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?
Foot-and-mouth disease (FMD) can affect a range of animals, including livestock. In South Africa, successful control has eliminated the infection from most of the country, however, infection risk remains in the areas surrounding the Kruger National Park. African buffalo (Syncerus caffer) maintain a high prevalence of FMD. Understanding how buffalo maintain the infection, when transmission from buffalo is most likely, and why transmission occurs are important to understanding FMD in South Africa. This article discusses an individual-based model that is guiding our field and experimental data collection in Kruger National Park.
Individual-based models of sexually transmitted infection epidemics allow us to account for heterogeneity of sexual behaviour in a way that is impractical with traditional differential-equation-based models. But do we increase our knowledge by using more complex models? When we use sexual behaviour data in our models are we generating outputs, such as estimates of prevalence and mortality, that reflect the real-world populations we are studying? In this article we present examples that help us explore this question.
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) are suited to combine heterogeneous within-and between-host interactions and offer many opportunities, especially to analyse targeted interventions for endemic infections and to model host behaviour. We advocate the exchange of (open-source) platforms and stress the need for consistent terminology and model “branding”. IBMs come at a computational cost but offer a very powerful and flexible framework to analyse disease transmission in depth.
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).
Mathematical models are often used to gain theoretical insights into the epidemiology of sexually transmitted infections (STIs) and to inform policy around the prevention and treatment of STIs. Yet these models differ greatly in the assumptions they make, and can sometimes produce vastly different estimates of the likely impact of STI control programmes. So which modelling approaches are most realistic? How much bias might we be introducing with certain simplifying assumptions? This article summarises a recent paper that attempted to address these questions by comparing two broad modelling approaches: deterministic, frequency-dependent models and individual-based, network models.
Sacema will expand its team in 2018. We are looking for mid-career researchers with doctoral degrees in a quantitative field and substantial experience in epidemiological modelling, study design, and data analysis. Areas of particular interest include TB, outbreak response, and emerging infections.
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.