Abstract
BackgroundModel-based geostatistical (MBG) methods have been extensively used to map malaria risk using community survey data in low-resource settings where disease registries are incomplete or non-existent. However, the wider adoption of MBG methods by national control programmes to inform health policy decisions is hindered by the lack of advanced statistical expertise and suitable computational equipment. Here, Maplaria, an interactive, user-friendly web-application that allows users to upload their own malaria prevalence data and carry out geostatistical prediction of annual malaria prevalence at any desired spatial scale, is introduced.MethodsIn the design of the Maplaria web application, two main criteria were considered: the application should be able to classify subnational divisions into the most likely endemicity levels; the web application should allow only minimal input from the user in the set-up of the geostatistical inference process. To achieve this, the process of fitting and validating the geostatistical models is carried out by statistical experts using publicly available malaria survey data from the Harvard database. The stage of geostatistical prediction is entirely user-driven and allows the user to upload malaria data, as well as vector data that define the administrative boundaries for the generation of spatially aggregated inferences.ResultsThe process of data uploading and processing is split into a series of steps spread across screens through the progressive disclosure technique that prevents the user being immediately overwhelmed by the length of the form. Each of these is illustrated using a data set from the Malaria Indicator carried out in Tanzania in 2017 as an example.ConclusionsMaplaria application provides a user-friendly solution to the problem making geostatistical methods more accessible to users that have not undertaken formal training in statistics. The application is a useful tool that can be used to foster ownership, among policy makers, of disease risk maps and promote better use of data for decision-making in low resource settings.
Highlights
Model-based geostatistical (MBG) methods have been extensively used to map malaria risk using community survey data in low-resource settings where disease registries are incomplete or non-existent
Across malaria endemic countries in sub-Saharan Africa (SSA), national malaria control programmes (NMCPs) develop guidelines, strategies, and policies that are adapted in accordance to local malaria endemicity levels [1]
The welcome page has a menu with three options which include applying for an account, signing in for existing account holders and accessing a demo account to explore the features of the web application
Summary
Model-based geostatistical (MBG) methods have been extensively used to map malaria risk using community survey data in low-resource settings where disease registries are incomplete or non-existent. Giorgi et al Malaria Journal (2021) 20:471 strategy for malaria 2016–2030 [2], NCMPs are required to stratify their countries sub-nationally based on endemicity, so as to realign their malaria control activities [3, 4]. Regions with prevalence lower than 1% may need to adapt their strategies for malaria elimination [5], whilst areas with a prevalence above 30% may still require intensive and sustained vector control [3]. To this end, NMCPs often require estimates of prevalence to be aggregated over geographical units used for decisionmaking, such as districts, regions or counties. Disease risk mapping is essential in providing timely and reliable information on the spatial heterogeneity of malaria endemicity in a country and how this changes over time
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