For individual-based models (IBMs) in epidemiology, thinking about links between a model’s complexity, its closeness to reality and its usefulness is pertinent. This is because the bottom-up, modular and hierarchical structure of this type of models makes it relatively easy to increase the level of heterogeneity and complexity represented by the model. Moreover, the rationale for doing so is often a desire to build more realistic models. The implicit belief is that by virtue of being more realistic, models also become more useful. But is this necessarily true?
This article summarises two papers that have used individual-based models (IBMs) to assess intervention strategies for measles, which give a flavour of the types of scenarios and questions for which IBMs have been used. Together, these two papers highlight that IBMs can have varying levels of complexity, should, where possible, be fitted to data, must be subject to thorough sensitivity analyses in the case of missing data, and can be very useful for the assessment of intervention strategies in specific times and places.
Individual-based models (IBMs) can be very useful for refining our mechanistic understanding of pathogen transmission and can help make inference about real-world epidemics, if based on real-world data. IBMs should not be discarded simply because they are complicated, have many parameters, or use assumptions. The type of inference made from an IBM should be closely tied to the data used to parameterize it.
Social mixing patterns can have an important effect on the spread of an infectious disease, and thus should be included in a model for the transmission of such a disease. Stride (a Simulator for the TRansmission of Infectious DisEases) is an open-source simulator for the transmission of infectious diseases. In Stride, the influence of age, context and type of day on social mixing patterns is explicitly modelled. After briefly introducing our model, we illustrate it by simulating the spread of Influenza in a synthetic population for Miami-Dade (Florida, USA).
Sacema will organise a practical introduction course to Individual-based Modelling in Epidemiology in May 2018. Postgrad students, postdocs and health science professionals whose work potentially involves the design and/or use of individual-based models in epidemiology are invited to attend.