Agent-Based Modeling
Science has a successful history of building analytic models: models of complex behavior that are composed of simple evolving parts. The success of such models in physics suggests also their importance in the design of models of disease state and disease progression.
In modeling HIV-1 response to broadly neutralizing antibodies, I found that agent based models were incredibly flexible in their representation. We can use all these different views to fix parameters in simple models: Birth-death processes, diffusion equations, deterministic ODE’s. Finally, as a continuous-time Markov process, we can always simulate them exactly using the Gillespie algorithm.
I believe that the flexibility of agent based models means we will be able to build unified approached for disease modeling that will allow us to predict disease progression for the purpose of optimizing therapeutic control. We design methods in this paradigm with hope that they will be extensible to any pathogenic evolving disease with dynamics that can be captured with an agent based model.