Journal Article

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

Ramirez, Christina M., Zhang, Lu, Suchard, Marc A., Zoller, Joseph A., Shamshoian, John, Xiong, Di, Rimoin, Anne W., Sundin, Phillip, Watson, Gregory L., Bufford, Teresa

2021

Overview

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 random forest algorithm trained on COVID-19 data and population-level characteristics, yielding daily projections and interval estimates for cases and deaths in U.S. states. We evaluated the model by training it on progressively longer periods of the pandemic and computing its predictive accuracy over 21-day forecasts. The substantial variation in predicted trajectories and associated uncertainty between states is illustrated by comparing three unique locations: New York, Colorado, and West Virginia. The sophistication and accuracy of this COVID-19 model offer reliable predictions and uncertainty estimates for the current trajectory of the pandemic in the U.S. and provide a platform for future predictions as shifting political and societal responses alter its course.

• Publisher: Public Library of Science,Public Library of Science (PLoS)
• Subject: Accuracy / Algorithms / Bayes Theorem / Bayesian analysis / Bayesian statistical decision theory / Computational Biology / Computer and Information Sciences / Coronaviruses / COVID-19 / COVID-19 - epidemiology / COVID-19 - mortality / COVID-19 - transmission / Disease transmission / Distribution / Epidemics / Forecasting / Forecasting - methods / Forecasts and trends / Growth models / Humans / Infections / Infectious diseases / Learning algorithms / Machine Learning / Mathematical models / Medicine and Health Sciences / Model accuracy / Model testing / Models, Statistical / Neural networks / Pandemics / Pandemics - statistics & numerical data / People and places / Physical Sciences / Population / Regression models / Research and Analysis Methods / SARS-CoV-2 / Severe acute respiratory syndrome coronavirus 2 / Statistical analysis / Statistical models / Time series / Time-series analysis / United States / United States - epidemiology