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      Quantifying the Risk and Cost of Active Monitoring for Infectious Diseases

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          Abstract

          During outbreaks of deadly emerging pathogens (e.g., Ebola, MERS-CoV) and bioterror threats (e.g., smallpox), actively monitoring potentially infected individuals aims to limit disease transmission and morbidity. Guidance issued by CDC on active monitoring was a cornerstone of its response to the West Africa Ebola outbreak. There are limited data on how to balance the costs and performance of this important public health activity. We present a framework that estimates the risks and costs of specific durations of active monitoring for pathogens of significant public health concern. We analyze data from New York City’s Ebola active monitoring program over a 16-month period in 2014–2016. For monitored individuals, we identified unique durations of active monitoring that minimize expected costs for those at “low (but not zero) risk” and “some or high risk”: 21 and 31 days, respectively. Extending our analysis to smallpox and MERS-CoV, we found that the optimal length of active monitoring relative to the median incubation period was reduced compared to Ebola due to less variable incubation periods. Active monitoring can save lives but is expensive. Resources can be most effectively allocated by using exposure-risk categories to modify the duration or intensity of active monitoring.

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          Preliminary epidemiological assessment of MERS-CoV outbreak in South Korea, May to June 2015.

          South Korea is experiencing the largest outbreak of Middle East respiratory syndrome coronavirus infections outside the Arabian Peninsula, with 166 laboratory-confirmed cases, including 24 deaths up to 19 June 2015. We estimated that the mean incubation period was 6.7 days and the mean serial interval 12.6 days. We found it unlikely that infectiousness precedes symptom onset. Based on currently available data, we predict an overall case fatality risk of 21% (95% credible interval: 14–31).
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            Estimating incubation period distributions with coarse data.

            The incubation period, the time between infection and disease onset, is important in the surveillance and control of infectious diseases but is often coarsely observed. Coarse data arises because the time of infection, the time of disease onset or both are not known precisely. Accurate estimates of an incubation period distribution are useful in real-time outbreak investigations and in modeling public health interventions. We compare two methods of estimating such distributions. The first method represents the data as doubly interval-censored. The second introduces a data reduction technique that makes the computation more tractable. In a simulation study, the methods perform similarly when estimating the median, but the first method yields more reliable estimates of the distributional tails. We conduct a sensitivity analysis of the two methods to violations of model assumption and we apply these methods to historical incubation period data on influenza A and respiratory syncytial virus. The analysis of reduced data is less computationally intensive and performs well for estimating the median under a wide range of conditions. However for estimation of the tails of the distribution, the doubly interval-censored analysis is the recommended procedure. (c) 2009 John Wiley & Sons, Ltd.
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              Smallpox in Europe, 1950-1971.

              T Mack (1972)
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                Author and article information

                Contributors
                nick@schoolph.umass.edu
                Journal
                Sci Rep
                Sci Rep
                Scientific Reports
                Nature Publishing Group UK (London )
                2045-2322
                18 January 2018
                18 January 2018
                2018
                : 8
                : 1093
                Affiliations
                [1 ]ISNI 0000 0001 2184 9220, GRID grid.266683.f, University of Massachusetts-Amherst, Department of Biostatistics and Epidemiology, ; Amherst, MA 01003 USA
                [2 ]ISNI 0000 0001 2171 9311, GRID grid.21107.35, Johns Hopkins Bloomberg School of Public Health, Department of Epidemiology, ; Baltimore, MD 21205 USA
                [3 ]ISNI 0000 0001 0320 6731, GRID grid.238477.d, New York City Department of Health and Mental Hygiene, ; New York, NY 11101 USA
                [4 ]ISNI 0000 0001 2163 0069, GRID grid.416738.f, Centers for Disease Control and Prevention, ; Atlanta, GA 30329 USA
                Author information
                http://orcid.org/0000-0003-3503-9899
                Article
                19406
                10.1038/s41598-018-19406-x
                5773605
                29348656
                203a1c52-8952-478c-848e-6a2995170edf
                © The Author(s) 2018

                Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 31 August 2017
                : 29 December 2017
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