Pandemic velocity: Forecasting COVID-19 in the US with a machine learning & Bayesian time series compartmental model.

ABSTRACT: Predictions of COVID-19 case growth and mortality are critical to the decisions of political leaders, businesses, and individuals grappling with the pandemic. This predictive task is challenging due to the novelty of the virus, limited data, and dynamic political and societal responses. We embed a Bayesian time series model and a random forest algorithm within an epidemiological compartmental model for empirically grounded COVID-19 predictions. The Bayesian case model fits a location-specific curve to the velocity (first derivative) of the log transformed cumulative case count, borrowing strength across geographic locations and incorporating prior information to obtain a posterior distribution for case trajectories. The compartmental model uses this distribution and predicts deaths using a

SUBMITTER: Watson GL 

PROVIDER: S-EPMC8031749 | biostudies-literature | 2021 Mar

REPOSITORIES: biostudies-literature