Large-scale cattle farm epidemic disease risk assessment method and system
By setting up sampling points in large-scale cattle farms, collecting and processing microbial samples for metagenomic sequencing, and assigning scores, the problem of inaccurate risk assessment caused by neglecting environmental factors in existing technologies has been solved, achieving more accurate disease risk assessment and prevention.
Patent Information
- Application Number
- CN202510840565.9
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-06-23
- Publication Date
- 2025-11-18
AI Technical Summary
Existing technologies for poultry disease risk assessment neglect environmental factors, leading to inaccurate risk assessments and affecting the effectiveness of disease prevention and control.
Multiple sampling points were set up in large-scale cattle farms to collect microbial samples and perform metagenomic sequencing. Anomalies in the samples were handled by combining pathogen species and abundance information, and scores were assigned. Environmental impact was taken into account to conduct disease risk assessment.
It improves the accuracy of disease risk assessment, helps farms identify and prevent potential risks, and take targeted measures to reduce the probability of disease outbreaks.
Smart Images
Figure CN120977556A_ABST
Abstract
Description
TECHNICAL FIELD
[0001] The present application belongs to the technical field of poultry epidemic prevention and control, and particularly relates to a large-scale cattle farm epidemic risk assessment method and system. BACKGROUND
[0002] Poultry farms are facing various infectious disease risks. The spread of epidemics not only affects the health and production efficiency of poultry, but also can have a profound impact on public health safety and economic development. In recent years, the frequent occurrence of avian influenza, bovine mycoplasma disease and other epidemics has brought huge economic losses to the breeding industry and has a serious impact on the stable supply of poultry and egg food and food safety. Environmental factors play an important role in the spread of animal epidemics. Conducting environmental epidemic risk assessment can provide a scientific basis for epidemic prevention and control. Through an effective risk assessment system, the farm can identify potential epidemic threats in advance and take necessary preventive measures. For high-risk pathogens, take measures to strengthen immunity, quickly control the epidemic, and reduce the risk to the minimum; for pathogens without risk, stop immunity, and apply the immune potential of animals to respond to high-risk pathogens, so as to carry out healthy breeding in the most economical and efficient way and produce better economic and social benefits.
[0003] For the poultry epidemic risk assessment in the prior art, it is usually determined according to the state of poultry, ignoring the influence of the environment, thereby leading to inaccurate actual epidemic risk assessment and affecting the epidemic prevention and control. SUMMARY
[0004] In order to solve the above technical problems, the present application provides a large-scale cattle farm epidemic risk assessment method and system for solving the technical problems proposed in the prior art.
[0005] In the first aspect, the embodiments of the present application provide the following technical scheme, a large-scale cattle farm epidemic risk assessment method, comprising: A plurality of sampling points are arranged in the large-scale cattle farm, and corresponding microbial samples are collected at each sampling point; The microbial samples are subjected to sample processing and metagenomic sequencing to obtain pathogen species and abundance information results corresponding to the samples of each sampling point; The pathogen species and abundance information results are subjected to sample anomaly processing to obtain a processing result; The pathogen nucleic acid abundance in the processing result is determined, and the corresponding pathogen is scored based on the pathogen nucleic acid abundance to obtain a pathogen score; The large-scale cattle farm epidemic risk is assessed based on the pathogen score.
[0006] Compared with the prior art, the application has the beneficial effects that: firstly, a plurality of sampling points are arranged in a large-scale cattle farm, and corresponding microbial samples are collected at each sampling point; sample processing and metagenomic sequencing are performed on the microbial samples to obtain pathogen species and abundance information results of the samples corresponding to each sampling point; sample anomaly processing is performed on the pathogen species and abundance information results to obtain processing results; pathogen nucleic acid abundance in the processing results is determined, and corresponding pathogens are scored based on the pathogen nucleic acid abundance to obtain pathogen scores; and disease risk assessment of the large-scale cattle farm is performed based on the pathogen scores. In the process of disease risk assessment, the application fully considers the influence of the environment, and performs corresponding processing on the data to eliminate abnormal data, so as to improve the accuracy of risk assessment, help the farm to identify and evaluate potential disease risks, and take targeted measures to prevent diseases.
[0007] Preferably, the sampling points include biological sampling points, in-shed environment sampling points, out-of-shed environment sampling points, non-breeding environment sampling points in the farm area, and peripheral environment sampling points in the farm area, and the microbial samples include cattle biological samples, in-shed samples, out-of-shed samples in the farm area, non-breeding area samples in the farm area, and peripheral samples in the farm area.
