Innovative research that harnesses maths and biology could help predict which infectious strains of disease have the potential to go global and become epidemics.

Dr Andrew Francis, from the University of Western Sydney and Dr Mark Tanaka, from the University of New South Wales, have designed a model for tracking tuberculosis (TB) infections.

The research, funded by an Australian Research Council Discovery Grant, appears this week in the prestigious international journal, "Proceedings of the National Academy of Sciences of the United States of America".

" We've believed for a number of years some strains of tuberculosis spread faster than others, but it has been difficult to spot the breakaway strains before they take hold," said Dr Francis.

" Our method combines computing and maths expertise with the principles of biology to provide a way to analyse and visualise TB transmission data."

Dr Tanaka, from UNSW, says the technique is unique.

" This is the first time a method has been proposed to identify TB strains travelling through a population at different speeds," said Dr Tanaka.

The researchers applied their technique to four published sets of TB data. Their analysis identified the oldest and most common strains of TB, but according to Dr Francis, it also highlighted younger strains that are infecting at a much faster rate.

" While health authorities are already aware of these strains' existence, the fact that they are spreading faster than others cannot be detected with existing molecular techniques and their transmission speed has never before been clocked," said Dr Francis.

Catching a disease outbreak early could mean the difference between a few infections and an epidemic.

However, modern science delivers so much information it's often difficult to spot the rapidly spreading strains among the sea of statistics.

" The introduction of molecular methods to epidemiology has generated data of increasing complexity and volume. Combining this with advances in genetics can cause an overload of information," said Dr Francis.

" Until now, current models can't make sense of the additional information and new techniques are required."

Tuberculosis is primarily an illness of the respiratory system, and is spread by coughing and sneezing. Every year about 1.7 million people die from the disease. While the disease is curable, drug resistant strains have emerged and are taking hold in some countries.

Dr Tanaka says tracking TB has never before been more critical but current methods fall short.

" Molecular technologies allow epidemiologists to genotype the TB bacterium from sputum samples, classify them and identify key strains. The presence of large clusters of identical types gives us a clue about the rate of spread of some strains, but these don't give us the complete picture," said Dr Tanaka.

Dr Francis says the new method helps to fill in the blanks about TB transmissions.

" A high number of infections by a particular strain of TB may indicate it's highly infectious, but the strain may have also been circulating in the population for a while - long enough to build up high numbers of infections," Dr Francis said.

" Our technique enables us to identify strains within a specific regional outbreak that are spreading considerably faster than others by factoring in the age of the strain."

Dr Tanaka says the relative age of a strain can be measured by counting the number of mutation events it has undergone and comparing it to others in the sample.

" The strains to look for are the ones that appear to be spreading too quickly to produce many mutations. These are the strains that are moving through the population faster than others," he said.

A strain which can infect people quickly is dangerous. It has the potential to spread widely, overwhelm a health service and ultimately kill thousands.

The technique can be used to track drug resistant strains of TB and, with further research, may be adapted to analyse data from other similar diseases.

There are also plans to provide a web based version of the technique allowing health authorities to easily input data which is automatically analysed.

Dr Andrew Francis is a mathematician and senior lecturer in School of Computing and Mathematics the University of Western Sydney.
Dr Mark Tanaka is a mathematical biologist in the School of Biotechnology and Biomolecular Sciences at the University of NSW.

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