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.articleNumber5575
dc.citation.journalTitleScientific Reports
dc.citation.volumeNumber14
dc.contributor.authorEnsor, Katherine B.
dc.contributor.authorSchedler, Julia C.
dc.contributor.authorSun, Thomas
dc.contributor.authorSchneider, Rebecca
dc.contributor.authorMulenga, Anthony
dc.contributor.authorWu, Jingjing
dc.contributor.authorStadler, Lauren B.
dc.contributor.authorHopkins, Loren
dc.date.accessioned2024-07-25T20:56:28Z
dc.date.available2024-07-25T20:56:28Z
dc.date.issued2024
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.
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/s41598-024-56175-2
dc.identifier.digitals41598-024-56175-2
dc.identifier.doihttps://doi.org/10.1038/s41598-024-56175-2
dc.identifier.urihttps://hdl.handle.net/1911/117544
dc.language.isoeng
dc.publisherSpringer Nature
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.
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.titleOnline trend estimation and detection of trend deviations in sub-sewershed time series of SARS-CoV-2 RNA measured in wastewater
dc.typeJournal article
dc.type.dcmiText
dc.type.publicationpublisher version
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