[0008] Preferably, the step of collecting corresponding microbial samples at each sampling point comprises: Randomly selecting cattle samples according to a preset proportion of feeding amount, and collecting samples in the form of whole blood, nasal swabs and anal swabs to obtain cattle biological samples; Sampling according to the coverage rate of cattle feeding pens, uniformly selecting a plurality of ground sampling points in the shed to collect cotton swab samples, and using an air / aerosol sampler to perform air sampling three times in the morning, noon and evening to obtain in-shed samples; Collecting wall corner cotton swab samples at intervals of 20 meters along the periphery of the shed to obtain out-of-shed samples in the farm area; Collecting ground, building or tree surface cotton swab samples at intervals of 50 meters along the edge of the breeding area to obtain non-breeding area samples in the farm area; Collecting road surface or building surface cotton swab samples at intervals of 100 meters along the periphery of the breeding area to obtain peripheral samples in the farm area.
[0009] Preferably, the step of performing sample processing and metagenomic sequencing on the microbial samples to obtain pathogen species and abundance information results of the samples corresponding to each sampling point comprises: Selecting a number of microbial samples, extracting bovine biological samples from the microbial samples, performing metagenomic sequencing on the bovine biological samples according to sample types using multiple individual mixed samples, extracting a barn sample, a field area outside the barn sample, a field area non-feeding area sample, and a field area peripheral sample from the microbial samples, performing metagenomic sequencing on the barn sample, the field area outside the barn sample, the field area non-feeding area sample, and the field area peripheral sample according to sample types using multiple similar samples combined into mixed samples, to obtain pathogen species and abundance information results.
[0010] Preferably, the step of performing sample anomaly processing on the pathogen species and abundance information results to obtain a processing result comprises: Obtaining sample data of corresponding samples in the pathogen species and abundance information results, selecting any data in the sample data as reference data, and calculating the reachable distance of the reference data . ; In the formula, , represents the distance between the reference data and the rest of the data , represents the first distance of the reference data ; Based on the reachable distance , the reachable density is calculated : ; In the formula, , represents the sample set whose distance between the rest of the data and the reference data in the sample data is less than the corresponding first distance, represents the sum ; Based on the reachable density , the first score is calculated : ; In the formula, , represents the reachable density of the rest of the data ; The sample data is input into a preset forest algorithm for processing, the results on each isolated tree are integrated, and the average path length is normalized to obtain a second score ; Based on the first score and the second score , a comprehensive score is calculated : ; wherein, represents a fusion weight; The data with a comprehensive score lower than a score threshold is rejected to obtain a processing result.
[0011] Preferably, the step of assigning a score to a corresponding pathogen based on the pathogenic nucleic acid abundance of the pathogen to obtain a pathogen score specifically comprises: If the pathogenic nucleic acid abundance of the pathogen ranks in the top 20 of the pathogenic nucleic acid abundance ranking, the pathogen is assigned a score of 100, if the pathogenic nucleic acid abundance of the pathogen does not rank in the top 20 of the pathogenic nucleic acid abundance ranking, the pathogen is assigned a score of 50, and if the pathogen is not detected, the pathogen is assigned a score of 0 to obtain a pathogen score.
[0012] Preferably, the step of performing epidemic risk assessment of the large-scale cattle farm based on the pathogen score specifically comprises: If the pathogen score is greater than a preset score and the corresponding pathogen is included in the target directory, the pathogen is a high-risk pathogen, if the pathogen score is greater than a preset score and the corresponding pathogen is not included in the target directory, the pathogen is a medium-risk pathogen, if the pathogen score is between 0 and the preset score and the corresponding pathogen is included in the target directory, the pathogen is a medium-risk pathogen, if the pathogen score is 0 and the corresponding pathogen is included in the target directory, the pathogen is a low-risk pathogen, and if the pathogen score is between 0 and the preset score and the corresponding pathogen is not included in the target directory, the pathogen is a medium-risk pathogen.
[0013] In a second aspect, the embodiments of the present application provide the following technical solutions, a large-scale cattle farm epidemic risk assessment system, the system comprises: A collection module is configured to set a plurality of sampling points in a large-scale cattle farm and collect corresponding microbial samples at each sampling point; A sequencing module is configured to perform sample processing and metagenomic sequencing on the microbial samples to obtain pathogenic species and abundance information results corresponding to the samples at each sampling point; A processing module is configured to perform sample anomaly processing on the pathogenic species and abundance information results to obtain a processing result; A scoring module is configured to determine pathogenic nucleic acid abundance in the processing result, assign a score to a corresponding pathogen based on the pathogenic nucleic acid abundance, and obtain a pathogen score; An assessment module is configured to perform epidemic risk assessment of the large-scale cattle farm based on the pathogen score.
[0014] In a third aspect, the embodiments of the present application provide a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, and the processor implements the large-scale cattle farm epidemic risk assessment method when executing the computer program.
