A systematic review of epidemiological modelling in response to lumpy skin disease outbreaks.

decision making lumpy skin disease modelling workflow outbreak response systematic review

Journal

Frontiers in veterinary science
ISSN: 2297-1769
Titre abrégé: Front Vet Sci
Pays: Switzerland
ID NLM: 101666658

Informations de publication

Date de publication:
2024
Historique:
received: 04 07 2024
accepted: 28 08 2024
medline: 8 10 2024
pubmed: 8 10 2024
entrez: 8 10 2024
Statut: epublish

Résumé

Lumpy skin disease (LSD) is an infectious disease currently spreading worldwide and poses a serious global threat. However, there is limited evidence and understanding to support the use of models to inform decision-making in LSD outbreak responses. This review aimed to identify modelling approaches that can be used before and during an outbreak of LSD, examining their characteristics and priorities, and proposing a structured workflow. We conducted a systematic review and identified 60 relevant publications on LSD outbreak modelling. The review identified six categories of question to be addressed following outbreak detection (origin, entry pathway, outbreak severity, risk factors, spread, and effectiveness of control measures), and five analytical techniques used to address them (descriptive epidemiology, risk factor analysis, spatiotemporal analysis, dynamic transmission modelling, and simulation modelling). We evaluated the questions each analytical technique can address, along with their data requirements and limitations, and accordingly assigned priorities to the modelling. Based on this, we propose a structured workflow for modelling during an LSD outbreak. Additionally, we emphasise the importance of pre-outbreak preparation and continuous updating of modelling post-outbreak for effective decision-making. This study also discusses the inherent limitations and uncertainties in the identified modelling approaches. To support this workflow, high-quality data must be collected in standardised formats, and efforts should be made to reduce inherent uncertainties of the models. The suggested modelling workflow can be used as a process to support rapid response for countries facing their first LSD occurrence and can be adapted to other transboundary diseases.

Identifiants

pubmed: 39376926
doi: 10.3389/fvets.2024.1459293
pmc: PMC11456570
doi:

Types de publication

Journal Article Systematic Review

Langues

eng

Pagination

1459293

Informations de copyright

Copyright © 2024 Lee, Baker, Sellens, Stevenson, Roche, Hall, Breed and Firestone.

Déclaration de conflit d'intérêts

RH was employed by Ausvet Pty Ltd. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Auteurs

Simin Lee (S)

Melbourne Veterinary School, Faculty of Science, The University of Melbourne, Parkville, VIC, Australia.

Christopher M Baker (CM)

School of Mathematics and Statistics, Faculty of Science, The University of Melbourne, Parkville, VIC, Australia.
Melbourne Centre for Data Science, The University of Melbourne, Parkville, VIC, Australia.
The Centre of Excellence for Biosecurity Risk Analysis, School of Biosciences, The University of Melbourne, Parkville, VIC, Australia.

Emily Sellens (E)

Epidemiology, Surveillance and Laboratory Section, Australian Government Department of Agriculture, Fisheries and Forestry, Canberra, ACT, Australia.

Mark A Stevenson (MA)

Melbourne Veterinary School, Faculty of Science, The University of Melbourne, Parkville, VIC, Australia.

Sharon Roche (S)

Epidemiology, Surveillance and Laboratory Section, Australian Government Department of Agriculture, Fisheries and Forestry, Canberra, ACT, Australia.

Robyn N Hall (RN)

Ausvet Pty Ltd., Canberra, ACT, Australia.

Andrew C Breed (AC)

Epidemiology, Surveillance and Laboratory Section, Australian Government Department of Agriculture, Fisheries and Forestry, Canberra, ACT, Australia.

Simon M Firestone (SM)

Melbourne Veterinary School, Faculty of Science, The University of Melbourne, Parkville, VIC, Australia.

Classifications MeSH