Mapping spatio-temporal patterns in malaria to inform malaria control in Malawi using routinely collected health facility data

Introduction
Reporting of malaria data through Malawi's Health Information System (DHIS2) has been improving over recent years. Combined with climatological and environmental data from satellite imagery, this provides an opportunity to construct maps that can be continually updated to inform targeted malaria control activities.
Methods
Monthly malaria case data was downloaded from DHIS2 for 2017 - 2020. Environmental and climatological covariates were extracted from MODIS satellite imagery obtained via Google Earth Engine. Geo-statistical modelling was used to produce a series of maps.
Results
A series of maps to describe the distribution of malaria burden across Malawi during the wet and dry season. Maps showing variation in timing of the malaria season across of Malawi. Maps identifying which areas have unexpected high or low reported malaria burden. Results from geo-statistical model showing the relative contributions of explanatory variables in dry season compared to the wet season. Maps to guide timing and location of malaria control in Malawi.
Discussion
This study demonstrates how DHIS2 data can be used to identify areas of interest for malaria control and variations in optimal timing for malaria control. Timing As reporting rates within DHIS2 improve in the future, monthly updated maps could support planning of interventions and allocation of resources.