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

Area of Science:

• Environmental microbiology
• Computational biology
• Public health genomics

Background:

• Urban sewage metagenomes offer insights into urban ecology and population health.
• Wastewater analysis, particularly during the COVID-19 pandemic, highlighted its potential for disease surveillance.
• Current methods using census data for population size estimation in wastewater studies are inaccurate and hinder data interpretation.

Purpose of the Study:

• To develop a novel method for estimating human population size in real-time using sewage samples.
• To establish a mathematical framework for accurate ecological and public health assessments based on wastewater metagenomics.

Main Methods:

• Utilized taxon abundance and sub-species diversity in gut-associated microbiomes as features for population estimation.
• Developed and applied the MicrobiomeCensus algorithm, employing maximum likelihood estimation based on deviations of multiple taxa's relative abundances from population means.
• Validated the algorithm using population-scale human gut microbiome data and data-driven simulation benchmarks.

Main Results:

• Demonstrated generalizable relationships between taxon abundance distributions in gut microbiomes and human population size.
• MicrobiomeCensus outperformed generic algorithms in simulations for estimating population size from sewage samples.
• Successfully detected population size differences in field data, confirming the algorithm's practical applicability.

Conclusions:

• Gut microbiome data provides a robust feature space for accurate human population estimation from sewage.
• The MicrobiomeCensus algorithm offers a reliable, real-time method for inferring population sizes, improving wastewater-based studies.
• This research enables more precise ecological and public health monitoring through sewage metagenome analysis.