152 results about "Outbreak" patented technology

The invention relates to the technical field of public health safety, in particular to an infectious disease transmission path prediction method and device and a medium, and the method comprises the following steps: extracting an initial tetrad of an event from infectious disease outbreak data, mapping the initial tetrad into a numerical value, and obtaining a numerical value tetrad; dividing the numerical tetrad according to time to obtain a time sequence knowledge graph; performing initialization embedding representation on entities and relationships in the time sequence knowledge graph; constructing a relational graph attention network, and learning structure information in the time sequence knowledge graph to obtain entity embedding and relation embedding; calling a gating circulation unit, and extracting time sequence information of the time sequence knowledge graph; and combining the structure information with the time sequence information by adopting a decoder, generating a prediction result, and realizing infectious disease transmission path prediction based on the prediction result. According to the method, the transmission path of the infectious diseases is predicted by adopting the time sequence knowledge graph, and compared with a traditional public health prevention method, the method has higher dynamic nature and multi-dimensional correlation analysis capability.

The invention provides a virulent bacteriophage and application thereof, the virulent bacteriophage is named as vBVpaPChan1 and has been preserved in China Center for Type Culture Collection on December 10, 2024, the preservation address is Wuhan University, Wuhan, Hubei, China, and the preservation number is CCTCC M 20242772. The bacteriophage is safe, environmentally friendly and free of toxic and side effects, and compared with known bacteriophages, the bacteriophage is short in incubation period, large in outbreak amount and capable of effectively killing diseases caused by vibrio parahaemolyticus chan02.

The invention relates to the technical field of infectious disease on-site simulation, in particular to a respiratory infectious disease on-site epidemic AI simulation experiment system based on environment evaluation. Performing virus region diffusion analysis based on a virus propagation risk assessment result, the environment data and the contact condition of nearby facilities, performing path virus risk assessment based on a path virus region diffusion analysis result, a personnel evacuation condition and a virus attenuation condition, and acquiring an evacuation path according to a path virus risk assessment result. By evaluating the risk of the virus in the path, the path with relatively low virus transmission risk can be accurately identified, and the path is selected for personnel evacuation in the period of infectious disease outbreak, so that the contact opportunity between evacuated personnel and the virus can be reduced to the greatest extent, the possibility of infectious disease infection is effectively reduced, and the life health and safety of the public are guaranteed.

Systems and methods for outbreak analysis by performing taxonomic classification of the first LRS data to assign one or more taxonomic identifiers to each record in the first LRS data; identifying a plurality of species-specific candidate genomes from a reference genome database based on the assigned taxonomic identifiers; aligning the first LRS data with each of the plurality of species-specific candidate genomes to determine a first species identity of a microorganism species present in the sample; identifying a first nearest strain of the identified microorganism species present in the first sample by aligning the first LRS data with a plurality of strain-specific candidate genome dataset.

The invention relates to the technical field of preventive medicine, and discloses a mouse-borne disease intelligent monitoring and early warning device based on multi-source data fusion, which comprises an environment sensor module, a mouse activity monitoring module, a pathogen detection module, a human case data acquisition module, a data fusion processing unit and an early warning and decision support unit, the data fusion processing unit is used for integrating and analyzing multi-source heterogeneous data from the above modules, and the early warning and decision support unit generates early warning information and prevention and control suggestions based on an analysis result. According to the method, the environment data, the mouse activity data, the pathogen detection data and the human case data are integrated, a multi-dimensional monitoring system is constructed, space-time correlation analysis is performed in combination with a deep learning algorithm, the prediction accuracy of disease outbreak is remarkably improved, the mouse recognition accuracy is high, and the pathogen detection specificity is excellent.

The invention discloses an infectious disease intelligent prediction and decision support system and method based on multi-source data, and relates to the technical field of infectious disease intelligent prediction, and the system comprises a prediction module and a decision module. Predicting the outbreak time period, the propagation scale and the propagation path of the target infectious disease; and the decision module is used for making a decision according to the predicted outbreak time period, the propagation scale and the propagation path of the target infectious disease. According to the method, the limitation of a traditional single data source is broken through by multi-source data integration, the multi-dimensional characteristics of infectious disease transmission can be comprehensively captured, and the accuracy and reliability of prediction are remarkably improved. The prevention and control strategy can be dynamically adjusted according to the real-time change of the epidemic situation, resource waste and excessive prevention and control are avoided, and the utilization efficiency of public health resources is improved.

