Many human diseases are climate-sensitive with climate acting as an important driver of spatial patterns, seasonal cycles, year-to-year variations (including epidemics), and longer-term trends. Public health policy makers and practitioners are increasingly concerned about the potential impact of climate change and extreme events on the health of populations. However the public health community lags behind others in the use of climate and environmental information for climate-sensitive decision-making. Although part of the explanation comes from the fact that climate is only one of the many factors coming into play, recent developments in climate science and, more recently, climate services, along with new technologies for data management, analysis and sharing provide unprecedented opportunities for rapidly advancing this area. A real challenge is that new developments respond to the real needs of the global health decision-making community and empower their associated research communities if this potential is to be fully realized.

This talk will report on-going activities, including research results, within the context of the potential use of climate information in Meningitis epidemic control and response in sub-Saharan Africa. The so-called Meningitis Belt in sub-Saharan Africa is the region where the majority of epidemics are located and which suffers the greatest burden of endemic disease. Meningitis is an infection of the thin lining that surrounds the brain and spinal cord. While there are many causes of meningitis, the epidemic form of the disease is caused by bacteria called ”Neissera Meningitidis”. In 1996-1997, the epidemics affected hundreds of thousands and killed more than 25,000 in 10 countries in the Meningitis Belt. In the 2009 epidemic season, 14 countries implementing enhanced surveillance, reported 88,199 suspected cases, including 5,352 deaths, the largest number since the 1996 epidemic. Epidemics and seasonal upsurges in endemic disease occur in the latter part of the dry season after the onset of the dusty Harmattan winds and usually subside at the onset of the rains.

In particular, we study potential statistical seasonal forecast models for Meningitis based on climate data and other determinants from national to district levels in Niger. From the climatic perspective the data used also includes dust model estimates and was produced in collaboration with the Barcelona Supercomputing Center.