Proud to share our final graduation project, where we conducted an in-depth statistical analysis of COVID-19 death trends in two different countries — Egypt and Italy — using real-world data, advanced distribution fitting, and regression modeling.
? What we did:
- Analyzed national COVID-19 death data (2021–2022)
- Fitted appropriate statistical distributions (Weibull for Egypt, Lomax for Italy)
- Built regression models (GLM, Poisson, Negative Binomial, ZINB)
- Investigated the impact of predictors: Diabetes, CVD, temperature & humidity
Key insights:
- Diabetes was the strongest mortality predictor in both countries
- Humidity had a protective effect in Egypt
- ZINB model best fit the Egyptian data due to zero inflation
- Italy’s mortality patterns fit best with a Negative Binomial model
Tools:
- Python (Pandas, SciPy, Seaborn)
- R (GLMs, stats, MASS)
