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

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 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. Based on the projection as of April 13, 2020 from the proposed model, the time for peak medical resource usage for US as a whole will be around the beginning of May. Also US will reach the peak in terms of daily new infected cases and death cases around middle of April. The total cumulative infected cases will reach to peak of 1.09 million people around June 12, 2020 and the estimated total death cases will reach to 90K in the end.