COVID-19 Visualization Platform Based on Population Density Propagation Model

Abstract

Based on the classical SIR model and CEMM intercity model, a new model was established by adding "population density" parameter to analyze and predict the spread of virus. In addition, the current trend of the epidemic and forecast data can be referenced to the public in an intuitive web view to improve the perception of risk information in the society. The real-time epidemic data interface was adopted to analyze the real-time pneumonia epidemic data captured by the deployment of timing crawler combined with the regional population density to build a model. Then, the diversified charts, Python and Web front-end technologies were used to realize the visualization of epidemic information. COVID-19 grows exponentially without obstruction, and when a place has a high population density, the spread of the virus accelerates and the number of people infected increases. The research shows that the integration of population density parameters can further improve the epidemic prediction function, provide epidemic data reference in a more effective and accurate way, and further improve the public's ability to perceive social risk information.