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White, Laura Forsberg

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Laura Forsberg

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White, Laura Forsberg

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2007 - 20092

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Author's Publications: search.filters.isAuthorOfPublication.95c1ffc5-6e62-44a6-90f0-e97f75acd4e2

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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
    (Wiley-Blackwell, 2009) White, Laura Forsberg; Wallinga, Jacco; Finelli, Lyn; Reed, Carrie; Riley, Steven; Lipsitch, Marc; Pagano, Marcello
    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 (mu) 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 mu. In a sensitivity analysis making use of previous estimates of the mean of the serial interval, both for this epidemic (mu = 1.91 days) and for seasonal influenza (mu = 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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    Effect of spatial resolution on cluster detection: a simulation study
    (BioMed Central, 2007) Ozonoff, Alexander; Jeffery, Caroline; Manjourides, Justin Daniel; White, Laura Forsberg; Pagano, Marcello
    Background: Aggregation of spatial data is intended to protect privacy, but some effects of aggregation on spatial methods have not yet been quantified. Methods: We generated 3,000 spatial data sets and evaluated power of detection at 12 different levels of aggregation using the spatial scan statistic implemented in SaTScan v6.0. Results: Power to detect clusters decreased from nearly 100% when using exact locations to roughly 40% at the coarsest level of spatial resolution. Conclusion: Aggregation has the potential for obfuscation.