Web Exclusives: Diseases
Modeling H1N1: Then and Now
As the United States prepares for the upcoming flu season, a group of researchers involved in the Models of Infectious Disease Agent Study (MIDAS) continues to simulate how the H1N1 flu could spread.
Ever since the first cases of the flu strain were reported in April 2009, MIDAS researchers have been gathering data on H1N1, including its spread and who got infected. Their computational models let them conduct virtual experiments of how the flu could spread with and without interventions, including vaccination, treatment with antiviral medications and school closures.
To better understand what the virus could do this fall and winter, a MIDAS team based at the University of Washington has developed a model representing the world population. Besides including demographic data, the model also incorporates 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.
Here are key findings from MIDAS' earlier work on H1N1.
Not Worse Than a Typical Season
Modeling work by epidemiologist Marc Lipsitch of the Harvard School of Public Health predicted that H1N1 wouldn't be any worse than a typical seasonal flu season. To forecast the likely severity of H1N1 in the fall and winter months following the initial outbreaks, Lipsitch's group analyzed patient care data from Milwaukee and New York City. The researchers estimated that about 1 in 70 people with flu symptoms were admitted to the hospital, 1 in 400 needed intensive care and 1 in 2,000 died. The modeling results indicated that H1N1 would be less severe or equal to that of seasonal flu. For disease modelers, the work also showed that it's possible to make predictions about severity using data from the early stages of an outbreak.
Vaccinating School-Aged Kids
According to research led by biostatistician Ira Longini at the University of Washington, vaccinating school-aged children greatly reduces overall disease spread and could prevent up to 100 million additional cases in the general population. But the modeling work also showed that these effects were less strong when the virus was more contagious or when fewer children were vaccinated. So, Longini's group concluded that vaccine distribution strategies should depend on a number of factors, including vaccine availability and viral spread.
Value of Employee Vaccination Programs
In one of the first analyses of the economic value of work-sponsored seasonal and pandemic flu vaccine programs, the MIDAS group led by infectious disease scientist Donald Burke at the University of Pittsburgh developed a model that estimated the employer cost to be less than $35 per vaccinated employee. The potential savings could be $15 to $1,494 per employee, depending on the infectiousness of the virus.
Differences in locales do matter when it comes to vaccine strategies, according to modeling work by MIDAS investigator and physicist Stephen Eubank at the Virginia Bioinformatics Institute. He and his team modeled Miami, Seattle and each county in Washington. For each place, vaccinating school-aged children was the best vaccination strategy. But the scientists also showed that characteristics of the local population—age, income, household size and social network patterns, for example—altered the ideal timing and overall effectiveness. These differences, Eubank reported, suggest that vaccination and probably other intervention strategies should take local demographics into account.
Stockpiling more than one type of medication given to lessen flu symptoms could keep H1N1 from developing resistance to such antiviral drugs. This finding comes from Lipsitch's collaborators at the University of Hong Kong. Infectious disease scientists Joseph Wu and Steven Riley used mathematical modeling to predict the likelihood that the H1N1 strain would develop resistance to the widespread use of antiviral medications. Their work showed that giving a secondary antiviral flu drug either before or in combination with a primary antiviral drug could reduce the chances of a resistant strain, as well as slow the spread of infection.
When to Close and Open Schools
Based on results from Burke's model of Allegheny County, Penn., schools should stay closed about 2 months. When strictly maintained for at least 8 weeks, school closure could delay the epidemic peak by up to 1 week, allowing additional time to develop and implement other interventions. However, the model also indicated that school closures lasting less than 2 weeks could actually facilitate flu spread by sending susceptible kids back to school in the middle of an outbreak. The results also showed that closing individual schools after they have identified cases may work as well as closing entire school systems.