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      Efficient use of sentinel sites: detection of invasive honeybee pests and diseases in the UK

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          Abstract

          Sentinel sites, where problems can be identified early or investigated in detail, form an important part of planning for exotic disease outbreaks in humans, livestock and plants. Key questions are: how many sentinels are required, where should they be positioned and how effective are they at rapidly identifying new invasions? The sentinel apiary system for invasive honeybee pests and diseases illustrates the costs and benefits of such approaches. Here, we address these issues with two mathematical modelling approaches. The first approach is generic and uses probabilistic arguments to calculate the average number of affected sites when an outbreak is first detected, providing rapid and general insights that we have applied to a range of infectious diseases. The second approach uses a computationally intensive, stochastic, spatial model to simulate multiple outbreaks and to determine appropriate sentinel locations for UK apiaries. Both models quantify the anticipated increase in success of sentinel sites as their number increases and as non-sentinel sites become worse at detection; however, unexpectedly sentinels perform relatively better for faster growing outbreaks. Additionally, the spatial model allows us to quantify the substantial role that carefully positioned sentinels can play in the rapid detection of exotic invasions.

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          A dynamic model of bovine tuberculosis spread and control in Great Britain.

          Bovine tuberculosis (TB) is one of the most complex, persistent and controversial problems facing the British cattle industry, costing the country an estimated £100 million per year. The low sensitivity of the standard diagnostic test leads to considerable ambiguity in determining the main transmission routes of infection, which exacerbates the continuing scientific debate. In turn this uncertainty fuels the fierce public and political disputes on the necessity of controlling badgers to limit the spread of infection. Here we present a dynamic stochastic spatial model for bovine TB in Great Britain that combines within-farm and between-farm transmission. At the farm scale the model incorporates stochastic transmission of infection, maintenance of infection in the environment and a testing protocol that mimics historical government policy. Between-farm transmission has a short-range environmental component and is explicitly driven by movements of individual cattle between farms, as recorded in the Cattle Tracing System. The resultant model replicates the observed annual increase of infection over time as well as the spread of infection into new areas. Given that our model is mechanistic, it can ascribe transmission pathways to each new case; the majority of newly detected cases involve several transmission routes with moving infected cattle, reinfection from an environmental reservoir and poor sensitivity of the diagnostic test all having substantive roles. This underpins our findings on the implications of control measures. Very few of the control options tested have the potential to reverse the observed annual increase, with only intensive strategies such as whole-herd culling or additional national testing proving highly effective, whereas controls focused on a single transmission route are unlikely to be highly effective.
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            Crow deaths as a sentinel surveillance system for West Nile virus in the northeastern United States, 1999.

            In addition to human encephalitis and meningitis cases, the West Nile (WN) virus outbreak in the summer and fall of 1999 in New York State resulted in bird deaths in New York, New Jersey, and Connecticut. From August to December 1999, 295 dead birds were laboratory-confirmed with WN virus infection; 262 (89%) were American Crows (Corvus brachyrhynchos). The New York State Department of Health received reports of 17,339 dead birds, including 5,697 (33%) crows; in Connecticut 1,040 dead crows were reported. Bird deaths were critical in identifying WN virus as the cause of the human outbreak and defining its geographic and temporal limits. If established before a WN virus outbreak, a surveillance system based on bird deaths may provide a sensitive method of detecting WN virus.
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              Spatial Contact Models for Ecological and Epidemic Spread

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                Author and article information

                Journal
                J R Soc Interface
                J R Soc Interface
                RSIF
                royinterface
                Journal of the Royal Society Interface
                The Royal Society
                1742-5689
                1742-5662
                April 2017
                26 April 2017
                26 April 2017
                : 14
                : 129
                : 20160908
                Affiliations
                [1 ]Zeeman Institute: SBIDER, University of Warwick , Coventry CV4 7AL, UK
                [2 ]Mathematics Institute, University of Warwick , Coventry CV4 7AL, UK
                [3 ]School of Life Sciences, University of Warwick , Coventry CV4 7AL, UK
                [4 ]Animal and Plant Health Agency , Sand Hutton, York YO41 1LZ, UK
                [5 ]Fera , Sand Hutton, York YO41 1LZ, UK
                [6 ]Institute for Agri-Food Research and Innovation, Newcastle University , Newcastle upon Tyne NE1 7RU, UK
                Author notes
                Author information
                http://orcid.org/0000-0003-4639-4765
                http://orcid.org/0000-0001-6175-2095
                Article
                rsif20160908
                10.1098/rsif.2016.0908
                5414905
                28446703
                61f5d001-94ab-4fdf-8435-4da6786acefb
                © 2017 The Authors.

                Published by the Royal Society under the terms of the Creative Commons Attribution License http://creativecommons.org/licenses/by/4.0/, which permits unrestricted use, provided the original author and source are credited.

                History
                : 11 November 2016
                : 3 April 2017
                Funding
                Funded by: Defra Strategic Evidence Partnering Fund;
                Award ID: Project number SEPF14-30
                Funded by: BBSRC, Defra, NERC, the Scottish Government and the Wellcome Trust, under the Insect Pollinators Initiative;
                Award ID: BB/I000615/1
                Award ID: BB/I000801/1
                Categories
                1004
                44
                24
                20
                Life Sciences–Mathematics interface
                Research Article
                Custom metadata
                April, 2017

                Life sciences
                invasion,eradication,pathogen,foulbrood,tropilaelaps,small hive beetle
                Life sciences
                invasion, eradication, pathogen, foulbrood, tropilaelaps, small hive beetle

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