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Mitigation strategies for pandemic influenzain the United StatesTimothy C. Germann*†, Kai Kadau*, Ira M. Longini, Jr.‡, and Catherine A. Macken**Los Alamos National Laboratory, Los Alamos, NM 87545; and‡Program of Biostatistics and Biomathematics, Fred Hutchinson Cancer Research Center andDepartment of Biostatistics, School of Public Health and Community Medicine, University of Washington, Seattle, WA 98109Communicated by G. Balakrish Nair, International Centre for Diarrhoeal Disease Research Bangladesh, Dhaka, Bangladesh, February 16, 2006(received for review January 10, 2006)Recent human deaths due to infection by highly pathogenic (H5N1)avian influenza A virus have raised the specter of a devastatingpandemic like that of 1917–1918, should this avian virus evolve tobecome readily transmissible among humans. We introduce anduse a large-scale stochastic simulation model to investigate thespread of a pandemic strain of influenza virus through the U.S.population of 281 million individuals for R0(the basic reproductivenumber) from 1.6 to 2.4. We model the impact that a variety oflevels and combinations of influenza antiviral agents, vaccines, andmodified social mobility (including school closure and travel re-strictions) have on the timing and magnitude of this spread. Oursimulations demonstrate that, in a highly mobile population,restricting travel after an outbreak is detected is likely to delayslightly the time course of the outbreak without impacting theeventual number ill. For R0< 1.9, our model suggests that the rapidproduction and distribution of vaccines, even if poorly matched tocirculating strains, could significantly slow disease spread and limitthe number ill to <10% of the population, particularly if childrenare preferentially vaccinated. Alternatively, the aggressive deploy-ment of several million courses of influenza antiviral agents in atargeted prophylaxis strategy may contain a nascent outbreakwith low R0, provided adequate contact tracing and distributioncapacities exist. For higher R0, we predict that multiple strategiesin combination (involving both social and medical interventions)will be required to achieve similar limits on illness rates.antiviral agents 兩 infectious diseases 兩 simulation modeling 兩social network dynamics 兩 vaccinesIt is inevitable that another influenza pandemic will occur, andrecent events sugge st that this might happen sooner rather thanlater (1). A highly pathogenic H5N1 influenza A virus appears tohave become endemic in avian hosts in Asia, and it is now spreadingin migratory birds westward across eastern Europe. Human infec-tions caused by this virus have a high case fatality rate; together withrecent genetic data that implicate direct transmission of avian-adapted influenza virus to humans as the cause of the 1918influenza pandemic (2), these conditions raise the specter ofanother devastating pandemic. To date, H5N1 viruse s cannottransmit readily from human to human, thus providing a window toplan for the pandemic that will occur should the virus evolve to bereadily transmissible among humans. If the nascent pandemic is notcontained by timely intervention at its source (3, 4), internationaltravel could carry pandemic viruses around the globe within weeksto months of the initiation of the outbreak, causing a worldwidepublic health emergency.Intensive pandemic planning is occurring at the national [U.S.Department of Health and Human Services (HHS) PandemicInf luenza Plan, www.hhs.gov兾pandemicflu兾plan) and interna-tional [World Health Organization (WHO) Global Inf luenzaP reparedness Plan, www.who.int兾csr兾resources兾publications兾influenza兾WHO㛭CDS㛭CSR㛭GIP㛭2005㛭5兾en兾index.html] levels.The most pressing public health questions are: what might be thetime c ourse and geographic spread of the outbreak, and what isthe most effective utilization of available therapeutic and socialresources to min imize the impact of the outbreak? Preciseplann ing is hampered by several unknowns, most critically theeventual human-to-human transmissibility of the human-adapted avian strain (characterized by the basic reproductivenumber R0, the average number of secondary infections causedby a single typical infected individual among a completelysusceptible population), and the supply of therapeutic agents.Manufacturers of neuraminidase inhibitors, such as oselt amivir,have committed to considerable increases in production over thenext 3–4 years. However, the production of vaccine, the tradi-tional first line of defense against influenza vir us infections, ishampered by the inability to predict the antigenic details of theevolved vir us at the time that it becomes a pandemic strain andthe consequent inability to prepare a highly effective vaccine inadvance of a pandemic outbreak. Given these uncertainties, it isimport ant to develop multiple mitigation strategies, involvingvac cination, prophylaxis with antiviral drugs, and both voluntaryand imposed changes in social patterns such as school closuresand travel restrictions.The course of an influenza outbreak is sensitive to many factors,particularly population mobility and the susceptibility of individualsto the virus. Traditional mathematical models of epidemics oftentake the form of deterministic SIR differential equations for thepopulation dynamics of susceptible (S), infectious (I), and re-moved兾recovered (R) individuals (5, 6). Such models have alsobeen extended to model the geographic spread of infectious dis-eases (7, 8). However, the population-based nature of this class ofmodels best describes the dynamics of an epidemic when largenumbers of individuals are infected, rather than the initial or finalstage s of an outbreak, when small numbers of individuals areinvolved and stochastic person-to-person transmission processesdominate. To satisfactorily model the initial seeding and finalquenching of small community-level outbreaks requires a funda-mentally different approach. To capture this crucial effect ofuncertainty in transmission on epidemic predictions, we developand use a stochastic agent-based discrete-time simulation model.This class of model has been used to assess vaccination and antiviralprophylaxis strategies on a local level (9–11); larger-scale versionshave recently been used to investigate strategie s at a regional levelfor containing an emerging pandemic influenza strain at its source(3, 4). Our national-level model combines an


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