Browse by author
Lookup NU author(s): Dr Marie McIntyreORCiD
This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).
Copyright © 2025 Clough, Chaters, Havelaar, McIntyre, Marsh, Hughes, Jemberu, Stacey, Afonso, Gilbert, Raymond and Rushton. Livestock provide nutritional and socio-economic security for marginalized populations in low and middle-income countries. Poorly-informed decisions impact livestock husbandry outcomes, leading to poverty from livestock disease, with repercussions on human health and well-being. The Global Burden of Animal Diseases (GBADs) programme is working to understand the impacts of livestock disease upon human livelihoods and livestock health and welfare. This information can then be used by policy makers operating regionally, nationally and making global decisions. The burden of animal disease crosses many scales and estimating it is a complex task, with extensive requirements for data and subsequent data synthesis. Some of the information that livestock decision-makers require is represented by quantitative estimates derived from field data and models. Model outputs contain uncertainty, arising from many sources such as data quality and availability, or the user’s understanding of models and production systems. Uncertainty in estimates needs to be recognized, accommodated, and accurately reported. This enables robust understanding of synthesized estimates, and associated uncertainty, providing rigor around values that will inform livestock management decision-making. Approaches to handling uncertainty in models and their outputs receive scant attention in animal health economics literature; indeed, uncertainty is sometimes perceived as an analytical weakness. However, knowledge of uncertainty is as important as generating point estimates. Motivated by the context of GBADs, this paper describes an analytical framework for handling uncertainty, emphasizing uncertainty management, and reporting to stakeholders and policy makers. This framework describes a hierarchy of evidence, guiding movement from worst to best-case sources of information, and suggests a stepwise approach to handling uncertainty in estimating the global burden of animal disease. The framework describes the following pillars: background preparation; models as simple as possible but no simpler; assumptions documented; data source quality ranked; commitment to moving up the evidence hierarchy; documentation and justification of modelling approaches, data, data flows and sources of modelling uncertainty; uncertainty and sensitivity analysis on model outputs; documentation and justification of approaches to handling uncertainty; an iterative, up-to-date process of modelling; accounting for accuracy of model inputs; communication of confidence in model outputs; and peer-review.
Author(s): Clough HE, Chaters GL, Havelaar AH, McIntyre KM, Marsh TL, Hughes EC, Jemberu WT, Stacey D, Afonso JS, Gilbert W, Raymond K, Rushton J
Publication type: Article
Publication status: Published
Journal: Frontiers in Veterinary Science
Year: 2025
Volume: 12
Online publication date: 07/03/2025
Acceptance date: 21/01/2025
Date deposited: 07/04/2025
ISSN (electronic): 2297-1769
Publisher: Frontiers Media SA
URL: https://doi.org/10.3389/fvets.2025.1459209
DOI: 10.3389/fvets.2025.1459209
Data Access Statement: The original contributions presented in the study are included in the article/supplementary material, further inquiries can be directed to the corresponding author.
Altmetrics provided by Altmetric
