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Mathematical models have become invaluable decision-making tools for public health officials. As demonstrated during the United Kingdom's foot-and-mouth epidemic of 2001, models can be useful in two ways: they can reveal the underlying characteristics of an infection and they can allow the comparison of alternative control measures. Often, however, such models make implicit assumptions that may systematically bias their predictions, say researchers at the University of Georgia.

In a paper published today in the online open-access journal PLoS (Public Library of Science) Medicine, Helen Wearing and Pejman Rohani of the Institute of Ecology at the University of Georgia and Matt Keeling of the University of Warwick, United Kingdom, showed that commonly used disease models may risk making overly optimistic predictions about the levels of public health interventions needed to bring a disease under control.

Wearing and her colleagues found that many off-the-shelf models used in infection management do not realistically account for the length of time that people harbor infections. The simplest models entirely ignore the latent period of a disease: the period of time when an individual is infected but not yet infectious. Other models often assume that the rate of progression from latent to infectious, and infectious to recovered, is constant, irrespective of the time already spent in that status. In such models, for example, many people have a very short infectious period while a few have a very long infectious period. In reality, most people are infectious for an average period of time. For the flu the average infectious period is around 4-5 days, with incredibly few people infectious for less than a couple of days or more than a week.

"Models which do not incorporate the latent period or assume unrealistic distributions of the latent and infectious period," said the researchers, "always resulted in underestimating the transmission potential of an in
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Source:Containerless Research, Inc. & National Science Foundation

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