As the United States prepares for the upcoming flu season, a group of researchers supported by the National Institutes of Health continues to model how H1N1 may spread.
The work is part of an effort, called the Models of Infectious Disease Agent Study (MIDAS), to develop computational models for conducting virtual experiments of how emerging pathogens could spread with and without interventions. The study involves more than 50 scientists with expertise in epidemiology, infectious diseases, computational biology, statistics, social sciences, physics, computer sciences and informatics.
As soon as the first cases of H1N1 infections were reported in April 2009, MIDAS researchers began gathering data on viral spread and affected populations. This information enabled them to model the potential outcomes of different interventions, including vaccination, treatment with antiviral medications and school closures. The work built upon earlier models the MIDAS scientists developed in response to concerns about a different potentially pandemic influenza strain, H5N1, or avian flu.
“Computational modeling can be a powerful tool for understanding how a disease outbreak is unfolding and predicting the implications of specific public health measures,” said Jeremy M. Berg, Ph.D., director of the National Institute of General Medical Sciences, which supports MIDAS. “During the H1N1 pandemic, MIDAS scientists applied their models to see what they could do to help in a real situation.”
Because the H1N1 flu strain is still circulating, a MIDAS group based at the University of Washington in Seattle is now studying the impact the virus could have this fall and winter. Its model, which represents the world population, includes information about immunity—how many people are protected by vaccination or prior infection—and the other circulating flu strains. Using the model, the scientists may be able to predict how H1N1 evolves and the possible role of the H3N2 strain, which historically has been the dominant seasonal flu virus. The results also may help forecast the potential effectiveness of the new flu vaccine that includes both the H1N1 and H3N2 viral strains.
Source: NIH
