Measuring infection and disease
To fight infections, we must be able to see them, count them, and characterise them. None of this is completely straightforward. For some conditions, like TB, even detecting them is a challenge. HIV is easy to detect reliably, but patients require life-long treatment with powerful drugs, and it becomes a challenge to efficiently monitor the severity of disease and to optimise long term treatment for each individual.
Refining our ability to measure infections - to distinguish such things as the strain of infectious agent (virus or bacterium), the severity of infection, natural sequential stages of illness, and the effects of treatment – has consequences far beyond the ability to provide appropriate treatment to patients. Epidemiological surveillance relies on efficient, reliable measures of a spectrum of infections and associated diseases. Basic studies of the biology of disease require a clear window into microscopic processes influenced by pathogens immune system dynamics, and pharmaceutical interventions. The planning and evaluation of health care systems requires knowledge of trends in disease patterns on all these.
At SACEMA, we invest significant effort in various investigations of ‘Biomarkers’ – biological traces of individual and population health, which can be measured in studies or in routine care. These projects cover a range of activities and applications. Repositories of specimens need to be either physically assembled of tracked across various studies contributing this material. Data gathered for originally different purposes needs to be combined into coherent analyses.
New fundamental biochemical and technological ideas need to be passed through various levels of evaluation to decide on additional investment of effort, course corrections, regulatory claims of utility, and so on. Laboratory processes require optimisation in the sense of yielding sufficient information without requiring excessive investment in infrastructure and consumables.
DSI-NRF Centre of Excellence in Epidemiological Modelling and