Research And Implementation Of COVID-19 Information Tracking And Trend Prediction System

The novel coronavirus pneumonia has been spreading and spreading rapidly worldwide.The daily confirmed cases are increasing exponentially,which has a serious impact on human society.The research on the transmission mechanism of the new crown pneumonia has become a global concern.During the novel coronavirus pneumonia epidemic situation,the epidemic data became the focus of public attention.A large number of information tracking and forecasting applications were sprung up,timely conveying the quantity information and temporal spatial distribution and changes of the epidemic to the public,helping the public to quickly understand the current situation of the epidemic situation and infer the trend of the epidemic.Based on the visual expression and information prediction of epidemic data,this paper studies and implements the information tracking and trend prediction system of COVID-19 based on the multi-source data acquisition and model prediction technology,and combining the functional requirements such as collection,extraction,analysis and prediction of COVID-19 data.The system is composed of foreground visualization module and background core computing module.The foreground visualization module adopts B/S architecture,and is designed and implemented based on Echarts middleware technology,combined with the world map,China map,bar chart,pie statistics chart,curve statistical chart and other middleware.The visualization data of the epidemic data obtained from the background core computing module can directly reflect the real-time information and historical development trend of COVID-19.The color difference was used to show the severity of the epidemic situation in each region,and the development trend of the epidemic situation in each province in the designated time domain was given.The back-end core computing module includes multi-source data collection and storage module,historical data query analysis and trend prediction comparison module.The multi-source data collection and storage module collects the epidemic data of different information sources in real time,cleans the data according to the time stamp rules,and stores the standardized data by classification.The novel coronavirus pneumonia prediction model is based on the real data set of Hubei province.The SIR model under the three hypothesis condition of COVID-19 is put forward to predict the trend of the data of the new crown pneumonia epidemic.The matching function of the least squares is obtained by processing the data by means of linear regression analysis and processing the difference formula.Thus,the optimal model parameters can be solved to further improve the accuracy of the model prediction results.The test results show that the system has good real-time information tracking and high accuracy of trend prediction,which can provide support for ordinary users to observe the epidemic situation in real time and the supervision department to continuously control the epidemic situation,and then strive for more time for epidemic control and vaccine development.According to the design and development process of software engineering,this paper introduces the system in detail from five parts: introduction,related technology introduction,system requirement analysis and design,key technology,system implementation and testing.