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Seasonality of meningitis in Africa and climate forcing: Aerosols stand out

  • L. Agier
  • , A. Deroubaix
  • , N. Martiny
  • , P. Yaka
  • , A. Djibo
  • , H. Broutin
  • Lancaster University
  • Institut Pasteur, Paris
  • Centre de Recherches de Climatologie, CNRS UMR 5210, Université de Bourgogne
  • UPMC
  • Direction Générale de la Météorologie (DGM)
  • Ministry of Health
  • UMR CNRS 5290-IRD224-UM1-UM2

Research output: Contribution to journalArticlepeer-review

96 Citations (Scopus)

Abstract

Bacterial meningitis is an ongoing threat for the population of the African Meningitis Belt, a region characterized by the highest incidence rates worldwide. The determinants of the disease dynamics are still poorly understood; nevertheless, it is often advocated that climate and mineral dust have a large impact. Over the last decade, several studies have investigated this relationship at a large scale. In this analysis, we scaled down to the district-level weekly scale (which is used for in-year response to emerging epidemics), and used wavelet and phase analysis methods to define and compare the time-varying periodicities of meningitis, climate and dust in Niger. We mostly focused on detecting time-lags between the signals that were consistent across districts. Results highlighted the special case of dust in comparison to wind, humidity or temperature: a strong similarity between districts is noticed in the evolution of the time-lags between the seasonal component of dust and meningitis. This result, together with the assumption of dust damaging the pharyngeal mucosa and easing bacterial invasion, reinforces our confidence in dust forcing on meningitis seasonality. Dust data should now be integrated in epidemiological and forecasting models to make them more realistic and usable in a public health perspective.

Original languageEnglish
Article number20120814
JournalJournal of the Royal Society Interface
Volume10
Issue number79
DOIs
Publication statusPublished - 6 Feb 2013
Externally publishedYes

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

  1. SDG 3 - Good Health and Well-being
    SDG 3 Good Health and Well-being
  2. SDG 13 - Climate Action
    SDG 13 Climate Action

Keywords

  • Africa
  • Climate and dust
  • Meningitis
  • Phase analysis
  • Seasonality
  • Wavelet methods

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