Understand Problem and Get Better Results Using Exploratory Data Analysis in Python: Practical Insights for Epidemiology

This article shows how Exploratory Data Analysis in Python can uncover essential patterns, trends, and anomalies in epidemiological datasets, enabling more accurate models and better-informed public health strategies.

Article Outline:

1. Introduction – Importance of thoroughly understanding epidemiological data before modelling, and the role of EDA in improving public health analysis.

2. What is Exploratory Data Analysis (EDA)? – Definition, objectives, and relevance in studying disease patterns, transmission dynamics, and health outcomes.

3. Key EDA Techniques in Epidemiology – Summary statistics, time-series trend analysis, demographic breakdowns, spatial visualisations, and correlation analysis.

4. Preparing the Dataset – Structure of epidemiological datasets including case counts, demographic attributes, geographic locations, and time variables.

5. EDA in Action Using Python – Step-by-step process with descriptive statistics, visualisation of trends, spatial patterns, and relationships.

6. Identifying Patterns, Risk Factors, and Transmission Insights – How EDA reveals seasonality, vulnerable groups, and geographic hotspots.

7. From EDA to Better Epidemiological Modelling – Using EDA findings to refine model structures, improve prediction accuracy, and guide interventions.

8. Conclusion – Reinforcing EDA as the foundation for robust and actionable epidemiological modelling and decision-making.

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