Modeling the positive testing rate of COVID-19 in South Africa using a semi-parametric smoother for binomial data

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dc.citation.articleNumber979230en_US
dc.citation.journalTitleFrontiers in Public Healthen_US
dc.citation.volumeNumber11en_US
dc.contributor.authorOwokotomo, Olajumoke Evangelinaen_US
dc.contributor.authorManda, Samuelen_US
dc.contributor.authorCleasen, Jürgenen_US
dc.contributor.authorKasim, Adetayoen_US
dc.contributor.authorSengupta, Rudradeven_US
dc.contributor.authorShome, Rahulen_US
dc.contributor.authorSubhra Paria, Soumyaen_US
dc.contributor.authorReddy, Taryleeen_US
dc.contributor.authorShkedy, Ziven_US
dc.date.accessioned2023-03-23T14:10:37Zen_US
dc.date.available2023-03-23T14:10:37Zen_US
dc.date.issued2023en_US
dc.description.abstractIdentification and isolation of COVID-19 infected persons plays a significant role in the control of COVID-19 pandemic. A country's COVID-19 positive testing rate is useful in understanding and monitoring the disease transmission and spread for the planning of intervention policy. Using publicly available data collected between March 5th, 2020 and May 31st, 2021, we proposed to estimate both the positive testing rate and its daily rate of change in South Africa with a flexible semi-parametric smoothing model for discrete data. There was a gradual increase in the positive testing rate up to a first peak rate in July, 2020, then a decrease before another peak around mid-December 2020 to mid-January 2021. The proposed semi-parametric smoothing model provides a data driven estimates for both the positive testing rate and its change. We provide an online R dashboard that can be used to estimate the positive rate in any country of interest based on publicly available data. We believe this is a useful tool for both researchers and policymakers for planning intervention and understanding the COVID-19 spread.en_US
dc.identifier.citationOwokotomo, Olajumoke Evangelina, Manda, Samuel, Cleasen, Jürgen, et al.. "Modeling the positive testing rate of COVID-19 in South Africa using a semi-parametric smoother for binomial data." <i>Frontiers in Public Health,</i> 11, (2023) Frontiers: https://doi.org/10.3389/fpubh.2023.979230.en_US
dc.identifier.digitalfpubh-11-979230en_US
dc.identifier.doihttps://doi.org/10.3389/fpubh.2023.979230en_US
dc.identifier.urihttps://hdl.handle.net/1911/114534en_US
dc.language.isoengen_US
dc.publisherFrontiersen_US
dc.rightsThis is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.en_US
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/en_US
dc.titleModeling the positive testing rate of COVID-19 in South Africa using a semi-parametric smoother for binomial dataen_US
dc.typeJournal articleen_US
dc.type.dcmiTexten_US
dc.type.publicationpublisher versionen_US
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