Abstract

Accurate prediction of the number of daily or weekly confirmed cases of COVID-19 is critical to the control of the pandemic. In this talk, I will introduce our recently developed method in forecasting the daily confirmed cases of COVID-19 by a hybrid model consisting of a mechanistic ordinary differential equations (ODE) model and a generalized boosting machine learning model (GBM). To calibrate the parameters, we create an inverse method that obtains the transmission rate from the other variables in the ODE model and then feed it into the GBM to connect with the policy data. We apply the method to both the pre-vaccination and post-vaccination cases, accordingly obtain retrospective forecasts of COVID-19 daily confirmed cases in the US, and identify the relative influence of the predictor variables. The approach used in this work can be helpful in designing improved forecasters as well as informing policymakers.

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