Tuberculosis has been classically studied from different points of view, with most efforts devoted to clinical, epidemiological or microbiologic aspects of the infection and the disease. Scientists have been lately discovering how mathematical approaches can help them to better understand complex processes as some of the mechanisms underlying infection, disease development or microbiological cultures, as recently published literature proves. However, the language used by the bioscientists and the mathematical modelers is so different that great efforts are needed in order to establish a fluent communication between both. As result, mathematical approaches are useful tools that still remain a mystery for most of the clinicians, researchers and vaccine and drug developers.
Mathematical models can measure epidemiological patterns, extrapolate predictions on the course of outbreaks and help designing health policies with important economic impact. But they can also be used to evaluate the effect of new drug and vaccine candidates while reducing the number of animals used in experimentation, and to measure the impact of vaccination strategies, for example.
By proposing this topic our aim is to disseminate how mathematical models can help to interpret the experimental data obtained and to learn more about TB natural history, the disease course and transmission; and to show how models can permit to better design and evaluate new diagnostic and therapeutic strategies.
By proposing selected experts in the field, we have planned to cover the mathematical models used to approach tuberculosis at all levels: microbiological cultures, granuloma formation, TB epidemiology and health economics.
To summarize, this research topic aims to explain how mathematical modeling available could help in a practical way the clinicians and researchers while representing an up-to-date for those already familiar with it, in order to achieve a multidisciplinary approach able to help tackling Tuberculosis.
Tuberculosis has been classically studied from different points of view, with most efforts devoted to clinical, epidemiological or microbiologic aspects of the infection and the disease. Scientists have been lately discovering how mathematical approaches can help them to better understand complex processes as some of the mechanisms underlying infection, disease development or microbiological cultures, as recently published literature proves. However, the language used by the bioscientists and the mathematical modelers is so different that great efforts are needed in order to establish a fluent communication between both. As result, mathematical approaches are useful tools that still remain a mystery for most of the clinicians, researchers and vaccine and drug developers.
Mathematical models can measure epidemiological patterns, extrapolate predictions on the course of outbreaks and help designing health policies with important economic impact. But they can also be used to evaluate the effect of new drug and vaccine candidates while reducing the number of animals used in experimentation, and to measure the impact of vaccination strategies, for example.
By proposing this topic our aim is to disseminate how mathematical models can help to interpret the experimental data obtained and to learn more about TB natural history, the disease course and transmission; and to show how models can permit to better design and evaluate new diagnostic and therapeutic strategies.
By proposing selected experts in the field, we have planned to cover the mathematical models used to approach tuberculosis at all levels: microbiological cultures, granuloma formation, TB epidemiology and health economics.
To summarize, this research topic aims to explain how mathematical modeling available could help in a practical way the clinicians and researchers while representing an up-to-date for those already familiar with it, in order to achieve a multidisciplinary approach able to help tackling Tuberculosis.
