joanitha25/Karagwe-malaria-prevalence

This involves GEE java script codes for satellite-derived environmental variables in relation to malaria prevalence, Google colab codes for model training and streamlit deployment codes # Machine Learning-Based Web Application for Predicting Malaria Prevalence Using Satellite Imagery ## Overview This repository contains the source code developed for the study: "Machine Learning-Based Web Application for Predicting Malaria Prevalence Using Satellite Imagery: A Case Study of Karagwe District, Tanzania." The study integrates satellite-derived environmental variables and machine learning to estimate Plasmodium falciparum parasite prevalence among children aged 2–10 years (PfPR2–10) in Karagwe District, Tanzania. ## Study Period 2020–2024 ## Study Area Karagwe District, Kagera Region, Tanzania. ## Data Sources The study uses data from: - Sentinel-2 - MODIS MOD11A1 - CHIRPS - SRTM DEM - JRC Global Surface Water - Malaria Atlas Project (MAP) ## Environmental Variables The environmental predictors include: - NDVI - NDWI - NDMI - MNDWI - NDBI - Rainfall - Land Surface Temperature - Elevation - Slope - Distance to Water ## Machine Learning The repository contains implementations of: - Random Forest Regression - XGBoost - Spatial cross-validation - Feature importance analysis - Model evaluation using R², RMSE and MAE ## Web Application The Streamlit application provides: - Prediction year selection - AOI upload - PfPR2–10 prediction - Interactive map visualisation - Environmental variable visualisation - GeoTIFF download - GeoJSON download ## Repository Structure GEE/ Google Earth Engine scripts Google_Colab/ Machine learning and data processing code Streamlit/ Web application source code Model/ Model configuration files Data/ The data used in this study is public available data