  Accurate prediction of the number of daily or weekly confirmed cases of
COVID-19 is critical to the control of the pandemic. Existing mechanistic
models nicely capture the disease dynamics. However, to forecast the future,
they require the transmission rate to be known, limiting their prediction
power. Typically, a hypothesis is made on the form of the transmission rate
with respect to time. Yet the real form is too complex to be mechanistically
modeled due to the unknown dynamics of many influential factors. We tackle this
problem by using a hypothesis-free machine-learning algorithm to estimate the
transmission rate from data on non-pharmaceutical policies, and in turn
forecast the confirmed cases using a mechanistic disease model. More
specifically, we build a hybrid model consisting of a mechanistic ordinary
differential equation (ODE) model and a generalized boosting model (GBM). To
calibrate the parameters, we develop an "inverse method" that obtains the
transmission rate inversely in time from the other variables in the ODE model
and then feed it into the GBM to connect with the policy data. The resulting
model forecasted the number of daily confirmed cases up to 35 days in the
future in the United States with an averaged mean absolute percentage error of
27%. Being partly data-driven, the method is more accurate than typical
mechanistic models and meanwhile more intuitive, and possibly reliable, than
purely data-based machine learning models. Moreover, it can identify the most
informative predictive variables, which can be helpful in designing improved
forecasters as well as informing policymakers.
