An ensemble approach to predicting the impact of vaccination on rotavirus disease in Niger

Jaewoo Park, Joshua Goldstein, Murali Haran, Matthew Ferrari

Research output: Contribution to journalArticlepeer-review

7 Citations (Scopus)

Abstract

Recently developed vaccines provide a new way of controlling rotavirus in sub-Saharan Africa. Models for the transmission dynamics of rotavirus are critical both for estimating current burden from imperfect surveillance and for assessing potential effects of vaccine intervention strategies. We examine rotavirus infection in the Maradi area in southern Niger using hospital surveillance data provided by Epicentre collected over two years. Additionally, a cluster survey of households in the region allows us to estimate the proportion of children with diarrhea who consulted at a health structure. Model fit and future projections are necessarily particular to a given model; thus, where there are competing models for the underlying epidemiology an ensemble approach can account for that uncertainty. We compare our results across several variants of Susceptible-Infectious-Recovered (SIR) compartmental models to quantify the impact of modeling assumptions on our estimates. Model-specific parameters are estimated by Bayesian inference using Markov chain Monte Carlo. We then use Bayesian model averaging to generate ensemble estimates of the current dynamics, including estimates of R0, the burden of infection in the region, as well as the impact of vaccination on both the short-term dynamics and the long-term reduction of rotavirus incidence under varying levels of coverage. The ensemble of models predicts that the current burden of severe rotavirus disease is 2.6–3.7% of the population each year and that a 2-dose vaccine schedule achieving 70% coverage could reduce burden by 39–42%.

Original languageEnglish
Pages (from-to)5835-5841
Number of pages7
JournalVaccine
Volume35
Issue number43
DOIs
Publication statusPublished - 2017 Oct 13

Bibliographical note

Funding Information:
The authors are grateful to Epicentre for providing the data sets for this research project. MF is funded by a grant from the Ecology and Evolution of Infectious Disease program of the NSF/NIH (award number 1 R01 GM105247-01 ).

Publisher Copyright:
© 2017

All Science Journal Classification (ASJC) codes

  • Molecular Medicine
  • Immunology and Microbiology(all)
  • veterinary(all)
  • Public Health, Environmental and Occupational Health
  • Infectious Diseases

Fingerprint

Dive into the research topics of 'An ensemble approach to predicting the impact of vaccination on rotavirus disease in Niger'. Together they form a unique fingerprint.

Cite this