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Network analysis of patterns and relevance of enteric pathogen co-infections among infants in a diarrhea-endemic setting.
- Source :
- PLoS Computational Biology, Vol 19, Iss 11, p e1011624 (2023)
- Publication Year :
- 2023
- Publisher :
- Public Library of Science (PLoS), 2023.
-
Abstract
- Despite significant progress in recent decades toward ameliorating the excess burden of diarrheal disease globally, childhood diarrhea remains a leading cause of morbidity and mortality in low-and-middle-income countries (LMICs). Recent large-scale studies of diarrhea etiology in these populations have revealed widespread co-infection with multiple enteric pathogens, in both acute and asymptomatic stool specimens. We applied methods from network science and ecology to better understand the underlying structure of enteric co-infection among infants in two large longitudinal birth cohorts in Bangladesh. We used a configuration model to establish distributions of expected random co-occurrence, based on individual pathogen prevalence alone, for every pathogen pair among 30 enteropathogens detected by qRT-PCR in both diarrheal and asymptomatic stool specimens. We found two pairs, Enterotoxigenic E. coli (ETEC) with Enteropathogenic E. coli (EPEC), and ETEC with Campylobacter spp., co-infected significantly more than expected at random (both pairs co-occurring almost 4 standard deviations above what one could expect due to chance alone). Furthermore, we found a general pattern that bacteria-bacteria pairs appear together more frequently than expected at random, while virus-bacteria pairs tend to appear less frequently than expected based on model predictions. Finally, infants co-infected with leading bacteria-bacteria pairs had more days of diarrhea in the first year of life compared to infants without co-infection (p-value
- Subjects :
- Biology (General)
QH301-705.5
Subjects
Details
- Language :
- English
- ISSN :
- 1553734X and 15537358
- Volume :
- 19
- Issue :
- 11
- Database :
- Directory of Open Access Journals
- Journal :
- PLoS Computational Biology
- Publication Type :
- Academic Journal
- Accession number :
- edsdoj.05d50a2b066a41b7bfcaf64230514caf
- Document Type :
- article
- Full Text :
- https://doi.org/10.1371/journal.pcbi.1011624&type=printable