[0015] In a fourth aspect, the embodiments of the present application provide a computer readable storage medium, and the computer readable storage medium stores a computer program, and the computer program is executable on a processor to implement the large-scale cattle farm epidemic risk assessment method. BRIEF DESCRIPTION OF DRAWINGS
[0016] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the following will briefly introduce the drawings needed to be used in the embodiments or prior art description. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without any creative labor based on these drawings.
[0017] Figure 1 The flow chart of the large-scale cattle farm epidemic risk assessment method provided by the embodiments of the present application; Figure 2 The krona chart of species and percentage abundance in the biological sample of the cattle provided by the embodiments of the present application; Figure 3 The krona chart of species and percentage abundance in the sample in the shed provided by the embodiments of the present application; Figure 4 The krona chart of species and percentage abundance in the sample outside the shed in the field area provided by the embodiments of the present application; Figure 5 The krona chart of species and percentage abundance in the sample in the non-feeding area in the field area provided by the embodiments of the present application; Figure 6 The krona chart of species and percentage abundance in the sample in the periphery of the field area provided by the embodiments of the present application; Figure 7 The structural block diagram of the large-scale cattle farm epidemic risk assessment system provided by the embodiments of the present application; Figure 8 The hardware structure schematic diagram of the computer device provided in the embodiments of the present application. DETAILED DESCRIPTION
[0018] Embodiments of the present invention are described in detail below, examples of which are illustrated in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and intended to explain embodiments of the present invention, and should not be construed as limiting the present invention.
[0019] Example 1 In Embodiment 1 of the present invention, as Figure 1 As shown, a method for assessing disease risk in large-scale cattle farms includes: S1. Set up several sampling points in a large-scale cattle farm and collect corresponding microbial samples at each sampling point; The sampling points include biological sampling points, indoor environmental sampling points, outdoor environmental sampling points, non-breeding environmental sampling points, and outdoor environmental sampling points. The microbial samples include bovine biological samples, samples from inside the pen, samples from outside the pen, samples from non-breeding areas, and samples from the perimeter of the farm.
[0020] In practice, the collected data is analyzed to identify the main risk factors affecting the occurrence of diseases. Risk factors can be divided into biological factors (such as viral load and host population), environmental factors (such as temperature and humidity), and management factors (such as stocking density and hygiene management). However, the contribution of environmental and management factors to risk can also be directly or indirectly reflected in biological factors. Therefore, this invention only considers biological factors.
[0021] Step S1 includes: S11. Randomly select cattle samples according to the preset ratio of the feeding quantity, and collect samples by whole blood, nasal swabs and anal swabs to obtain cattle biological samples; Specifically, bovine biological samples were obtained by randomly selecting 0.5% to 1.0% of the animal population, including one whole blood sample, one nasal swab, and one anal swab.
[0022] S12. Sampling was carried out according to the coverage of cattle feeding pens. Several ground sampling points were evenly selected in the pen to collect cotton swab samples. Air samples were collected three times a day, in the morning, noon and evening, using an air / aerosol sampler to obtain samples from inside the pen. Specifically, sampling was conducted at 50% pen coverage, with 3-4 ground sampling points evenly selected within the pen to collect cotton swab samples; air sampling was conducted three times a day, in the morning, noon, and evening, using an air / aerosol sampler, with each sampling time not less than 10 minutes, to obtain samples from within the pen.
[0023] S13. Collect cotton swab samples from the corners of the enclosures at 20-meter intervals to obtain samples from outside the enclosures in the field area.
[0024] S14, at the edge in contact with the breeding area, collect cotton swab samples of the ground, buildings or tree surfaces every 50 meters to obtain field area non-feeding area samples.
[0025] S15, collect cotton swab samples of the road surface or building surface every 100 meters along the periphery of the breeding area to obtain field area periphery samples.
[0026] S2, sample processing and metagenomic sequencing are performed on the microorganism samples to obtain pathogen species and abundance information results corresponding to each sampling point; The step S2 is specifically: A number of microorganism samples are selected, and bovine biological samples are extracted from the microorganism samples. The bovine biological samples are subjected to metagenomic sequencing according to sample types using multiple individual mixed samples. The shed-in sample, the field area shed-out sample, the field area non-feeding area sample, and the field area periphery sample are extracted from the microorganism samples. The shed-in sample, the field area shed-out sample, the field area non-feeding area sample, and the field area periphery sample are subjected to metagenomic sequencing according to sample types using multiple similar samples combined into mixed samples to obtain pathogen species and abundance information results.
[0027] Specifically, the steps of step S2 are mainly used for DNA / RNA extraction and quality evaluation, library construction and high-throughput sequencing, quality control, host removal, metagenomic annotation, microorganism species and abundance evaluation, and other analyses of the sequencing results. One evaluation uses one second-generation high-throughput sequencing chip to determine 32 nucleic acid samples, and the sample allocation is as follows: (1) 12 bovine biological samples, including 3 nasal swabs, 3 blood samples, and 6 anal swabs. For each type of sample, multiple individual mixed samples are used for detection. (2) 20 environmental samples, including 5 shed-in samples, 5 field area shed-out samples, 5 field area non-feeding area samples, and 5 field area periphery samples. For each type of sample, multiple similar samples are combined into mixed samples for detection.
