Related Experiment Video
Updated: Jul 10, 2025
A High-throughput Platform for the Screening of Salmonella spp./Shigella spp.
Published on: November 7, 2018
Network analysis of patterns and relevance of enteric pathogen co-infections among infants in a diarrhea-endemic
E Ross Colgate1,2, Connor Klopfer3, Dorothy M Dickson1,2
1Translational Global Infectious Disease Research Center, University of Vermont, Burlington, Vermont, United States of America.
Insights
Childhood diarrhea in low-income countries is often caused by multiple pathogens. Network analysis revealed specific bacterial co-infections, like ETEC and EPEC, are more common than random and linked to increased diarrhea days.
Area of Science:
- Infectious Disease Epidemiology
- Network Science
- Microbial Ecology
Background:
- Childhood diarrhea remains a major global health challenge, particularly in low- and middle-income countries (LMICs).
- Studies show frequent co-infections with multiple enteric pathogens in both symptomatic and asymptomatic children.
- Understanding co-infection patterns is crucial for effective disease control strategies.
Purpose of the Study:
- To investigate the structure of enteric co-infections in infants using network science methods.
- To determine if pathogen co-occurrences deviate from random expectations.
- To assess the impact of specific co-infections on childhood diarrhea burden.
Main Methods:
- Applied network science and ecological models to analyze co-infection data from two large birth cohorts in Bangladesh.
- Utilized a configuration model to compare observed pathogen pair frequencies against random co-occurrence probabilities.
- Detected 30 enteropathogens using qRT-PCR in diarrheal and asymptomatic stool specimens.
Main Results:
- Identified significant co-infection of Enterotoxigenic E. coli (ETEC) with Enteropathogenic E. coli (EPEC), and ETEC with Campylobacter spp., occurring far more frequently than random chance.
- Observed a trend of increased co-occurrence for bacteria-bacteria pairs and decreased co-occurrence for virus-bacteria pairs compared to model predictions.
- Infants with co-infections involving key bacteria-bacteria pairs experienced significantly more days of diarrhea in their first year of life (p < 0.0001).
Conclusions:
- Network analysis reveals non-random structures in enteric co-infections among infants.
- Specific bacterial co-infections are strongly associated with increased diarrhea morbidity.
- Findings can inform targeted interventions to reduce common infection sources or understand co-infection mechanisms.
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 <0.0001). Our methods and results help us understand the structure of enteric co-infection which can guide further work to identify and eliminate common sources of infection or determine biologic mechanisms that promote co-infection.
