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Efficient Simulation of the Spatial Transmission Dynamics of Influenza

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      Abstract

      Early data from the 2009 H1N1 pandemic (H1N1pdm) suggest that previous studies over-estimated the within-country rate of spatial spread of pandemic influenza. As large spatially resolved data sets are constructed, the need for efficient simulation code with which to investigate the spatial patterns of the pandemic becomes clear. Here, we present a significant improvement to the efficiency of an individual-based stochastic disease simulation framework commonly used in multiple previous studies. We quantify the efficiency of the revised algorithm and present an alternative parameterization of the model in terms of the basic reproductive number. We apply the model to the population of Taiwan and demonstrate how the location of the initial seed can influence spatial incidence profiles and the overall spread of the epidemic. Differences in incidence are driven by the relative connectivity of alternate seed locations. The ability to perform efficient simulation allows us to run a batch of simulations and take account of their average in real time. The averaged data are stable and can be used to differentiate spreading patterns that are not readily seen by only conducting a few runs.

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      Most cited references 17

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      Pandemic potential of a strain of influenza A (H1N1): early findings.

      A novel influenza A (H1N1) virus has spread rapidly across the globe. Judging its pandemic potential is difficult with limited data, but nevertheless essential to inform appropriate health responses. By analyzing the outbreak in Mexico, early data on international spread, and viral genetic diversity, we make an early assessment of transmissibility and severity. Our estimates suggest that 23,000 (range 6000 to 32,000) individuals had been infected in Mexico by late April, giving an estimated case fatality ratio (CFR) of 0.4% (range: 0.3 to 1.8%) based on confirmed and suspected deaths reported to that time. In a community outbreak in the small community of La Gloria, Veracruz, no deaths were attributed to infection, giving an upper 95% bound on CFR of 0.6%. Thus, although substantial uncertainty remains, clinical severity appears less than that seen in the 1918 influenza pandemic but comparable with that seen in the 1957 pandemic. Clinical attack rates in children in La Gloria were twice that in adults ( /=15 years: 29%). Three different epidemiological analyses gave basic reproduction number (R0) estimates in the range of 1.4 to 1.6, whereas a genetic analysis gave a central estimate of 1.2. This range of values is consistent with 14 to 73 generations of human-to-human transmission having occurred in Mexico to late April. Transmissibility is therefore substantially higher than that of seasonal flu, and comparable with lower estimates of R0 obtained from previous influenza pandemics.
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        Strategies for mitigating an influenza pandemic.

        Development of strategies for mitigating the severity of a new influenza pandemic is now a top global public health priority. Influenza prevention and containment strategies can be considered under the broad categories of antiviral, vaccine and non-pharmaceutical (case isolation, household quarantine, school or workplace closure, restrictions on travel) measures. Mathematical models are powerful tools for exploring this complex landscape of intervention strategies and quantifying the potential costs and benefits of different options. Here we use a large-scale epidemic simulation to examine intervention options should initial containment of a novel influenza outbreak fail, using Great Britain and the United States as examples. We find that border restrictions and/or internal travel restrictions are unlikely to delay spread by more than 2-3 weeks unless more than 99% effective. School closure during the peak of a pandemic can reduce peak attack rates by up to 40%, but has little impact on overall attack rates, whereas case isolation or household quarantine could have a significant impact, if feasible. Treatment of clinical cases can reduce transmission, but only if antivirals are given within a day of symptoms starting. Given enough drugs for 50% of the population, household-based prophylaxis coupled with reactive school closure could reduce clinical attack rates by 40-50%. More widespread prophylaxis would be even more logistically challenging but might reduce attack rates by over 75%. Vaccine stockpiled in advance of a pandemic could significantly reduce attack rates even if of low efficacy. Estimates of policy effectiveness will change if the characteristics of a future pandemic strain differ substantially from those seen in past pandemics.
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          Strategies for containing an emerging influenza pandemic in Southeast Asia.

          Highly pathogenic H5N1 influenza A viruses are now endemic in avian populations in Southeast Asia, and human cases continue to accumulate. Although currently incapable of sustained human-to-human transmission, H5N1 represents a serious pandemic threat owing to the risk of a mutation or reassortment generating a virus with increased transmissibility. Identifying public health interventions that might be able to halt a pandemic in its earliest stages is therefore a priority. Here we use a simulation model of influenza transmission in Southeast Asia to evaluate the potential effectiveness of targeted mass prophylactic use of antiviral drugs as a containment strategy. Other interventions aimed at reducing population contact rates are also examined as reinforcements to an antiviral-based containment policy. We show that elimination of a nascent pandemic may be feasible using a combination of geographically targeted prophylaxis and social distancing measures, if the basic reproduction number of the new virus is below 1.8. We predict that a stockpile of 3 million courses of antiviral drugs should be sufficient for elimination. Policy effectiveness depends critically on how quickly clinical cases are diagnosed and the speed with which antiviral drugs can be distributed.
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            Author and article information

            Affiliations
            [1]Institute of Information Science, Academia Sinica, Taipei, Taiwan
            [2]Epidemic Intelligence Center, Centers for Disease Control, Taipei, Taiwan
            [3]Department of Computer Science and Information Engineering, National Taiwan University, Taipei, Taiwan
            [4]Centers for Disease Control, Taipei, Taiwan
            [5]Department of Infectious Disease Epidemiology, University of Hong Kong, Hong Kong
            [6]Department of Radiation Oncology, Far Eastern Memorial Hospital, Taipei, Taiwan
            [7]Department of Computer Science, University of Virginia, Charlottesville, Virginia, United States of America
            Dana-Farber Cancer Institute, United States of America
            Author notes

            Conceived and designed the experiments: MTT TCMC JHC HSK CJL SR BJS CHS DWW TSH. Performed the experiments: MTT TCMC JHC CWH HSK CJL SR BJS CHS DWW TSH. Analyzed the data: MTT TCMC JHC CWH HSK CJL SR BJS CHS DWW TSH. Contributed reagents/materials/analysis tools: MTT TCMC JHC CWH HSK CJL SR BJS CHS DWW TSH. Wrote the paper: MTT TCMC SR BJS CHS DWW TSH.

            Contributors
            Role: Editor
            Journal
            PLoS One
            plos
            plosone
            PLoS ONE
            Public Library of Science (San Francisco, USA)
            1932-6203
            2010
            4 November 2010
            : 5
            : 11
            2973967
            21079810
            10-PONE-RA-18947R1
            10.1371/journal.pone.0013292
            (Editor)
            Tsai 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.
            Counts
            Pages: 8
            Categories
            Research Article
            Computer Science/Information Technology
            Infectious Diseases/Epidemiology and Control of Infectious Diseases
            Public Health and Epidemiology/Epidemiology
            Public Health and Epidemiology/Infectious Diseases

            Uncategorized

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