A system for AI-driven predictive healthcare for pandemic prevention, which includes the following: a data collection module configured to capture health-related data from IoT-based wearable devices, electronic health records (EHRs), environmental sensors, and social media in real time; an AI-driven analysis module that is operationally linked to the data collection module and configured to use deep learning and natural language processing (NLP) techniques to process and analyze collected data in order to detect potential disease outbreaks; a predictive modeling module configured to use hybrid epidemiological and AI models to predict disease transmission patterns based on mobility data, population density, and historical infection trends; a warning and response module that automatically generates and disseminates notifications to health authorities, medical professionals, and vulnerable individuals based on predicted outbreak risks; a blockchain-based data security module configured to ensure the secure storage, access control, and verification of health-related data through decentralized ledger technology and smart contracts; and a decision support module that is operationally linked to the predictive modeling module and provides AI-driven recommendations for healthcare resource allocation, vaccination strategies, quarantine measures and public health measures.

The invention discloses an avian influenza space-time distribution characteristic sampling monitoring method and device, and relates to the field of avian influenza data processing, and the method comprises the steps: obtaining multi-source data of a research region; the multi-source data comprises an avian influenza outbreak data set, a land utilization data set, a vegetation index data set, a climate change data set, a population density data set and a topographic data set; performing time sequence analysis on the outbreak starting time according to months by using the avian influenza outbreak data set, counting the outbreak frequency per month, and determining the avian influenza outbreak time characteristics of the research area; performing avian influenza outbreak risk hot spot area and key factor identification according to the multi-source data by using a maximum entropy model to obtain avian influenza outbreak space distribution characteristics of the research area; the bird flu outbreak time characteristic and the bird flu outbreak space distribution characteristic form a bird flu space-time distribution characteristic, and the bird flu outbreak time characteristic and the bird flu outbreak space distribution characteristic aim at overcoming the defects in the existing monitoring technology and providing scientific basis and technical support for bird flu prevention and control.

The invention relates to the technical field of environmental monitoring and water ecological safety, in particular to a eukaryotic algae outbreak early warning method and system based on genus-level specific recognition. The method comprises the following steps: collecting a water body sample at a monitoring position according to a preset sampling plan, collecting an environment measurement value, and respectively obtaining environment parameter data, a water sample sampling identifier and a sampling timestamp; extracting nucleic acid from the water sample at the monitoring position based on the water sample sampling identifier, performing targeted amplification, and performing high-throughput sequencing at the same time to obtain eDNA original sequencing data; therefore, by constructing the eukaryotic algae outbreak early warning process based on genus-level specific recognition, the problems that in a traditional method, sampling disturbance is uncontrollable, sequence judgment precision is insufficient, trend recognition is lagged, and an early warning link is not transparent are solved, and the accuracy, stability and traceability of early judgment of algae outbreak are improved.

The invention discloses a content auditing abnormity monitoring and early warning method and system based on intelligent alarm suppression, and the method comprises the following steps: obtaining real-time content auditing data collected in multiple dimensions, determining multi-dimensional data, carrying out the data cleaning and standardization processing of the multi-dimensional data, and carrying out the monitoring and early warning of the content auditing abnormity. Storing the multi-dimensional data subjected to data cleaning and standardization processing into a database; and constructing a deep reinforcement learning model, determining data input of the deep reinforcement learning model based on the standardized multi-dimensional data, and driving the deep reinforcement learning model to optimize an alarm mode in a training process. According to the method, hidden violation and semantic evolution trends are recognized through semantic clustering, and the traditional detection bottleneck based on a numerical threshold value is broken through; according to the invention, by realizing cross-cycle trend evolution analysis, early signals before abnormal outbreak can be identified in advance; the method has the cross-event semantic comparison capability, and the false alarm rate and the redundant data volume are remarkably reduced.

