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      Optimizing Tactics for Use of the U.S. Antiviral Strategic National Stockpile for Pandemic Influenza

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          Abstract

          In 2009, public health agencies across the globe worked to mitigate the impact of the swine-origin influenza A (pH1N1) virus. These efforts included intensified surveillance, social distancing, hygiene measures, and the targeted use of antiviral medications to prevent infection (prophylaxis). In addition, aggressive antiviral treatment was recommended for certain patient subgroups to reduce the severity and duration of symptoms. To assist States and other localities meet these needs, the U.S. Government distributed a quarter of the antiviral medications in the Strategic National Stockpile within weeks of the pandemic's start. However, there are no quantitative models guiding the geo-temporal distribution of the remainder of the Stockpile in relation to pandemic spread or severity. We present a tactical optimization model for distributing this stockpile for treatment of infected cases during the early stages of a pandemic like 2009 pH1N1, prior to the wide availability of a strain-specific vaccine. Our optimization method efficiently searches large sets of intervention strategies applied to a stochastic network model of pandemic influenza transmission within and among U.S. cities. The resulting optimized strategies depend on the transmissability of the virus and postulated rates of antiviral uptake and wastage (through misallocation or loss). Our results suggest that an aggressive community-based antiviral treatment strategy involving early, widespread, pro-rata distribution of antivirals to States can contribute to slowing the transmission of mildly transmissible strains, like pH1N1. For more highly transmissible strains, outcomes of antiviral use are more heavily impacted by choice of distribution intervals, quantities per shipment, and timing of shipments in relation to pandemic spread. This study supports previous modeling results suggesting that appropriate antiviral treatment may be an effective mitigation strategy during the early stages of future influenza pandemics, increasing the need for systematic efforts to optimize distribution strategies and provide tactical guidance for public health policy-makers.

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          Most cited references15

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          Transmissibility of 1918 pandemic influenza

          The 1918 influenza pandemic killed 20–40 million people worldwide 1 , and is seen as a worst-case scenario for pandemic planning. Like other pandemic influenza strains, the 1918 A/H1N1 strain spread extremely rapidly. A measure of transmissibility and of the stringency of control measures required to stop an epidemic is the reproductive number, which is the number of secondary cases produced by each primary case 2 . Here we obtained an estimate of the reproductive number for 1918 influenza by fitting a deterministic SEIR (susceptible-exposed-infectious-recovered) model to pneumonia and influenza death epidemic curves from 45 US cities: the median value is less than three. The estimated proportion of the population with A/H1N1 immunity before September 1918 implies a median basic reproductive number of less than four. These results strongly suggest that the reproductive number for 1918 pandemic influenza is not large relative to many other infectious diseases 2 . In theory, a similar novel influenza subtype could be controlled. But because influenza is frequently transmitted before a specific diagnosis is possible and there is a dearth of global antiviral and vaccine stores, aggressive transmission reducing measures will probably be required. Supplementary information The online version of this article (doi:10.1038/nature03063) contains supplementary material, which is available to authorized users.
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            Containing pandemic influenza with antiviral agents.

            I Longini (2004)
            For the first wave of pandemic influenza or a bioterrorist influenza attack, antiviral agents would be one of the few options to contain the epidemic in the United States until adequate supplies of vaccine were available. The authors use stochastic epidemic simulations to investigate the effectiveness of targeted antiviral prophylaxis to contain influenza. In this strategy, close contacts of suspected index influenza cases take antiviral agents prophylactically. The authors compare targeted antiviral prophylaxis with vaccination strategies. They model an influenza pandemic or bioterrorist attack for an agent similar to influenza A virus (H2N2) that caused the Asian influenza pandemic of 1957-1958. In the absence of intervention, the model predicts an influenza illness attack rate of 33% of the population (95% confidence interval (CI): 30, 37) and an influenza death rate of 0.58 deaths/1,000 persons (95% Cl: 0.4, 0.8). With the use of targeted antiviral prophylaxis, if 80% of the exposed persons maintained prophylaxis for up to 8 weeks, the epidemic would be contained, and the model predicts a reduction to an illness attack rate of 2% (95% Cl: 0.2, 16) and a death rate of 0.04 deaths/1,000 persons (95% CI: 0.0003, 0.25). Such antiviral prophylaxis is nearly as effective as vaccinating 80% of the population. Vaccinating 80% of the children aged less than 19 years is almost as effective as vaccinating 80% of the population. Targeted antiviral prophylaxis has potential as an effective measure for containing influenza until adequate quantities of vaccine are available.
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              Estimates of the Prevalence of Pandemic (H1N1) 2009, United States, April–July 2009

              Through July 2009, a total of 43,677 laboratory-confirmed cases of influenza A pandemic (H1N1) 2009 were reported in the United States, which is likely a substantial underestimate of the true number. Correcting for under-ascertainment using a multiplier model, we estimate that 1.8 million–5.7 million cases occurred, including 9,000–21,000 hospitalizations.
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                Author and article information

                Contributors
                Role: Editor
                Journal
                PLoS One
                plos
                plosone
                PLoS ONE
                Public Library of Science (San Francisco, USA )
                1932-6203
                2011
                19 January 2011
                : 6
                : 1
                : e16094
                Affiliations
                [1 ]ORIE Program, University of Texas at Austin, Austin, Texas, United States of America
                [2 ]Integrative Biology, University of Texas at Austin, Austin, Texas, United States of America
                [3 ]Preparedness Modeling Unit, U.S. Centers for Disease Control and Prevention, Atlanta, Georgia, United States of America
                [4 ]Weill Cornell Medical College, New York, New York, United States of America
                [5 ]Mathematical Modeling, British Columbia Centre for Disease Control, Vancouver, British Columbia, Canada
                [6 ]The Santa Fe Institute, Santa Fe, New Mexico, United States of America
                University of Hong Kong, Hong Kong
                Author notes

                Conceived and designed the experiments: NBD SG LAM. Performed the experiments: NBD SG LAM. Analyzed the data: NBD SG NH BP LAM. Contributed reagents/materials/analysis tools: NBD SG NH BP LAM. Wrote the paper: NBD SG NH BP LAM.

                Article
                PONE-D-10-00330
                10.1371/journal.pone.0016094
                3023704
                21283514
                5879d16b-140c-4379-9e42-4c4e599c1c6e
                Dimitrov et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
                History
                : 3 August 2010
                : 10 December 2010
                Page count
                Pages: 10
                Categories
                Research Article
                Biology
                Computational Biology
                Population Modeling
                Infectious Disease Modeling
                Medicine
                Infectious Diseases
                Infectious Disease Control
                Infectious Disease Modeling

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