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.
The main contributions in this edition of the Quarterly give us a good indication of the kinds of questions that warrant modelling, and with which non-modellers can also better engage if they are willing to at least frame their thinking explicitly in model-like terms.
Recent years have seen increasing recognition of the importance of canine rabies to human health: the scientific community has increasingly embraced dog rabies as an interesting and important field of study, and the public-health community has ramped up control efforts, leading to striking control successes, particularly in the Americas. This article discusses a series of papers published from 2007 based on ground-breaking work on tracing canine rabies in Tanzania, as well as analyses of data from other parts of the world.
Human cells have a finite lifespan (Hayflick limit). The existence of this Hayflick limit with regards to immune cells known as T cells, implies that infections have a long-term immunological cost (IC) to the individual because they drive immune cells towards the end of their lifespan, eventually making most of those cells unavailable to respond to other infections. The development of an accurate IC measure will lead to a better predictor of the age of the adaptive immune system than chronological age.