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.

大多数传染病暴发涉及从动物到人类的传播，称为人畜共患溢出。几项研究提供了证据，表明气候变化会影响人畜共患病的频率和发生。尽管如此，当前的数学模型在很大程度上忽略了气候变化对人畜共动性溢出的影响。 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.研究小组将利用数十年的天气，野生动植物种群和人畜共患病数据来确定可以纳入模型中的重要变量，并准确估计溢出的流行病学预测因子（例如，疾病的力量，速度和疾病的方向以及基本的繁殖数量和基本的繁殖数量），作为重要的天气和环境因素的功能。 校准模型的数值模拟将有助于研究人员阐明管理人畜共患病生态的潜在机制，并预测气候变化的可能影响。 此外，这项研究基于现有的病原体和种群散布的波浪理论，以促进旅行和固定波的理论知识，包括它们的存在，独特性，稳定性和渐近行为。用于增强建模和模拟人畜共患模型的分析和计算工具，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的法定任务，并被认为是值得通过基金会的知识分子优点和更广泛影响的审查审查的评估来通过评估来支持的。