  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.
