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      Early Estimation of the Reproduction Number in the Presence of Imported Cases: Pandemic Influenza H1N1-2009 in New Zealand

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      1 , * , 2 , 3
      PLoS ONE
      Public Library of Science

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          Abstract

          We analyse data from the early epidemic of H1N1-2009 in New Zealand, and estimate the reproduction number . We employ a renewal process which accounts for imported cases, illustrate some technical pitfalls, and propose a novel estimation method to address these pitfalls. Explicitly accounting for the infection-age distribution of imported cases and for the delay in transmission dynamics due to international travel, was estimated to be (95% confidence interval: ). Hence we show that a previous study, which did not account for these factors, overestimated . Our approach also permitted us to examine the infection-age at which secondary transmission occurs as a function of calendar time, demonstrating the downward bias during the beginning of the epidemic. These technical issues may compromise the usefulness of a well-known estimator of - the inverse of the moment-generating function of the generation time given the intrinsic growth rate. Explicit modelling of the infection-age distribution among imported cases and the examination of the time dependency of the generation time play key roles in avoiding a biased estimate of , especially when one only has data covering a short time interval during the early growth phase of the epidemic.

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          Emergence and pandemic potential of swine-origin H1N1 influenza virus.

          Influenza viruses cause annual epidemics and occasional pandemics that have claimed the lives of millions. The emergence of new strains will continue to pose challenges to public health and the scientific communities. A prime example is the recent emergence of swine-origin H1N1 viruses that have transmitted to and spread among humans, resulting in outbreaks internationally. Efforts to control these outbreaks and real-time monitoring of the evolution of this virus should provide us with invaluable information to direct infectious disease control programmes and to improve understanding of the factors that determine viral pathogenicity and/or transmissibility.
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            Estimation of the reproductive number and the serial interval in early phase of the 2009 influenza A/H1N1 pandemic in the USA

            Background  The United States was the second country to have a major outbreak of novel influenza A/H1N1 in what has become a new pandemic. Appropriate public health responses to this pandemic depend in part on early estimates of key epidemiological parameters of the virus in defined populations. Methods  We use a likelihood‐based method to estimate the basic reproductive number (R 0) and serial interval using individual level U.S. data from the Centers for Disease Control and Prevention (CDC). We adjust for missing dates of illness and changes in case ascertainment. Using prior estimates for the serial interval we also estimate the reproductive number only. Results  Using the raw CDC data, we estimate the reproductive number to be between 2·2 and 2·3 and the mean of the serial interval (μ) between 2·5 and 2·6 days. After adjustment for increased case ascertainment our estimates change to 1·7 to 1·8 for R 0 and 2·2 to 2·3 days for μ. In a sensitivity analysis making use of previous estimates of the mean of the serial interval, both for this epidemic (μ = 1·91 days) and for seasonal influenza (μ = 3·6 days), we estimate the reproductive number at 1·5 to 3·1. Conclusions  With adjustments for data imperfections we obtain useful estimates of key epidemiological parameters for the current influenza H1N1 outbreak in the United States. Estimates that adjust for suspected increases in reporting suggest that substantial reductions in the spread of this epidemic may be achievable with aggressive control measures, while sensitivity analyses suggest the possibility that even such measures would have limited effect in reducing total attack rates.
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              A likelihood-based method for real-time estimation of the serial interval and reproductive number of an epidemic.

              We present a method for the simultaneous estimation of the basic reproductive number, R(0), and the serial interval for infectious disease epidemics, using readily available surveillance data. These estimates can be obtained in real time to inform an appropriate public health response to the outbreak. We show how this methodology, in its most simple case, is related to a branching process and describe similarities between the two that allow us to draw parallels which enable us to understand some of the theoretical properties of our estimators. We provide simulation results that illustrate the efficacy of the method for estimating R(0) and the serial interval in real time. Finally, we implement our proposed method with data from three infectious disease outbreaks.
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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
                26 May 2011
                : 6
                : 5
                : e17835
                Affiliations
                [1 ]Centre for Mathematical Biology, Institute of Information and Mathematical Sciences, and New Zealand Institute for Advanced Study, Massey University, Auckland, New Zealand
                [2 ]PRESTO, Japan Science and Technology Agency, Saitama, Japan
                [3 ]Theoretical Epidemiology, University of Utrecht, Utrecht, The Netherlands
                University of Oxford, Viet Nam
                Author notes

                Conceived and designed the experiments: MGR HN. Performed the experiments: HN. Analyzed the data: MGR HN. Contributed reagents/materials/analysis tools: MGR HN. Wrote the paper: MGR HN.

                Article
                PONE-D-10-03644
                10.1371/journal.pone.0017835
                3102662
                21637342
                e4470927-4189-4575-9599-f2bd2cd8add9
                Roberts, Nishiura. 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
                : 15 October 2010
                : 16 February 2011
                Page count
                Pages: 9
                Categories
                Research Article
                Biology
                Microbiology
                Virology
                Viral Transmission and Infection
                Population Biology
                Epidemiology
                Epidemiological Methods
                Infectious Disease Epidemiology
                Mathematics
                Statistics
                Biostatistics
                Confidence Intervals
                Statistical Methods
                Medicine
                Clinical Research Design
                Epidemiology
                Epidemiology
                Epidemiological Methods
                Infectious Disease Epidemiology
                Infectious Diseases
                Viral Diseases
                Influenza
                Infectious Disease Modeling

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                Uncategorized

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