Modeling targeted layered containment of an influenza pandemic in the United States

doi:10.1073/pnas.0706849105

Modeling targeted layered containment of an influenza pandemic in the United States

^¶Virginia Bioinformatics Institute, Virginia Polytechnical Institute and State University, Blacksburg, VA 24061;
^††Graduate School of Public Health, University of Pittsburgh, Pittsburgh, PA 15261;
**Research Triangle Institute, Research Triangle Park, NC 27709;
^§Department of Infectious Disease Epidemiology, Imperial College, London W21PG, England;
^‖Los Alamos National Laboratories, Los Alamos, NM 87545;
*Department of Biostatistics, School of Public Health and Community Medicine, University of Washington, Seattle, WA 98195; and
^†Program in Biostatistics and Biomathematics, Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, WA 98109

Edited by Barry R. Bloom, Harvard School of Public Health, Boston, MA, and approved January 15, 2008 (received for review July 23, 2007)

Abstract

Planning a response to an outbreak of a pandemic strain of influenza is a high public health priority. Three research groups using different individual-based, stochastic simulation models have examined the consequences of intervention strategies chosen in consultation with U.S. public health workers. The first goal is to simulate the effectiveness of a set of potentially feasible intervention strategies. Combinations called targeted layered containment (TLC) of influenza antiviral treatment and prophylaxis and nonpharmaceutical interventions of quarantine, isolation, school closure, community social distancing, and workplace social distancing are considered. The second goal is to examine the robustness of the results to model assumptions. The comparisons focus on a pandemic outbreak in a population similar to that of Chicago, with ≈8.6 million people. The simulations suggest that at the expected transmissibility of a pandemic strain, timely implementation of a combination of targeted household antiviral prophylaxis, and social distancing measures could substantially lower the illness attack rate before a highly efficacious vaccine could become available. Timely initiation of measures and school closure play important roles. Because of the current lack of data on which to base such models, further field research is recommended to learn more about the sources of transmission and the effectiveness of social distancing measures in reducing influenza transmission.

Footnotes

^‡To whom correspondence should be addressed. E-mail: betz{at}u.washington.edu
Author contributions: M.E.H., I.M.L., S.X., and C.A.M. designed research; M.E.H., N.M.F., S.E., D.A.T.C., B.L., S.X., C.F., A.V., T.C.G., R.B., K.K., and C.B. performed research; M.E.H., N.M.F., S.E., I.M.L., D.W., and P.C. analyzed data; and M.E.H., N.M.F., I.M.L., and D.S.B. wrote the paper.
The authors declare no conflict of interest.
This article is a PNAS Direct Submission.
This article contains supporting information online at www.pnas.org/cgi/content/full/0706849105/DC1.
Freely available online through the PNAS open access option.