How infections spread through populations, and what the mathematics says you can do about it. The SIR model and its nonlinear transmission term, the basic reproduction number, the epidemic threshold and why the peak arrives exactly when the susceptible fraction crosses 1/R0, herd immunity, the final size equation and the overshoot past the threshold, and the effective reproduction number. Then SEIR and the latent period, early growth and doubling time, peak prevalence and what flattening the curve really buys, SIS and endemic equilibrium, vaccination coverage and when elimination is impossible, and waning immunity. It closes with the ways the well-mixed assumption fails: heterogeneous contact, superspreading and overdispersion, contact networks and the friendship paradox, case fatality against infection fatality, what partial interventions buy, and which model to reach for. Scoped against modeling, datascience, probability and stochastic, which own compartment mixing models, diagnostic test performance, base rates and percolation, and branching processes.
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Every idea is taught with motivation and a worked example before the drills, and an FSRS spaced-repetition engine schedules each review for the moment just before you'd forget it. A short placement check finds what you already know, so you start Epidemiology exactly where it's useful.