[0028] S3, sample abnormality processing is performed on the pathogen species and abundance information results to obtain a processing result; The step S3 includes: S31, obtaining sample data of the corresponding sample in the pathogen species and abundance information results, selecting any data in the sample data as reference data, and calculating the reachable distance of the reference data : ; In the formula, the distance between the reference data and the rest of the data , the distance between the reference data The distance; Specifically, the first one here The distance is specifically represented as the minimum existence of a certain number of samples in the data. Each sample is chosen such that its distance from the benchmark data is no greater than the distance between the benchmark data and the remaining data. The distance between them, and at most the existence Each sample is chosen such that its distance from the benchmark data is smaller than the distance between the benchmark data and the remaining data. The distance between them.
[0029] S32, Based on the reachable distance Calculate achievable density : ; In the formula, This indicates the remaining data in the sample data compared to the baseline data. The distance between them is less than the corresponding first The distance sample set, This indicates a summation.
[0030] S33, Based on the achievable density Calculate the first fraction : ; In the formula, Represents the remaining data The achievable density.
[0031] S34. The sample data is input into a preset forest algorithm for processing. The results from each isolated tree are combined and the average path length is normalized to obtain the second score. ; Specifically, the preset forest algorithm here is the isolated forest algorithm in the existing technology, which will not be elaborated here.
[0032] S35, Based on the first score With the second score Calculate the overall score : ; In the formula, Indicates the fusion weights; Specifically, the fusion weight here is 0.5.
[0033] S36. Remove data whose overall score is lower than the score threshold to obtain the processing result; Specifically, after processing through the above steps, abnormal data caused by environmental factors or the data collection process can be eliminated.
[0034] S4, determining the pathogenic nucleic acid abundance in the processing result, assigning a score to the corresponding pathogen based on the pathogenic nucleic acid abundance to obtain a pathogen score; The step S4 is specifically as follows: If the pathogenic nucleic acid abundance of the pathogen enters the top 20 of the pathogenic nucleic acid abundance ranking, the pathogen is assigned a score of 100, if the pathogenic nucleic acid abundance of the pathogen does not enter the top 20 of the pathogenic nucleic acid abundance ranking, the pathogen is assigned a score of 50, and if the pathogen is not detected, the pathogen is assigned a score of 0 to obtain the pathogen score.
[0035] In the present application, the krona plot of species and percentage abundance in the cow biological sample is as shown in Figure 2 The top 20 microorganisms in the abundance ranking in the cow biological sample are shown in Table 1 as follows: Table 1
[0036] Wherein, the first letter of the species means: kingdom-k, phylum-p, class-c, order-o, family-f, genus-g, species-s.
[0037] The pathogen score of the pathogen in the cow biological sample is shown in Table 2 as follows: Table 2
[0038] In the present application, the krona plot of species and percentage abundance in the shed sample is as shown in Figure 3 The top 20 microorganisms in the abundance ranking in the shed sample are shown in Table 3 as follows: Table 3
[0039] The pathogen score of the pathogen in the shed sample is shown in Table 4 as follows: Table 4
[0040] In the present application, the krona plot of species and percentage abundance in the shed sample in the field area is as shown in Figure 4 The top 20 microorganisms in the abundance ranking in the shed sample in the field area are shown in Table 5 as follows: Table 5
[0041] The pathogen score of the pathogen in the shed sample in the field area is shown in Table 6 as follows: Table 6
[0042] In the present application, the krona plot of species and percentage abundance in the non-feeding area samples in the field area is as shown in Figure 5 The top 20 microorganisms in the non-feeding area samples in the field area in order of abundance are shown in Table 7 below: Table 7
[0043] The pathogenic score of the pathogen in the non-feeding area samples in the field area is shown in Table 8: Table 8
[0044] In the present application, the krona plot of species and percentage abundance in the field area peripheral samples is as shown in Figure 6 The top 20 microorganisms in the field area peripheral samples in order of abundance are shown in Table 9 below: Table 9
[0045] The pathogenic score of the pathogen in the barn samples is shown in Table 10: Table 10
[0046] S5, based on the pathogenic score, performing disease risk assessment of the large-scale cattle farm; Wherein, the step S5 is specifically: If the pathogenic score is greater than the preset score and the corresponding pathogen is included in the target directory, the pathogen is a high-risk pathogen, if the pathogenic score is greater than the preset score and the corresponding pathogen is not included in the target directory, the pathogen is a medium-risk pathogen, if the pathogenic score is between 0 and the preset score and the corresponding pathogen is included in the target directory, the pathogen is a medium-risk pathogen, if the pathogenic score is 0 and the corresponding pathogen is included in the target directory, the pathogen is a low-risk pathogen, if the pathogenic score is between 0 and the preset score and the corresponding pathogen is not included in the target directory, the pathogen is a medium-risk pathogen.
