  This paper gives an introduction to rule-based modelling applied to topics in
infectious diseases. Rule-based models generalise reaction-based models with
reagents that have internal state and may be bound together to form complexes,
as in chemistry. Rule-based modelling is directly transferable from molecular
biology to epidemiology and allows us to express a broad class of models for
processes of interest in epidemiology that would not otherwise be feasible in
compartmental models. This includes dynamics commonly found in compartmental
models such as the spread of a virus from an infectious to a susceptible
population, and more complex dynamics outside the typical scope of such models
such as social behaviours and decision-making, testing capacity constraints,
and tracing of people exposed to a virus but not yet symptomatic. We propose
that such dynamics are well-captured with rule-based models, and that doing so
combines intuitiveness and transparency of representation with scalability and
compositionality. We demonstrate this feasibility of our approach using a suite
of seven models to describe a spread of infectious diseases under different
scenarios: wearing masks, infection via fomites and prevention by hand-washing,
the concept of vector-borne diseases, testing and contact tracing
interventions, disease propagation within motif-structured populations with
shared environments such as schools, and superspreading events. The
machine-readable description of these models corresponds closely to the
mathematical description and also functions as a human-readable format so that
one knows readily "what is in the model".
