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dc.contributor.author
Guinat, Claire
dc.contributor.author
Porphyre, Thibaud
dc.contributor.author
Gogin, Andrey
dc.contributor.author
Dixon, Linda
dc.contributor.author
Pfeiffer, Dirk U.
dc.contributor.author
Gubbins, Simon
dc.date.accessioned
2020-09-24T12:51:22Z
dc.date.available
2020-09-22T11:31:31Z
dc.date.available
2020-09-24T12:51:22Z
dc.date.issued
2018-04
dc.identifier.issn
1865-1674
dc.identifier.issn
1865-1682
dc.identifier.other
10.1111/tbed.12748
en_US
dc.identifier.uri
http://hdl.handle.net/20.500.11850/441889
dc.identifier.doi
10.3929/ethz-b-000441889
dc.description.abstract
Mortality data are routinely collected for many livestock and poultry species, and they are often used for epidemiological purposes, including estimating transmission parameters. In this study, we infer transmission rates for African swine fever virus (ASFV), an important transboundary disease of swine, using mortality data collected from nine pig herds in the Russian Federation with confirmed outbreaks of ASFV. Parameters in a stochastic model for the transmission of ASFV within a herd were estimated using approximate Bayesian computation. Estimates for the basic reproduction number varied amongst herds, ranging from 4.4 to 17.3. This was primarily a consequence of differences in transmission rate (range: 0.7–2.2), but also differences in the mean infectious period (range: 4.5–8.3 days). We also found differences amongst herds in the mean latent period (range: 5.8–9.7 days). Furthermore, our results suggest that ASFV could be circulating in a herd for several weeks before a substantial increase in mortality is observed in a herd, limiting the usefulness of mortality data as a means of early detection of an outbreak. However, our results also show that mortality data are a potential source of data from which to infer transmission parameters, at least for diseases which cause high mortality.
en_US
dc.format
application/pdf
en_US
dc.language.iso
en
en_US
dc.publisher
Wiley
en_US
dc.rights.uri
http://creativecommons.org/licenses/by/4.0/
dc.subject
approximate Bayesian computation
en_US
dc.subject
disease control
en_US
dc.subject
epidemiology
en_US
dc.subject
modelling
en_US
dc.subject
mortality data
en_US
dc.subject
pigs
en_US
dc.title
Inferring within-herd transmission parameters for African swine fever virus using mortality data from outbreaks in the Russian Federation
en_US
dc.type
Journal Article
dc.type
Journal Article
dc.rights.license
Creative Commons Attribution 4.0 International
dc.date.published
2017-11-09
ethz.journal.title
Transboundary and Emerging Diseases
ethz.journal.volume
65
en_US
ethz.journal.issue
2
en_US
ethz.journal.abbreviated
Transbound Emerg Dis
ethz.pages.start
e264
en_US
ethz.pages.end
e271
en_US
ethz.version.deposit
publishedVersion
en_US
ethz.identifier.scopus
ethz.publication.place
Berlin
en_US
ethz.publication.status
published
en_US
ethz.leitzahl
ETH Zürich::00002 - ETH Zürich::00012 - Lehre und Forschung::00007 - Departemente::02060 - Dep. Biosysteme / Dep. of Biosystems Science and Eng.::09490 - Stadler, Tanja / Stadler, Tanja
en_US
ethz.leitzahl.certified
ETH Zürich::00002 - ETH Zürich::00012 - Lehre und Forschung::00007 - Departemente::02060 - Dep. Biosysteme / Dep. of Biosystems Science and Eng.::09490 - Stadler, Tanja / Stadler, Tanja
en_US
ethz.identifier.orcidWorkCode
69496432
ethz.date.deposited
2020-09-22T11:31:42Z
ethz.source
FORM
ethz.eth
no
en_US
ethz.availability
Open access
en_US
ethz.rosetta.installDate
2020-09-24T12:51:32Z
ethz.rosetta.lastUpdated
2022-03-29T03:12:17Z
ethz.rosetta.versionExported
true
ethz.COinS
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