This document discusses computational models of infectious disease transmission. It begins by introducing the objectives and importance of modeling infectious diseases. It then describes the classical Susceptible-Infected-Recovered (SIR) model, including its assumptions, predictions regarding the basic reproductive number (R0), and extensions adding vital dynamics and vaccination. Variations including stochastic models, modeling emotion diffusion, and an example NetLogo spatial SIR outbreak model are also covered. The document discusses analyzing model assumptions, writing more efficient code, and exploring alternative models of recovery times and additional disease states.