在一个大规模计划中处理不确定性的框架,估计全球动物疾病负担
Helen E Clough1, Gemma L Chaters1,2, Arie H Havelaar3
1Department of Livestock and One Health, Institute of Infection, Veterinary and Ecological Sciences, University of Liverpool, Liverpool, United Kingdom.
Frontiers in veterinary science
|March 24, 2025
概括
了解和管理牲畜疾病负担估计中的不确定性对于明智决策至关重要. 该框架指导决策者应对不确定性,以改善牲畜健康和经济结果.
科学领域:
- 动物健康经济学 动物健康经济学
- 全球疾病负担估计
- 社会经济影响评估.
背景情况:
- 牲畜对于低收入和中等收入国家的粮食安全和生计至关重要.
- 畜牧业疾病对人类健康,福祉和经济稳定有重大影响.
- 全球动物疾病负担 (GBADs) 计划旨在为政策指导量化这些影响.
研究的目的:
- 提出一个分析框架,用于管理和报告动物疾病负担估计的不确定性.
- 为了解决动物健康经济学文献中对不确定性给予的有限关注.
- 为决策者提供一个结构化的方法,有效地利用不确定的数据.
主要方法:
- 描述证据的层次结构,以指导信息采购.
- 建议采取逐步的方法来处理疾病负担模型中的不确定性.
- 强调模型和数据的文档,证明和代改进.
主要成果:
- 提出了一个全面的框架,用于疾病负担估计的不确定性管理.
- 该框架包括数据质量排名,敏感性分析和明确的信任沟通等支柱.
- 它倡导假设,数据流和建模选择的透明度.
结论:
- 识别和报告不确定性与生成畜牧疾病负担点估计一样重要.
- 拟议的框架提高了估计的严谨性和稳定性,以告知畜牧管理决策.
- 有效的不确定性管理支持更好的政策制定,以改善动物健康和人类福利.
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