TABB model for adaptation to health related effects of climate change and its application in Africa

There is now broad scientific consensus that climate change can be expected to increasingly and adversely affect global health in the near future. In the last decade, remote sensing technology has steadily acquired importance as an innovative epidemiological tool, providing data needed to respond to epidemic threats quickly, and predicting the future of infectious diseases in areas where it was never a main concern. Remote sensing, coupled with an effective early warning system, can significantly strengthen a country's capacity to monitor and allocate resources for prevention and treatment of climate sensitive diseases and natural disasters. This paper provides an analysis of the effects of climate change on health, and the utility of incorporating a remote sensing-based early warning system for climate change and health adaptation in Africa. We review several models that utilize remote sensing surveillance and early warning to benefit public health and based on a comparative analysis of each of their approaches, we conclude that USAID's Famine Early Warning Systems Network approach provides the most effective foundation for creating an early warning model for health and climate change. FEWS NET helps countries build their capacity to predict and mitigate food insecurity through innovative technology, targeted information and policy relevant reporting. Using this foundation and incorporating important additions and modifications based on specific climate change and public health needs in Africa, we create the TAAB model for climate change and health, which will facilitate surveillance, early warning and adaptation to negative health outcomes of climate change.