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dc.contributor.authorLarson, Richard Charles
dc.contributor.authorBerman, Oded
dc.contributor.authorNourinejad, Mehdi
dc.date.accessioned2020-10-07T17:37:32Z
dc.date.available2020-10-07T17:37:32Z
dc.date.issued2020-10
dc.date.submitted2020-06
dc.identifier.issn1932-6203
dc.identifier.urihttps://hdl.handle.net/1721.1/127833
dc.description.abstractAbout 50% of individuals infected with the novel Coronavirus (SARS-CoV-2) suffer from intestinal infection as well as respiratory infection. They shed virus in their stool. Municipal sewage systems carry the virus and its genetic remnants. These viral traces can be detected in the sewage entering a wastewater treatment plant (WTP). Such virus signals indicate community infections but not locations of the infection within the community. In this paper, we frame and formulate the problem in a way that leads to algorithmic procedures homing in on locations and/or neighborhoods within the community that are most likely to have infections. Our data source is wastewater sampled and real-time tested from selected manholes. Our algorithms dynamically and adaptively develop a sequence of manholes to sample and test. The algorithms are often finished after 5 to 10 manhole samples, meaning that—in the field—the procedure can be carried out within one day. The goal is to provide timely information that will support faster more productive human testing for viral infection and thus reduce community disease spread. Leveraging the tree graph structure of the sewage system, we develop two algorithms, the first designed for a community that is certified at a given time to have zero infections and the second for a community known to have many infections. For the first, we assume that wastewater at the WTP has just revealed traces of SARS-CoV-2, indicating existence of a “Patient Zero” in the community. This first algorithm identifies the city block in which the infected person resides. For the second, we home in on a most infected neighborhood of the community, where a neighborhood is usually several city blocks. We present extensive computational results, some applied to a small New England city.en_US
dc.publisherPublic Library of Science (PLoS)en_US
dc.relation.isversionofhttp://dx.doi.org/10.1371/journal.pone.0240007en_US
dc.rightsCreative Commons Attribution 4.0 International licenseen_US
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/en_US
dc.sourcePLoSen_US
dc.titleSampling manholes to home in on SARS-CoV-2 infectionsen_US
dc.typeArticleen_US
dc.identifier.citationLarson, Richard C. et al. "Sampling manholes to home in on SARS-CoV-2 infections." PLoS ONE 15, 10 (October 2020): e0240007 © 2020 Larson et al.en_US
dc.contributor.departmentMassachusetts Institute of Technology. Institute for Data, Systems, and Societyen_US
dc.relation.journalPLoS ONEen_US
dc.eprint.versionFinal published versionen_US
dc.type.urihttp://purl.org/eprint/type/JournalArticleen_US
eprint.statushttp://purl.org/eprint/status/PeerRevieweden_US
dspace.date.submission2020-10-07T12:21:54Z
mit.journal.volume15en_US
mit.journal.issue10en_US
mit.licensePUBLISHER_CC
mit.metadata.statusComplete


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