MicrobiomeCensus estimates human population sizes from wastewater samples based on inter-individual variability in gut microbiomes

Lin Zhang, Likai Chen, Xiaoqian Yu, Claire Duvallet, Siavash Isazadeh, Chengzhen Dai, Shinkyu Park, Katya Frois-Moniz, Fabio Duarte, Carlo Ratti, Eric J. Alm, Fangqiong Ling*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review


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.

Original languageEnglish (US)
Article numbere1010472
JournalPLoS computational biology
Issue number9
StatePublished - Sep 2022

Bibliographical note

Funding Information:
Funding:FLacknowledgessupportfromthe NationalScienceFoundationDirectorateof EngineeringFacultyEarlyCareerDevelopment Program(2047470),AlfredP.SloanFoundation MicrobiologyoftheBuiltEnvironmentPostdoctoral Fellowship(2015-14164),aswellasWashington

Funding Information:
FL acknowledges support from the National Science Foundation Directorate of Engineering Faculty Early Career Development Program (2047470), Alfred P. Sloan Foundation Microbiology of the Built Environment Postdoctoral Fellowship (2015-14164), as well as Washington University in St. Louis Mckelvey School of Engineering Faculty Startup Fund. EJA and CR acknowledge funding from the Kuwait Foundation for Advancement of Sciences (KFAS). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. We thank Cho C. Yiu, David E Hingston, and Joseph S. Monteiro from the MIT Facilities department for assistance with sewage sampling. We thank Noriko Endo, Sean Gibbons, Tami Lieberman, Xiaofang Jiang and Shijie Zhao for valuable discussions. We thank Mariana Matus and Newsha Ghaeli for acquisition and access of 24-hr sewage time series data.

Publisher Copyright:
© 2022 Zhang et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

ASJC Scopus subject areas

  • Ecology, Evolution, Behavior and Systematics
  • Modeling and Simulation
  • Ecology
  • Molecular Biology
  • Genetics
  • Cellular and Molecular Neuroscience
  • Computational Theory and Mathematics


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