Castonguay Adam C, Chowdhury Sukanta, Shanta Ireen Sultana, Schrijver Bente, Schrijver Remco, Wang Shiyong, Soares Magalhães Ricardo J
Queensland Alliance for One Health Sciences, School of Veterinary Sciences, The University of Queensland, St. Lucia, QLD 4072, Australia.
International Centre for Diarrheal Diseases Research, Bangladesh (icddr,b), Dhaka 1213, Bangladesh.
Trop Med Infect Dis. 2024 Aug 21;9(8):188. doi: 10.3390/tropicalmed9080188.
Emerging and re-emerging zoonotic diseases pose a significant threat to global health and economic security. This threat is further aggravated by amplifying drivers of change, including climate hazards and landscape alterations induced by climate change. Given the complex relationships between climate change and zoonotic disease health outcomes, a structured decision-making process is required to effectively identify pathogens of greatest concern to prioritize prevention and surveillance efforts. Here, we describe a workshop-based expert elicitation process in six steps to prioritize climate-sensitive zoonoses based on a structured approach to defining criteria for climate sensitivity. Fuzzy analytical hierarchy process methodology is used to analyze data provided by experts across human, animal, and environmental health sectors accounting for uncertainties at different stages of the prioritization process. We also present a new interactive expert elicitation interface that facilitates data collection and real-time visualization of prioritization results. The novel approach presented in this paper offers a generalized platform for prioritizing climate-sensitive zoonoses at a national or regional level. This allows for a structured decision-making support process when allocating limited financial and personnel resources to enhance preparedness and response to zoonotic diseases amplified by climate change.
新出现和再度出现的人畜共患疾病对全球健康和经济安全构成重大威胁。气候变化引发的气候灾害和景观改变等驱动变化因素进一步加剧了这一威胁。鉴于气候变化与人畜共患疾病健康结果之间的复杂关系,需要一个结构化的决策过程来有效识别最值得关注的病原体,以便优先开展预防和监测工作。在此,我们描述了一个基于研讨会的专家征询过程,该过程分六个步骤,基于定义气候敏感性标准的结构化方法,对气候敏感的人畜共患病进行优先级排序。模糊层次分析法用于分析人类、动物和环境卫生部门专家提供的数据,同时考虑优先级排序过程不同阶段的不确定性。我们还展示了一个新的交互式专家征询界面,该界面便于数据收集和优先级排序结果的实时可视化。本文提出的新方法为在国家或区域层面上对气候敏感的人畜共患病进行优先级排序提供了一个通用平台。这使得在分配有限的财政和人力资源以加强对气候变化引发的人畜共患疾病的防范和应对时,能够有一个结构化的决策支持过程。