0, 100%]);

[0110] a: Number of matching bases in the two sequences (number of complementary base pairs in AT and GC); b: Number of mismatched bases (number of non-complementary base pairs).

[0111] c: Number of gaps (number of inserted / deleted base fragments in sequence alignment);

[0112] Mismatch penalty coefficient (value 1.0, default setting by the ClustalW algorithm);

[0113] Gap penalty coefficient (value 0.5, default setting by the ClustalW algorithm);

[0114] L: Total length of the aligned sequence (including the total number of bases with the gap);

[0115] The formula for calculating the Kimura two-parameter model is as follows:

[0116]

[0117] in,

[0118] Genetic distance between two viral sequences (indicating the degree of evolutionary difference between sequences; the larger the value, the greater the difference).

[0119] Sequence switching rate (the frequency of base substitutions between purines or pyrimidines, such as A→G, T→C).

[0120] Sequence transversion rate (the frequency of base substitutions between purine and pyrimidine or pyrimidine and purine, such as A→T, G→C);

[0121] when If a mutation is detected at a key antigenic site (such as the RBS region of the HA protein), it is identified as a "novel variant strain".

[0122] An adjacency tree is constructed based on genetic distance d, with branch lengths corresponding to genetic distances, to visually demonstrate the kinship between different strains.

[0123] Step S6: Dynamic risk assessment and early warning;

[0124] Machine learning models (random forest model and logistic regression model) trained and optimized with historical monitoring data were used. Combined with virus mutation analysis results (mutations of cross-species transmission-related sites), wildlife activity trajectory data (location of residential areas and farms around monitoring points), and human activity area distribution data, the risk level of cross-species transmission of the virus (low risk, medium risk, high risk, and extremely high risk) was assessed through a cross-species transmission risk index model.

[0125] When the risk level reaches medium or above, early warning information will be pushed to disease control centers and public health management departments at all levels through multiple channels such as SMS, dedicated APP, and email. The information includes the scope of the risk area, the genetic characteristics of the risk strain, and targeted prevention and control recommendations (strengthening disinfection, restricting the movement of people, and increasing the frequency of monitoring in farms).

[0126] The formula for calculating the cross-species transmission risk index is as follows:

[0127]

[0128] in,

[0129] Cross-species transmission risk index (value range: [0,1], the larger the value, the higher the risk);

[0130] , , Weight coefficients (obtained from training the random forest model, satisfying...) Based on historical case verification (Contribution of viral mutation) (Host-human overlap contribution) (Human density contribution));

[0131] V: Virus mutation coefficient (standardized value [0,1]), Key genes include HA and NA genes, with a total length of approximately 2000 bp.

[0132] O: Host-human activity overlap (normalized value [0,1]), );

[0133] H: Human activity density (standardized value [0,1]), (÷1000, i.e., the population per square kilometer divided by 1000 and then standardized).

[0134] The risk level determination rules are as follows:

[0135] High risk: ;

[0136] Medium risk: ;

[0137] Low risk: ;

[0138] Extremely low risk: .

[0139] Each quarter, based on the monitoring data from the previous quarter, the actual occurrence of the epidemic, and new scientific research conclusions (such as the discovery of new cross-species transmission-related sites), the model input parameters and weights are updated, and the model performance is evaluated through 5-fold cross-validation to ensure that the risk assessment accuracy is not less than 90%. After the early warning information is pushed, the reception status is tracked in real time, and three reminders are given for unread information to ensure that relevant departments receive it in a timely manner.

[0140] like Figure 2 As shown, a real-time influenza virus monitoring system based on wild animal hosts implements the aforementioned real-time influenza virus monitoring method based on wild animal hosts, including:

[0141] The dynamic sampling module includes a mobile monitoring station, a non-invasive sampling tool, a portable sample pretreatment device, and a sample identification unit. The mobile monitoring station is equipped with a GIS positioning module and environmental monitoring sensors to provide real-time feedback of geographic coordinates and environmental parameters. The portable sample pretreatment device includes temperature control, nucleic acid extraction, and dual quality control functions. The sample identification unit enables sample traceability via QR code / RFID.

