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

Emanuele Giorgi1, Peter M Macharia2,3, Jack Woodmansey2

  • 1Centre for Health Informatics, Computing, and Statistics, Lancaster Medical School, Lancaster University, Lancaster, UK. e.giorgi@lancaster.ac.uk.

Malaria Journal
|December 21, 2021
PubMed
Summary
This summary is machine-generated.

Maplaria is a user-friendly web app making malaria risk mapping accessible. It empowers users to upload data for geostatistical predictions, aiding decision-making in low-resource settings.

Keywords:
Cross-sectional surveysMalariaMalaria mappingModel based geostatisticsNational malaria control programmeSub Saharan AfricaWeb application

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Area of Science:

  • Epidemiology
  • Geostatistics
  • Public Health

Background:

  • Model-based geostatistical (MBG) methods are crucial for mapping malaria risk in areas with incomplete disease registries.
  • Limited statistical expertise and computational resources hinder the adoption of MBG methods by national control programs.
  • Maplaria, a new web application, aims to bridge this gap by simplifying geostatistical malaria risk prediction.

Purpose of the Study:

  • To introduce Maplaria, an interactive web application for malaria risk mapping.
  • To enable users to upload their own malaria prevalence data for geostatistical prediction.
  • To facilitate the classification of subnational divisions into endemicity levels with minimal user input.

Main Methods:

  • The Maplaria application was designed with two key criteria: classifying endemicity levels and minimizing user input.
  • Geostatistical model fitting and validation are performed by experts using existing malaria data.
  • Users can upload their own malaria and vector data for geostatistical prediction at desired spatial scales.

Main Results:

  • The application features a step-by-step data uploading process using progressive disclosure to avoid overwhelming users.
  • An example using 2017 Tanzanian Malaria Indicator Survey data illustrates the application's functionality.
  • The user-driven prediction stage allows for spatially aggregated inferences based on uploaded administrative boundaries.

Conclusions:

  • Maplaria offers a user-friendly solution, making advanced geostatistical methods accessible to non-statisticians.
  • The tool promotes data ownership among policymakers for improved decision-making.
  • It is a valuable resource for enhancing malaria control strategies in low-resource settings.