🦠 COVID-19 Global Impact Analysis

Overview

This project investigates the global impact of COVID-19 using datasets containing country-level statistics on deaths, vaccinations, and development indicators. The analysis explores how factors like GDP per capita, Human Development Index (HDI), government policy (stringency index), and health infrastructure affected COVID-19 outcomes including total deaths, death rates, and vaccination coverage.

Data cleaning, transformation, and exploratory data analysis (EDA) were performed using SQL (https://github.com/movahed-abdolahi/COVID-19-Analysis-with-SQL) and visualized using Power BI. The goal was to uncover critical insights into the spread, severity, and control of the pandemic across the globe.

---

Dataset Information

Files Used:

• COVID-dataset.xlsx: Combined and cleaned dataset containing case counts, deaths, vaccinations, and country-level socioeconomic data.
• Date.xlsx: Custom calendar table for time-based analysis in Power BI.
• Power BI Report:
  • Covid-Analysis.pbix: Final dashboard with KPIs, visuals, and filters.

---

Creating date table

Custom Date Table (DAX)

Used to support dynamic filtering and time intelligence in Power BI:

Date = 
ADDCOLUMNS(
    CALENDAR(DATE(2019,1,1), DATE(2025,12,31)),
    "Year", YEAR([Date]),
    "Month", FORMAT([Date], "MMMM"),
    "MonthNumber", MONTH([Date]),
    "Quarter", "Q" & FORMAT([Date], "Q"),
    "Day", DAY([Date]),
    "Weekday", FORMAT([Date], "dddd"),
    "WeekdayNumber", WEEKDAY([Date], 2)
)

---

Dashboard Overview (Power BI)

Key pages and features in the .pbix dashboard:

🌍 Global Overview

• Total cases, deaths, vaccinations
• Fatality and vaccination rates by continent
• Interactive map
• Drillthrough button to see detailed information when selecting a country on the map

📈 Country details page

• R-value vs case count over time
• Filterable by year and month
• Aggregated case/death/fatality rate by month and year
• Table visual with custom formattings showing case, death, fatality and percentage vaccinated for the selected country
• Country-level breakdown showing:
  • GDP, HDI, Median Age, Hospital Beds, Poverty Rate
  • Days segmented by fatality risk (low, medium, high)

---

Key Insights

• Higher HDI and GDP didn’t always correlate with lower death rates.
• Government stringency impacted reproduction rate and case curves.
• Rolling vaccination analysis shows when countries ramped up efforts.
• Europe and Asia led in case and death counts, but also had high vaccination rates.

---

Conclusion

This project demonstrates how real-world data analysis can uncover meaningful insights from global crises. Using SQL for data prep and Power BI for storytelling, we built a multi-dimensional view of COVID-19’s impact, influenced by economic, healthcare, and policy factors. The combination of structured query logic and compelling visuals brings clarity to complex global events.

---

Files

• 📊 COVID-dataset.xlsx
• 📅 Date.xlsx
• 📊 Covid-Analysis.pbix

---

License

This project is licensed under the MIT License. See the LICENSE file for more details.

---

Author

Movahed Abdolahi
🎓 Data & BI Analyst | Power BI | SQL | Python
🔗 LinkedIn Profile