While we all believe in ‘inter-disciplinary research’, the reality often falls short of the intention. How then can we begin to learn each others languages, hear what others are saying, use our joint knowledge and understanding to throw light on important problems, and hopefully make the world a slightly better place?
The most recent South African National HIV Prevalence, Incidence and Behaviour Survey conducted in 2012 incorporated new tools for generating greater information about the current state of the HIV epidemic from the national household survey. The results from the direct HIV incidence estimates from the multi-assay recent infection algorithm from the survey and estimates from more conventional modelling approaches to estimating incidence were found to generate fairly similar results. The 2012 household survey data also provided an opportunity to externally validate model projections over time.
Tsetse flies (genus Glossina) can threaten health and agriculture by transmitting the parasites that cause the potentially fatal diseases of sleeping sickness in humans and nagana in livestock. The fact that only parts of sub-Saharan Africa are infested is attributable to many causes, including the temperature in the area. This raises the possibility that climate change will affect the abundance and distribution of tsetse – the leading questions being how great and how rapid the effects will be. SACEMA is addressing these questions by a combination of field work and simulation modelling.
In populations where most subjects know their HIV status, population-based prevalence HIV estimates can be heavily biased due to high rates of non-response to HIV testing. Inverse probability weighting could potentially be used to correct for non-response to HIV testing in order to derive sub-national level HIV statistics, especially where the data at these levels are sparse. Its usefulness can be enhanced by incorporating antenatal clinics’ HIV data, often the only source of HIV prevalence.
Alide Dasnois, a South African journalist and former editor of the Cape Times, has written an an article titled “The long battle to get the mines to cough up” which is about compensating miners for the burden of lung disease. The importance of this issue has been highlighted before in a SACEMA Quarterly article by Tony Davies giving an historical overview on occupational lung disease in South Africa.
In the September 2015 SACEMA Quarterly, we published an editorial on the importance of interactive storytelling in epidemiology as well as a short on narratives and paradigms. When we came across a review of the book Houston, We Have a Narrative by Randy Olson, we thought that this would be interesting to share with you as well. The reviewer Rafael E. Luna is the author of The Art of Scientific Storytelling: Transform Your Research Manuscript with a Step-By-Step Formula.
In modelling hierarchical data we can take into account spatial and temporal correlations by introducing spatiotemporal random effects in the model. Several other hurdles have to be overcome when modelling hierarchical mortality data, but Bayesian techniques with the aid of the Markov chain Monte Carlo (MCMC) simulation methods have successfully overcome these and fit spatiotemporal random effects for reasonably sized geo-locations. However, as the number of geo-locations increases, MCMC computations become infeasible or extremely slow, which is a norm in Big Data Analytics (BDA). This problem is popularly known as the “big m” or “big N”.