Utibeobongutin/Nigeria-Disease-Burden-Analysis

# Nigeria Disease Burden Analysis: Malaria, TB & HIV (1990-2024) **Author:** Utibeobong Utin **Tools:** Python (pandas, matplotlib) **Data Source:** WHO Global Health Observatory (GHO) **Dataset Coverage:** Nigeria | 1990-2024 **Last Updated:** 2024 --- ## Project Overview This project analyses three decades of disease burden trends in Nigeria across three major infectious diseases: Malaria, Tuberculosis (TB), and HIV. Using official WHO Global Health Observatory data updated through 2024, the analysis tracks incidence trends, gender disparities, and the intersection of TB and HIV co-infection to surface policy-relevant insights about Nigeria's public health trajectory. Nigeria carries one of the heaviest infectious disease burdens globally: - **27% of the world's malaria cases** - **One of the highest TB burdens** in Africa - **The largest HIV-positive population** in West Africa This analysis asks: is Nigeria winning or losing the fight against these three diseases? --- ## Data Source - **Provider:** World Health Organization - Global Health Observatory - **URL:** who.int - **Format:** CSV - **Indicators used:** - Estimated malaria incidence (per 1,000 population at risk) - Number of incident tuberculosis cases - Number of incident tuberculosis cases (HIV-positive) - Number of incident tuberculosis cases in children aged 0-14 - Incidence of tuberculosis per 100,000 population (HIV-positive) - New HIV infections per 1,000 uninfected population (by gender) --- ## Data Cleaning Steps 1. **Loaded three separate WHO datasets** : malaria, TB, and HIV - each containing 35 columns of global health indicators 2. **Retained only relevant columns** : Indicator, Location, Period, Dim1 (gender), FactValueNumeric, FactValueNumericLow, FactValueNumericHigh 3. **Filtered for Nigeria** across all three datasets using the Location column 4. **Added a disease label column** to each dataset before concatenating into a single unified dataframe 5. * …