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

doi:10.3201/eid2703.203364

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

Biological Sciences

Research output: Contribution to journal › Article › peer-review

11 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 language	English (US)
Pages (from-to)	767-778
Number of pages	12
Journal	Emerging infectious diseases
Volume	27
Issue number	3
DOIs	https://doi.org/10.3201/eid2703.203364
State	Published - Mar 2021

ASJC Scopus subject areas

Epidemiology
Microbiology (medical)
Infectious Diseases

Access to Document

10.3201/eid2703.203364

Cite this

@article{304f8d0968754a67a10295522530fba2,

title = "Daily forecasting of regional epidemics of coronavirus disease with bayesian uncertainty quantification, United States",

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.",

author = "Lin, {Yen Ting} and Jacob Neumann and Miller, {Ely F.} and Posner, {Richard G.} and Abhishek Mallela and Cosmin Safta and Jaideep Ray and Gautam Thakur and Supriya Chinthavali and Hlavacek, {William S.}",

year = "2021",

month = mar,

doi = "10.3201/eid2703.203364",

language = "English (US)",

volume = "27",

pages = "767--778",

journal = "Emerging infectious diseases",

issn = "1080-6040",

publisher = "Centers for Disease Control and Prevention (CDC)",

number = "3",

}

TY - JOUR

T1 - Daily forecasting of regional epidemics of coronavirus disease with bayesian uncertainty quantification, United States

AU - Lin, Yen Ting

AU - Neumann, Jacob

AU - Miller, Ely F.

AU - Posner, Richard G.

AU - Mallela, Abhishek

AU - Safta, Cosmin

AU - Ray, Jaideep

AU - Thakur, Gautam

AU - Chinthavali, Supriya

AU - Hlavacek, William S.

PY - 2021/3

Y1 - 2021/3

N2 - 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.

AB - 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.

UR - http://www.scopus.com/inward/record.url?scp=85101497665&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85101497665&partnerID=8YFLogxK

U2 - 10.3201/eid2703.203364

DO - 10.3201/eid2703.203364

M3 - Article

C2 - 33622460

AN - SCOPUS:85101497665

SN - 1080-6040

VL - 27

SP - 767

EP - 778

JO - Emerging infectious diseases

JF - Emerging infectious diseases

IS - 3

ER -

Daily forecasting of regional epidemics of coronavirus disease with bayesian uncertainty quantification, United States

Abstract

ASJC Scopus subject areas

Access to Document

Other files and links

Fingerprint

Cite this