A machine learning model for nowcasting epidemic incidence.

Due to delay in reporting, the daily national and statewide COVID-19 incidence counts are often unreliable and need to be estimated from recent data. This process is known in economics as nowcasting. We describe in this paper a simple random forest statistical model for nowcasting the COVID-19 daily new infection counts based on historic data along with a set of simple covariates, such as the currently reported infection counts, day of the week, and time since first reporting. We apply the model to adjust the daily infection counts in Ohio, and show that the predictions from this simple data-driven method compare favorably both in quality and computational burden to those obtained from the state-of-the-art hierarchical Bayesian model employing a complex statistical algorithm. The interactive notebook for performing nowcasting is available online at https://tinyurl.com/simpleMLnowcasting. • Delays in reporting often make daily statewide COVID-19 incidence counts unreliable. • We developed a simple model for nowcasting the daily counts from historic data. • To calibrate the model we use historic rates of backfilling COVID-19 cases in Ohio. • On the Ohio COVID-19 dataset our method outperforms the standard model. • The algorithm is implemented in jupyter notebook environment for public use. [ABSTRACT FROM AUTHOR]