As the United States prepares for the upcoming flu season, a group of researchers supported by the NIH 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, according to a press release.
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.
“Computational modeling can be a powerful tool for understanding how a disease outbreak is unfolding and predicting the implications of specific public health measures,” Jeremy M. Berg, PhD, director of the National Institute of General Medical Sciences, which supports MIDAS, stated in the release. “During the H1N1 pandemic, MIDAS scientists applied their models to see what they could do to help in a real situation.”
To predict the likely severity of H1N1 in the fall and winter months following the initial outbreaks, the MIDAS group led by Marc Lipsitch, DPhil, of the Harvard School of Public Health in Boston analyzed patient care data from Milwaukee and New York City. The researchers estimated that about 1 in 70 symptomatic people were admitted to the hospital, 1 in 400 required intensive care and 1 in 2,000 died. They predicted H1N1 to be no more and possibly even less severe than the typical seasonal flu strain. The work, which factored in local differences in flu detection and reporting, also showed that it is possible to make predictions about severity using data from the early stages of an outbreak.
To determine if a vaccination strategy would likely have the same effect in different locations, a team led by MIDAS investigator Stephen Eubank, PhD, of the Virginia Bioinformatics Institute at Virginia Tech in Blacksburg developed models representing the demographics of Miami, Seattle and each county in Washington. The models indicated that while vaccinating school-aged children was the best strategy in each place, the optimal timing and overall effectiveness of the approach varied due to specific characteristics of the local population, such as age, income, household size and social network patterns. These differences, Eubank concluded, suggest that vaccination and probably other intervention strategies should take local demographics into account.
Lipsitch’s collaborators Joseph Wu, PhD, and Steven Riley, DPhil, at the University of Hong Kong used mathematical modeling to predict the likelihood that the H1N1 strain would develop resistance to the widespread use of antiviral medications taken to lessen flu symptoms. Their work showed that giving a secondary antiviral flu drug either prior to or in combination with a primary antiviral could mitigate the emergence of resistant strains in addition to slowing the spread of infection. The results, the researchers concluded, point to the value of stockpiling more than one type of antiviral drug.