The invention relates to the technical field of biological outbreak prediction, in particular to a marine organism outbreak prediction method and system based on real-time monitoring, and the method comprises the following steps: based on a sea area microsensor array, collecting environment and plankton data, processing interference and noise, normalizing parameters, and combining ecological baseline adjustment; and analyzing the environment and biological response relationship between the nodes, identifying a key area and a change trend, and judging plankton extension characteristics. According to the method, synchronous linkage of environmental factors and plankton response is realized through a joint discrimination mechanism of real-time acquisition, multi-parameter dynamic normalization and fusion of spatial distribution and time sequence data, sea area key change signals are identified by depending on ecological baseline reference, abnormal response is automatically screened in a monitoring network, and an extension situation is tracked; the space-time evolution trend of a risk area is effectively reflected, rapid identification and prospective early warning of potential explosion phenomena are realized through whole-process dynamic evaluation, and a reliable data basis is provided for ecological intervention and scientific management.

The invention relates to the technical field of epidemic disease monitoring, and discloses an infected population tracing method and system. The system comprehensively utilizes various data sources such as medical records, public health reports, social media dynamic and geographic position data and the like to realize real-time monitoring and accurate prediction of infectious disease epidemic situations. The system identifies high-risk areas and key propagation nodes through an advanced machine learning algorithm and a mathematical model, and provides accurate prevention and control measure suggestions. The method improves the traceability efficiency, optimizes the resource allocation, effectively supports public health decisions, and provides technical support for epidemic prevention and control. The system can guide a targeted intervention strategy by identifying key nodes and super propagators in a propagation chain, so that the overall propagation risk is reduced.

The invention discloses a medical health data analysis method based on privacy protection and information security, and relates to the technical field of health management, and the method comprises the steps: forming a data security management mechanism; obtaining corrected health data; analyzing and correcting the health data to form a disease distribution coefficient; predicting and forming a disease development trend based on the disease distribution coefficient; key preventive diseases are selected, and outbreak time points of the key preventive diseases are formed; analyzing to obtain a coping database of key preventive diseases; and the key preventive diseases are prevented in advance by using the data in the coping database. By correcting the abnormal value of the health data, forming a data security management mechanism and forming an outbreak time point for key disease prevention, abnormal data can be found in time, encryption can be carried out by using algorithms with different complexity degrees according to different privacy requirements of the data, excessive encryption is avoided, and the security of the health data is improved. And the more accurate outbreak time prediction can be carried out on the possible outbreak diseases.

The invention discloses an early warning method and system for outbreak of drug-resistant bacteria in a hospital based on dynamic aggregation degree evaluation. The method comprises the following steps: acquiring hospitalization information of a patient, microbiological inspection data and spatial layout information of an inpatient area to obtain initial data; judging whether a new case of drug-resistant bacteria is detected on the current day; if not, gradually reducing the historical aggregation degree by using an exponential decay formula until the historical aggregation degree returns to zero; if yes, calculating a newly added detection rate, a space aggregation degree and a time aggregation degree, and calculating a total aggregation degree of the day; dividing an inpatient area; dynamically generating a threshold value for the high-frequency inpatient area by adopting a quartile method to carry out abnormal value judgment, and identifying an abnormal aggregation degree for the low-frequency inpatient area by using a fixed absolute value threshold mechanism to obtain an early warning level; and early warning information of different levels is pushed according to the early warning level, and a prevention and control suggestion plan is attached. By implementing the method provided by the invention, the accuracy and efficiency of early warning can be remarkably improved, so that patients and medical personnel are better protected from the threat of drug-resistant bacterium infection.

The invention discloses a medical instrument demand management system and method based on big data, and the system comprises a demand trend module, a demand estimation module, a market analysis module, and a supply module, and relates to the technical field of medical instrument management. The technical key points are as follows: the use proportion of each age group is analyzed according to the deviation condition of the demands of different age groups for various medical instruments, so that the market demand of each medical instrument can be accurately speculated according to the age change condition of population; secondly, the diffusion trend and prevention and control requirements of various infectious diseases in a market area are known by analyzing treatment data, so that adjustment and planning are conveniently made before virus outbreak, the hysteresis quality of medical instrument supply is changed, and the requirements during market change are met; meanwhile, the upper limit of expansion of each supplier in the current market area can be known in combination with competitive product information and the market proportion, so that the production scale can be reasonably adjusted, and excessive competition and resource idleness are avoided.

