Editing Epidemiology, Disease Dynamics, and Population Health (section)

== <span style="color: #FFFFFF;">Understanding</span> ==
Epidemiology is understood through '''population''' and '''causation'''.

'''Why R₀ Is Not Fixed''': R₀ depends not just on pathogen biology but on social behavior, population density, and interventions. COVID-19's original Wuhan R₀ was ~2-3; Delta was ~6-8; Omicron ~15-18 — reflecting both viral evolution and the network structure of contacts. Masking, distancing, and ventilation reduce transmission probability per contact; lockdowns reduce contact frequency. This is why epidemic forecasting is inherently conditional — models project "what will happen IF behavior remains X," not deterministic predictions.

'''Confounding and Causality''': Epidemiology's central challenge is distinguishing association from causation in observational data. The classic example: countries with higher chocolate consumption have more Nobel Prize winners per capita. This is confounded by wealth — richer countries eat more chocolate and also invest more in science. Bradford Hill's criteria were developed precisely to discipline this inference. The randomized controlled trial is the gold standard for establishing causality, but most exposure-disease questions cannot be randomized ethically.
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