Toward Using Climate to Increase Lead-Time of a Malaria Early Warning System in Mozambique

Abstract:

Malaria is one of the greatest recurring threats to public health in Mozambique with ~10 million reported cases and thousands of deaths observed annually. Although a malaria early warning system (MEWS) is currently being established in the country, it is focused on short-term (4-8 week) prediction windows. Increased understanding of the links between quasi-predictable interannual climate variability and malaria could lengthen MEWS lead-times and enhance planning and actionable mitigation efforts by public health officials in the country. To accomplish this, we performed an empirical orthogonal function analysis of processed weekly district-level malaria rates from 2010-2017 and identified two dominant spatiotemporal patterns that collectively account for 81% of the interannual variability of malaria in Mozambique. These modes are shown to be closely related to precipitation variability. Linear regressions of sea surface temperatures onto a precipitation index tie the dominant patterns of spatiotemporal variability in malaria to the El Niño-Southern Oscillation (ENSO) and the Subtropical Indian Ocean Dipole (SIOD). Beyond linking interannual variability of malaria and climate in retrospect, we also examine the ability to use forecasts of modes of tropical climate variability for actionable decisions within Mozambique at different time scales, as well as an analysis of how the actual (i.e., non-anomalous) rates of malaria respond to various climatic events. Ultimately, these results will inform an enhancement of the MEWS in Mozambique and similar analyses are possible for other countries whose climate is dominated by tropical modes of climate variability.