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

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

Abstract

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.

Original languageEnglish (US)
Pages (from-to)767-778
Number of pages12
JournalEmerging infectious diseases
Volume27
Issue number3
DOIs
StatePublished - Mar 2021

ASJC Scopus subject areas

  • Epidemiology
  • Microbiology (medical)
  • Infectious Diseases

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