A framework for handling uncertainty in a large-scale programme estimating the Global Burden of Animal Diseases (GBADs)

Helen E. Clough ^1* Gemma L. Chaters ² Arie Hendrik Havelaar ³ K. Marie McIntyre ⁴ Thomas L. Marsh ⁵ Ellen C. Hughes ¹ Wudu Temesgen Jemberu ⁶ Deborah Stacey ⁷ Joao Sucena Afonso ¹ William Gilbert ¹ Kassy Raymond ⁷ Jonathan Rushton ¹

• ¹ University of Liverpool, Liverpool, United Kingdom
• ² Lancaster University, Lancaster, England, United Kingdom
• ³ University of Florida, Gainesville, Florida, United States
• ⁴ Newcastle University, Newcastle upon Tyne, North East England, United Kingdom
• ⁵ Washington State University, Pullman, Washington, United States
• ⁶ International Livestock Research Institute (Ethiopia), Addis Ababa, Addis Ababa, Ethiopia
• ⁷ University of Guelph, Guelph, Ontario, Canada

Livestock provide nutritional and socio-economic security for marginalised 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 recognised, accommodated, and accurately reported. This enables robust understanding of synthesised 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, emphasising uncertainty management, and reporting to stakeholders and policy makers. This framework describes a hierarchy of evidence, guiding movement from worst to bestcase 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.