The impact of climate change on vector borne diseases such as malaria is well-documented in published literature. While there exist multiple lines of evidence for the influence of climate change on malaria and the risk posed to vulnerable communities, there is insufficient understanding of the complexity underlying the spread of the disease at the community level. This is partly due to a lack of reliable and quality controlled meteorological and disease data, but more importantly, the malaria transmission cycle is complex and is not only influenced by climate change but also by other environmental, biological and socio-economic factors. These factors influence malaria transmission either independently or by modifying the effects of climate at a local level and can have a substantially greater influence on malaria transmission than climate alone. Drawing upon published literature and informed by existing vulnerability and transmission frameworks, we apply systems thinking to propose a detailed conceptual model illustrating the multiple drivers of malaria transmission. We use this conceptual model to construct a linked integrated assessment framework that considers indicators of both biophysical and social vulnerability. We propose that this integrated assessment framework can be applied in data poor regions and at a community level using both quantitative and qualitative methods with stakeholder engagement. Furthermore, this framework can be implemented using Bayesian Belief Models to undertake sensitivity analysis and explore policy interventions under different scenarios.

(Interest in presenting during the Vulnerable Communities Network Session)