The invention belongs to the technical field of microbiological detection, and particularly relates to a primer group, a kit and a method for detecting a coxsackie virus group A type 6 virus whole genome and application. A set of specific primer group aiming at CV-A6 whole genome sequence amplification is designed, aiming at different CV-A6 strains (different regions and different separation time), the super-multiplex PCR amplification can be realized, the whole genome sequence is further captured, the pathogen capture can be quickly realized at low cost in disease outbreak and epidemic periods, and the primer group has good practical value.

The invention relates to the technical field of industrial big data and intelligent breeding, and particularly discloses a pig farm breeding management system and method based on industrial big data. The system comprises a multi-source heterogeneous data acquisition module, a data fusion and feature engineering module, a subclinical period anomaly detection module, a causal inference and root cause tracing module and a dynamic resource optimization scheduling module. High-order features are extracted through multi-source data fusion, a double-layer adaptive model is adopted to realize sub-clinical period anomaly detection, then root causes are deduced and positioned in combination with causality, and finally an accurate resource scheduling instruction is generated based on multi-objective optimization. According to the invention, extremely early warning and accurate intervention of the health state of the swinery can be realized, the disease outbreak risk is reduced, and the breeding benefit is improved.

The invention relates to a jellyfish disaster early warning method, jellyfish disaster early warning equipment and a medium. The method comprises the following steps: acquiring the copepod dominance of a target water area on a date T and the diatom abundance of a date T-# imgabs0 #; if the copepod dominance is equal to or greater than a first threshold value and the diatom abundance is equal to or greater than a second threshold value, determining a jellyfish outbreak risk index of the target water area on a date T + # imgabs1 # according to the copepod dominance and the diatom abundance; otherwise, inputting the copepod dominance of the target water area on the date T, the diatom abundance of the date T-# imgabs2 # and the water quality parameter of the date T into a target prediction model corresponding to the target water area, and outputting a jellyfish outbreak risk index of the target water area on the date T + # imgabs3 #; and determining a jellyfish outbreak risk level of the target water area according to the jellyfish outbreak risk index. According to the scheme, the jellyfish outbreak risk index can be predicted in advance by combining the copepod dominance degree of the date T and the diatom abundance of the date T-# imgabs4 #.

The invention relates to the technical field of medical artificial intelligence and public health data management crossing, in particular to an intelligent tuberculosis screening and data analysis management system which comprises a data acquisition module, an intelligent screening module, a data analysis management module, a clinical interaction module and a system management module. The multi-source data fusion and the dynamic threshold reasoning enable the early-stage tiny focus detection rate to be larger than or equal to 85%, the screening time is shortened to 5 minutes per case (80% higher than that of manual film reading), cross-mechanism and cross-dimension data integrated acquisition and sharing are achieved, 'hospital-disease control-grassroots' data intercommunication is achieved, and the screening efficiency is improved. Complete data support is provided for epidemiological analysis, longitudinal individual tracking and transverse group analysis are combined, the early warning period of regional outbreak is larger than or equal to 2 weeks, and a public health prevention and control gateway is assisted to move forwards.

A system and method for outbreak analysis by performing a taxonomic classification of the first LRS data to assign one or more taxonomic identifiers to each record of the first LRS data, identifying a plurality of species-specific candidate genomes from a reference genome database based on the assigned taxonomic identifiers, aligning the first LRS data with each of the plurality of species-specific candidate genomes to determine a first species identity of a microbial species present in the sample, and identifying a first closest strain of the identified microbial species present in the first sample by aligning the first LRS data with a plurality of strain-specific candidate genome datasets.

The present invention belongs to the field of data mining technology and relates to a method for discovering the influence of key elements based on native-derivative topic transfer learning, including obtaining information including native topics and derived topics and related user information from an API interface provided by a social platform; constructing the early propagation network topology and propagation timing of derived topics, including using a joint distribution adaptive method to perform cross-domain feature adaptation on the content space of native topics and derived topics, and considering the sparsity of early data of derived topics, using an adversarial transfer learning method to compensate for the network structure; constructing a message-path-user ternary association graph of derived topics and performing cyclic iterative scoring to rank the influence of key elements of derived topics; the present invention can timely and accurately mine key elements in the early stage of the outbreak of derived topics, and the present invention can also be widely used in the precise placement of product advertisements, the discovery of important pathogenic genes, the prediction of popular research results, and the prevention of computer virus propagation.

