Social network analysis reveals the failure of between-farm movement restrictions to reduce Salmonella transmission.

Control measures Livestock movements Network analysis Salmonella Dublin Simulation model

Journal

Journal of dairy science
ISSN: 1525-3198
Titre abrégé: J Dairy Sci
Pays: United States
ID NLM: 2985126R

Informations de publication

Date de publication:
22 May 2024
Historique:
received: 16 12 2023
accepted: 01 04 2024
medline: 25 5 2024
pubmed: 25 5 2024
entrez: 24 5 2024
Statut: aheadofprint

Résumé

An increasing number of countries are investigating options to stop the spread of the emerging zoonotic infection Salmonella (S.) Dublin, which mainly spreads among bovines and with cattle manure. Detailed surveillance and cattle movement data from an 11-year period in Denmark provided an opportunity to gain new knowledge for mitigation options through a combined social network and simulation modeling approach. The analysis revealed similar network trends for non-infected and infected cattle farms despite stringent cattle movement restrictions imposed on infected farms in the national control program. The strongest predictive factor for farms becoming infected was their cattle movement activities in the previous month, with twice the effect of local transmission. The simulation model indicated an endemic S. Dublin occurrence, with peaks in outbreak probabilities and sizes around observed cattle movement activities. Therefore, pre- and post-movement measures within a 1-mo time-window may help reduce S. Dublin spread.

Identifiants

pubmed: 38788850
pii: S0022-0302(24)00816-6
doi: 10.3168/jds.2023-24554
pii:
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Informations de copyright

The Authors. Published by Elsevier Inc. on behalf of the American Dairy Science Association®. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).

Auteurs

B Conrady (B)

Department of Veterinary and Animal Sciences, University of Copenhagen, Gr⊘nnegårdsvej 8, 1870 Frederiksberg C, Denmark; Complexity Science Hub Vienna, Josefstädter Straße 39, 1080 Vienna, Austria. Electronic address: bcon@sund.ku.dk.

E H Dervic (EH)

Complexity Science Hub Vienna, Josefstädter Straße 39, 1080 Vienna, Austria; Supply Chain Intelligence Institute Austria, Josefstädter Straße 39, 1080 Vienna, Austria.

P Klimek (P)

Complexity Science Hub Vienna, Josefstädter Straße 39, 1080 Vienna, Austria; Supply Chain Intelligence Institute Austria, Josefstädter Straße 39, 1080 Vienna, Austria; Section for Science of Complex Systems, Medical University of Vienna, Spitalgasse 23, 1090 Vienna, Austria.

L Pedersen (L)

Department of Veterinary and Animal Sciences, University of Copenhagen, Gr⊘nnegårdsvej 8, 1870 Frederiksberg C, Denmark; SEGES Innovation P/S, Skejby, Agro Food Park 15, 8200 Aarhus N, Denmark.

M Merhi Reimert (MM)

Department of Veterinary and Animal Sciences, University of Copenhagen, Gr⊘nnegårdsvej 8, 1870 Frederiksberg C, Denmark.

P Rasmussen (P)

Department of Veterinary and Animal Sciences, University of Copenhagen, Gr⊘nnegårdsvej 8, 1870 Frederiksberg C, Denmark.

O O Apenteng (OO)

Department of Veterinary and Animal Sciences, University of Copenhagen, Gr⊘nnegårdsvej 8, 1870 Frederiksberg C, Denmark.

L R Nielsen (LR)

Department of Veterinary and Animal Sciences, University of Copenhagen, Gr⊘nnegårdsvej 8, 1870 Frederiksberg C, Denmark.

Classifications MeSH