Phylogenetic modeling of viral transmission dynamics at the human-wildlife interface in Uganda

许多威胁人类的传染病起源于野生动物，但我们所知相对较少 关于现实世界的生态条件，使溢出事件。尽管其重要性，识别 新的病毒病原体和表征其传播动力学仍然很困难，因为它需要 先进的基因测序技术，对可能携带病原体的野生动物进行采样， 人类和复杂的建模技术。我们将在基巴莱国家公园研究红疣猴 公园，乌干达，其他非人类灵长类动物，以及与这些野生动物种群相邻的人， 物种内和物种间的传播动力学。我们的团队将收集红色的行为生态学数据 疣猴生活在不同程度的人为干扰的森林地区， 与居住在公园边界沿着的人们进行访谈， 人畜共患病我们会对市民及个别可辨认的红疣猴进行重复抽样 猴子分析肠道病毒组，评估已知感染肠道寄生虫的感染， 红疣猴和人，发现以前没有记录的病毒多样性，检测新的 人类、红疣猴和其他灵长类动物关注的病原体（例如SARS-CoV-2），以及 追踪检测到的病原体的进化传播。为了模拟红疣相关病毒 传播，我们将开发新的生态动态模型，允许纵向生态和地理 数据来构建时间异质性流行病学事件发生率。我们还将创建、测试和分发 用于模拟、贝叶斯推理和基于深度学习的推理的新软件， 传染病在各种各样的生态系统一级传播情景中传播。我们提出的 该项目将有利于公共卫生和野生动物保护，并扩大美国的STEM培训， 乌干达我们将与乌干达社区合作，共同制定解决方案，以应对人畜共患病的风险。 疾病传播和减少传播途径的检测缓解战略。

Many infectious diseases that threaten humans originated among wildlife, yet we know relatively little about the real-world ecological conditions that enable spillover events. Despite its importance, identifying novel viral pathogens and characterizing their transmission dynamics remains difficult because it requires advanced genetic sequencing technologies, sampling wildlife likely to harbor pathogens of concern to humans, and sophisticated modeling techniques. We will study red colobus monkeys in Kibale National Park, Uganda, other nonhuman primates, and people who neighbor these wildlife populations to quantify transmission dynamics within and between species. Our team will collect behavioral ecology data on red colobus monkeys living in areas of the forest with different degrees of anthropogenic disturbance and conduct interviews with people living along the boundary of the park with varying exposure risks for zoonotic diseases. We will conduct repeat sampling of people and individually identifiable red colobus monkeys to analyze the gut virome, assess infection with gastrointestinal parasites known to infect both red colobus and people, discover previously undocumented viral diversity, detect the presence of novel pathogens of concern to humans, red colobus monkeys, and other primates (e.g. SARS-CoV-2), and track the evolutionary spread of detected pathogens. To model how red colobus-associated viruses spread, we will develop new phylodynamic models that allow longitudinal ecological and biogeographical data to structure time-heterogenous epidemiological event rates. We will also create, test, and distribute new software for simulation, Bayesian inference, and deep learning-based inference to model how infectious diseases spread in a wide variety of ecosystem-level transmission scenarios. Our proposed project will benefit public health and wildlife conservation and expand STEM training in the USA and Uganda. Working with Ugandan communities, we will co-create solutions to address risks for zoonotic disease transmission and test mitigation strategies to reduce transmission pathways.