Maplaria: a user friendly web-application for spatio-temporal malaria prevalence mapping

Abstract

Background 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. 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. Methods In 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. Results The 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. Conclusions Maplaria 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.

Optimizing Routine Malaria Surveillance Data in Urban Environments: A Case Study in Maputo City, Mozambique

“…When transmission is very low, household-level information becomes important as part of focus investigations to target residual transmission and confirm absence of transmission. 21 Furthermore, the additional data collected provided a more detailed overview of the malaria epidemiology in Maputo City. To enhance this malaria surveillance platform further, use of an electronic data capture system recording information on each case would facilitate both the quality of the data and support built-in real-time analytical and data visualization functions and ensure that malaria programs can quickly respond and adapt to changing malaria trends.…”

“…When transmission is very low, household-level information becomes important as part of focus investigations to target residual transmission and confirm absence of transmission. 21 …”

A new way of analyzing malaria data: A non-stationary geostatistical modeling approach

“…Malaria remains one of the major causes of mortality in Africa and Asia. In recent decades spatial models have been established as an important tool to identify malaria hotspot areas, predicting the prevalence of malaria at unsampled locations and hence guiding the development of strategies to control it (Macharia et al (2018); Giorgi et al (2021)). Malaria prevalence maps are useful, not only for enabling a more precise malaria risk stratification, but also for guiding the planning of more reliable spatial control programmes by identifying affected areas (?Gething 2012). )…”