Although rare, pandemic flu is highly infectious and people have little or no immunity because they have not previously been exposed to the virus. Unlike seasonal flu, where only high-risk groups are at risk of serious complications, healthy people can be affected during a pandemic and the symptoms are often more severe. It is therefore important to prepare for a flu pandemic in advance to ensure that adequate supplies of vaccines and antivirals are available. Mathematical and computational modeling strategies are increasingly being used to predict the spread of flu in order to be more prepared in case of a pandemic.

Research published in BMC Medicine this week has been centered around flu modeling. As part of a large international collaboration, Alessandro Vespignani and colleagues validated the Global Epidemic and Mobility Model, a computational model for the spread of disease, using surveillance data from the 2009 flu pandemic. The authors showed that computational forecasts of flu spread using this model are in good agreement with real-life data. They conclude that the Global Epidemic and Mobility Model could be used to predict the spread of disease during a pandemic, so long as high quality data are used to build the model.

 In another research article, Jonathan A McCullers and colleagues from Entropy Research Institute and the University of Tennessee used mathematical modeling to investigate flu mortality in different age groups. The authors assessed mortality data for all flu pandemics in the 20^th century, and showed that emerging virus strains are similar to those that were present previously. They concluded that mortality is not shifted to younger age groups during a pandemic, but is instead affected by the virulence of the current strain.

These research articles highlight the value of mathematical models for predicting the spread and mortality of flu in a pandemic. The usefulness of such models is discussed in a review article by BMC Medicine’s Editorial Board Member Gerardo Chowell and colleagues from Arizona State University. There have been many recent advances in forecasting flu transmission, as exemplified by recent research published in BMC Medicine; Chowell and colleagues describe how further improvements to mathematical models will lead to the integration of models with contingency plans for managing infection during mass gatherings. We look forward to following the progress of research on these valuable models to help prevent the spread of flu and other infectious diseases.