[0047] Specifically, the calculation formula of the pathogenic score is: assuming that the pathogenic score of a certain pathogen in the cattle biological samples, the barn samples, the field area barn outside samples, the field area non-feeding area samples, and the field area peripheral samples is 100, 50, 50, 0, and 0, respectively, and the proportion of the five samples is 30, 25, 20, 15, and 10, respectively, then the final score is equal to (100x30+50x25+50x20+0x15+0x10) / (30+25+20+15+10)= ) ÷ 100 = 52.5, and the preset score is 30 points, the target directory here is "Animal Disease List of Class I, II and III", and according to the above content, the high-risk pathogens include: Clostridium bovis, bovine viral diarrhea virus, Mycoplasma bovis, and it is recommended that the farm focus on the prevention and control of these diseases, and strengthen immunity and detection; list the medium-risk pathogens; medium-risk pathogens include: Mannheimia, double-nodulated Babesia, Moraxella, intracellular Lawsonia, bovine mammary gland adenovirus B type, bovine gamma herpes virus 6 type, and it is recommended that the farm continue to maintain prevention and control; low-risk pathogens: in addition to the above high-risk and medium-risk pathogens, other pathogens related to bovine diseases are included in "Animal Disease List of Class I, II and III", and it is recommended that the farm consider reducing the prevention and control level, such as reducing the current number of immunization. In addition, the detection results of the top-ranked pathogens with high abundance and pathogenicity to animals include conditional pathogens such as Propionibacterium acnes, Streptococcus, and Campylobacter. These pathogens pose a potential threat to animals, and attention should be paid to prevention and control.
[0048] The large-scale cattle farm disease risk assessment method provided by the embodiment one of the present application first sets a plurality of sampling points in the large-scale cattle farm, collects corresponding microbial samples at each sampling point, processes the microbial samples and performs metagenomic sequencing to obtain pathogen species and abundance information results corresponding to the samples at each sampling point, processes the pathogen species and abundance information results to obtain a processing result, determines the pathogen nucleic acid abundance in the processing result, assigns scores to the corresponding pathogens based on the pathogen nucleic acid abundance to obtain pathogen scores, and performs disease risk assessment of the large-scale cattle farm based on the pathogen scores. In the process of disease risk assessment, the present application fully considers the influence of the environment, and processes the data accordingly to eliminate abnormal data, thereby improving the accuracy of risk assessment, helping the farm to identify and assess potential disease risks, and taking targeted measures to prevent diseases.
[0049] Embodiment two As Figure 2 shown, the embodiment two of the present application provides a large-scale cattle farm disease risk assessment system, which comprises: The acquisition module 1 is used for setting a plurality of sampling points in the large-scale cattle farm, and collecting corresponding microbial samples at each sampling point; The sequencing module 2 is used for processing the microbial samples and performing metagenomic sequencing to obtain pathogen species and abundance information results corresponding to the samples at each sampling point; The processing module 3 is used for processing the pathogen species and abundance information results to obtain a processing result; The scoring module 4 is configured to determine the pathogenic nucleic acid abundance in the processing result, assign scores to corresponding pathogens based on the pathogenic nucleic acid abundance, and obtain pathogen scores. The evaluation module 5 is configured to determine pathogen scores based on the sampling point weights and the pathogen scores, and evaluate the epidemic risk of the large-scale cattle farm based on the pathogen scores. The collection module 1 comprises: The first collection submodule is configured to randomly select cattle samples according to a preset proportion of feeding amount, and collect samples in the form of whole blood, nasal swabs and anal swabs to obtain cattle biological samples. The second collection submodule is configured to sample according to the coverage rate of cattle feeding pens, uniformly select a plurality of ground sampling points in the shed to collect cotton swab samples, and use an air / aerosol sampler to perform air sampling three times in the morning, noon and evening to obtain shed samples. The third collection submodule is configured to collect wall corner cotton swab samples at intervals of 20 meters along the periphery of the shed to obtain field shed outside samples. The fourth collection submodule is configured to collect ground, building or tree surface cotton swab samples at intervals of 50 meters along the edge in contact with the breeding area to obtain field non-breeding area samples. The fifth collection submodule is configured to collect road surface or building surface cotton swab samples at intervals of 100 meters along the periphery of the breeding area to obtain field periphery samples.
