  Since December 2019, A novel coronavirus (2019-nCoV) has been breaking out in
China, which can cause respiratory diseases and severe pneumonia. Mathematical
and empirical models relying on the epidemic situation scale for forecasting
disease outbreaks have received increasing attention. Given its successful
application in the evaluation of infectious diseases scale, we propose a
Susceptible-Undiagnosed-Infected-Removed (SUIR) model to offer the effective
prediction, prevention, and control of infectious diseases. Our model is a
modified susceptible-infected-recovered (SIR) model that injects undiagnosed
state and offers pre-training effective reproduction number. Our SUIR model is
more precise than the traditional SIR model. Moreover, we combine domain
knowledge of the epidemic to estimate effective reproduction number, which
addresses the initial susceptible population of the infectious disease model
approach to the ground truth. These findings have implications for the
forecasting of epidemic trends in COVID-19 as these could help the growth of
estimating epidemic situation.
