Mathematical modelling

Published on September 13, 2013 by

Modelling “Tik” abuse in the presence of drug-supply chains

Methamphetamine (MA), commonly known by the street name “tik” in South Africa, is a highly addictive stimulant whose production and abuse has increased dramatically. Many questions remain unanswered as to how prevalent is drug abuse and the implications of drug use, especially on disease burden, healthcare and budgetary demands as well as risky behaviour. There is a need to understand the problem, measure drug use trends, design appropriate intervention measures and evaluate the success of these interventions. As is demonstrated here mathematical models can help in modelling the “tik” epidemic.

Published on June 12, 2013 by

Modelling – a universal practice in all of science

Welcome to a special edition of SACEMA quarterly epidemiological update – dedicated to SACEMA’s annual ‘research days’ event and other SACEMA related work. This is a glimpse into exciting trends in public health research, where mathematical methods are increasingly applied to a range of problems, to help leverage limited data, think about prospects for interventions, and formulate new hypotheses and experiments. The article also includes a reflection on modelling as a universal practice in all of science and all that differs are the kinds of models, and the techniques used to set them up and manipulate them.

Short item Published on March 18, 2013

Biology as Population Dynamics: Heuristics for Transmission Risk

Population-type models, accounting for phenomena such as population lifetimes, mixing patterns, recruitment patterns, genetic evolution and environmental conditions, can be usefully applied to the biology of HIV infection and viral replication.  A simple dynamic model can explore the effect of a vaccine-like stimulus on the mortality and infectiousness, which formally looks like fertility, of invading Read More

Short item Published on March 18, 2013

Clinic on the Meaningful Modelling of Epidemiological Data

The South African Centre for Epidemiological Modelling (SACEMA) invites applications to the fourth annual Clinic on the Meaningful Modelling of Epidemiological Data. This two-week modelling clinic, mounted in collaboration with the International Clinics on Infectious Disease Dynamics and Data (ICI3D) Program, and the African Institute for Mathematical Sciences (AIMS), will emphasize the use of data in understanding Read More

Published on November 30, 2012 by

Modelling the control of Trypanosomiasis using trypanocides or insecticide-treated livestock

Across sub-Saharan Africa, several species of trypanosome, transmitted by tsetse flies (Glossina spp), cause human and animal trypanosomiasis. While interventions can be directed against either the vector or the parasite, emphasis has usually been on the use of drugs to treat the disease both in humans and in livestock. Several advances in our understanding of tsetse biology and ecology and improvements in the cost-effectiveness of tsetse control have revived interest in the vector control approach to disease management. This article discusses and compares two different approaches to the control of trypanosomiasis in cattle: either we can control the disease by treating cattle with insecticides that kill the tsetse vectors without having any direct effect on the trypanosomes. Or we can inject the cattle with trypanocides that kill the parasites but leave the tsetse flies unharmed.

Published on November 30, 2012 by

Challenges with using estimates when calculating ART need among adults in South Africa

Annually, the Foundation for Professional Development (FPD) collects information on HIV/AIDS service provision in the City of Tshwane and estimates service needs. In order to estimate need for ART among adults, data on the number on ART are used from the Department of Health, and data on the number in need from the Statistics South Africa mid-year population estimates report (based on Spectrum Software estimates). When comparing the 2010 and 2011 Statistics South Africa reports the number in need of ART dramatically decreased, and became even lower than the number receiving ART. Although the difference is most likely due to changes in the calculation done by Statistics South Africa, no detailed and confirmed explanation can be offered at the moment. This article intends to provide a constructive contribution to the discussion about the use of model-derived estimates of the need for ART.

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