Abstract
Mathematical is a valuable tool in Immunology, enabling us to understand complex mechanisms at different scales and make predictions about their behaviour. However, designing a model that accurately represents a system can be challenging. One important consideration is the level of detail required to make the model interpretable because, often, adding more levels of detail turns the model unidentifiable, i.e., it cannot be uniquely estimable from data. In this talk, we will explore the importance of identifiability in model analysis and design and discuss strategies for finding the optimal level of model detail. We will examine several case studies highlighting challenges and opportunities in balancing model complexity with identifiability and point to some examples where simplicity trumps excessive focus on details.
Identifiability matters: a closer look at the art of simple mathematical models for complex systems
