Daily Forecasting of Regional Epidemics of Coronavirus Disease with Bayesian Uncertainty Quantification, United States
Yen Ting Lin, Jacob Neumann, Ely F. Miller, Richard G. Posner, Abhishek Mallela, Cosmin Safta, Jaideep Ray, Gautam Thakur, Supriya Chinthavali, William S. Hlavacek
Abstract
C oronavirus disease (COVID-19), caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) (1), was detected in the United States in January 2020 (2). Researchers documented deaths in the United States caused by COVID-19 in February (3). Thereafter, surveillance testing expanded nationwide (4). These and other efforts revealed community spread across the United States and exponential growth of new COVID-19 cases throughout most of March. Growth of cases during February-April had a doubling time of 2-3 days (5), similar to the doubling time of the initial outbreak in China ( The rapid increase in cases prompted broad adoption of social distancing practices such as teleworking, travel restrictions, use of face masks, and government mandates prohibiting public gatherings (7). The United States soon became a hotspot of the COVID-19 pandemic. In the United States, detection of new cases peaked in late April and steadily declined until mid-June (4). The decline in case numbers suggest that mandates and social distancing interventions effectively slowed COVID-19 transmission. Efforts to quantify the effects of these measures indicate that they substantially reduced disease prevalence (8,9).