Ken-Urrah/malaria-burden-analysis-2026

This project explores malaria burden across African countries through data visualization and statistical analysis using data from the World Health Organization (WHO) Global Health Observatory. What began as an effort to build an interactive dashboard evolved into a deeper investigation into the patterns behind malaria outcomes across the c # malaria-burden-analysis-2026 This project explores malaria burden across African countries through data visualization and statistical analysis using data from the World Health Organization (WHO) Global Health Observatory. What began as an effort to build an interactive dashboard evolved into a deeper investigation into the patterns behind malaria outcomes across the continent. Through the use of Excel, Python, and Power BI, I analyzed trends in malaria incidence, examined relationships with environmental and socioeconomic indicators, and explored how healthcare investment and intervention strategies may influence disease burden. The analysis focused on understanding: 1. Which countries carry the highest malaria burden, 2. Whether malaria outcomes are improving or worsening over time, 3. How factors such as precipitation, GDP per capita, population density, and health expenditure relate to incidence, 4. The role of Insecticide-Treated Nets (ITNs), 5. How historical patterns may inform future risk. One of the most important insights from this work was realizing that malaria cannot be explained through a single variable or intervention. The findings suggest that health outcomes emerge from an interconnected system involving healthcare access, environmental conditions, and broader socioeconomic realities. This report represents both a public health analytics case study and a personal exploration of how data science can move beyond visualization to support understanding, decision-making, and meaningful impact. doi.org