Practitioner perspectives on informing decisions in One Health sectors with predictive models

Kim M. Pepin (), Keith Carlisle, Richard B. Chipman, Dana Cole, Dean P. Anderson, Michael G. Baker, Jackie Benschop, Michael Bunce, Rachelle N. Binny, Nigel French, Suzie Greenhalgh, Dion RJ O’Neale, Scott McDougall, Fraser J. Morgan, Petra Muellner, Emil Murphy, Michael J. Plank, Daniel M. Tompkins and David TS Hayman
Additional contact information
Kim M. Pepin: National Wildlife Research Center
Keith Carlisle: National Wildlife Research Center
Richard B. Chipman: National Rabies Management Program
Dana Cole: National Feral Swine Damage Management Program
Dean P. Anderson: Manaaki Whenua - Landcare Research
Michael G. Baker: University of Otago
Jackie Benschop: Palmerston North
Michael Bunce: Department of Conservation
Rachelle N. Binny: Manaaki Whenua - Landcare Research
Nigel French: Palmerston North
Suzie Greenhalgh: Manaaki Whenua - Landcare Research
Dion RJ O’Neale: University of Auckland
Scott McDougall: Cognosco
Fraser J. Morgan: University of Auckland
Petra Muellner: Palmerston North
Emil Murphy: Deer Industry New Zealand
Michael J. Plank: University of Canterbury
Daniel M. Tompkins: Predator Free 2050 Limited
David TS Hayman: Palmerston North

Palgrave Communications, 2025, vol. 12, issue 1, 1-15

Abstract: Abstract The continued emergence of challenges in human, animal, and environmental health (One Health sectors) requires public servants to make management and policy decisions about system-level ecological and sociological processes that are complex, poorly understood, and change over time. Relying on intuition, evidence, and experience for robust decision-making is challenging without a formal assimilation of these elements (a model), especially when the decision needs to consider potential impacts if an action is or is not taken. Models can provide assistance to this challenge, but effective development and use of model-based evidence in decision-making (‘model-to-decision workflow’) can be challenging. To address this gap, we examined conditions that maximize the value of model-based evidence in decision-making in One Health sectors by conducting 41 semi-structured interviews of researchers, science advisors, operational managers, and policy decision-makers with direct experience in model-to-decision workflows (‘Practitioners’) in One Health sectors. Broadly, our interview guide was structured to understand practitioner perspectives about the utility of models in health policy or management decision-making, challenges and risks with using models in this capacity, experience with using models, factors that affect trust in model-based evidence, and perspectives about conditions that lead to the most effective model-to-decision workflow. We used inductive qualitative analysis of the interview data with iterative coding to identify key themes for maximizing the value of model-based evidence in One Health applications. Our analysis describes practitioner perspectives for improved collaboration among modelers and decision-makers in public service, and priorities for increasing accessibility and value of model-based evidence in One Health decision-making. Two emergent priorities include establishing different standards for development of model-based evidence before or after decisions are made, or in real-time versus preparedness phases of emergency response, and investment in knowledge brokers with modeling expertise working in teams with decision-makers.

Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
http://link.springer.com/10.1057/s41599-025-05077-3 Abstract (text/html)
Access to full text is restricted to subscribers.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:pal:palcom:v:12:y:2025:i:1:d:10.1057_s41599-025-05077-3

Ordering information: This journal article can be ordered from
https://www.nature.com/palcomms/about

DOI: 10.1057/s41599-025-05077-3

Access Statistics for this article

More articles in Palgrave Communications from Palgrave Macmillan
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().