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Towards realtime spatiotemporal prediction of district level meningitis incidence in sub-Saharan Africa

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  • Michelle C. Stanton
  • and Lydiane Agier
  • Benjamin M. Taylor
  • Peter J. Diggle

Abstract

type="main" xml:id="rssa12033-abs-0001"> Within an area of sub-Saharan Africa termed ‘the meningitis belt’, meningococcal meningitis epidemics are a major public health concern. The epidemic control strategy that is currently utilized is reactive, such that a vaccination programme is initiated in a district once a predefined weekly incidence threshold has been exceeded. We report progress towards the development of an early warning system based on statistical modelling of district level weekly incidence data. Four modelling approaches are considered and their forecasting performances are compared by using weekly epidemiological data from Niger for the period 1986–2007. We conclude that the models under consideration are advantageous in different situations. The three-state Markov model described in which observed incidence is categorized according to policy-defined thresholds gives the most reliable short-term forecasts, whereas the dynamic linear model proposed, using log-transformed weekly incidence as the response variable, gives more reliable predictions of annual epidemics.

Suggested Citation

  • Michelle C. Stanton & and Lydiane Agier & Benjamin M. Taylor & Peter J. Diggle, 2014. "Towards realtime spatiotemporal prediction of district level meningitis incidence in sub-Saharan Africa," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 177(3), pages 661-678, June.
    • Handle: RePEc:bla:jorssa:v:177:y:2014:i:3:p:661-678
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    File URL: http://hdl.handle.net/10.1111/rssa.2014.177.issue-3
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    Cited by:

    1. Y. Hagar & M. Hayden & C. Wiedinmyer & V. Dukic, 2017. "Comparison of Models Analyzing a Small Number of Observed Meningitis Cases in Navrongo, Ghana," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 22(1), pages 76-104, March.

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