  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.
