Evaluation of a Parsimonious COVID-19 Outbreak Prediction Model: Heuristic Modeling Approach Using Publicly Available Data Sets

dc.contributor.authorGupta, Agrayan K.
dc.contributor.authorGrannis, Shaun J.
dc.contributor.authorKasthurirathne, Suranga N.
dc.contributor.departmentFamily Medicine, School of Medicineen_US
dc.date.accessioned2022-02-24T20:04:03Z
dc.date.available2022-02-24T20:04:03Z
dc.date.issued2021-07
dc.description.abstractBackground: The COVID-19 pandemic has changed public health policies and human and community behaviors through lockdowns and mandates. Governments are rapidly evolving policies to increase hospital capacity and supply personal protective equipment and other equipment to mitigate disease spread in affected regions. Current models that predict COVID-19 case counts and spread are complex by nature and offer limited explainability and generalizability. This has highlighted the need for accurate and robust outbreak prediction models that balance model parsimony and performance. Objective: We sought to leverage readily accessible data sets extracted from multiple states to train and evaluate a parsimonious predictive model capable of identifying county-level risk of COVID-19 outbreaks on a day-to-day basis. Methods: Our modeling approach leveraged the following data inputs: COVID-19 case counts per county per day and county populations. We developed an outbreak gold standard across California, Indiana, and Iowa. The model utilized a per capita running 7-day sum of the case counts per county per day and the mean cumulative case count to develop baseline values. The model was trained with data recorded between March 1 and August 31, 2020, and tested on data recorded between September 1 and October 31, 2020. Results: The model reported sensitivities of 81%, 92%, and 90% for California, Indiana, and Iowa, respectively. The precision in each state was above 85% while specificity and accuracy scores were generally >95%. Conclusions: Our parsimonious model provides a generalizable and simple alternative approach to outbreak prediction. This methodology can be applied to diverse regions to help state officials and hospitals with resource allocation and to guide risk management, community education, and mitigation strategies.en_US
dc.eprint.versionFinal published versionen_US
dc.identifier.citationGupta, A. K., Grannis, S. J., & Kasthurirathne, S. N. (2021). Evaluation of a Parsimonious COVID-19 Outbreak Prediction Model: Heuristic Modeling Approach Using Publicly Available Data Sets. Journal of Medical Internet Research, 23(7), e28812. https://doi.org/10.2196/28812en_US
dc.identifier.issn1438-8871en_US
dc.identifier.urihttps://hdl.handle.net/1805/27952
dc.language.isoen_USen_US
dc.publisherJMIRen_US
dc.relation.isversionof10.2196/28812en_US
dc.relation.journalJournal of Medical Internet Researchen_US
dc.rightsAttribution 4.0 United States
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/*
dc.sourcePublisheren_US
dc.subjectemerging outbreaken_US
dc.subjectCovid-19en_US
dc.subjectmodeling disease outbreaken_US
dc.subjectprecision public healthen_US
dc.subjectpredictive modelingen_US
dc.titleEvaluation of a Parsimonious COVID-19 Outbreak Prediction Model: Heuristic Modeling Approach Using Publicly Available Data Setsen_US
dc.typeArticleen_US
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