  Since the onset of the COVID-19 outbreak in Wuhan, China, numerous
forecasting models have been proposed to project the trajectory of coronavirus
infection cases. We propose a new discrete-time Markov chain transition matrix
model that directly incorporates stochastic behavior and for which parameter
estimation is straightforward from available data. Using such data from China's
Hubei province (for which Wuhan is the provincial capital city), the model is
shown to be flexible, robust, and accurate. As a result, it has been adopted by
the first Shanghai assistance medical team in Wuhan's Jinyintan Hospital, which
was the first designated hospital to take COVID-19 patients in the world. The
forecast has been used for preparing medical staff, intensive care unit (ICU)
beds, ventilators, and other critical care medical resources and for supporting
real-time medical management decisions. Empirical data from China's first two
months (January/February) of fighting COVID-19 was collected and used to
enhance the model by embedding NPI efficiency into the model. We applied the
model to forecast Italy, South Korea, and Iran on March 9. Later we made
forecasts for Spain, Germany, France, US on March 24. Again, the model has
performed very well, proven to be flexible, robust, and accurate for most of
these countries/regions outside China.
