Dynamic models in infectious disease and health and healthcare more generally integrate information and processes across many domains. Model modularity serves as a key enabler for smooth and flexible coordination between domains --- for instance, between pathogen transmission, human behavior, and genomic data. Such modularity allows domain experts to independently build and refine clearly delineated model components which are composed into a single complex model. This approach divides the complexity of model building into two factors: the development of submodels by domain experts and the integration of the submodels into the whole. Applied category theory, the mathematics of composition, tackles this second challenge. Composing models using the tools of applied category theory provides visual transparency for stakeholders, formal analyzability, alignment between types of model heterogeneity (e.g., modular stratification), and opportunities for optimization and parallelization. In this course, we will show how these mathematical tools and their implementation in the open-source AlgebraicJulia programming environment can be used to rapidly develop transparent dynamic models in health, with a particular emphasis on models of infectious diseases.

Beyond teaching these fundamental advances in modeling methodology and theory, the course will explore the strong application potential for these platforms, where students will gain experience in use of existing toolsets that make practical a variety of types of compositional modeling. These tools include interactive application programming interfaces as well as browser-based interactive, collaborative, graphical user interfaces for compositional modeling. Examples model will be drawn from conditions such as COVID-19, measles, pertussis, and other infectious, zoonotic and chronic diseases.