Climate and Health in Africa: climate variability, forecasting, health models and intervention: an end-to-end approach with "some" gaps

Background The burden of disease in sub-Saharan Africa is great; the probability of children dying between birth and 15 years is 20 times higher (Murray and Lopez, 1997) than in counties with established market economies. The proportion of deaths caused by communicable disease (infectious and parasitic diseases, respiratory infections etc.) plus maternal and perinatal disorders when compared with non-communicable disease is almost 33 times greater in sub-Saharan Africa than in so called developed countries (Murray and Lopez, 1997). Most if not all of these communicable diseases are preventable with many related to climate and the environment as are, directly or indirectly, some of the perinatal and maternal disorders. The semi-arid areas in Africa, which are home to 140 million people, are particularly prone to a number of infectious epidemic diseases including malaria and meningitis, both of which have climatic variability drivers (IRI, 2006). Rainfall variability causes food security risks and water availability issues. Health is also intrinsically linked to nutrition and the supply of safe water. The impacts research of the AMMA project (http://www.amma-international.org/), in West Africa, are working on a number of these climate variability - human dimension issues and are in the process of assimilating 17 new African partners into the impacts section of the project (see Thorncroft et al. article). These regions have limited resources to tackle epidemics and need effective decision making support to help break the cycle between disease burden and its impact on economic growth. The Roll Back Malaria programme of the WHO (http://www.rbm.who.int/) indicates that in some African countries, malaria alone reduced economic growth by 1.3% per year. Utilisation of probabilistic long range forecasts of climate variability An early warning system must target, in advance of the epidemic, regions that have the greatest predicted epidemic risk to help guide and target intervention strategies. Epidemic risk is not driven by climate variability alone and antecedent environmental conditions and human factors including immunity need to be considered. Forecasts of varying lead times (e.g. seasonal forecasts from ensemble prediction systems) are produced routinely but rarely applied to health early warning. This is a complex task requiring an integrated forecasting - decision support system which takes data from a multi-model global ensemble forecasting system to support an 'on the ground' intervention action. There are a number of steps in this chain:- 1. The production and update of user defined, skilful forecasts (at seasonal lead times from global forecast models ensemble prediction systems (EPS) or using a statistical modelling approach normally based on sea surface temperature - rainfall relationships) with downscaling to regional and local scales. These forecasts should be updated with sub-seasonal to short range products, from EPS systems, which provide information for intraseasonal and weather forecast time scales. These forecast products, that are routinely produced, are used to plan safe transportation and storage of vaccines and related medical equipment, given the poor road infrastructure in the region. Significant losses of these medical supplies sometimes occur due to