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This section contains information on statistics, dynamical systems, probabilities, and other preliminaries necessary to understand as you approach building, configuring, and calibrating models to simulate disease transmission or other epidemiological processes. Much of this foundational knowledge is essential to informing decisions you make during the modeling processes.

As you work through this curriculum, keep in mind that the process of disease modeling and model calibration is often an iterative one. For example, a common process is to configure a very simple statistical or compartmental model, attempt to calibrate it to real-world data, and then identify where it cannot reproduce that data. Then, add more complexity to close that gap and repeat the process until you have a model that fits the data well.

Generally, you want to use the simplest model that still provides a good fit to the data. Simpler models are often:

• Easier to understand and interpret, including when communicating findings to policymakers
• Less computationally expensive to run
• Quick to provide insights into different scenarios or intervention strategies