Appreciating the complexity of localized malaria risk in Ghana: Spatial data challenges and solutions

Abstract

Various factors have been associated with the ongoing high prevalence of malaria in Ghana. Among these are poor sanitation, low socioeconomic status (SES), building construction and other proximate micro environmental risks, and individual behaviors. What makes the curbing of malaria more challenging, is that for many of the most impacted areas there is little data for modeling or predictions, which are needed, as risk is not homogenous at the sub-neighborhood scale. In this study we use available local surveillance data combined with novel on-the ground fine scale environmental data collection, to gain an initial understanding of malaria risk for the Teshie township of Accra, Ghana. Mapped environmental risk factors include open drains, stagnant water and trash. Overlaid onto these were clinical data of reported malaria cases collected between 2012 and 2016 at LEKMA hospital. We then enrich these maps with local context using a new method for malaria research, spatial video geonarratives (SVGs). These SVGs provide insights into the underlying spatial-social patterns of risks, to reveal where traditional data collection is lacking, and how and where to develop local intervention strategies.