Data from: A method that accounts for differential detectability in mixed samples of long-term infections with applications to the case of Chronic Wasting Disease in cervids

1. Surveillance of wildlife diseases is logistically difficult, and imperfect detection is a recurrent challenge for disease estimation. Using citizen science can increase sample sizes, but it is associated with a cost in terms of the anatomical type and quality of the sample. Additionally, biologic...

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Main Authors: Viljugrein, Hildegunn, Hopp, Petter, Benestad, Sylvie, Nilsen, Erlend B., Våge, Jørn, Tavornpanich, Saraya, Rolandsen, Christer, Strand, Olav, Mysterud, Atle
Format: Dataset
Language:unknown
Published: 2019
Subjects:
geo
Online Access:https://doi.org/10.5061/dryad.q84p862
id fttriple:oai:gotriple.eu:50|dedup_wf_001::78b1f2477375b59082e7e6e7dd20a40d
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spelling fttriple:oai:gotriple.eu:50|dedup_wf_001::78b1f2477375b59082e7e6e7dd20a40d 2023-05-15T18:04:26+02:00 Data from: A method that accounts for differential detectability in mixed samples of long-term infections with applications to the case of Chronic Wasting Disease in cervids Viljugrein, Hildegunn Hopp, Petter Benestad, Sylvie Nilsen, Erlend B. Våge, Jørn Tavornpanich, Saraya Rolandsen, Christer Strand, Olav Mysterud, Atle 2019-08-24 https://doi.org/10.5061/dryad.q84p862 undefined unknown http://dx.doi.org/10.5061/dryad.q84p862 https://dx.doi.org/10.5061/dryad.q84p862 lic_creative-commons oai:services.nod.dans.knaw.nl:Products/dans:oai:easy.dans.knaw.nl:easy-dataset:117174 10.5061/dryad.q84p862 oai:easy.dans.knaw.nl:easy-dataset:117174 10|eurocrisdris::fe4903425d9040f680d8610d9079ea14 10|openaire____::9e3be59865b2c1c335d32dae2fe7b254 10|re3data_____::94816e6421eeb072e7742ce6a9decc5f re3data_____::r3d100000044 10|re3data_____::84e123776089ce3c7a33db98d9cd15a8 Life sciences medicine and health care 2016-2017 chronic wasting disease Rangifer tarandus (:tba) envir geo Dataset https://vocabularies.coar-repositories.org/resource_types/c_ddb1/ 2019 fttriple https://doi.org/10.5061/dryad.q84p862 2023-01-22T16:51:02Z 1. Surveillance of wildlife diseases is logistically difficult, and imperfect detection is a recurrent challenge for disease estimation. Using citizen science can increase sample sizes, but it is associated with a cost in terms of the anatomical type and quality of the sample. Additionally, biological tissue samples from remote areas lose quality due to autolysis. These challenges are faced in the case of emerging Chronic Wasting Disease (CWD) in cervids. 2. Here, we develop a stochastic scenario tree model of diagnostic sensitivity, allowing for a mixture of tissue sample types (lymph nodes and brain) and qualities while accounting for different detection probabilities during the CWD infection, lasting 2-3 years. We apply the diagnostic sensitivity in a Bayesian framework, enabling estimation of age-class-specific true prevalence, including the prevalence in latent, recently infected stages. We provide a simulation framework to estimate the sensitivity of the surveillance system (i.e., the probability of detecting the infection in a given population), when detectability varies among individuals due to different disease progression. 3. We demonstrate the utility of our framework by applying it to the recent emergence of CWD in a European population of reindeer. We estimated apparent CWD prevalence at 1.2 % of adults in the infected population of wild reindeer, while the true prevalence was 1.6 %. The sensitivity estimation of the CWD surveillance was performed in an adjacent small (~500) and a large (~10,000) reindeer population, demonstrating low certainty of CWD absence. 4. Our method has immediate application to the mandatory testing for CWD in EU countries commencing in 2018. Similar approaches that account for latent stages and a serial disease progression in various tissues with a temporal pattern of diagnostic sensitivity may enhance the estimation of the prevalence of wildlife diseases more generally. datViljugrein_etal2018Data on tested reindeer for Nordfjella and Hardangervidda used in application 1 ... Dataset Rangifer tarandus Unknown Nordfjella ENVELOPE(11.034,11.034,64.546,64.546)
institution Open Polar
collection Unknown
op_collection_id fttriple
language unknown
topic Life sciences
medicine and health care
2016-2017
chronic wasting disease
Rangifer tarandus
(:tba)
envir
geo
spellingShingle Life sciences
medicine and health care
2016-2017
chronic wasting disease
Rangifer tarandus
(:tba)
envir
geo
Viljugrein, Hildegunn
Hopp, Petter
Benestad, Sylvie
Nilsen, Erlend B.
