Research And Implementation Of Visualization Analysis Of COVID-19 Data Based On Machine Learning

With the full-scale outbreak of the new crown epidemic,it has spread all over the world.During the development stage of the epidemic,the number of daily confirmed cases has increased rapidly,which has had a serious impact on the life and production of the entire society.Researchers from various countries are working on the new crown epidemic.Research on the mechanism of pneumonia transmission.During the COVID-19 epidemic,the various data of the epidemic are also a matter of great concern to the general public.It is very urgent to track and predict various information on the epidemic.Display information to the public to help people understand the real-time information of the epidemic in a timely manner.and future development trends.In this paper,through the study of information visualization technology,combined with the characteristics of the new crown epidemic data,using a variety of epidemic data acquisition technologies to collect,extract,analyze and predict the new crown epidemic data,and study the current information tracking and prediction system technology of the new crown epidemic.This article uses web crawler technology to obtain the data of the new crown pneumonia epidemic in various places,and uses Echarts technology to realize the visual display of the data.Through visual display,users can more intuitively understand the development trend of the global epidemic situation and the epidemic situation in various places.In terms of data display,this paper uses various forms such as world map,China map,line chart,and histogram to display epidemic data at different levels,such as global,national,provincial,and municipal.In addition to data display,this article also uses the MTS-LSTM model to predict the trend of the epidemic.By analyzing the epidemic data in Wuhan and other parts of China,the model proposes a multivariate long-short-term memory prediction model to predict the development trend of the global epidemic.Through comparative analysis with other prediction models,this paper proves that the prediction effect of the MTS-LSTM model is more accurate.Finally,this paper designs and develops a visual analysis system using Python’s Django framework.The system adopts B/S structure,including visualization module and background computing center module.The front end adopts Layui template and Echarts middleware technology,combined with world map,China map,various statistical graphs,etc.to realize.The background mainly uses real-time acquisition of data,calculates the prediction results,and displays them on the front desk.Using this visual component,it can intuitively display the real-time information of the new crown epidemic,and use different colors to indicate the severity of the epidemic in different regions.The research and implementation results of this paper show that the machine learning-based visualization and analysis system of COVID-19 data can provide important support for government decision-making,public health management,and epidemic prevention.The system can better display the epidemic data,help users better understand the epidemic trend,and predict the future development of the epidemic.