Objectives:
The primary goal of this analysis is to monitor and track the spread of the 2024 MonkeyPox outbreak using real-time epidemiological data. The analysis aims to:
- Analyze the geographical spread, infection rates, and demographic trends.
- Provide insights on containment efforts, case surges, and risk factors.
- Facilitate decision-making for public health interventions by tracking trends in real time.
- Visualize key metrics such as infection rates, recovery rates, mortality rates, and hot spots for more effective interpretation.
Tools Used
- Excel: As the main tool, Excel was used for data cleaning, processing, and visualization. Pivot tables, conditional formatting, and advanced charting techniques were applied to organize the data.
- Flourish: I used this for visualisation of data. The last 4 charts on the dashboard are prepared utilising Flourish.
- Data Sources: Public health organizations, WHO, The World Bank, official epidemiological data, and real-time reports were integrated into the model.
Methodology
- Data Collection: Epidemiological data from government reports, health organizations, and public data repositories were collated. This included daily new cases, recoveries, deaths, and transmisson rates.
- Data Processing: Using Excel, the raw data was cleaned and structured into tables, enabling smooth analysis.
- Visualization: Key charts and graphs were created to visualize trends. These include:
- Line charts tracking case numbers over time.
- Heatmaps showing infection density across regions.
- Pie charts showing the main syptoms of patients.
- Bar charts comparing the number of recoveries, deaths, and active cases.
Excel dashboard
Charts
Conclusion:
This Excel-based epidemiological tracking model provides a robust framework for real-time monitoring of the MonkeyPox outbreak. It allows healthcare authorities and policymakers to make informed, data-driven decisions aimed at managing the outbreak efficiently.
This analysis underscores the power of Excel in handling real-time epidemiological data for meaningful public health interventions.
