Exploring Trends in HIV Prevalence and Neonatal Mortality in Sub-Saharan Africa (2000–2023)

This project presents a longitudinal, cross-country analysis of two critical public health indicators: HIV burden and neonatal mortality across Sub-Saharan Africa from 2000 to 2023. The study combines publicly available datasets from the World Health Organization and UNICEF/UN IGME, using Python and R to conduct in-depth trend analysis, visualize regional disparities, and explore relationships between disease burden and child survival outcomes.

Goals and Scope:
Analyze the temporal trends of people living with HIV across African nations, with emphasis on high-burden regions

Examine neonatal mortality rate (NMR) trends across wealth quintiles, years, and sexes

Explore possible associations between HIV prevalence and neonatal mortality rates using side-by-side analysis and correlation-based exploration

Data Sources:
HIV Dataset (2000–2023):

Indicator: "Estimated number of people (all ages) living with HIV"

Country-level data, disaggregated by year and region

Extracted from WHO global datasets

Neonatal Mortality Dataset (UN IGME Estimates):

Indicator: "Neonatal mortality rate per 1,000 live births"

Dimensions: Sex, Wealth Quintile, Year

Covers Sub-Saharan Africa, sourced from UNICEF and UN IGME

Technologies and Methods:
Tools: Python (pandas, seaborn, matplotlib), R (ggplot2, tidyverse)

Processes:

Data wrangling and unification

Time-series analysis

Grouped summaries by country, region, sex, and socioeconomic group

Dual-axis plotting and heatmap visualizations

Optional regression or correlation analysis to explore interdependence

Key Insights:
HIV burden remained critically high in select Sub-Saharan countries despite global treatment efforts; progress varied across nations

Neonatal mortality showed an overall decline, but inequities persist across wealth quintiles and demographic groups

Preliminary findings suggest a geospatial and developmental correlation between regions with higher HIV burden and persistent neonatal mortality, especially in under-resourced health systems

Impact and Value:
Builds a scalable, data-driven template for health systems analysis

Provides a dual-outcome lens for policymakers, NGOs, and healthcare leaders to understand compound public health vulnerabilities

Demonstrates multi-source data integration, health analytics, and longitudinal analysis skills critical for public health data scientists