Mapping an area at risk of epidemics of meningococcal meningitis in Africa has significant implications for their prevention and case treatment, through the targeted development of improved surveillance systems and control policies. Such an area was described using information obtained from published and unpublished reports of meningitis epidemics between 1980 and 1999 and cases of meningococcal disease reported by surveillance systems to WHO. The Sahel bore the greatest epidemic burden, with over two-thirds of documented outbreaks and high attack rates. In addition to those already in the Meningitis Belt, countries affected included Guinea-Bissau, Guinea, Côte d'Ivoire, Togo, the Central African Republic and Eritrea. Elsewhere epidemics were reported from a band of countries around the Rift Valley and Great Lakes regions extending as far south as Mozambique and from here west to Angola and Namibia in southern Africa. The cumulative pan-continental analysis provided evidence of an epidemic-susceptible area which extends beyond the region accepted as the Meningitis Belt and which, moreover, may be partially determined by the physical environment, as shown by a striking correspondence to the 300-1100-mm mean annual rainfall isohyets.

Abstract. In line with the renewed World Health Organization Global Malaria Control Strategy, we have advocated the use of satellite imagery by control services to provide environmental information for malaria stratification, monitoring, and early warning. To achieve this operationally, appropriate methodologies must be developed for integrating environmental and epidemiologic data into models that can be used by decision-makers for improved resource allocation. Using methodologies developed for the Famine Early Warning Systems and spatial statistics, we show a significant association between age related malaria infection in Gambian children and the amount of seasonal environmental greenness as measured using the normalized difference vegetation index derived from satellite data. The resulting model is used to predict changes in malaria prevalence rates in children resulting from different bed net control scenarios.

No abstract

No abstract