The invention belongs to the technical field of aquatic pathogen detection, and provides a primer and probe combination with strong specificity and high sensitivity, a kit and a cdPCR detection method in order to solve the problems of dependence of absolute quantification of an existing qPCR technology on a standard curve and low repeatability and stability caused by uncontrollable quality of a standard substance. The primer probe can generate specific amplification on LYCIV and has no cross reaction on other common aquatic pathogens (such as NNV, DIV1 and the like), so that the specificity of a detection result is fundamentally ensured, a false positive result caused by the cross reaction is effectively avoided, and the diagnosis accuracy is improved. The method has the characteristics of extremely high sensitivity and absolute quantification, the lower limit of detection reaches 6.2 copies / mu L, the method has more advantages in quantification of samples with extremely low concentration, early diagnosis and detection of extremely low virus load can be realized, viruses with extremely low content in fish bodies can be detected earlier, precious time is provided for disease early warning and early intervention, and the method is worthy of popularization and application. Disease outbreak is effectively prevented.

The invention provides a fully humanized monoclonal antibody for resisting SARS-CoV and SARS-CoV-2 and application of the fully humanized monoclonal antibody, and belongs to the technical field of immunology, and the amino acid sequence of a heavy chain variable region of the monoclonal antibody is as shown in SEQ ID NO: 1; the amino acid sequence of a light chain variable region of the monoclonal antibody SARS2-1 is as shown in SEQ ID NO: 2; the antibody has a unique CDR partition, can identify S proteins of the two viruses at the same time, and has cross-neutralization activity on pseudoviruses of the two viruses. The antibody has the characteristics of high expression, full human source and good stability, is suitable for industrial production, and has potential application value for coping with outbreak prevalence caused by coronavirus possibly occurring at present and in the future.

The invention relates to the technical field of infectious disease monitoring, and discloses a respiratory infectious disease early warning method and system based on big data. The method comprises the following steps: identifying a first type of change nodes and calculating a second type of change nodes by calculating a patient number change value; a central area and a peripheral area are determined, and when it is monitored that a new case appears in the peripheral area, a first early warning signal is output based on the risk level of the central area; and defining the geographic position of the new case as a new seed location, updating the central area, and outputting a second early warning signal. According to the method, the infectious disease evolution time period is divided into at least one infectious disease development stage, so that differentiated risk assessment can be carried out for different infectious disease development stages, and when the geographical location of the infectious disease is transferred, the monitoring and early warning focus can focus on a new risk outbreak point, and the risk assessment accuracy is improved. The timeliness and geographical accuracy of early warning are ensured, and the utilization efficiency of prevention and control resources is improved.

This invention discloses a remote water quality monitoring system for secondary water supply systems, belonging to the field of water quality testing technology. It solves the problems of Legionella outbreaks caused by the coupling of hydraulic stagnation and high-temperature environments in secondary water supply systems, where the random distribution of pathogens in isolated pipe sections leads to spatial randomness in detection, false negatives, and high costs due to monitoring blind spots. The system includes a main control and computing module, an electrochemical impedance spectroscopy (EIS) measurement module, a microcurrent measurement module, a multi-channel switching module, a temperature sensing module, a bus power supply and communication module, and a three-electrode sensing patch. The main control and computing module coordinates and controls the operation sequence of all modules, executes EIS fitting and microcurrent trend analysis algorithms, and makes risk decisions based on the fusion results of multi-source data. This invention automatically monitors the water quality at the end of each pipeline, measures temperature, performs EIS scanning, analyzes weak metabolic currents, and fuses data to assess the risk of biofilm formation in real time, ultimately uploading early warning information.

