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Recently developed vaccines provide a new way of controlling rotavirus insub-Saharan Africa. Models for the transmission dynamics of rotavirus arecritical both for estimating current burden from imperfect surveillance and forassessing potential effects of vaccine intervention strategies. We examinerotavirus infection in the Maradi area in southern Niger using hospitalsurveillance data provided by Epicentre collected over two years. Additionally,a cluster survey of households in the region allows us to estimate theproportion of children with diarrhea who consulted at a health structure. Modelfit and future projections are necessarily particular to a given model; thus,where there are competing models for the underlying epidemiology an ensembleapproach can account for that uncertainty. We compare our results acrossseveral variants of Susceptible-Infectious-Recovered (SIR) compartmental modelsto quantify the impact of modeling assumptions on our estimates. Model-specificparameters are estimated by Bayesian inference using Markov chain Monte Carlo.We then use Bayesian model averaging to generate ensemble estimates of thecurrent dynamics, including estimates of $R_0$, the burden of infection in theregion, as well as the impact of vaccination on both the short-term dynamicsand the long-term reduction of rotavirus incidence under varying levels ofcoverage. The ensemble of models predicts that the current burden of severerotavirus disease is 2.9 to 4.1% of the population each year and that a 2-dosevaccine schedule achieving 70% coverage could reduce burden by 37-43%.

An Ensemble Approach to Predicting the Impact of Vaccination on Rotavirus Disease in Niger