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dc.contributor.authorZhang, Lin
dc.contributor.authorChen, Likai
dc.contributor.authorYu, Xiaoqian Annie
dc.contributor.authorDuvallet, Claire
dc.contributor.authorIsazadeh, Siavash
dc.contributor.authorDai, Chengzhen
dc.contributor.authorPark, Shinkyu
dc.contributor.authorFrois-Moniz, Katya
dc.contributor.authorDuarte, Fabio
dc.contributor.authorRatti, Carlo
dc.contributor.authorAlm, Eric J
dc.contributor.authorLing, Fangqiong
dc.date.accessioned2023-01-26T13:54:16Z
dc.date.available2023-01-26T13:54:16Z
dc.date.issued2022
dc.identifier.urihttps://hdl.handle.net/1721.1/147715
dc.description.abstract<jats:p>The metagenome embedded in urban sewage is an attractive new data source to understand urban ecology and assess human health status at scales beyond a single host. Analyzing the viral fraction of wastewater in the ongoing COVID-19 pandemic has shown the potential of wastewater as aggregated samples for early detection, prevalence monitoring, and variant identification of human diseases in large populations. However, using census-based population size instead of real-time population estimates can mislead the interpretation of data acquired from sewage, hindering assessment of representativeness, inference of prevalence, or comparisons of taxa across sites. Here, we show that taxon abundance and sub-species diversisty in gut-associated microbiomes are new feature space to utilize for human population estimation. Using a population-scale human gut microbiome sample of over 1,100 people, we found that taxon-abundance distributions of gut-associated multi-person microbiomes exhibited generalizable relationships with respect to human population size. Here and throughout this paper, the human population size is essentially the sample size from the wastewater sample. We present a new algorithm, MicrobiomeCensus, for estimating human population size from sewage samples. MicrobiomeCensus harnesses the inter-individual variability in human gut microbiomes and performs maximum likelihood estimation based on simultaneous deviation of multiple taxa’s relative abundances from their population means. MicrobiomeCensus outperformed generic algorithms in data-driven simulation benchmarks and detected population size differences in field data. New theorems are provided to justify our approach. This research provides a mathematical framework for inferring population sizes in real time from sewage samples, paving the way for more accurate ecological and public health studies utilizing the sewage metagenome.</jats:p>en_US
dc.language.isoen
dc.publisherPublic Library of Science (PLoS)en_US
dc.relation.isversionof10.1371/JOURNAL.PCBI.1010472en_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.titleMicrobiomeCensus estimates human population sizes from wastewater samples based on inter-individual variability in gut microbiomesen_US
dc.typeArticleen_US
dc.identifier.citationZhang, Lin, Chen, Likai, Yu, Xiaoqian Annie, Duvallet, Claire, Isazadeh, Siavash et al. 2022. "MicrobiomeCensus estimates human population sizes from wastewater samples based on inter-individual variability in gut microbiomes." PLoS Computational Biology, 18 (9).
dc.contributor.departmentMassachusetts Institute of Technology. Department of Biological Engineeringen_US
dc.relation.journalPLoS Computational Biologyen_US
dc.eprint.versionFinal published versionen_US
dc.type.urihttp://purl.org/eprint/type/JournalArticleen_US
eprint.statushttp://purl.org/eprint/status/PeerRevieweden_US
dc.date.updated2023-01-26T13:44:57Z
dspace.orderedauthorsZhang, L; Chen, L; Yu, XA; Duvallet, C; Isazadeh, S; Dai, C; Park, S; Frois-Moniz, K; Duarte, F; Ratti, C; Alm, EJ; Ling, Fen_US
dspace.date.submission2023-01-26T13:45:03Z
mit.journal.volume18en_US
mit.journal.issue9en_US
mit.licensePUBLISHER_CC
mit.metadata.statusAuthority Work and Publication Information Neededen_US


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