Prompt use of social-distancing measures, antiviral treatment and prophylaxis could control an outbreak of pandemic influenza in the United States, pending the availability of a vaccine, according to a recently-published study.
Three teams of researchers in the U.S, and England, in close collaboration with Government officials, studied various intervention combinations to guide national pandemic planning. The three research teams and an informatics group that participated are part of the Models of Infectious Disease Agent Study (MIDAS) Network, an effort funded by the National Institute of General Medical Sciences, or NIGMS.
"The federal government wanted three separate infectious-disease-modeling groups working on the same problem just to make sure the results were robust, since this data would be used to inform national pandemic planning," said the study’s co-author, Ira M. Longini Jr., Ph.D. "We got the highest level of input."
The researchers concentrated on assessing the effectiveness of a blend of antiviral and social-distancing interventions (like closing schools) in preventing a flu pandemic, since a vaccine was unavailable at the time. Other studies had shown that a vaccine would be very helpful in slowing a pandemic.
"The good news was that all three of the disease-modeling groups involved in the study found that an outbreak of pandemic flu similar to the pandemic of 1918 could be mitigated if these measures were implemented quickly," said M. Elizabeth Halloran, M.D., D.Sc., the study’s lead author.
To conduct the study, the researchers used three separate but similar computer models to calculate the spread of influenza within a population similar to that of Chicago, with approximately 8.6 million people. Members of this virtual community interacted the way people normally do: within households, schools and workplaces, and the community at large. All three models were set up to have attack-rate patterns similar to those of past U.S. flu pandemics.
Predicting the spread of an infectious disease such pandemic influenza requires much more than simply connecting dots on a map. Instead, Halloran and colleagues rely on a tool called stochastic modeling to take into account real-world unpredictability, as well as many factors about the disease and the affected population. In constructing these models, the researchers begin with assumptions about how people interact and how the virus spreads. They also introduce and evaluate the effectiveness of various intervention strategies.
These were the findings published in the online Early Edition of PNAS, a publication of the Proceedings of the National Academy of Sciences of the United States of America. M. Elizabeth Halloran, M.D., D.Sc., was the lead author, Ira M. Longini Jr., Ph.D., was the co-author. Both are researchers at Fred Hutchinson Cancer Research Center and professors of biostatistics at the University of Washington, and use mathematical and statistical methods to study the natural course of infectious diseases. Prior to publication, Longini presented the findings at the White House and at the Institute of Medicine.