Covid Cases in Scotland

This project explores how COVID-19 case numbers in Scotland evolved in relation to vaccination rates and national lockdowns, using publicly available NHS Scotland datasets. Conducted in Python using a Jupyter Notebook, the analysis combines time-series visualizations with data cleaning and transformation techniques.

The case dataset required formatting adjustments, notably converting date fields to proper datetime objects. Summary statistics were generated to understand the scope of the data and identify trends. A key visualisation plots daily COVID-19 cases over time, with vertical markers indicating major lockdown dates. These annotations help illustrate how restrictions impacted case rates.

The vaccination dataset was filtered to include total doses administered across all age groups. A change in reporting after March 2022 required a calculated field (calculated dose) to ensure continuity. Before the change, official daily figures were used; after, the daily difference in cumulative doses was computed. Data was cleaned to remove invalid or missing entries.

Results showed a clear inverse relationship between vaccination rates and daily case numbers, particularly in the months following major vaccine rollouts. Combined with lockdown measures, vaccination appears to have significantly reduced case counts.

The project showcases the effective use of pandas and Matplotlib for public health data analysis and visualisation. It highlights how data science can be used to evaluate the impact of health interventions and guide evidence-based policy decisions.