Metagenomic strain tracking among children in urban and rural communities in low- and middle-income countries.

Childhood bacterial enteric infections remain a significant public health challenge in low- and middle-income countries. However, the mechanisms underlying bacterial exchange among humans are poorly understood. Here, we collected 511 stool samples from children and mothers across urban and rural communities in Kenya and applied a machine learning-based strain-tracking framework to investigate bacterial strain-sharing dynamics and evaluate the impact of community-level water chlorination. This study provides new insights into how bacterial strains disseminate within and between households and communities in low- and middle-income settings. By integrating metagenomic sequencing with machine learning, it elucidates both the microbial dissemination dynamics and the extent to which community-scale water treatment can disrupt microbial spread, informing strategies to mitigate pathogen transmission in vulnerable populations.