Estimation of the transmission dynamics of African swine fever virus within a swine house

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Estimation of the transmission dynamics of African swine fever virus within a swine house. / Nielsen, J. P.; Larsen, T. S.; Halasa, T.; Christiansen, L. E.

I: Epidemiology and Infection, Bind 145, Nr. 13, 01.10.2017, s. 2787-2796.

Publikation: Bidrag til tidsskriftTidsskriftartikelForskningfagfællebedømt

Harvard

Nielsen, JP, Larsen, TS, Halasa, T & Christiansen, LE 2017, 'Estimation of the transmission dynamics of African swine fever virus within a swine house', Epidemiology and Infection, bind 145, nr. 13, s. 2787-2796. https://doi.org/10.1017/S0950268817001613

APA

Nielsen, J. P., Larsen, T. S., Halasa, T., & Christiansen, L. E. (2017). Estimation of the transmission dynamics of African swine fever virus within a swine house. Epidemiology and Infection, 145(13), 2787-2796. https://doi.org/10.1017/S0950268817001613

Vancouver

Nielsen JP, Larsen TS, Halasa T, Christiansen LE. Estimation of the transmission dynamics of African swine fever virus within a swine house. Epidemiology and Infection. 2017 okt. 1;145(13):2787-2796. https://doi.org/10.1017/S0950268817001613

Author

Nielsen, J. P. ; Larsen, T. S. ; Halasa, T. ; Christiansen, L. E. / Estimation of the transmission dynamics of African swine fever virus within a swine house. I: Epidemiology and Infection. 2017 ; Bind 145, Nr. 13. s. 2787-2796.

Bibtex

@article{8dff84b9fa0e4df8bffe71ed68bbe55f,
title = "Estimation of the transmission dynamics of African swine fever virus within a swine house",
abstract = "The spread of African swine fever virus (ASFV) threatens to reach further parts of Europe. In countries with a large swine production, an outbreak of ASF may result in devastating economic consequences for the swine industry. Simulation models can assist decision makers setting up contingency plans. This creates a need for estimation of parameters. This study presents a new analysis of a previously published study. A full likelihood framework is presented including the impact of model assumptions on the estimated transmission parameters. As animals were only tested every other day, an interpretation was introduced to cover the weighted infectiousness on unobserved days for the individual animals (WIU). Based on our model and the set of assumptions, the within- and between-pen transmission parameters were estimated to β w = 1·05 (95% CI 0·62-1·72), β b = 0·46 (95% CI 0·17-1·00), respectively, and the WIU = 1·00 (95% CI 0-1). Furthermore, we simulated the spread of ASFV within a pig house using a modified SEIR-model to establish the time from infection of one animal until ASFV is detected in the herd. Based on a chosen detection limit of 2·55% equivalent to 10 dead pigs out of 360, the disease would be detected 13-19 days after introduction.",
keywords = "African swine fever, epidemiology, maximum likelihood, modelling, transmission",
author = "Nielsen, {J. P.} and Larsen, {T. S.} and T. Halasa and Christiansen, {L. E.}",
year = "2017",
month = oct,
day = "1",
doi = "10.1017/S0950268817001613",
language = "English",
volume = "145",
pages = "2787--2796",
journal = "Epidemiology and Infection",
issn = "0950-2688",
publisher = "Cambridge University Press",
number = "13",

}

RIS

TY - JOUR

T1 - Estimation of the transmission dynamics of African swine fever virus within a swine house

AU - Nielsen, J. P.

AU - Larsen, T. S.

AU - Halasa, T.

AU - Christiansen, L. E.

PY - 2017/10/1

Y1 - 2017/10/1

N2 - The spread of African swine fever virus (ASFV) threatens to reach further parts of Europe. In countries with a large swine production, an outbreak of ASF may result in devastating economic consequences for the swine industry. Simulation models can assist decision makers setting up contingency plans. This creates a need for estimation of parameters. This study presents a new analysis of a previously published study. A full likelihood framework is presented including the impact of model assumptions on the estimated transmission parameters. As animals were only tested every other day, an interpretation was introduced to cover the weighted infectiousness on unobserved days for the individual animals (WIU). Based on our model and the set of assumptions, the within- and between-pen transmission parameters were estimated to β w = 1·05 (95% CI 0·62-1·72), β b = 0·46 (95% CI 0·17-1·00), respectively, and the WIU = 1·00 (95% CI 0-1). Furthermore, we simulated the spread of ASFV within a pig house using a modified SEIR-model to establish the time from infection of one animal until ASFV is detected in the herd. Based on a chosen detection limit of 2·55% equivalent to 10 dead pigs out of 360, the disease would be detected 13-19 days after introduction.

AB - The spread of African swine fever virus (ASFV) threatens to reach further parts of Europe. In countries with a large swine production, an outbreak of ASF may result in devastating economic consequences for the swine industry. Simulation models can assist decision makers setting up contingency plans. This creates a need for estimation of parameters. This study presents a new analysis of a previously published study. A full likelihood framework is presented including the impact of model assumptions on the estimated transmission parameters. As animals were only tested every other day, an interpretation was introduced to cover the weighted infectiousness on unobserved days for the individual animals (WIU). Based on our model and the set of assumptions, the within- and between-pen transmission parameters were estimated to β w = 1·05 (95% CI 0·62-1·72), β b = 0·46 (95% CI 0·17-1·00), respectively, and the WIU = 1·00 (95% CI 0-1). Furthermore, we simulated the spread of ASFV within a pig house using a modified SEIR-model to establish the time from infection of one animal until ASFV is detected in the herd. Based on a chosen detection limit of 2·55% equivalent to 10 dead pigs out of 360, the disease would be detected 13-19 days after introduction.

KW - African swine fever

KW - epidemiology

KW - maximum likelihood

KW - modelling

KW - transmission

U2 - 10.1017/S0950268817001613

DO - 10.1017/S0950268817001613

M3 - Journal article

C2 - 28768556

AN - SCOPUS:85026783015

VL - 145

SP - 2787

EP - 2796

JO - Epidemiology and Infection

JF - Epidemiology and Infection

SN - 0950-2688

IS - 13

ER -

ID: 203327247