With the current situation in South Africa, showing only a modest decline in new TB cases since 2012, new avenues and strategies to identify TB cases need to be explored, tested and implemented. Systematic symptom screening in high risk populations, when this translates to screening everyone in the community, is not very sensitive or cost-effective. The TB programme might therefore consider screening all individuals at primary healthcare facility level, irrespective of their reason for attending. The use of a screening tool with improved sensitivity in comparison to symptom screening alone would be preferable, followed by the current diagnostic algorithm.
Models of HIV and TB are well established and it is tempting to model the combination of HIV and TB by repeating a suitable TB model a number of times corresponding to the various states of HIV. This can, however, lead to a very complex model with tens, if not hundreds of parameters, requiring considerable computing power to run. Fortunately, the time scales over which the two infections progress are very different, allowing us to greatly simplify the problem.
These short courses will take place at SACEMA, Stellenbosch: Introduction to R: Management, Exploration, and Communication of Data (2-6 July 2018) and Advanced Epidemiological Methods (30 July-2 August 2018).
The Conference theme: “STEP UP – Let’s embrace all to end TB!” provides a unique opportunity to reflect on what we have done across the entire spectrum of our programmes in response to TB, what has been effective and what not, what we need to do to find the missing cases and giving attention to Read More
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.