Daily Forecasting of Regional Epidemics of Coronavirus Disease with Bayesian Uncertainty Quantification, United States

• Main Authors: Yen Ting Lin, Jacob Neumann, Ely F. Miller, Richard G. Posner, Abhishek Mallela, Cosmin Safta, Jaideep Ray, Gautam Thakur, Supriya Chinthavali, William S. Hlavacek
• Format: Article
• Language: English
• Published: Centers for Disease Control and Prevention 2021-03-01
• Series: Emerging Infectious Diseases
• Subjects: mathematical model; statistics; uncertainty; epidemics; coronavirus disease; COVID-19
• Online Access: https://wwwnc.cdc.gov/eid/article/27/3/20-3364_article
• ISSN: 1080-6040; 1080-6059

To increase situational awareness and support evidence-based policymaking, we formulated a mathematical model for coronavirus disease transmission within a regional population. This compartmental model accounts for quarantine, self-isolation, social distancing, a nonexponentially distributed incubation period, asymptomatic persons, and mild and severe forms of symptomatic disease. We used Bayesian inference to calibrate region-specific models for consistency with daily reports of confirmed cases in the 15 most populous metropolitan statistical areas in the United States. We also quantified uncertainty in parameter estimates and forecasts. This online learning approach enables early identification of new trends despite considerable variability in case reporting.