Understand Problem and Get Better Results Using Exploratory Data Analysis in R: Practical Insights for Disease Modelling

This article demonstrates how Exploratory Data Analysis in R can uncover vital patterns, relationships, and anomalies in disease-related datasets, enabling more accurate modelling and informed public health decision-making.

Article Outline:

1. Introduction – Importance of understanding data through EDA in disease modelling for accurate prediction, monitoring, and intervention planning.

2. What is Exploratory Data Analysis (EDA)? – Definition, objectives, and role in analysing epidemiological and health surveillance data.

3. Key EDA Techniques in Disease Modelling – Summary statistics, distribution analysis, time-series exploration, spatial pattern identification, and correlation analysis.

4. Preparing the Dataset – Structure of disease-related datasets including case counts, demographic factors, time periods, and geographic identifiers.

5. EDA in Action Using R – Step-by-step exploration with descriptive statistics, visualisations, correlation analysis, and trend identification in disease data.

6. Identifying Patterns, Risk Factors, and Transmission Trends – How EDA highlights seasonality, hotspots, and relationships between variables in disease spread.

7. From EDA to Better Disease Modelling – Using findings to refine epidemiological models, improve forecasts, and guide public health decisions.

8. Conclusion – Reinforcing the role of EDA as a foundation for effective disease modelling and intervention strategies.

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