A random forest model for forecasting regional COVID-19 cases utilizing reproduction number estimates and demographic data.

ABSTRACT: During the COVID-19 pandemic, predicting case spikes at the local level is important for a precise, targeted public health response and is generally done with compartmental models. The performance of compartmental models is highly dependent on the accuracy of their assumptions about disease dynamics within a population; thus, such models are susceptible to human error, unexpected events, or unknown characteristics of a novel infectious agent like COVID-19. We present a relatively non-parametric random forest model that forecasts the number of COVID-19 cases at the U.S. county level. Its most prioritized training features are derived from easily accessible, standard epidemiological data (i.e., regional test positivity rate) and the effective reproduction number ( Rt ) from compartmental mod

SUBMITTER: Galasso J 

PROVIDER: S-EPMC8731233 | biostudies-literature | 2022 Mar

REPOSITORIES: biostudies-literature