[0050] The sequencing module 2 is specifically configured to: select a plurality of microbial samples, extract cattle biological samples from the microbial samples, perform metagenomic sequencing on the cattle biological samples according to sample types using a plurality of individual mixed samples, extract shed samples, field shed outside samples, field non-breeding area samples and field periphery samples from the microbial samples, perform metagenomic sequencing on the shed samples, field shed outside samples, field non-breeding area samples and field periphery samples according to sample types using a plurality of similar samples combined into mixed samples, and obtain pathogenic species and abundance information results.
[0051] The processing module 3 comprises: The first calculation submodule is configured to obtain sample data of corresponding samples in the pathogenic species and abundance information results, select any data in the sample data as reference data, and calculate the reachable distance of the reference data : ; In the formula, the distance between the reference data and the remaining data , the first distance; a second calculation submodule, configured to calculate a reachable density based on the reachable distance calculate reachable density : ; wherein, denotes the distance between the rest of the sample data and the reference data is less than the corresponding first distance of the sample set, denotes summation; a third calculation submodule, configured to calculate a first score based on the reachable density : ; wherein, denotes the reachable density of the rest of the data ; a fourth calculation submodule, configured to input the sample data into a preset forest algorithm for processing, integrate the results on each isolated tree, and normalize the average path length to obtain a second score ; a fifth calculation submodule, configured to calculate a comprehensive score based on the first score and the second score : ; wherein, denotes a fusion weight; a sixth calculation submodule, configured to remove data with a comprehensive score lower than a score threshold to obtain a processing result.
[0052] The scoring module 4 is specifically configured to: if the pathogenic nucleic acid abundance of the pathogen enters the top 20 of the pathogenic nucleic acid abundance ranking, the pathogen is assigned a score of 100; if the pathogenic nucleic acid abundance of the pathogen does not enter the top 20 of the pathogenic nucleic acid abundance ranking, the pathogen is assigned a score of 50; if the pathogen is not detected, the pathogen is assigned a score of 0, to obtain a pathogen score.
[0053] The evaluation module 5 is specifically configured to: If the pathogen score is greater than the preset score and the corresponding pathogen is included in the target directory, the pathogen is a high-risk pathogen; if the pathogen score is greater than the preset score and the corresponding pathogen is not included in the target directory, the pathogen is a medium-risk pathogen; if the pathogen score is between 0 and the preset score and the corresponding pathogen is included in the target directory, the pathogen is a medium-risk pathogen; if the pathogen score is 0 and the corresponding pathogen is included in the target directory, the pathogen is a low-risk pathogen; if the pathogen score is between 0 and the preset score and the corresponding pathogen is not included in the target directory, the pathogen is a medium-risk pathogen.
[0054] In some embodiments of the present application, the present application provides a computer, comprising a memory 102, a processor 101, and a computer program stored in the memory 102 and capable of running on the processor 101, wherein the processor 101 implements the large-scale cattle farm epidemic risk assessment method as described above when executing the computer program.
[0055] Specifically, the processor 101 can include a central processing unit (CPU), or an application specific integrated circuit (ASIC), or one or more integrated circuits configured to implement embodiments of the present application.
[0056] The memory 102 can include mass storage for data or instructions. By way of example, and not limitation, the memory 102 can include a Hard Disk Drive (HDD), a floppy disk drive, a Solid State Drive (SSD), a flash drive, a Compact Disc Read Only Memory (CD-ROM), a magneto-optical disk, magnetic tape, or a Universal Serial Bus (USB) drive or a combination of two or more of these. Storage memory 102, where appropriate, can be removable or non-removable (or fixed) media. Storage memory 102, where appropriate, can be internal or external to data processing apparatus. In particular embodiments, storage memory 102 is nonvolatile memory. In particular embodiments, storage memory 102 includes Read-Only Memory (ROM) and Random Access Memory (RAM). Where appropriate, this ROM can be mask- programmed ROM, Programmable Read-Only Memory (PROM), Erasable Programmable Read-Only Memory (EPROM), Electrically Erasable Programmable Read-Only Memory (EEPROM), Electrically Alterable Read-Only Memory (EAROM), or flash memory (FLASH), or a combination of two or more of these. Where appropriate, this RAM can be Static Random-Access Memory (SRAM) or Dynamic Random Access Memory (DRAM), which can be Fast Page Mode Dynamic Random Access Memory (FPMDRAM), Extended Data Output Dynamic Random Access Memory (EDO DRAM), Extended Data Output
[0057] The memory 102 can be used to store or buffer various data files needed for processing and / or communication, and possible computer program instructions executed by the processor 101.
[0058] The processor 101 realizes the above-mentioned large-scale cattle farm epidemic risk assessment method by reading and executing the computer program instructions stored in the memory 102.
