A multi-state spatio-temporal Markov model for categorized incidence of meningitis in sub-Saharan Africa

L. Agier, M. Stanton, G. Soga, P. J. Diggle

Research output: Contribution to journalArticlepeer-review

Abstract

Meningococcal meningitis is a major public health problem in the African Belt. Despite the obvious seasonality of epidemics, the factors driving them are still poorly understood. Here, we provide a first attempt to predict epidemics at the spatio-temporal scale required for in-year response, using a purely empirical approach. District-level weekly incidence rates for Niger (1986-2007) were discretized into latent, alert and epidemic states according to pre-specified epidemiological thresholds. We modelled the probabilities of transition between states, accounting for seasonality and spatio-temporal dependence. One-week-ahead predictions for entering the epidemic state were generated with specificity and negative predictive value >99%, sensitivity and positive predictive value >72%. On the annual scale, we predict the first entry of a district into the epidemic state with sensitivity 65·0%, positive predictive value 49·0%, and an average time gained of 4·6 weeks. These results could inform decisions on preparatory actions.

Original languageEnglish (US)
Pages (from-to)1764-1771
Number of pages8
JournalEpidemiology and Infection
Volume141
Issue number8
DOIs
StatePublished - Aug 2013
Externally publishedYes

Keywords

  • Infectious disease surveillance
  • Markov multinomial model
  • meningitis
  • spatio-temporal statistics
  • sub-Saharan Africa

ASJC Scopus subject areas

  • Infectious Diseases
  • Epidemiology

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