ArticleEpidemiology and infection2013
A multi-state spatio-temporal Markov model for categorized incidence of meningitis in sub-Saharan Africa.
Article in Epidemiology and infection, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Monitoring the impact of desert dust outbreaks for air quality for health studies.Environment international · 2019Review
- Comparison of Models Analyzing a Small Number of Observed Meningitis Cases in Navrongo, Ghana.Journal of agricultural, biological, and environmental statistics · 2017Article
- Spatio-temporal factors associated with meningococcal meningitis annual incidence at the health centre level in Niger, 2004-2010.PLoS neglected tropical diseases · 2014Article
- Seasonality of meningitis in Africa and climate forcing: aerosols stand out.Journal of the Royal Society, Interface · 2013Article
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Authors and funding
4 authors.
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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.
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