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

  • L. AGIER (a1), M. STANTON (a2), G. SOGA (a3) and P. J. DIGGLE (a1) (a4)

Summary

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.

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Copyright

Corresponding author

*Author for correspondence: Ms. L. Agier, CHICAS, Physics Building, Lancaster University, Lancaster LA1 4YD, UK. (Email: l.agier@lancaster.ac.uk)

References

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

  • L. AGIER (a1), M. STANTON (a2), G. SOGA (a3) and P. J. DIGGLE (a1) (a4)

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