eMB: Enhancing Mathematical Models to Investigate the Influences of Climate Change on Zoonotic Spillover

eMB：增强数学模型以研究气候变化对人畜共患病溢出效应的影响

• 批准号: 2325267
• 负责人: Majid Bani-Yaghoub
• 金额: $ 26.5万
• 依托单位: University of Missouri-Kansas City
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-09-01 至 2026-08-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2325267&HistoricalAwards=false
• 关键词: eMB; Enhancing; Mathematical; Models; Investigate

Most infectious disease outbreaks involve transmission from animals to humans, known as zoonotic spillover. Several studies provide evidence that climate change can influence the frequency and occurrence of zoonotic spillover. Nonetheless, current mathematical models have largely overlooked the effects of climate change on zoonotic spillover. By enhancing the modeling approaches, the researchers of this multidisciplinary project seek to understand what challenges zoonotic pathogens must overcome to transmit from wild animal hosts to humans or other animals, how climate change can reduce these challenges and make it more plausible for zoonotic pathogens to live within and between new species, and what kinds of environments have a higher likelihood of zoonotic spillover in the view of climate change. The research team will use decades of weather, wildlife population, and zoonotic disease data to identify significant variables that can be incorporated into the models and to accurately estimate epidemiological predictors of spillover (e.g., the force, speed, and direction of disease spread and the basic reproduction number) as functions of significant weather and environmental factors. The numerical simulations of the calibrated models will help the researchers elucidate the underlying mechanisms governing the ecology of zoonotic disease and predict possible influences of climate change. Furthermore, this study builds on the existing wave theory of pathogen and population dispersal to advance the theoretical knowledge of traveling and stationary waves, including their existence, uniqueness, stability, and asymptotic behaviors. The analytical and computational tools, template codes, and tutorials for enhanced modeling and simulating zoonotic spillover will be released on a GitHub page dedicated to this project.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

大多数传染病的爆发涉及从动物到人类的传播，即人畜共患病溢出。几项研究提供的证据表明，气候变化可以影响人畜共患病溢出的频率和发生。尽管如此，目前的数学模型在很大程度上忽略了气候变化对人畜共患病溢出的影响。通过改进建模方法，这个多学科项目的研究人员试图了解人畜共患病病原体必须克服哪些挑战才能从野生动物宿主传播到人类或其他动物，气候变化如何减少这些挑战并使人畜共患病病原体在新物种内和新物种之间生活的可能性更大，以及从气候变化的角度来看，什么样的环境人畜共患病溢出的可能性更高。研究小组将使用数十年的天气、野生动物种群和人畜共患病数据，以确定可以纳入模型的重要变量，并准确估计作为重要天气和环境因素的函数的溢出的流行病学预测因素(例如，疾病传播的力量、速度和方向以及基本繁殖数量)。校准模型的数值模拟将帮助研究人员阐明控制人畜共患病生态的潜在机制，并预测气候变化可能产生的影响。此外，本研究在现有的病原体波动理论和种群扩散理论的基础上，进一步发展了行波和驻波的理论知识，包括行波和驻波的存在性、唯一性、稳定性和渐近行为。用于增强建模和模拟人畜共患病溢出效应的分析和计算工具、模板代码和教程将在专门针对该项目的GitHub页面上发布。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。