7 Conclusion and Additional Resources
7.1 Closing Statement
We’ve come to the end of our introduction and overview of SIR models! Although this marks the end of this module, we hope this is just the beginning of your journey to learning, applying, and appreciating the world of disease modeling. There is still so much to learn that we have not gone over: constructing and analyzing compartmental models with different compartments (like the SEIR model which has the “exposed” compartment), doing MLE with incidence instead of prevalence data, finding the final epidemic size, and so much more.
We also hope that, in addition to gaining a new appreciation for disease modeling, you have developed a greater sense of computational literacy, or using computers to explore, analyze, and better understand problems in biology, epidemiology, mathematics, and beyond.
7.2 Conclusion: What Computational Thinking Gets You
The SIR model is simple. Three compartments, two parameters, three equations. And yet, working through this module computationally, not just mathematically, you have done something more than learned a model. You have learned to:
- Decompose a biological process (disease spread) into computable pieces (state variables, rates, parameters)
- Translate between mathematical notation and code, and recognize they are the same statement in different languages
- Interrogate a model by changing its parameters and watching what changes and what doesn’t
- Verify analytical results numerically, and notice when they agree or diverge
- Extend the framework: the endemic model uses the same structure as the epidemic model, just with added terms. From here, SEIR, SIRS, and host-pathogen models with predators follow the same logic.
This is the computational literacy that makes quantitative ecology accessible. The math does not have to be a black box. The code does not have to be a black box either. When both are visible and connected, the biology becomes clearest.
7.3 Feedback Survey
A feedback survey will be linked here — TBD.