The aquaculture disease network model (AquaNet-Mod): A simulation model to evaluate disease spread and controls for the salmonid industry in England and Wales.


Journal

Epidemics
ISSN: 1878-0067
Titre abrégé: Epidemics
Pays: Netherlands
ID NLM: 101484711

Informations de publication

Date de publication:
09 2023
Historique:
received: 22 03 2023
revised: 27 07 2023
accepted: 28 07 2023
medline: 8 9 2023
pubmed: 11 8 2023
entrez: 10 8 2023
Statut: ppublish

Résumé

Infectious disease causes significant mortality in wild and farmed systems, threatening biodiversity, conservation and animal welfare, as well as food security. To mitigate impacts and inform policy, tools such as mathematical models and computer simulations are valuable for predicting the potential spread and impact of disease. This paper describes the development of the Aquaculture Disease Network Model, AquaNet-Mod, and demonstrates its application to evaluating disease epidemics and the efficacy of control, using a Viral Haemorrhagic Septicaemia (VHS) case study. AquaNet-Mod is a data-driven, stochastic, state-transition model. Disease spread can occur via four different mechanisms, i) live fish movement, ii) river based, iii) short distance mechanical and iv) distance independent mechanical. Sites transit between three disease states: susceptible, clinically infected and subclinically infected. Disease spread can be interrupted by the application of disease mitigation measures and controls such as contact tracing, culling, fallowing and surveillance. Results from a VHS case study highlight the potential for VHS to spread to 96% of sites over a 10 year time horizon if no disease controls are applied. Epidemiological impact is significantly reduced when live fish movement restrictions are placed on the most connected sites and further still, when disease controls, representative of current disease control policy in England and Wales, are applied. The importance of specific disease control measures, particularly contact tracing and disease detection rate, are also highlighted. The merit of this model for evaluation of disease spread and the efficacy of controls, in the context of policy, along with potential for further application and development of the model, for example to include economic parameters, is discussed.

Identifiants

pubmed: 37562182
pii: S1755-4365(23)00047-6
doi: 10.1016/j.epidem.2023.100711
pii:
doi:

Types de publication

Journal Article Research Support, Non-U.S. Gov't

Langues

eng

Sous-ensembles de citation

IM

Pagination

100711

Informations de copyright

Crown Copyright © 2023. Published by Elsevier B.V. All rights reserved.

Déclaration de conflit d'intérêts

Declaration of Competing Interest The authors confirm that there are no known conflicts of interest associated with this publication and there has been no significant financial support for this work that could have influenced its outcome.

Auteurs

James Guilder (J)

Centre for Environment, Fisheries & Aquaculture Science (Cefas), Weymouth Laboratory, DT4 8UB, UK.

David Ryder (D)

Centre for Environment, Fisheries & Aquaculture Science (Cefas), Weymouth Laboratory, DT4 8UB, UK.

Nick G H Taylor (NGH)

Centre for Environment, Fisheries & Aquaculture Science (Cefas), Weymouth Laboratory, DT4 8UB, UK.

Sarah R Alewijnse (SR)

Centre for Environment, Fisheries & Aquaculture Science (Cefas), Weymouth Laboratory, DT4 8UB, UK.

Rebecca S Millard (RS)

Centre for Environment, Fisheries & Aquaculture Science (Cefas), Weymouth Laboratory, DT4 8UB, UK.

Mark A Thrush (MA)

Centre for Environment, Fisheries & Aquaculture Science (Cefas), Weymouth Laboratory, DT4 8UB, UK.

Edmund J Peeler (EJ)

Centre for Environment, Fisheries & Aquaculture Science (Cefas), Weymouth Laboratory, DT4 8UB, UK.

Hannah J Tidbury (HJ)

Centre for Environment, Fisheries & Aquaculture Science (Cefas), Weymouth Laboratory, DT4 8UB, UK. Electronic address: hannah.tidbury@cefas.gov.uk.

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