Individual-Based Models

Published on November 30, 2017 by

Editorial: On complexity, realism and usefulness of individual-based models

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?

Published on November 30, 2017 by

Individual-based approaches to infectious disease modelling; the example of measles

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.

Published on November 30, 2017 by

Representing reality with individual-based models: Opportunity or illusion?

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.

Published on November 30, 2017 by

Modelling heterogeneous social contact patterns in a Simulator for the Transmission of Infectious Diseases (Stride)

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).