ajalil-123/Impact-of-COVID-19-on-Population-Mobility-Patterns-in-Rwanda

This project analyzes the impact of COVID-19 on population mobility patterns in Rwanda and examines their relationship with government interventions and disease burden. The analysis uses mobility data, epidemiological indicators, and socio-economic variables to uncover trends, correlations, and impacts over time. # COVID-19 Mobility and Impact Analysis (Rwanda) ## Overview This project analyzes the impact of COVID-19 on population mobility patterns in Rwanda and examines their relationship with government interventions and disease burden. The analysis uses mobility data, epidemiological indicators, and socio-economic variables to uncover trends, correlations, and impacts over time. ## Activities - Data Preparation - Impact Analysis - Trend Analysis - Correlation Analysis ## Data Preparation The dataset contains daily COVID-19 mobility, health, and socio-economic indicators from February 2020 to March 2022. Data cleaning involved: - Handling missing values - Removing sparse vaccination-related variables - Renaming mobility indicators for clarity - Exporting a cleaned dataset for analysis ## Impact Analysis Mobility indicators (retail, grocery, parks, workplaces, transit, residential) were analyzed alongside: - Government stringency index - New COVID-19 cases - New COVID-19 deaths Scatter plots were used to visualize the relationship between mobility patterns, policy measures, and disease outcomes. ## Trend Analysis Interactive time-series visualizations were developed to explore mobility trends across different locations and time periods. Users can dynamically select date ranges to observe changes in mobility before, during, and after policy interventions. ## Correlation Analysis Correlation analysis was conducted to quantify relationships between mobility indicators, government response measures, and COVID-19 statistics. A heatmap was used to highlight strong positive and negative correlations, revealing how mobility restrictions and residential stay patterns relate to disease spread and policy stringency. ## Tools & Libraries - Python - Pandas, NumPy - Matplotlib, Seaborn - Plotly, Cufflinks ## Output - Cleaned dataset: `clean_data.csv` - Visual insights on mobility trends, policy impacts, and COVID-19 outcomes