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

AGIER, L.; STANTON, M.; SOGA, G.; DIGGLE, P. J.

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.

SUB-Saharan Africa; MENINGITIS; MARKOV processes; DISEASE incidence; DISEASE progression; PUBLIC health; EPIDEMICS; COMMUNICABLE diseases

Epidemiology & Infection, 2013, Vol 141, Issue 8, p1764

0950-2688

Academic Journal

10.1017/S0950268812001926