Cynthia-Nwajiobi/malaria-trends-africa-analysis

This project analyses the incidence of Malaria in Africa, challenges, improvements and the way forward. # Malaria Trends Analysis ## Project Overview Malaria is still a significant public health issue till date, most especially in sub-Saharan Africa, accounting for the vast majority of global malaria cases and deaths, with over half a million deaths annually, mostly among children under five. This project aims to look through the malaria trends in Africa gotten from public datasets which cover malaria occurrence from 2007 to 2017. Through this analysis, we will see the progress made by some African countries and the countries still battling with high burdens. ### Objectives Analyse malaria trends across African countries. Timeframe: 2007–2017 #### Key Questions: 1. How have malaria trends evolved? 2. Which countries succeeded in elimination? 2. Where is the burden still high? ### Methodology Datasets were sourced from a Kaggle dataset aggregated from WHO. A lot of columns were irrelevant to the analysis. #### Actions taken: - Cleaned missing/inconsistent values - Created a pivot table on Excel - Focused on confirmed cases by year and country - Used Python for exploratory data analysis ##### Tools Used: Excel, Python, Looker ### Findings & Insights Between 2008 and 2017, Burkina Faso appeared to have one of the highest reported malaria cases and also had the highest malaria incidence rate, meaning that new cases were occurring rapidly within that period. While Sierra Leone, Central African Republic, Mali, and Benin had the highest incidence rates on the continent, none of them were among the top 5 countries with the highest reported cases, indicating that the malaria incidence in these countries was handled critically. ### Limitations with the data The goal was to work with datasets up till 2022 and to also find a correlation between several factors that could lead to the rate of malaria cases in high-burden and low-burden countries. However, the data was limited in the sense that: 1. The dataset wasn’t up-to-date (only available until 2017) 2. The supposedl …