The invention discloses a rapid detection method of a streptomyces specific bacteriophage for fermentation production, which comprises the following steps: placing a prepared double-layer detection flat plate in a to-be-detected environment for fixed-point and fixed-time detection, taking back the double-layer detection flat plate, culturing at constant temperature for 24-28 hours under the conditions that the temperature is 32-34 DEG C and the humidity is 40-45%, and observing and recording a result, so that the detection of the bacteriophage in the environment can be realized. Therefore, pollution to factory environment and laboratories can be completely eradicated by early finding and timely taking measures such as disinfection, and the possibility of outbreak can be completely eradicated. Meanwhile, the test liquid is added to a double-layer detection flat plate, and after uniform coating, constant-temperature culture is performed for 24-28 hours under the conditions that the temperature is 32-34 DEG C and the humidity is 40-45%, so that the detection of the test liquid infected bacteriophage is realized. According to the method, the existence of the bacteriophage can be visually observed, the number and the sedimentation rate of the bacteriophage can be detected, and the method is an effective, rapid, simple and convenient detection method and can prevent bacteriophage infection and outbreak.

The present disclosure relates to the technical field of data analysis, and particularly relates to a disease monitoring and early warning method, a storage medium, and a terminal device. The method includes: obtaining feature data for a preset monitoring period; determining a single feature sequence set according to the feature data in combination with feature weight data corresponding to a disease to be evaluated, wherein the single feature sequence includes the weighted sum result of each feature and the corresponding feature weight for each day within the monitoring period; inputting the single feature sequence set into a trained risk assessment model to obtain an outbreak risk assessment result of the disease to be evaluated within the preset monitoring period. This solution can improve the accuracy of the outbreak risk early warning for infectious diseases.

The invention provides a vibrio parahaemolyticus bacteriophage RKP-VP23248 and application of the vibrio parahaemolyticus bacteriophage RKP-VP23248, the vibrio parahaemolyticus bacteriophage is preserved in the China General Microbiological Culture Collection Center on September 27, 2024, and the preservation number of the vibrio parahaemolyticus bacteriophage RKP-VP23248 is CGMCC (China General Microbiological Culture Collection Center) No. 46193. The invention further provides a bacteriophage composition, and the bacteriophage composition comprises the vibrio parahaemolyticus bacteriophage RKP-VP23248. The bacteriophage provided by the invention is separated from natural water environments such as seawater, river water and water sediment, still keeps high-level titer under continuous passage, has relatively strong genetic stability and thermal stability, and can effectively cope with disease outbreak caused by mass propagation of pathogenic bacteria when water environment conditions change. The bacteriophage has strong lysis property on the drug-resistant high-pathogenicity vibrio, the lysis rate is up to 88.51%, and the problem of vibrio drug resistance enhancement caused by antibiotic abuse can be effectively solved, so that the problems of large-scale acute death and the like of aquatic animals such as fishes, shrimps and the like caused by the high-pathogenicity vibrio are solved.

This invention provides a quantitative prediction method for the spatiotemporal distribution changes of vole populations, belonging to the field of species distribution prediction in the ecological environment. The method includes: determining vole habitats based on their activity range; dividing the study area of ​​the vole habitat into an unstructured grid based on triangular units using a hydrodynamic model and a two-dimensional sediment model; constructing a vole habitat quality assessment model, a vole subpopulation migration model, a vole population size change model, and a vole stored energy change model; coupling these models for calculation, and predicting the distribution changes of the vole population based on the calculation results. This invention, through the coupled calculation of different models, can accurately predict the distribution range of vole based on the calculation results, which is of great significance for effectively controlling vole outbreaks.

This application discloses an indoor artificial rearing method for the rice stem borer, belonging to the field of insect rearing technology. This method addresses the problems of drowning in young larvae and frequent disease outbreaks in older larvae caused by homogeneous rearing across all generations. It constructs a constant-temperature environment and prepares granular dry feed for young larvae and slightly dehydrated flake feed for older larvae based on differences in larval mouthparts. A heterogeneous three-dimensional rearing device is used. In the young larvae stage, a slow-release substrate at the bottom maintains a vertical humidity gradient, and layered feeding prevents mechanical damage to the larvae. In the older larvae stage, substrate-free bare-bottom rearing and full-volume replacement feeding are employed. Timely removal of filamentous netting blocks the breeding environment for soft rot. Furthermore, the method combines pupation induction with corrugated paper induction and non-contact moisturizing, humidification during the adult dark period to delay gelatinous hardening, and overall substrate replacement during the light period to achieve damage-free egg mass harvesting. This application, through precise age-based management, significantly improves the survival rate and population propagation efficiency of the rice stem borer across all generations.