Simulation-free estimation of an individual-based SEIR model for evaluating nonpharmaceutical interventions with an application to COVID-19 in the District of Columbia

Daniel K Sewell; Aaron Miller; CDC MInD-Healthcare Program

doi:10.1371/journal.pone.0241949

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Simulation-free estimation of an individual-based SEIR model for evaluating nonpharmaceutical interventions with an application to COVID-19 in the District of Columbia

Journal article

Open access

Peer reviewed

Simulation-free estimation of an individual-based SEIR model for evaluating nonpharmaceutical interventions with an application to COVID-19 in the District of Columbia

Daniel K Sewell, Aaron Miller and CDC MInD-Healthcare Program

PloS one, Vol.15(11), pp.e0241949-e0241949

2020

DOI: 10.1371/journal.pone.0241949

PMCID: PMC7654811

PMID: 33170871

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Abstract

The ongoing COVID-19 pandemic has overwhelmingly demonstrated the need to accurately evaluate the effects of implementing new or altering existing nonpharmaceutical interventions. Since these interventions applied at the societal level cannot be evaluated through traditional experimental means, public health officials and other decision makers must rely on statistical and mathematical epidemiological models. Nonpharmaceutical interventions are typically focused on contacts between members of a population, and yet most epidemiological models rely on homogeneous mixing which has repeatedly been shown to be an unrealistic representation of contact patterns. An alternative approach is individual based models (IBMs), but these are often time intensive and computationally expensive to implement, requiring a high degree of expertise and computational resources. More often, decision makers need to know the effects of potential public policy decisions in a very short time window using limited resources. This paper presents a computation algorithm for an IBM designed to evaluate nonpharmaceutical interventions. By utilizing recursive relationships, our method can quickly compute the expected epidemiological outcomes even for large populations based on any arbitrary contact network. We utilize our methods to evaluate the effects of various mitigation measures in the District of Columbia, USA, at various times and to various degrees. Rcode for our method is provided in the supplementry material, thereby allowing others to utilize our approach for other regions.

Algorithms

Betacoronavirus - isolation & purification

Coronavirus Infections - diagnosis

Coronavirus Infections - epidemiology

Coronavirus Infections - prevention & control

Coronavirus Infections - virology

COVID-19

Disease Outbreaks

District of Columbia - epidemiology

Humans

Masks

Models, Theoretical

Pandemics - prevention & control

Pneumonia, Viral - diagnosis

Pneumonia, Viral - epidemiology

Pneumonia, Viral - prevention & control

Pneumonia, Viral - virology

Quarantine

SARS-CoV-2

Details

Title: Subtitle: Simulation-free estimation of an individual-based SEIR model for evaluating nonpharmaceutical interventions with an application to COVID-19 in the District of Columbia
Creators: Daniel K Sewell - Department of Biostatistics, University of Iowa, Iowa City, IA, United States of America
Aaron Miller - Department of Epidemiology, University of Iowa, Iowa City, IA, United States of America
CDC MInD-Healthcare Program
Resource Type: Journal article
Publication Details: PloS one, Vol.15(11), pp.e0241949-e0241949
DOI: 10.1371/journal.pone.0241949
PMID: 33170871
PMCID: PMC7654811
NLM abbreviation: PLoS One
ISSN: 1932-6203
eISSN: 1932-6203
Grant note: 1 U01 CK000594-01-00 / NIH HHS 5 U01 CK000531-02 / NIH HHS
Language: English
Date published: 2020
Academic Unit: Infectious Diseases; Biostatistics; Public Policy Center (Archive); Internal Medicine
Record Identifier: 9984214840602771

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6 Times Cited - Web of Science