Online trend estimation and detection of trend deviations in sub-sewershed time series of SARS-CoV-2 RNA measured in wastewater

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dc.citation.articleNumber5575en_US
dc.citation.journalTitleScientific Reportsen_US
dc.citation.volumeNumber14en_US
dc.contributor.authorEnsor, Katherine B.en_US
dc.contributor.authorSchedler, Julia C.en_US
dc.contributor.authorSun, Thomasen_US
dc.contributor.authorSchneider, Rebeccaen_US
dc.contributor.authorMulenga, Anthonyen_US
dc.contributor.authorWu, Jingjingen_US
dc.contributor.authorStadler, Lauren B.en_US
dc.contributor.authorHopkins, Lorenen_US
dc.date.accessioned2024-07-25T20:56:28Zen_US
dc.date.available2024-07-25T20:56:28Zen_US
dc.date.issued2024en_US
dc.description.abstractWastewater surveillance has proven a cost-effective key public health tool to understand a wide range of community health diseases and has been a strong source of information on community levels and spread for health departments throughout the SARS- CoV-2 pandemic. Studies spanning the globe demonstrate the strong association between virus levels observed in wastewater and quality clinical case information of the population served by the sewershed. Few of these studies incorporate the temporal dependence present in sampling over time, which can lead to estimation issues which in turn impact conclusions. We contribute to the literature for this important public health science by putting forward time series methods coupled with statistical process control that (1) capture the evolving trend of a disease in the population; (2) separate the uncertainty in the population disease trend from the uncertainty due to sampling and measurement; and (3) support comparison of sub-sewershed population disease dynamics with those of the population represented by the larger downstream treatment plant. Our statistical methods incorporate the fact that measurements are over time, ensuring correct statistical conclusions. We provide a retrospective example of how sub-sewersheds virus levels compare to the upstream wastewater treatment plant virus levels. An on-line algorithm supports real-time statistical assessment of deviations of virus level in a population represented by a sub-sewershed to the virus level in the corresponding larger downstream wastewater treatment plant. This information supports public health decisions by spotlighting segments of the population where outbreaks may be occurring.en_US
dc.identifier.citationEnsor, K. B., Schedler, J. C., Sun, T., Schneider, R., Mulenga, A., Wu, J., Stadler, L. B., & Hopkins, L. (2024). Online trend estimation and detection of trend deviations in sub-sewershed time series of SARS-CoV-2 RNA measured in wastewater. Scientific Reports, 14(1), 5575. https://doi.org/10.1038/s41598-024-56175-2en_US
dc.identifier.digitals41598-024-56175-2en_US
dc.identifier.doihttps://doi.org/10.1038/s41598-024-56175-2en_US
dc.identifier.urihttps://hdl.handle.net/1911/117544en_US
dc.language.isoengen_US
dc.publisherSpringer Natureen_US
dc.rightsExcept where otherwise noted, this work is licensed under a Creative Commons Attribution (CC BY) license.  Permission to reuse, publish, or reproduce the work beyond the terms of the license or beyond the bounds of fair use or other exemptions to copyright law must be obtained from the copyright holder.en_US
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/en_US
dc.titleOnline trend estimation and detection of trend deviations in sub-sewershed time series of SARS-CoV-2 RNA measured in wastewateren_US
dc.typeJournal articleen_US
dc.type.dcmiTexten_US
dc.type.publicationpublisher versionen_US
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