Learning and Collaborating: To Translate Data and Climate Information into an Early Warning System for Malaria
The case study highlights the importance for learning, collaboration as well as adoption of experiences, with a specific focus on translating information into interventions to develop early warning systems to predict changes in malaria incidence and validating climate-driven models for malaria early warning.
Annual cycles of malaria transmission are closely linked to seasonal weather patterns. Because of climate change Malaria transmission is shifting to higher elevations. 6.5 million people are estimated to be at increased risk of malaria. This case study brings forward two critical subcomponents of collaborating, learning, and adapting (CLA). First, it builds on local External Collaboration with more than a decade of collaborative research on malaria-climate relationships and malaria forecasting and support for shared resources, funding and ensuring local program ownership for longer term sustainability. A Technical Evidence Base was used to translate data and information into intervention to develop early warning systems to predict changes in malaria incidence and validating climate-driven models for malaria early warning. Surveillance provides critical information that informs appropriate interventions such as the purchase of commodities in anticipation of malaria epidemics, outreach to health centers and the distribution of commodities. There is potential to incorporate new data to reduce the burden of disease and mortality for malaria and provide information that extends to other diseases. The long-term record of malaria surveillance data in Ethiopia combined with the wealth of climate data from Earth-observing satellites presents a challenge and opportunity.
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