Daily Forecasting of New Cases for Regional Epidemics of Coronavirus Disease 2019 with Bayesian Uncertainty Quantification

TL;DR

This paper develops Bayesian models to forecast COVID-19 cases in US metropolitan areas, enabling early trend detection and uncertainty quantification for better policy support.

Contribution

It introduces a Bayesian calibration approach for epidemic models that adapt daily to new data, capturing uncertainties and early trend signals.

Findings

Identified significant upward trends in five US metro areas.

Models successfully calibrated with daily case reports.

Provided uncertainty estimates for future case predictions.

Abstract

To increase situational awareness and support evidence-based policy-making, we formulated two types of mathematical models for COVID-19 transmission within a regional population. One is a fitting function that can be calibrated to reproduce an epidemic curve with two timescales (e.g., fast growth and slow decay). The other is a compartmental model that accounts for quarantine, self-isolation, social distancing, a non-exponentially distributed incubation period, asymptomatic individuals, and mild and severe forms of symptomatic disease. Using Bayesian inference, we have been calibrating our models daily for consistency with new reports of confirmed cases from the 15 most populous metropolitan statistical areas in the United States and quantifying uncertainty in parameter estimates and predictions of future case reports. This online learning approach allows for early identification of new trends despite considerable variability in case reporting. We infer new significant upward trends for five of the metropolitan areas starting between 19-April-2020 and 12-June-2020.