[0142] The real-time detection and analysis module includes a portable real-time fluorescent RT-PCR detection device, a viral subtype typing database, and a fault self-checking unit. The portable real-time fluorescent RT-PCR detection device is pre-stored with multi-subtype-specific primers and probes to enable rapid detection and typing. The fault self-checking unit monitors the device status and provides repair guidance.

[0143] The data transmission and sharing module includes a dual-mode communication unit, a data encryption unit, and a cloud data center. The dual-mode communication unit ensures real-time transmission between the field and the cloud. The data encryption unit uses the SM4 algorithm to provide data encryption. The cloud data center enables data synchronization, hierarchical sharing, and disaster recovery backup.

[0144] The bioinformatics analysis module includes an automated sequence alignment unit, a virus mutation identification unit, a phylogenetic tree construction unit, and a mutation site annotation unit, which are used for sequence alignment, mutation identification, dynamic updating of the phylogenetic tree, and functional annotation of mutation sites.

[0145] The risk assessment and early warning module includes a machine learning model library, a risk level determination unit, and an early warning push unit. The model library contains iteratively optimized risk assessment models. The risk level determination unit classifies risks into four levels. The early warning push unit pushes early warning information through multiple channels and tracks the reception status.

[0146] The host tracking linkage module establishes a connection with wildlife tracking equipment, receives real-time host activity data, provides a basis for the dynamic sampling module to adjust the sampling points, and links the host trajectory with the virus detection results to clarify the virus transmission path.

[0147] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A method for real-time monitoring of influenza viruses based on wild animal hosts, characterized in that, Includes the following steps: Step S1: Deploy dynamic monitoring points; Step S2: Sample collection and preprocessing; Step S3: Real-time detection and virus typing; Step S4: Real-time data transmission and sharing; Step S5: Virus sequence alignment and mutation analysis; Step S6: Dynamic risk assessment and early warning.

2. The method for real-time monitoring of influenza virus based on wild animal hosts according to claim 1, characterized in that, Step S1, setting up dynamic monitoring points, includes: Based on historical data on wildlife migration routes and habitat distribution, combined with seasonal variation patterns, climate prediction information, and data on the distribution of human activity areas, an initial monitoring point network is constructed using a Geographic Information System (GIS). At the same time, real-time activity data transmitted by wildlife tracking devices is accessed. Combined with historical migration path models, aggregation areas are predicted using aggregation area probability prediction algorithms. The location and number of monitoring points are dynamically adjusted to form an adaptive monitoring network covering key areas of migratory bird passageways, wetland reserves, and wildlife aggregation areas.

3. The method for real-time monitoring of influenza virus based on wild animal hosts according to claim 1, characterized in that, Step S2, sample collection and preprocessing, includes: At each dynamic monitoring point, non-invasive sampling methods were used to obtain wild animal samples. The sample identification module recorded the information of the sample collector, the collection time, the collection environment parameters and the host species to achieve full traceability of the sample. Then, portable sample pretreatment equipment was used to process the samples. Impurities were removed by centrifugation, and viral nucleic acid was extracted based on the magnetic bead method. During the extraction process, the quality control effect was verified by the nucleic acid concentration quality control formula.

4. The method for real-time monitoring of influenza virus based on wild animal hosts according to claim 1, characterized in that, Step S3, real-time detection and typing of viruses, includes: The pretreated viral nucleic acid is introduced into a portable real-time fluorescent RT-PCR detection device. This device is pre-stored with a variety of influenza virus subtype-specific primers and probes. The virus positivity and subtype classification of the sample are determined by a fluorescence signal threshold determination algorithm, and an electronic report is automatically generated after the test is completed.

