Coronavirus kit detection method based on big data

Abstract

The application relates to the technical field of medical detection, and discloses a new crown kit detection method based on big data, which comprises the following steps: S1, data-driven dynamic kit design, relying on a global virus gene database; S2, multi-level detection process design, the detection process is divided into three layers; S3, a comprehensive big data analysis platform is constructed under the support of a big data analysis platform; S4, a dynamic feedback and target optimization mechanism based on real-time monitoring and sequencing results of the big data analysis platform; and S5, integrated equipment deployment, integrated equipment is arranged in each community. By relying on the global virus gene database and artificial intelligence technology, new nucleic acid amplification targets and antigen detection schemes are generated in real time, rapid iteration of detection kits is realized, the mechanism effectively adapts to the continuous variation of the new coronavirus, high sensitivity and high specificity of detection are maintained, and missed detection and misjudgment caused by mutations are avoided, so that the accuracy and effectiveness of detection are ensured.

Description

Technical Field

[0001] This invention relates to the field of medical testing technology, specifically to a COVID-19 test kit method based on big data. Background Technology

[0002] COVID-19 test kits are medical tools used to detect infection with the novel coronavirus. They help determine whether a person is infected by detecting the presence of specific genetic material or antibodies against the SARS-CoV-2 virus in the body. The process primarily involves collecting nasopharyngeal swabs, throat swabs, or saliva samples, extracting viral RNA, and performing reverse transcription polymerase chain reaction (RT-PCR) detection. This method can accurately detect the genetic material of the SARS-CoV-2 virus and is suitable for early detection.

[0003] The COVID-19 test kit method based on big data mainly relies on technologies such as data analysis, machine learning, and artificial intelligence (AI). It combines traditional medical testing with the processing of large-scale data, optimizes the testing process, and improves accuracy, efficiency, and scalability. By analyzing a large amount of case data, test kit results, and viral genome information, it is possible to identify viral mutation patterns, transmission routes, and the differences in the effectiveness of different types of testing methods in different populations.

[0004] Traditional COVID-19 test kits typically use fixed nucleic acid amplification targets and antigen detection schemes designed based on known viral gene sequences. During testing, sample processing and analysis rely on large laboratory equipment, making the process complex, time-consuming, and unable to respond promptly to rapid viral mutations. Due to continuous changes in the viral genome, fixed-design detection targets cannot effectively identify new variants, leading to decreased detection sensitivity and false negatives. The specificity of the tests is also low. Cross-reactions with other pathogens similar to COVID-19 may occur, leading to false positives. Furthermore, the update speed of test kits is slow. Summary of the Invention

[0005] To address the shortcomings of existing technologies, this invention provides a COVID-19 test kit detection method based on big data. This method solves the problems of complex and time-consuming testing processes that rely on large laboratory equipment, the inability to respond promptly to rapid viral mutations, the inability of fixed-design detection targets to effectively identify new variants leading to missed detections, and the potential for cross-reactions with other pathogens similar to COVID-19, resulting in misdiagnosis.

[0006] To achieve the above objectives, the present invention provides the following technical solution: a COVID-19 test kit method based on big data, comprising the following steps: S1. Data-driven dynamic reagent kit design relies on a global viral gene database to collect and analyze the mutation information of the novel coronavirus in real time. By using artificial intelligence algorithms, it identifies the information of the novel coronavirus and designs dynamically adjusted nucleic acid amplification targets, antigen detection and reagent kits based on this information. The reagent kits are used to cope with different viral mutations and detection needs. S2. Multi-level detection process design: The detection process is divided into three levels. The first level is rapid initial screening through antigen or antibody detection, which is suitable for large-scale population screening and serves to reduce costs and improve efficiency. The second level is nucleic acid detection confirmation through digital PCR or CRISPR technology, and a multi-target amplification scheme is designed to cover key gene regions and common mutation sites. The third level is for abnormal samples or suspected missed samples, which uses high-throughput sequencing and artificial intelligence to analyze mutations. S3, the big data analysis platform supports the construction of a comprehensive big data analysis platform. Then, through the information from big data analysis, machine learning and statistical modeling are used to predict the trend of virus transmission. The platform analyzes and detects data in real time to identify high-risk groups or areas. S4. Dynamic feedback and target optimization mechanism: Based on real-time monitoring and sequencing results from the big data analysis platform, a target dynamic adjustment mechanism is established. The mechanism will automatically trigger the artificial intelligence model to generate new primers and probes. Then, combined with the dynamic module in the kit, it can adapt to the mutation information and complete the rapid update by inserting the dynamic module to replace it. This mechanism is used to ensure that the kit maintains high sensitivity and specificity for new variants and avoids the problems of missed detection and false positives. S5. Integrated equipment deployment: The integrated equipment is set up in various communities, medical institutions or remote areas to realize on-site testing. The information of each processed sample is analyzed by combining the intelligent analysis capabilities of the equipment. Then, the analysis information is directly uploaded to the platform through the network function to form real-time feedback and analysis.

