  The unprecedented coronavirus disease 2019 (COVID-19) pandemic is still a
worldwide threat to human life since its invasion into the daily lives of the
public in the first several months of 2020. Predicting the size of confirmed
cases is important for countries and communities to make proper prevention and
control policies so as to effectively curb the spread of COVID-19. Different
from the 2003 SARS epidemic and the worldwide 2009 H1N1 influenza pandemic,
COVID-19 has unique epidemiological characteristics in its infectious and
recovered compartments. This drives us to formulate a new infectious dynamic
model for forecasting the COVID-19 pandemic within the human mobility network,
named the SaucIR-model in the sense that the new compartmental model extends
the benchmark SIR model by dividing the flow of people in the infected state
into asymptomatic, pathologically infected but unconfirmed, and confirmed.
Furthermore, we employ dynamic modeling of population flow in the model in
order that spatial effects can be incorporated effectively. We forecast the
spread of accumulated confirmed cases in some provinces of mainland China and
other countries that experienced severe infection during the time period from
late February to early May 2020. The novelty of incorporating the geographic
spread of the pandemic leads to a surprisingly good agreement with published
confirmed case reports. The numerical analysis validates the high degree of
predictability of our proposed SaucIR model compared to existing resemblance.
The proposed forecasting SaucIR model is implemented in Python. A web-based
application is also developed by Dash (under construction).
