Evidence map›Paper›PMID 36149894›Full record

ArticlePLoS computational biology2022

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

Lin Zhang, Likai Chen, Xiaoqian Annie Yu, Claire Duvallet, Siavash Isazadeh, Chengzhen Dai, Shinkyu Park, Katya Frois-Moniz, Fabio Duarte, Carlo Ratti and 2 more

Open access · goldAbstract read
In one paragraph

Article in PLoS computational biology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
0.3field-weighted citation impact, top 44% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed, 4 citations in OpenAlex.

  1. Article
  2. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

12 authors at 3 institutions in 1 country.

Lin ZhangDepartment of Energy, Environmental and Chemical Engineering, Washington University in St. Louis, St. Louis, Missouri, United States of America.
Likai ChenDepartment of Mathematics, Washington University in St. Louis, St. Louis, Missouri, United States of America.ORCID 0000-0002-4603-1676
Xiaoqian Annie YuDepartment of Biology, Massachusetts Institute of Technology, Boston, Massachusetts, United States of America.ORCID 0000-0002-8105-8444
Claire DuvalletDepartment of Biological Engineering, Massachusetts Institute of Technology, Boston, Massachusetts, United States of America.ORCID 0000-0002-8093-8394
Siavash IsazadehDepartment of Biological Engineering, Massachusetts Institute of Technology, Boston, Massachusetts, United States of America.
Chengzhen DaiSENSEable City Lab, Massachusetts Institute of Technology, Boston, Massachusetts, United States of America.
Shinkyu ParkSENSEable City Lab, Massachusetts Institute of Technology, Boston, Massachusetts, United States of America.
Katya Frois-MonizDepartment of Biological Engineering, Massachusetts Institute of Technology, Boston, Massachusetts, United States of America.
Fabio DuarteSENSEable City Lab, Massachusetts Institute of Technology, Boston, Massachusetts, United States of America.ORCID 0000-0003-0909-5379
Carlo RattiSENSEable City Lab, Massachusetts Institute of Technology, Boston, Massachusetts, United States of America.
Eric J AlmDepartment of Biological Engineering, Massachusetts Institute of Technology, Boston, Massachusetts, United States of America.
Fangqiong LingDepartment of Energy, Environmental and Chemical Engineering, Washington University in St. Louis, St. Louis, Missouri, United States of America.ORCID 0000-0003-1546-5647
Massachusetts Institute of Technology · USWashington University in St. Louis · USBroad Institute · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

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.

Indexed as

COVID-19Gastrointestinal MicrobiomeHumansPandemicsPopulation DensitySewageWastewaterSewageWastewater

Identifiers

PMID36149894
PMCPMC9534451
OpenAlexW4296800684

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.