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

Agier, L., Stanton, Michelle ORCID: https://orcid.org/0000-0002-1754-4894, Soga, G. and Diggle, P. J. (2013) 'A multi-state spatio-temporal Markov model for categorized incidence of meningitis in sub-Saharan Africa'. Epidemiology and Infection, Vol 141, Issue 8, pp. 1764-1771.

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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.

Item Type: Article
Subjects: WA Public Health > WA 105 Epidemiology
WA Public Health > Health Problems of Special Population Groups > WA 395 Health in developing countries
WC Communicable Diseases > Infection. Bacterial Infections > Bacterial Infections > WC 245 Meningococcal infections
Faculty: Department: Biological Sciences > Department of Tropical Disease Biology
Digital Object Identifer (DOI): https://doi.org/10.1017/s0950268812001926
Depositing User: Lynn Roberts-Maloney
Date Deposited: 06 Feb 2015 09:46
Last Modified: 06 Feb 2018 13:08
URI: http://archive.lstmed.ac.uk/id/eprint/4856

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