Lecture 1: Disease Modelling Introduction
Why It Matters
Effective disease modeling enables proactive public‑health interventions, reducing health and economic losses during epidemics.
Key Takeaways
- •Disease models translate data into actionable public‑health insights.
- •Model choice depends on research question and data availability.
- •Mechanistic, statistical, ML, and dynamic models each have trade‑offs.
- •Structured, semi‑structured, and unstructured health data dictate modeling approach.
- •Early modeling improves response speed during outbreaks like COVID‑19.
Summary
The lecture provides a foundational overview of disease modeling, aimed at public‑health, data‑science, and AI students, and explains why models are essential for turning health data into policy decisions.
It categorizes four principal model families—mechanistic (e.g., SIR), statistical (GLM), machine‑learning (random forest, gradient boosting), and dynamic continuous‑time (neural ODEs)—highlighting each approach’s trade‑offs between interpretability, data requirements, and predictive accuracy.
Real‑world examples illustrate the stakes: delayed modeling during Ebola and COVID‑19 slowed lockdowns and hospital preparedness, while structured registries support statistical models and unstructured clinical notes demand advanced machine‑learning techniques.
The key implication is that model selection must align with the specific research question and available data type, a principle that will guide more advanced applications in subsequent sessions.
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