Kingsley-amg/ghana-malaria-climate

Climate and malaria across Ghana's 16 regions: an R spatial-statistics study (ecological regression, GAM, Moran's I) linking rainfall, temperature and humidity to malaria. DHS + NASA POWER + World Bank data. # Climate and Malaria across Ghana's 16 Regions An ecological and **spatial-statistics** study of how rainfall, temperature and humidity relate to malaria across **Ghana's 16 regions**, set in the African context. Built in **R** from open, up-to-date data (DHS, World Bank, NASA POWER, geoBoundaries). > Climate normals explain about **72% of the between-region variation** in malaria > prevalence (adjusted R²; 79% via a GAM). Malaria is strongly **spatially > clustered** (Moran's I = 0.49, p northern and middle-belt regions. --- ## 🔗 Read the report **▶ Full report (HTML)**, maps, models and interpretation, knitted from R Markdown. A **PDF version** is in `report/`. ## 🔬 What it does - **Africa context**, Ghana's malaria incidence vs other African countries (World Bank, to 2024) and the national trend. - **Climate by region**, monthly rainfall, temperature and humidity (NASA POWER, 2014-2024) at each region's centroid, summarised to long-run normals, plus a seasonality profile (single-peak north vs double-peak south). - **Malaria by region**, DHS rapid-test prevalence (2022), mapped across all 16 regions. - **Advanced statistics:** - Ecological **multiple regression** (standardised, with 95% CIs and a VIF multicollinearity check) - A **generalised additive model (GAM, mgcv)** for the non-linear temperature effect - **Spatial autocorrelation**, Moran's I on malaria and on regression residuals (spdep) ## 🔑 Key findings - Malaria prevalence ranges from ~3% (Greater Accra) to ~34% in the north. - The strongest climate correlates are **humidity (-)** and **temperature (+)**; the warmer, drier north carries the highest burden. - **Significant spatial clustering** (Moran's I = 0.49, p < 0.001). - Supports **climate-informed, regionally targeted** malaria control. ## 🗂️ Structure ``` ghana-malaria-climate/ ├── 01_extract.R # pull malaria + climate data and region boundaries ├── malaria_climate_analysis.Rmd # the full analysis (source) ├── data/ …