Kemri-Wellcome Trust Research Programme: Spatial data science for seasonal malaria risk mapping at sub-national levels in Kenya

Despite the recent investments in malaria, the burden in sub-Saharan Africa remains distributed unevenly between and within countries and its seasonal variation is poorly described at a sub-national level. Recent mapping efforts continue to rely on interpolation of parasite prevalence from community surveys. These surveys do not capture the intra-annual and seasonal variation of malaria risk and heterogeneity at sub-national scale. Test positivity rates from routine health facility data will be assembled and combined with climate data to improve a) mapping malaria risk and seasonality characterisation and b) malaria risk stratification and understating of uncertainty from routine data. Improving risk estimation, stratification and understating of uncertainty is important for disease control, targeting of interventions, and to understanding future data quality needs to track the impact of interventions. Improvement in data quality (through DHIS 2) provides a platform to improve our understanding of risk and seasonality. This, however, requires the development of scientific approaches for not only risk estimation but also interpreting biases or uncertainties related to information systems data and related products that support decision making. This proposal aims to address this need by improving and expanding upon current approaches of mapping malaria risk and stratification in Kenya.

Programme: International Training Fellowship

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