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?
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.
Two months ago Lander Willem and I organized the first edition of the short course “Individual-based modelling in epidemiology: A practical introduction”. The feedback at the end of the course was overwhelmingly positive, which left us feeling empowered and encouraged to not leave it at this first edition. Participants of the next edition should expect an even more hands-on course, with more time to acquire skills in developing, exploring and fitting individual-based models.
As epidemiologists we constantly think about indicators and metrics. Given the well-known limitations of simplifying complex dependencies to one-dimensional indicators, isn’t it surprising that many academics have bought into the practice of measuring the quality and impact of their work by a handful of metrics? While books have been written about the need for more and better indicators of impact and excellence in academia, surprisingly little attention is given to the challenge and value of being engaged and excelling in non-academic activities. Some ideas around this are presented in this editorial.
Early initiation of antiretroviral therapy (ART) significantly improves the survival of people living with HIV (PLWH) and reduces HIV transmission to uninfected partners. Mathematical models suggest that treatment-as-prevention programmes could lead to HIV elimination. How the clinical efficacy of ART in preventing HIV transmission translates to real-life settings depends in large part on the capacity of HIV programmes to engage and retain PLWH. The effects of ART on HIV incidence may also depend on changes in sexual network dynamics during the course of ART scale-up which are discussed in this article.
Effective HIV prevention requires knowledge of the structure and dynamics of the social networks across which infections are transmitted. These networks are most commonly comprised of chains of sexual relationships. Whereas network data have long been collected during survey interviews, new data sources have become increasingly common in recent years. In this article, we review current and emerging methods for collecting HIV-related network data, as well as modelling frameworks commonly used to infer network parameters and map potential HIV transmission pathways within the network.
Policy makers, fellow scientists, media reporters and students are more likely to pay attention to epidemiologists who are able to articulate new research findings through a captivating narrative, a vivid mental picture or a striking infographic. Recent software developments have made it easy to produce web applications and reports that dynamically combine text, mathematical expressions, code chunks and the output of complex computations. The result is that researchers can adopt a more engaging, interactive form of storytelling.
2015 signifies the deadline for the Millennium Development Goals (MDGs) which include reduction of the under-five mortality rate by two-thirds, reduction of the maternal mortality ratio by three quarters (both relative to the 1990 figures), and universal access to reproductive health. This issue of the SACEMA Quarterly focuses on various aspects of maternal and child health, and the role of statistical and mathematical modelling techniques in this area of research.
According to the President’s Emergency Plan for AIDS Relief (PEPFAR), an AIDS-free generation entails that first, no one will be born with the virus; second, that as people get older, they will be at a far lower risk of becoming infected than they are today; and third, that if they do acquire HIV, they will get treatment that keeps them healthy and prevents them from transmitting the virus to others. We argue that an AIDS-free generation is possible in Southern Africa, but not unless the high rates of incident infections in key populations are reduced.
SACEMA’s experience is that low competence and poor self-efficacy in the use of statistical software packages is a major obstacle to acquiring and expanding expertise in statistical analysis. It was therefore decided to increase our efforts to strengthen hands-on capacity and confidence in data management, exploration and visualisation, using the versatile, open-source package R. We plan to offer an intensive one-week course in June 2014 which will include computer practicals with participants’ own data. The course also offers an opportunity to conduct education research.