Extending the susceptible-exposed-infected-removed (SEIR) model to handle the false negative rate and symptom-based administration of COVID-19 diagnostic tests: SEIR-fansy

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dc.contributor.authorBhaduri, Ritwiken
dc.contributor.authorKundu, Ritobanen
dc.contributor.authorPurkayastha, Soumiken
dc.contributor.authorKleinsasser, Michaelen
dc.contributor.authorBeesley, Lauren J.en
dc.contributor.authorMukherjee, Bhramaren
dc.contributor.authorDatta, Jyotishkaen
dc.date.accessioned2022-07-21T15:02:17Zen
dc.date.available2022-07-21T15:02:17Zen
dc.date.issued2022-06-15en
dc.description.abstractFalse negative rates of severe acute respiratory coronavirus 2 diagnostic tests, together with selection bias due to prioritized testing can result in inaccurate modeling of COVID-19 transmission dynamics based on reported "case" counts. We propose an extension of the widely used Susceptible-Exposed-Infected-Removed (SEIR) model that accounts for misclassification error and selection bias, and derive an analytic expression for the basic reproduction number R0 as a function of false negative rates of the diagnostic tests and selection probabilities for getting tested. Analyzing data from the first two waves of the pandemic in India, we show that correcting for misclassification and selection leads to more accurate prediction in a test sample. We provide estimates of undetected infections and deaths between April 1, 2020 and August 31, 2021. At the end of the first wave in India, the estimated under-reporting factor for cases was at 11.1 (95% CI: 10.7,11.5) and for deaths at 3.58 (95% CI: 3.5,3.66) as of February 1, 2021, while they change to 19.2 (95% CI: 17.9, 19.9) and 4.55 (95% CI: 4.32, 4.68) as of July 1, 2021. Equivalently, 9.0% (95% CI: 8.7%, 9.3%) and 5.2% (95% CI: 5.0%, 5.6%) of total estimated infections were reported on these two dates, while 27.9% (95% CI: 27.3%, 28.6%) and 22% (95% CI: 21.4%, 23.1%) of estimated total deaths were reported. Extensive simulation studies demonstrate the effect of misclassification and selection on estimation of R0 and prediction of future infections. A R-package SEIRfansy is developed for broader dissemination.en
dc.description.notesDivision of Cancer Prevention, National Cancer Institute, Grant/Award Number: 5P30CA046592-27; Michigan Institute of Data Science (MIDAS), Precision Health Initiative and Rogel Scholar Fund at the University of Michigan; Division of Mathematical Sciences, Grant/Award Number: 1712933; National Human Genome Research Institute, Grant/Award Numbers: 5R01HG008773-05, P30 CA046592; National Science Foundation, Grant/Award Numbers: 2015460, 1712933en
dc.description.sponsorshipDivision of Cancer Prevention, National Cancer Institute [5P30CA046592-27]; Michigan Institute of Data Science (MIDAS) at the University of Michigan; Rogel Scholar Fund at the University of Michigan; Division of Mathematical Sciences [1712933]; National Human Genome Research Institute [5R01HG008773-05, P30 CA046592]; National Science Foundation [1712933, 2015460]; Precision Health Initiative at the University of Michiganen
dc.description.versionPublished versionen
dc.format.mimetypeapplication/pdfen
dc.identifier.doihttps://doi.org/10.1002/sim.9357en
dc.identifier.eissn1097-0258en
dc.identifier.issn0277-6715en
dc.identifier.issue13en
dc.identifier.pmid35224743en
dc.identifier.urihttp://hdl.handle.net/10919/111312en
dc.identifier.volume41en
dc.language.isoenen
dc.publisherWileyen
dc.rightsCreative Commons Attribution-NonCommercial 4.0 Internationalen
dc.rights.urihttp://creativecommons.org/licenses/by-nc/4.0/en
dc.subjectcompartmental modelsen
dc.subjectinfection fatality rateen
dc.subjectR package SEIRfansyen
dc.subjectreproduction numberen
dc.subjectselection biasen
dc.subjectsensitivityen
dc.subjectundetected infectionsen
dc.titleExtending the susceptible-exposed-infected-removed (SEIR) model to handle the false negative rate and symptom-based administration of COVID-19 diagnostic tests: SEIR-fansyen
dc.title.serialStatistics in Medicineen
dc.typeArticle - Refereeden
dc.type.dcmitypeTexten

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