[0007] Preferably, in S1, the viral gene database includes GISAID or the National Gene Bank data platform, the real-time mutation information is used to construct a dynamically updated genome database, the COVID-19 information includes high-frequency mutation sites and key gene regions, the kit includes a nucleic acid module, an antigen module and an antibody module, and the artificial intelligence algorithm includes convolutional neural networks, graph neural networks, recurrent neural networks, generative adversarial networks, reinforcement learning networks, deep belief networks, long short-term memory networks and transfer learning.

[0008] Preferably, in S2, the rapid initial screening includes detection using saliva or serum samples; the multi-target amplification is used to ensure the sensitivity and coverage specificity of the detection; the mutation status is used to verify the detection results and optimize the kit design; each layer of the three-layer detection process has a detection method and sample type, wherein different detection methods and sample types are used to complement and connect with each other.

[0009] Preferably, in S3, the big data analysis platform is used to summarize test results, viral genome information and epidemiological data, as well as to provide real-time analysis, prediction, optimization support and data sharing functions. The high-risk population or area is used to assist in precise epidemic prevention and control decision-making.

[0010] Preferably, in S4, the target dynamic adjustment mechanism is used to update the viral genome data in real time. The variation information includes changes in gene sequence, spike protein variation, key site mutation, protein structure change, antigenic epitope change, and viral strain classification and evolutionary relationship.

[0011] Preferably, in S5, the integrated device is formed by integrating rapid antigen detection, nucleic acid amplification detection, and mutation screening functions into a single device. The networking function adopts at least one of 5G, WiFi, or wired transmission. The integrated device is used to provide testing support for regional epidemic prevention and control in medical institutions or remote mountainous areas. The integrated device is suitable for operation by non-professionals and can be widely used in various testing scenarios.

[0012] This invention provides a COVID-19 test kit detection method based on big data. It has the following beneficial effects: 1. This invention relies on a global viral gene database and artificial intelligence technology to dynamically collect and analyze viral mutation information, generate new nucleic acid amplification targets and antigen detection schemes in real time, and achieve rapid iteration of detection kits. Through this mechanism, it can effectively adapt to the continuous mutation of the novel coronavirus, maintain high sensitivity and high specificity of detection, avoid missed detection and misjudgment caused by mutations, and ensure the accuracy and effectiveness of detection.

[0013] 2. This invention achieves comprehensive coverage from large-scale rapid screening to high-precision diagnosis through initial screening, nucleic acid detection confirmation, and high-throughput sequencing verification. The initial screening stage can reduce testing costs and improve screening efficiency, while nucleic acid detection confirmation provides accurate diagnosis, reducing false positives and false negatives. High-throughput sequencing verification allows for in-depth analysis of complex samples. When used, it can optimize reagent kit design and improve prevention and control strategies, forming a precise and efficient testing system.

[0014] 3. This invention integrates antigen detection, nucleic acid amplification detection, and mutation screening functions into a portable all-in-one device. The device automatically processes samples, supports real-time network connectivity and data upload, and is suitable for remote areas, community screening, and non-laboratory environments. At the same time, the device is easy to operate and suitable for non-professionals, achieving high efficiency and widespread availability of the testing process. It also provides instant data feedback, providing real-time support for epidemic prevention and control decisions. Attached Figure Description

[0015] Figure 1 This is a flowchart of the COVID-19 test kit detection method based on big data according to the present invention. Detailed Implementation

[0016] The technical solution of the present invention will now be clearly and completely described 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.