Våge, Jørn
Tavornpanich, Saraya
Rolandsen, Christer
Strand, Olav
Mysterud, Atle
Data from: A method that accounts for differential detectability in mixed samples of long-term infections with applications to the case of Chronic Wasting Disease in cervids
topic_facet Life sciences
medicine and health care
2016-2017
chronic wasting disease
Rangifer tarandus
(:tba)
envir
geo
description 1. Surveillance of wildlife diseases is logistically difficult, and imperfect detection is a recurrent challenge for disease estimation. Using citizen science can increase sample sizes, but it is associated with a cost in terms of the anatomical type and quality of the sample. Additionally, biological tissue samples from remote areas lose quality due to autolysis. These challenges are faced in the case of emerging Chronic Wasting Disease (CWD) in cervids. 2. Here, we develop a stochastic scenario tree model of diagnostic sensitivity, allowing for a mixture of tissue sample types (lymph nodes and brain) and qualities while accounting for different detection probabilities during the CWD infection, lasting 2-3 years. We apply the diagnostic sensitivity in a Bayesian framework, enabling estimation of age-class-specific true prevalence, including the prevalence in latent, recently infected stages. We provide a simulation framework to estimate the sensitivity of the surveillance system (i.e., the probability of detecting the infection in a given population), when detectability varies among individuals due to different disease progression. 3. We demonstrate the utility of our framework by applying it to the recent emergence of CWD in a European population of reindeer. We estimated apparent CWD prevalence at 1.2 % of adults in the infected population of wild reindeer, while the true prevalence was 1.6 %. The sensitivity estimation of the CWD surveillance was performed in an adjacent small (~500) and a large (~10,000) reindeer population, demonstrating low certainty of CWD absence. 4. Our method has immediate application to the mandatory testing for CWD in EU countries commencing in 2018. Similar approaches that account for latent stages and a serial disease progression in various tissues with a temporal pattern of diagnostic sensitivity may enhance the estimation of the prevalence of wildlife diseases more generally. datViljugrein_etal2018Data on tested reindeer for Nordfjella and Hardangervidda used in application 1 ...
format Dataset
author Viljugrein, Hildegunn
Hopp, Petter
Benestad, Sylvie
Nilsen, Erlend B.
Våge, Jørn
Tavornpanich, Saraya
Rolandsen, Christer
Strand, Olav
Mysterud, Atle
author_facet Viljugrein, Hildegunn
Hopp, Petter
Benestad, Sylvie
Nilsen, Erlend B.
Våge, Jørn
Tavornpanich, Saraya
Rolandsen, Christer
Strand, Olav
Mysterud, Atle
author_sort Viljugrein, Hildegunn
title Data from: A method that accounts for differential detectability in mixed samples of long-term infections with applications to the case of Chronic Wasting Disease in cervids
title_short Data from: A method that accounts for differential detectability in mixed samples of long-term infections with applications to the case of Chronic Wasting Disease in cervids
title_full Data from: A method that accounts for differential detectability in mixed samples of long-term infections with applications to the case of Chronic Wasting Disease in cervids
title_fullStr Data from: A method that accounts for differential detectability in mixed samples of long-term infections with applications to the case of Chronic Wasting Disease in cervids
title_full_unstemmed Data from: A method that accounts for differential detectability in mixed samples of long-term infections with applications to the case of Chronic Wasting Disease in cervids
title_sort data from: a method that accounts for differential detectability in mixed samples of long-term infections with applications to the case of chronic wasting disease in cervids
publishDate 2019
url https://doi.org/10.5061/dryad.q84p862
long_lat ENVELOPE(11.034,11.034,64.546,64.546)
geographic Nordfjella
geographic_facet Nordfjella
genre Rangifer tarandus
genre_facet Rangifer tarandus
op_source oai:services.nod.dans.knaw.nl:Products/dans:oai:easy.dans.knaw.nl:easy-dataset:117174
10.5061/dryad.q84p862
oai:easy.dans.knaw.nl:easy-dataset:117174
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op_relation http://dx.doi.org/10.5061/dryad.q84p862
https://dx.doi.org/10.5061/dryad.q84p862
op_rights lic_creative-commons
op_doi https://doi.org/10.5061/dryad.q84p862
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