aykahsay/Spatial-Analysis-of-Malaria-Epidemiology-in-Kenya

Domain:

healthcaregeospatial

Record type:

project

Creator:

ayk

Host:

# Spatial Analysis of Malaria Epidemiology in Kenya 🇰🇪 ## **Project Overview** This project conducts a **spatial econometric analysis** of malaria incidence across the **47 counties of Kenya**. By integrating epidemiological, climatic, demographic, and socio-economic variables, the study identifies statistically significant **malaria hotspots** and models the key drivers of disease transmission using a **Spatial Lag Model (SLM)**. The findings show that malaria in Kenya is a **spatially dependent phenomenon**, influenced by rainfall patterns, ecological zones, and transmission spillover across neighboring counties. --- ## **Key Objectives** ### **1. Visualization** * Produce choropleth maps showing malaria incidence across counties. * Explore spatial clustering including hotspots and coldspots. ### **2. Spatial Diagnostics** * Compute **Global Moran’s I** to assess overall clustering. * Use **Local Indicators of Spatial Association (LISA)** to detect specific high-burden zones. ### **3. Spatial Econometric Modeling** * Fit **Spatial Lag (SLM)** and **Spatial Error (SEM)** models. * Test significance of climatic (rainfall, temperature), demographic (population), and socio-economic (poverty) drivers. --- ## **Data Sources** | Dataset Type | Source | Description | | ---------------------- | --------------------------- | --------------------------------------- | | **Spatial Boundaries** | GADM v4.1 | Kenya county shapefiles (vector data) | | **Epidemiology** | Ministry of Health | Malaria incidence per 1,000 population | | **Climate** | Ecological zone simulations | Annual Rainfall (mm) & Temperature (°C) | | **Socio-Economic** | KNBS (2022) | Poverty Headcount Index | | **Demographics** | KNBS Projections (2025) | Population totals per county | --- Link to data Sources : - **Geo Data Soure** h …

Visit

github.com

Licenses

MIT

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aykahsay/Ekegusii-LLM-Translation

# 🌍 Ekegusii-LLM-Translation: Resource-Aware Adaptation of Multilingual Large Language Models for Lo