[0017] Example: Please see the appendix Figure 1 This invention provides a COVID-19 test kit detection method based on big data, including the following steps: S1. Data-driven dynamic reagent kit design relies on a global viral gene database to collect and analyze the mutation information of the novel coronavirus in real time. By using artificial intelligence algorithms, it identifies the information of the novel coronavirus and designs dynamically adjusted nucleic acid amplification targets, antigen detection and reagent kits based on this information. The reagent kits are used to cope with different viral mutations and detection needs. S2. Multi-level detection process design: The detection process is divided into three levels. The first level is rapid initial screening through antigen or antibody detection, which is suitable for large-scale population screening and serves to reduce costs and improve efficiency. The second level is nucleic acid detection confirmation through digital PCR or CRISPR technology, and a multi-target amplification scheme is designed to cover key gene regions and common mutation sites. The third level is for abnormal samples or suspected missed samples, which uses high-throughput sequencing and artificial intelligence to analyze mutations. S3, the big data analysis platform supports the construction of a comprehensive big data analysis platform. Then, through the information from big data analysis, machine learning and statistical modeling are used to predict the trend of virus transmission. The platform analyzes and detects data in real time to identify high-risk groups or areas. S4. Dynamic feedback and target optimization mechanism: Based on real-time monitoring and sequencing results from the big data analysis platform, a target dynamic adjustment mechanism is established. The mechanism will automatically trigger the artificial intelligence model to generate new primers and probes. Then, combined with the dynamic module in the kit, it can adapt to the mutation information and complete the rapid update by inserting the dynamic module to replace it. This mechanism is used to ensure that the kit maintains high sensitivity and specificity for new variants and avoids the problems of missed detection and false positives. S5. Integrated equipment deployment: The integrated equipment is set up in various communities, medical institutions or remote areas to realize on-site testing. The information of each processed sample is analyzed by combining the intelligent analysis capabilities of the equipment. Then, the analysis information is directly uploaded to the platform through the network function to form real-time feedback and analysis.

[0018] In S1, the viral gene database includes GISAID or the National Gene Bank data platform. Real-time mutation information is used to construct a dynamically updated genome database. The COVID-19 virus information includes high-frequency mutation sites and key gene regions. The kit includes modules covering nucleic acid, antigen, and antibody. Artificial intelligence algorithms include convolutional neural networks, graph neural networks, recurrent neural networks, generative adversarial networks, reinforcement learning networks, deep belief networks, long short-term memory networks, and transfer learning.

[0019] Specifically, this invention relies on a global viral gene database and artificial intelligence algorithms, including convolutional neural networks, long short-term memory networks, and transfer learning, to dynamically collect and analyze mutation information of the novel coronavirus, generating nucleic acid amplification targets and antigen detection targets in real time. Based on this modular design, the kit encompasses nucleic acid, antigen, and antibody detection modules, enabling rapid adaptation to new variants while maintaining high sensitivity and specificity. Utilizing a dynamically updated genome database and high-frequency mutation analysis, the kit's nucleic acid and antigen target design can adapt to rapidly mutating viral strains, significantly reducing the false negative rate. During use, the automatic learning mechanism of artificial intelligence can quickly discover new high-frequency mutation sites, shortening the kit iteration cycle from the traditional weeks to a few days, achieving rapid iteration of detection technology. The multi-target amplification scheme of the nucleic acid module covers conserved regions and mutation hotspots, ensuring detection sensitivity and stable detection even with viral mutations. The antigen module is optimized based on the mutated protein epitope structure, maintaining the broad spectrum and specificity of antigen detection. The antibody module detects patient antibody levels through dynamically designed virus-specific antigen epitopes, improving the ability to discriminate patient infection history and immune status.

[0020] In S2, rapid initial screening includes testing using saliva or serum samples. Multi-target amplification is used to ensure the sensitivity and coverage specificity of the test. Mutation analysis is used to verify the test results and optimize the kit design. Each of the three-layer testing process has a test method and sample type, and the different test methods and sample types are used to complement and connect with each other.

