Musene, K. | bioRxiv Science

bioRxiv2025

Gut microbiome associations with poliovirus vaccine seroconversion in children in the Democratic Republic of the Congo

BackgroundDespite efforts to increase global immunization, vaccination seroconversion in low and middle income countries (LMICs) is often lower than in high income countries (HICs). The reasons for this disparity are not fully understood. Given the role of the gut microbiome in immune development, we investigated the relationship between the gut microbiome and polio vaccine seroconversion in children in the Democratic Republic of the Congo (DRC). MethodsThis cross-sectional analysis included children ages 6-24 months old (n=90) enrolled in the DRC. Vaccine history was obtained from health records and/or self-report and poliovirus serostatus surveyed using dried blood spots. Nutritional status was evaluated using anthropomorphic measures. Microbiome profiling (16S rRNA gene) was performed and associations with poliovirus serostatus and malnutrition were examined. ResultsThe average age of the study population was 13.6 months (SD=5.6) with 58% female. Poliovirus seropositivity was 65.5% and 22% of the children were malnourished. We found that presence of Campylobacter and Veillonella, especially at an early age, was associated with low poliovirus vaccination seroconversion. These bacterial taxa differed from those associated with malnutrition. ConclusionsThe presence of enteropathogens such as Campylobacter at a young age could be an important factor contributing to low vaccination seroconversion in children in LMICs.

microbiology↗

bioRxiv2018

Real-time projections of epidemic transmission and estimation of vaccination impact during an Ebola virus disease outbreak in the Eastern region of the Democratic Republic of Congo

BackgroundAs of October 12, 2018, 211 cases of Ebola virus disease (EVD) were reported in North Kivu Province, Democratic Republic of Congo. Since the beginning of October the outbreak has largely shifted into regions in which active armed conflict is occurring, and in which EVD cases and their contacts are difficult for health workers to reach. We used available data on the current outbreak with case-count time series from prior outbreaks to project the short-term and long-term course of the outbreak.\n\nMethodsFor short and long term projections we modeled Ebola virus transmission using a stochastic branching process that assumes gradually quenching transmission estimated from past EVD outbreaks, with outbreak trajectories conditioned on agreement with the course of the current outbreak, and with multiple levels of vaccination coverage. We used a negative binomial autoregression for short-term projections, a Theil-Sen regression model for final sizes, and a baseline minimum-information projection using Gotts law to construct an ensemble of forecasts to be compared and recorded for future evaluation against final outcomes. From August 20 to October 13, short-term model projections were validated against actual case counts.\n\nResultsDuring validation of short-term projections, from one week to four weeks, we found models consistently scored higher on shorter-term forecasts. Based on case counts as of October 13, the stochastic model projected a median case count of 226 cases by October 27 (95% prediction interval: 205-268) and 245 cases by November 10 (95% prediction interval: 208-315), while the auto-regression model projects median case counts of 240 (95% prediction interval: 215-307) and 259 (95% prediction interval: 216-395) cases for those dates, respectively. Projected median final counts range from 274 to 421. Except for Gotts law, the projected probability of an outbreak comparable to 2013-2016 is exceedingly small. The stochastic model estimates that vaccine coverage in this outbreak is lower than reported in its trial setting in Sierra Leone.\n\nConclusionsBased on our projections we believe that the epidemic had not yet peaked at the time of these estimates, though a trajectory on the scale of the West African outbreak is exceedingly improbable. Validating our models in real time allowed us to generate more accurate short-term forecasts, and this process may provide a useful roadmap for real-time short-term forecasting. We estimate that transmission rates are higher than would be seen under target levels of 62% coverage due to contact tracing and vaccination, and this model estimate may offer a surrogate indicator for the outbreak response challenges.

epidemiology↗