Lecture 10: Dynamic Disease Modelling
Why It Matters
Choosing the right disease model enables policymakers to allocate resources efficiently, design targeted interventions, and anticipate epidemic outcomes, ultimately saving lives and reducing economic disruption.
Key Takeaways
- •Dynamic models predict disease spread, resource needs, and intervention impact.
- •SIR and SEIR frameworks use transmission, recovery, and incubation parameters.
- •Agent‑based, network, and stochastic models capture heterogeneity and randomness.
- •Meta‑population models link regional outbreaks via travel and migration.
- •Model choice depends on data availability, complexity, and policy objectives.
Summary
The lecture introduces dynamic disease modeling as a tool for forecasting infection trajectories, estimating healthcare demand, and evaluating control measures such as vaccination, testing, and quarantine. It emphasizes that models translate epidemiological parameters—transmission rate (β), recovery rate (γ), incubation period (η), and basic reproduction number (R₀)—into actionable predictions for planners. Key concepts covered include the classic SIR model, its SEIR extension that adds an exposed compartment, and the calculation of R₀ to gauge epidemic potential. Real‑world examples illustrate these ideas: a 1978 measles outbreak in a UK school (R₀≈3.8, requiring ~74% immunization), COVID‑19 SEIR parameters (5‑day incubation, 2.9‑day infectious period, R₀≈2.6), and an Ebola stochastic simulation showing a 70% chance of early extinction when cases are isolated. The session also surveys advanced approaches—agent‑based models that simulate individual behaviors, network models that capture heterogeneous contact patterns, stochastic simulations (Gillespie algorithm, τ‑leaping) for small populations, and meta‑population frameworks that link regional SIR dynamics via travel. Tools such as R, NetLogo, and open‑source ABM platforms are highlighted for implementation. Finally, the lecturer provides a decision matrix for selecting the appropriate model based on data richness, system complexity, and policy goals, underscoring that accurate model choice directly influences public‑health preparedness, resource allocation, and intervention effectiveness.
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