In Ethiopia, malaria continues to be a major public health concern with an estimated
two thirds of the national population at risk of infection. Tackling a key driver, this
workshop was convened to advance the understanding of the impact of climate
variability and change in relation to the malaria burden to better inform policy
decisions related to current control and future elimination strategies. To achieve
this, the workshop explored data, methodologies and tools that could be used by
national public health researchers to improve malaria risk assessments.
The motivation for the workshop came from an NIH funded project entitled “Climate
Variability and Change: Implications for Malaria Control and Elimination in Africa”
with the goal of supporting malaria researchers in affected countries in East Africa
to identify opportunities for improving the effectiveness of prevention, control and
elimination strategies by incorporating an understanding of likely short and longer
term changes in the climate in their analysis. In particular, the workshop aimed to
address the challenge of varied drivers of the climate, acting at multiple timescales
including year to year variability, 10-20 year climate shifts and long term trends
associated with climate change.
Data sources explored during the workshop included the newly developed
Enhanced National Climate Services (ENACTS) rainfall and temperature products
disseminated by the Ethiopian National Meteorological Agency (NMA), as well as
globally available climate products freely distributed online. The Climate
Predictability Tool (CPT), developed by the International Research Institute for
Climate and Society (IRI), to assist climatologists in making robust predictions was
tested for the first time as a potential tool for the malaria research community to
assess the relationship of malaria to large-scale climate processes. In addition, a
multi-model malaria platform (MMMP) was presented during the workshop to
explore uncertainty associated with the predictability of malaria over time using a
series of process-based models
