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Spatial interpolation of health and demographic variables: predicting malaria indicators with and without covariates

Domaine:

healthcaregeospatial

Type de record:

datasetsoftware
Créateur:
Cam
Hôte:avatar
This repository contains data and material to model and predict indicators of the Demographic and Health Surveys (DHS) by comparing three categories of methods : (1) spatial interpolation methods (i.e. inverse distance weighting, thin plate splines, kriging), (2) ensemble methods (i.e. random forest), and (3) Bayesian geostatistical models (i.e. with and without covariates). We focus on DHS indicators that potentially drive malaria in Senegal and classify them into socioeconomic and malaria prevention indicators. The outputs of the different methods are continuous surfaces of DHS indicators at 1 km spatial resolution.

This repository is based on work published in "Morlighem, C., Nnanatu, C.C., Visée, C., Fall, A., & Linard, C. (2025). Spatial interpolation of health and demographic variables: Predicting malaria indicators with and without covariates. PLOS One, 20(5), e0322819. https://doi.org/10.1371/jou…". Please refer to the ReadMe file for instructions on how to use these codes.


Visit

figshare.com

Tags

Geospatial information systems and geospatial data modellingDemographic and Health Surveys (DHS)spatial interpolationmalaria vulnerabilitySustainable Development GoalsBayesian geospatial modelling

Licenses

MIT

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