Towards realtime spatiotemporal prediction of district level meningitis incidence in sub-Saharan Africa

Michelle C. Stanton, and Lydiane Agier, Benjamin M. Taylor, Peter J. Diggle

Research output: Contribution to journalArticlepeer-review

3 Scopus citations


Within an area of sub-Saharan Africa termed 'the meningitis belt', meningococcal meningitis epidemics are a major public health concern. The epidemic control strategy that is currently utilized is reactive, such that a vaccination programme is initiated in a district once a predefined weekly incidence threshold has been exceeded. We report progress towards the development of an early warning system based on statistical modelling of district level weekly incidence data. Four modelling approaches are considered and their forecasting performances are compared by using weekly epidemiological data from Niger for the period 1986-2007. We conclude that the models under consideration are advantageous in different situations. The three-state Markov model described in which observed incidence is categorized according to policy-defined thresholds gives the most reliable short-term forecasts, whereas the dynamic linear model proposed, using log-transformed weekly incidence as the response variable, gives more reliable predictions of annual epidemics.

Original languageEnglish (US)
Pages (from-to)661-678
Number of pages18
JournalJournal of the Royal Statistical Society. Series A: Statistics in Society
Issue number3
StatePublished - 2014
Externally publishedYes


  • Dynamic generalized linear models
  • Epidemic control
  • Markov chain
  • Meningitis belt
  • Meningococcal meningitis

ASJC Scopus subject areas

  • Statistics and Probability
  • Economics and Econometrics
  • Social Sciences (miscellaneous)
  • Statistics, Probability and Uncertainty


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