OBJECTIVES: Meningococcal meningitis is a major public health problem in Africa. This report explores the potential for climate/environmental models to predict the probability of occurrence of meningitis epidemics.
METHODS: Time series of meningitis cases by month and district were obtained for Burkina Faso, Niger, Mali and Togo (536 district-years). Environmental information (1989-1999) for the continent [soil and land-cover type, aerosol index, vegetation greenness (NDVI), cold cloud duration (CCD) and rainfall] was used to develop models to predict the incidence of meningitis. Meningitis incidence, dust, rainfall, NDVI and CCD were analysed as anomalies (mean minus observed value). The models were developed using univariate and stepwise multi-variate linear regression.
RESULTS: Anomalies in annual meningitis incidence at district level were related to monthly climate anomalies. Significant relationships were found for both estimates of rainfall and dust in the pre-, post- and epidemic season. While present in all land-cover classes these relationships were strongest in savannah areas.
CONCLUSIONS: Predicting epidemics of meningitis could be feasible. To fully develop this potential, we require (a) a better understanding of the epidemiological and environmental phenomena underpinning epidemics and how satellite derived climate proxies reflect conditions on the ground and (b) more extensive epidemiological and environmental datasets. Climate forecasting tools capable of predicting climate variables 3-6 months in advance of an epidemic would increase the lead-time available for control strategies. Our increased capacity for data processing; the recent improvements in meningitis surveillance in preparation for the distribution of the impending conjugate vaccines and the development of other early warning systems for epidemic diseases in Africa, favours the creation of these models.
- Africa, Western
- Air Pollutants
- Cold Temperature
- Disease Outbreaks
- Geographic Information Systems
- Meningitis, Meningococcal
- Models, Theoretical
- Plant Physiological Phenomena