Real-Time Differential Epidemic Analysis and Prediction for COVID-19 Pandemic

Tan, Sheldon X.D.; Chen, Liang

In this paper, we propose a new real-time differential virus transmission model, which can give more accurate and robust short-term predictions of COVID-19 transmitted infectious disease with benefits of near-term trend projection. Different from the existing Susceptible-Exposed-Infected-Removed (SEIR) based virus transmission models, which fits well for pandemic modeling with sufficient historical data, the new model, which is also SEIR based, uses short history data to find the trend of the changing disease dynamics for the infected, the dead and the recovered so that it can naturally accommodate the adaptive real-time changes of disease mitigation, business activity and social behavior of populations. As the parameters of the improved SEIR models are trained by short history window data for accurate trend prediction, our differential epidemic model, essentially are window-based time-varying SEIR model. Since SEIR model still is a physics-based disease transmission model, its near-term (like one month) projection can still be very instrumental for policy makers to guide their decision for disease mitigation and business activity policy change in a real-time. This is especially useful if the pandemic lasts more than one year with different phases across the world like 1918 flu pandemic. Numerical results on the recent COVID-19 data from China, Italy and US, California and New York states have been analyzed.