5. The method for real-time monitoring of influenza virus based on wild animal hosts according to claim 1, characterized in that, Step S4, real-time data transmission and sharing, includes: Through satellite communication and dual-mode transmission modules, the electronic reports and metadata are transmitted end-to-end using the national cryptographic SM4 encryption algorithm. The encryption process follows the SM4 block cipher algorithm standard. Data is received through a cloud data center, and real-time connection is established with the national influenza database and the World Health Organization Global Influenza Program database to automatically synchronize detection data. At the same time, hierarchical access permissions are set to achieve secure data sharing. The encryption formula is as follows:

6. Among them, : Encrypted ciphertext data; Session key; Plaintext data to be encrypted; : SM4 encryption function.

7. The method for real-time monitoring of influenza virus based on wild animal hosts according to claim 1, characterized in that, Step S5, viral sequence alignment and mutation analysis, includes: The cloud data center has a built-in bioinformatics analysis unit that uses the BLAST algorithm to compare newly acquired virus sequences with known influenza virus sequences in the database in real time. It calculates sequence homology using a sequence homology calculation model and then identifies gene mutation sites using a multiple sequence alignment algorithm to determine whether new variant strains have emerged. At the same time, it constructs and updates the virus evolutionary tree based on the neighbor-joining method. During the construction process, it calculates genetic distance using the Kimura two-parameter model to intuitively display the virus evolution path and kinship.

8. The method for real-time monitoring of influenza virus based on wild animal hosts according to claim 1, characterized in that, Step S6, dynamic risk assessment and early warning, includes: A machine learning model trained and optimized with historical monitoring data was used, combined with virus mutation analysis results, wildlife activity trajectory data and human activity area distribution data, to assess the risk level of cross-species transmission of the virus through a cross-species transmission risk index model; When the risk level reaches medium risk or above, early warning information will be pushed to disease control centers and public health management departments at all levels through multiple channels such as SMS, dedicated APP, and email. The information includes the scope of the risk area, the genetic characteristics of the risk strain, and targeted prevention and control recommendations.

9. A real-time influenza virus monitoring system based on wild animal hosts, implementing the real-time influenza virus monitoring method based on wild animal hosts as described in any one of claims 1-7, characterized in that, include: The dynamic sampling module includes a mobile monitoring station, a non-invasive sampling tool, a portable sample pretreatment device, and a sample identification unit. The mobile monitoring station is equipped with a GIS positioning module and environmental monitoring sensors to provide real-time feedback of geographic coordinates and environmental parameters. The portable sample pretreatment device includes temperature control, nucleic acid extraction, and dual quality control functions. The sample identification unit enables sample traceability via QR code / RFID. The real-time detection and analysis module includes a portable real-time fluorescent RT-PCR detection device, a viral subtype typing database, and a fault self-checking unit. The portable real-time fluorescent RT-PCR detection device is pre-stored with multi-subtype-specific primers and probes to enable rapid detection and typing. The fault self-checking unit monitors the device status and provides repair guidance. The data transmission and sharing module includes a dual-mode communication unit, a data encryption unit, and a cloud data center. The dual-mode communication unit ensures real-time transmission between the field and the cloud. The data encryption unit uses the SM4 algorithm to provide data encryption. The cloud data center enables data synchronization, hierarchical sharing, and disaster recovery backup. The bioinformatics analysis module includes an automated sequence alignment unit, a virus mutation identification unit, a phylogenetic tree construction unit, and a mutation site annotation unit, which are used for sequence alignment, mutation identification, dynamic updating of the phylogenetic tree, and functional annotation of mutation sites. The risk assessment and early warning module includes a machine learning model library, a risk level determination unit, and an early warning push unit. The model library contains iteratively optimized risk assessment models. The risk level determination unit classifies risks into four levels. The early warning push unit pushes early warning information through multiple channels and tracks the reception status. The host tracking linkage module establishes a connection with wildlife tracking equipment, receives real-time host activity data, provides a basis for the dynamic sampling module to adjust the sampling points, and links the host trajectory with the virus detection results to clarify the virus transmission path.

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