Infectious disease modeling is increasingly being used to inform policy, practice, and research. This course will provide an introduction to the epidemiological and mathematical concepts underlying infectious disease modeling as well as the application these concepts through hands-on model implementation. This course will be taught in an alternating lecture and lab style; we will be coding in R software. We will discuss the use of models in making predictions, selecting interventions, and assessing counterfactuals. Student will develop skills identifying the important underlying processes and assumptions in the infectious disease systems they want to model. We will explore the basic reproduction number, its importance to infectious disease dynamics, and how it is calculated. We will compare and contrast compartmental, stochastic, and agent-based model frameworks, as well as deterministic and stochastic model implementations. We will consider how models can be connected to data, introducing parameter identifiability, parameter estimation, and uncertainty quantification.Prerequisites: experience with modeling or good quantitative background, including statistics and differential equations; familiarity with R software.