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 language | English (US) |
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Pages (from-to) | 1764-1771 |
Number of pages | 8 |
Journal | Epidemiology and Infection |
Volume | 141 |
Issue number | 8 |
DOIs | |
State | Published - Aug 2013 |
Externally published | Yes |
Keywords
- Infectious disease surveillance
- Markov multinomial model
- meningitis
- spatio-temporal statistics
- sub-Saharan Africa
ASJC Scopus subject areas
- Infectious Diseases
- Epidemiology