[0059] In some embodiments, the computer can further include a communication interface 103 and a bus 100. In which, as shown in the figure, the processor 101, the memory 102, the communication interface 103 are connected through the bus 100 and complete the communication between each other. Figure 3
[0060] The communication interface 103 is used to realize the communication between each module, device, unit and / or equipment in the embodiments of the present application. The communication interface 103 can also realize data communication with other components, such as: external devices, image / data acquisition devices, databases, external storage, and image / data processing workstations, etc.
[0061] Bus 100 includes hardware, software, or both, to couple components of computer device to each other and to couple components of computer device to other devices. Bus 100 includes, but is not limited to, at least one of the following: a data bus, an address bus, a control bus, an expansion bus, a local bus, etc. By way of example and not limitation, bus 100 can include an Accelerated Graphics Port (AGP) or other graphics bus, an Extended Industry Standard Architecture (EISA) bus, a Front Side Bus (FSB), a Hyper Transport (HT) interconnect, an Industry Standard Architecture (ISA) bus, an InfiniBand (IB) interconnect, a Low Pin Count (LPC) bus, a memory bus, a Micro Channel Architecture (MCA) bus, a Peripheral Component Interconnect (PCI) bus, a PCI-Express (PCI-X) bus, a Serial Advanced Technology Attachment (SATA) bus, a Video Electronics Standards Association Local (VLB) bus, or another suitable bus or combination of two or more of these. Where appropriate, bus 100 can include one or more buses. Although the present embodiments describe and show a particular bus, the present embodiments contemplate any suitable bus or interconnect.
[0062] The computer can execute the large-scale cattle farm epidemic risk assessment method according to the large-scale cattle farm epidemic risk assessment system, thereby realizing the large-scale cattle farm epidemic risk assessment.
[0063] In some embodiments of the present application, in combination with the large-scale cattle farm epidemic risk assessment method described above, the present embodiments provide the following technical solutions: a storage medium, the storage medium has a computer program stored thereon, and the computer program is executed by a processor to realize the large-scale cattle farm epidemic risk assessment method described above.
[0064] Those skilled in the art will appreciate that the logic and / or steps represented in the flow diagrams, or otherwise described herein, can be embodied in
[0065] More specific examples (a non-exhaustive list) of the computer readable medium include the following: an electrical connection (electronic) having one or more wires, a portable computer diskette (magnetic), a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or Flash memory), an optical fiber device, and a portable compact disc read-only memory (CDROM). Additionally, the computer readable medium can also be paper or another suitable medium upon which the program is printed, as the program can be electronically captured, for example, via an optical scanner, then compiled, interpreted or otherwise processed in a suitable manner, if necessary, and then stored in a computer memory.
[0066] It should be understood that aspects of the application can be implemented in hardware, software, firmware or combinations thereof. In the above embodiments, various steps or methods can be implemented in software or firmware that is stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, any of the following technologies, or combinations thereof, can be used: a discrete logic circuit having logic gates for implementing logic functions upon data signals, an application specific integrated circuit having appropriate combinational logic gates, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.
[0067] The technical features of the above-described embodiments can be combined in any manner, and in order to make the description concise, not all possible combinations of the technical features in the above-described embodiments are described, however, as long as the combinations of the technical features do not contradict each other, they should be considered within the scope of the present specification.
[0068] The above embodiments only express several implementation manners of the present application, and the description is relatively specific and detailed, but should not be understood as a limitation on the patent scope of the present application. It should be noted that, for ordinary skilled persons in the art, several modifications and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the patent of the present application should be subject to the appended claims.
Claims
1. A method for assessing disease risk in large-scale cattle farms, characterized in that, include: Several sampling points were set up in the large-scale cattle farm, and corresponding microbial samples were collected at each sampling point; The microbial samples were processed and metagenomically sequenced to obtain the pathogen species and abundance information of the samples corresponding to each sampling point; The pathogen species and abundance information results are processed to obtain the processing results; Determine the abundance of pathogen nucleic acids in the processing results, and assign scores to the corresponding pathogens based on the abundance of pathogen nucleic acids to obtain pathogen scores; Disease risk assessment for large-scale cattle farms is conducted based on the pathogen scores.
2. The method for assessing disease risk in large-scale cattle farms according to claim 1, characterized in that, The sampling points include biological sampling points, indoor environmental sampling points, outdoor environmental sampling points, non-breeding environmental sampling points, and outdoor environmental sampling points. The microbial samples include bovine biological samples, samples inside the pen, samples outside the pen, samples from non-breeding areas, and samples from the perimeter of the farm.
