Abstract

COVID-19 pandemic is the defining global health crisis of our time that has posed unprecedented challenges to the society. We discuss statistical methods to predict COVID-19 pandemic course and compare mitigation measures across states to inform decision making.  We propose a robust and parsimonious survival-convolution model to forecast the disease epidemic. We account for transmission during a pre-symptomatic period and use a piece-wise infection rate function to reflect the temporal trend and change in response to a public health intervention. We estimate the intervention effect using a natural experiment design and quantify uncertainty by permutation. To compare state policies, we adjust for between-state differences in demographics, testing capacity, social economic vulnerability, and mobility. Our weekly realtime forecasts are included in the COVID ensemble modeling hub and CDC forecasts.

Dr. Wang is a Professor in the Department of Biostatistics and Department of Psychiatry at Columbia University, and a core member of the Division of Biostatistics at New York State Psychiatry Institute. Dr. Wang’s methodological interests include precision medicine, machine learning, analytics for large-scale complex data and emerging challenges for neuropsychiatric disorders, and novel design and analysis of clinical trials. Her substantive research area of interest includes applications to psychiatric disorders and neurological disorders.

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