Spillover of Ebola and other filoviruses at ecological boundaries

埃博拉和其他丝状病毒在生态边界的溢出

基本信息

批准号：
10404564
负责人：
Patrick Stephens
金额：
$ 38.39万
依托单位：
OKLAHOMA STATE UNIVERSITY STILLWATER
依托单位国家：
美国
项目类别：
财政年份：
2020
资助国家：
美国
起止时间：
2020-06-17 至 2025-05-31
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10404564
关键词：
Affect Africa Agriculture Animals Biodiversity Biohazardous Substance Brucellosis Cessation of life Characteristics Communicable Diseases Communities Data Set Databases Dimensions Discipline Disease Disease Outbreaks Ebola Ebola virus Economic Conditions Economics Epidemic Filovirus Goals Gorilla gorilla Guinea Habitats Health Health system Human Human Activities Individual Investments Laws Malaria Maps Marburgvirus Methods Modeling Pattern Phylogenetic Analysis Physiological Processes Population Process Research Research Personnel Resources Risk Socioeconomic Factors Source Statistical Models System Testing Theoretical model Time Variant Vector-transmitted infectious disease Virus Weather West Nile virus Work Zoonoses anthropogenesis computerized tools contagion disease transmission human subject insight iterative design land conversion large scale data mathematical model mortality pathogen pathogen spillover social socioeconomics spillover event theories tool transmission process zoonotic spillover

项目摘要

More than half of all infectious disease outbreaks across the globe are zoonotic, involving pathogen spillover from animal reservoirs to humans. Ebola and other filoviruses rank among the most deadly zoonoses. Recent large outbreaks with mortality in the thousands both in humans and wildlife underscore the pressing need to better understand the factors promoting filovirus spillover. Although spillover is commonly defined as a pathogen crossing species boundaries, there are relatively few empirical studies or modeling frameworks that explicitly consider ecological boundaries across which spillover occurs. Crossing ecological boundaries involves processes that occur at many levels of organization: physiological processes at the individual level, interspecies interactions between individuals at the population level, interactions between populations of different species at the community level, and interactions between ecological communities within landscapes. Processes accelerating spillover often involve human activities such as habitat encroachment and land conversion, which are themselves ultimately driven by socioeconomic factors. In the context of Ebola and other filoviruses in Africa, we will develop the data sets, theoretical models and statistical tools needed for a general descriptive and predictive framework for spillover at ecological boundaries. Our project will follow an iterative design where results from mechanistic models are used to refine patterns that we test for empirically, and statistical models of large-scale data allow us to more realistically parameterize mechanistic models. Our work will test the generality of specific theories that so far have been applied only to a limited number of study systems. For example, ours will be among the first attempts to test the influence of Schmalhausen’s law -- an evolutionary theory that may explain the tendency for large outbreaks to occur at the edges of species ranges or during unusual weather conditions and which to date has primarily been investigated in the context of malaria -- in pathogens that rely on direct transmission. This work will demonstrate how new methods can provide unifying insight into patterns in critically important disease transmission systems and will enhance our ability to predict spillover of both filoviruses and many other zoonotic pathogens. Note that no human subjects, biohazards, or select agents will be involved in this project.

全球超过一半的传染病爆发是人畜共患，埃博拉病毒和其他丝状病毒是最致命的人畜共患病，人类和野生动物的死亡人数达到数千人，这凸显了迫切需要。为了更好地理解促进丝状病毒溢出的因素，尽管溢出通常被定义为跨越物种边界的病原体，但明确考虑溢出发生的生态边界的实证研究或建模框架相对较少。发生在许多组织层面的相互作用：个体层面的生理过程、种群层面个体之间的物种间相互作用、群落层面不同物种种群之间的相互作用以及景观内生态群落之间的相互作用。加速溢出的过程通常涉及人类活动。例如栖息地侵占和土地转变，这些问题本身最终是由社会经济因素驱动的。在非洲埃博拉病毒和其他丝状病毒的背景下，我们将开发溢出的一般描述和预测框架所需的数据集、理论模型和统计工具。在生态我们的项目将遵循迭代设计，其中机械模型的结果用于完善我们凭经验测试的模式，而大规模数据的统计模型使我们能够更现实地参数化机械模型。迄今为止，这些理论仅适用于有限数量的研究系统，例如，我们的理论将是测试施马尔豪森定律影响的首批尝试之一，这是一种可以解释大规模爆发的趋势的进化理论。的边缘这项工作将展示新方法如何能够提供对极其重要的疾病传播系统和模式的统一见解。将增强我们预测丝状病毒和许多其他人畜共患病原体溢出的能力。请注意，该项目不会涉及人类受试者、生物危害或选定的病原体。