AI4Pandemics Talk #30: Xiunan Wang, The University of Tennessee at Chattanooga

Title: From Policy to Prediction: Forecasting COVID-19 Dynamics Under Imperfect Vaccination

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Abstract: In this talk, I will introduce a novel method in forecasting the daily number of confirmed cases of infectious diseases by combining a mechanistic ordinary differential equation (ODE) model for infectious classes and a generalized boosting machine learning model (GBM) for predicting how public health policies and mobility data affect the transmission rate in the ODE model. To illustrate, I will show how to apply the method to obtain a retrospective forecast of COVID-19 daily confirmed cases in the USA, and identify the relative influence of the policies used as the predictor variables. Our results indicate that the inclusion of data on non-pharmaceutical interventions can significantly improve the accuracy of the predictions. Moreover, the most influential predictor variables are the policies of restrictions on gatherings, testing and school closing. The modeling approach used in this work is helpful in designing improved forecasters as well as informing policymakers.