[0021] Specifically, the testing process is divided into three layers. The first layer, rapid initial screening, uses saliva or serum samples for antigen or antibody testing to screen potential infected individuals in a low-cost and efficient manner. The second layer, nucleic acid testing, uses a multi-target amplification strategy to cover key gene regions and mutation hotspots, ensuring the sensitivity and specificity of the test. The third layer uses high-throughput sequencing to analyze mutations in abnormal samples, verifying test results and optimizing reagent kit design. These three layers, through the connection and complementarity of different sample types and testing methods, form a complete testing system. This allows for a rapid initial screening stage suitable for large-scale population screening, reducing testing costs and improving screening efficiency; a nucleic acid testing confirmation stage providing accurate diagnosis, significantly reducing false positives and missed detections; and a high-throughput sequencing verification stage for in-depth analysis of difficult samples, optimizing reagent kit target design. The overall process balances efficiency and accuracy, adapting to different scenario needs, achieving full coverage from rapid screening to accurate diagnosis, effectively supporting epidemic prevention and control and public health decision-making.

[0022] In S3, the big data analysis platform is used to summarize test results, viral genome information and epidemiological data, as well as to provide real-time analysis, prediction, optimization support and data sharing functions. It is also used to assist in precise epidemic prevention and control decision-making for high-risk groups or areas.

[0023] Specifically, the big data analysis platform of this invention aggregates test results, viral genome information, and epidemiological data. Through machine learning and statistical modeling, it analyzes the epidemic transmission trend in real time, predicts potential risk areas and populations, and provides optimization support for testing strategies. The platform supports real-time data sharing, enabling coordinated testing and control strategies across different regions. By identifying high-risk areas and populations, it provides precise prevention and control recommendations. Thus, in practice, the platform achieves real-time monitoring and accurate prediction of epidemic transmission, effectively identifies high-risk populations or areas, and provides scientific decision-making support for public health departments. Through data sharing and optimization support, it improves the efficiency and targeting of the testing process, optimizes resource allocation, reduces the risk of epidemic spread, and significantly enhances the responsiveness and overall efficiency of epidemic prevention and control.

[0024] In S4, the target dynamic adjustment mechanism is used to update viral genome data in real time. The variation information includes changes in gene sequence, spike protein variation, key site mutation, protein structure change, antigenic epitope change, and viral strain classification and evolutionary relationship.

[0025] Specifically, the target dynamic adjustment mechanism acquires viral genome data in real time, identifying gene sequence changes, spike protein mutations, key site mutations, protein structure alterations, antigenic epitope changes, and the classification and evolutionary relationships of viral strains. Artificial intelligence algorithms analyze this variation information to dynamically generate optimized nucleic acid amplification targets and antigen detection targets, which are then rapidly updated through modular reagent kits, achieving efficient adaptation to new variants. This target dynamic adjustment mechanism ensures the detection system can quickly respond to the challenges posed by viral mutations, avoiding missed detections and misdiagnoses due to mutations. By accurately capturing variation information and optimizing target design in real time, the sensitivity and specificity of the reagent kit are significantly improved, while the kit iteration cycle is shortened, production costs are reduced, and strong support is provided for rapid response to sudden outbreaks and the spread of new variants.

[0026] In S5, the integrated device combines rapid antigen detection, nucleic acid amplification detection, and mutation screening into a single device. The networking function adopts at least one of 5G, WiFi, or wired transmission. The integrated device is used to provide testing support for epidemic prevention and control in medical institutions or remote mountainous areas. The integrated device is suitable for operation by non-professionals and can be widely used in various testing scenarios.

[0027] Specifically, the integrated device of this invention integrates rapid antigen detection, nucleic acid amplification detection, and mutation screening functions, consolidating multiple detection technologies into a single device. The device automates sample processing and detection procedures, combining 5G, WiFi, or wired transmission to achieve real-time data upload and result feedback. Designed for operation by non-professionals, it provides a simple and intuitive user interface, enabling deployment in various scenarios such as medical institutions, community testing sites, and remote mountainous areas, providing on-site testing support for epidemic prevention and control. The integrated device significantly improves testing efficiency and accessibility, reducing sample processing time and increasing detection accuracy through the integration of multiple detection functions; its network connectivity ensures real-time upload of test results and integration with the epidemic monitoring system, enabling immediate response and precise decision support. The device's portability and ease of operation expand its application scope, providing an effective solution for areas with scarce medical resources and large-scale screening, enhancing the flexibility and coverage of epidemic prevention and control.