3. The method for assessing disease risk in large-scale cattle farms according to claim 2, characterized in that, The step of collecting corresponding microbial samples at each of the sampling points includes: Cattle samples were randomly selected according to a predetermined ratio of the number of cattle raised, and samples were collected using whole blood, nasal swabs, and anal swabs to obtain bovine biological samples. Sampling was conducted according to the coverage of cattle feeding pens. Several ground sampling points were evenly selected in the pen to collect cotton swab samples. Air samples were collected three times a day, in the morning, noon and evening, using an air / aerosol sampler to obtain samples from inside the pen. Swab samples were collected from the corners of the enclosures at 20-meter intervals to obtain samples from outside the enclosures in the field area; Along the edge of the breeding area, collect cotton swab samples from the ground, building or tree surfaces every 50 meters to obtain samples from the non-breeding area of the farm. Cotton swab samples were collected from the surface of roads or buildings every 100 meters along the perimeter of the breeding area to obtain samples from the perimeter of the farm.
4. The method for assessing disease risk in large-scale cattle farms according to claim 1, characterized in that, The specific steps for processing and metagenomic sequencing the microbial samples to obtain the pathogen species and abundance information for each sampling point are as follows: A number of microbial samples were selected, and bovine biological samples were extracted from the microbial samples. Metagenomic sequencing was performed on the bovine biological samples using multiple individual mixed samples according to sample type. Samples from inside the pen, outside the pen, non-feeding area, and perimeter of the pen were extracted from the microbial samples. Metagenomic sequencing was performed on the samples from inside the pen, outside the pen, non-feeding area, and perimeter of the pen, using multiple samples of the same type according to sample type, to obtain pathogen species and abundance information.
5. The method for assessing disease risk in large-scale cattle farms according to claim 1, characterized in that, The steps for performing sample anomaly processing on the pathogen species and abundance information results to obtain the processing results include: Obtain sample data of the corresponding samples from the pathogen species and abundance information results, select any data from the sample data as the baseline data, and calculate the reachability distance of the baseline data. : ; In the formula, Representing benchmark data With the rest of the data The distance between them Representing benchmark data The distance; Based on the reachable distance Calculate achievable density : ； In the formula, This indicates the remaining data in the sample data compared to the baseline data. The distance between them is less than the corresponding first The distance sample set, To express summation; Based on the achievable density Calculate the first fraction : ； In the formula, Represents the remaining data achievable density; The sample data is input into a preset forest algorithm for processing. The results from each isolated tree are combined, and the average path length is normalized to obtain the second score. ; Based on the first score With the second score Calculate the overall score : ； In the formula, Indicates the fusion weights; Data with a comprehensive score below the score threshold is removed to obtain the processing result.
6. The method for assessing disease risk in large-scale cattle farms according to claim 1, characterized in that, The specific steps for assigning scores to corresponding pathogens based on the abundance of pathogen nucleic acids to obtain pathogen scores are as follows: If the abundance of pathogen nucleic acid is among the top 20 in the pathogen nucleic acid abundance ranking, the pathogen is assigned a score of 100. If the abundance of pathogen nucleic acid is not among the top 20 in the pathogen nucleic acid abundance ranking, the pathogen is assigned a score of 50. If the pathogen is not detected, the pathogen is assigned a score of 0, thus obtaining the pathogen score.
7. The method for assessing disease risk in large-scale cattle farms according to claim 1, characterized in that, The specific steps for conducting disease risk assessment of large-scale cattle farms based on the pathogen score are as follows: If the pathogen score is greater than the preset score and the corresponding pathogen is included in the target directory, then the pathogen is a high-risk pathogen. If the pathogen score is greater than the preset score and the corresponding pathogen is not included in the target directory, then the pathogen is a medium-risk pathogen. If the pathogen score is between 0 and the preset score and the corresponding pathogen is included in the target directory, then the pathogen is a medium-risk pathogen. If the pathogen score is 0 and the corresponding pathogen is included in the target directory, then the pathogen is a low-risk pathogen. If the pathogen score is between 0 and the preset score and the corresponding pathogen is not included in the target directory, then the pathogen is a medium-risk pathogen.
8. A disease risk assessment system for large-scale cattle farms, characterized in that, include: The collection module is used to set up several sampling points in a large-scale cattle farm and collect corresponding microbial samples at each sampling point; The sequencing module is used to process and perform metagenomic sequencing on the microbial samples to obtain the pathogen species and abundance information of the samples corresponding to each sampling point; The processing module is used to perform sample anomaly processing on the pathogen species and abundance information results to obtain the processing results; The scoring module is used to determine the abundance of pathogen nucleic acids in the processing results, and assign scores to the corresponding pathogens based on the abundance of pathogen nucleic acids to obtain pathogen scores; The assessment module is used to assess the disease risk of large-scale cattle farms based on the pathogen score.
9. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the method for assessing the risk of disease in large-scale cattle farms as described in any one of claims 1 to 7.
10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when executed by a processor, implements the method for assessing the risk of disease in large-scale cattle farms as described in any one of claims 1 to 7.