[0028] 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 COVID-19 test kit detection method based on big data, characterized in that, Includes the following steps: S1. Data-driven dynamic reagent kit design relies on a global viral gene database to collect and analyze the mutation information of the novel coronavirus in real time. By using artificial intelligence algorithms, it identifies the information of the novel coronavirus and designs dynamically adjusted nucleic acid amplification targets, antigen detection and reagent kits based on this information. The reagent kits are used to cope with different viral mutations and detection needs. S2. Multi-level detection process design: The detection process is divided into three levels. The first level is rapid initial screening through antigen or antibody detection, which is suitable for large-scale population screening and serves to reduce costs and improve efficiency. The second level is nucleic acid detection confirmation through digital PCR or CRISPR technology, and a multi-target amplification scheme is designed to cover key gene regions and common mutation sites. The third level is for abnormal samples or suspected missed samples, which uses high-throughput sequencing and artificial intelligence to analyze mutations. S3, the big data analysis platform supports the construction of a comprehensive big data analysis platform. Then, through the information from big data analysis, machine learning and statistical modeling are used to predict the trend of virus transmission. The platform analyzes and detects data in real time to identify high-risk groups or areas. S4. Dynamic feedback and target optimization mechanism: Based on real-time monitoring and sequencing results from the big data analysis platform, a target dynamic adjustment mechanism is established. The mechanism will automatically trigger the artificial intelligence model to generate new primers and probes. Then, combined with the dynamic module in the kit, it can adapt to the mutation information and complete the rapid update by inserting the dynamic module to replace it. This mechanism is used to ensure that the kit maintains high sensitivity and specificity for new variants and avoids the problems of missed detection and false positives. S5. Integrated equipment deployment: The integrated equipment is set up in various communities, medical institutions or remote areas to realize on-site testing. The information of each processed sample is analyzed by combining the intelligent analysis capabilities of the equipment. Then, the analysis information is directly uploaded to the platform through the network function to form real-time feedback and analysis.

2. The COVID-19 test kit detection method based on big data according to claim 1, characterized in that: In S1, the viral gene database includes GISAID or the National Gene Bank data platform; the real-time mutation information is used to construct a dynamically updated genome database; the COVID-19 virus information includes high-frequency mutation sites and key gene regions; the kit includes modules covering nucleic acid, antigen, and antibody; and the artificial intelligence algorithm includes convolutional neural networks, graph neural networks, recurrent neural networks, generative adversarial networks, reinforcement learning networks, deep belief networks, long short-term memory networks, and transfer learning.

3. The COVID-19 test kit detection method based on big data according to claim 1, characterized in that: In S2, the rapid initial screening includes detection using saliva or serum samples. The multi-target amplification is used to ensure the sensitivity and coverage specificity of the detection. The mutation status is used to verify the detection results and optimize the kit design. Each layer of the three-layer detection process has a detection method and sample type, and the different detection methods and sample types are used to complement and connect with each other.

4. The COVID-19 test kit detection method based on big data according to claim 1, characterized in that: In S3, the big data analysis platform is used to summarize test results, viral genome information and epidemiological data, as well as to provide real-time analysis, prediction, optimization support and data sharing functions. The high-risk population or area is used to assist in precise epidemic prevention and control decision-making.

5. The COVID-19 test kit detection method based on big data according to claim 1, characterized in that: In S4, the target dynamic adjustment mechanism is used to update the viral genome data in real time. The variation information includes changes in gene sequence, spike protein variation, key site mutation, protein structure change, antigenic epitope change, and viral strain classification and evolutionary relationship.

6. The COVID-19 test kit detection method based on big data according to claim 1, characterized in that: In S5, the integrated device is formed by integrating rapid antigen detection, nucleic acid amplification detection, and mutation screening functions into a single device. The networking function adopts at least one of 5G, WiFi, or wired transmission. The integrated device is used to provide testing support for regional epidemic prevention and control in medical institutions or remote mountainous areas. The integrated device is suitable for operation by non-professionals and can be widely used in various testing scenarios.

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