Cross-scale dynamics of LASV spillover within human-driven ecosystems

动物疾病向人类的传播对全球公共卫生和经济产生了重大后果，新冠肺炎大流行就是明证。在评估疾病风险时，政策决策通常依赖于数据可视化技术，如风险地图。然而，对于广泛的风险图与影响疾病从动物向人类传播的局部范围过程之间的关系，人们的理解有限。例如，风险图往往缺少关于疾病风险与气候、土地使用和贫困等因素之间的广泛关联是如何由影响人类感染概率的人、动物和环境之间的相互作用驱动的重要信息。另一方面，揭示细微尺度疾病过程的本地化研究往往不适合为更广泛的空间尺度上的风险预测提供信息。该项目通过对拉沙热的研究弥合了这一差距，拉沙热是一种在西非具有公共卫生意义的啮齿动物传播的出血热，也是全球卫生优先事项。进行实地研究是为了检查支持水库种群的人与环境的相互作用以及导致Lassa病毒暴露的行为。这些数据可以提高我们对风险因素的理解，并为公共卫生政策提供信息。这项研究使用参与性方法，让处于全球卫生挑战前沿的当地社区参与知识和管理战略的建设。它还通过为科学家和决策者制作强有力的数据产品和改进流行病防备来促进公共卫生。该项目使用一种精细的量化和参与性建模方法，明确地将地方规模实地研究的结果纳入广泛的风险模型，以确定在人类驱动的生态系统内推动拉萨病毒溢出的模式和过程。实地研究从拉沙热风险的已知大范围驱动因素中抽样，以了解当地规模的进程如何在不同规模上变化(例如，人类土地利用如何影响水库人口动态，或贫困如何转化为高风险人类行为等)。关于啮齿动物种群动态、运动和感染的数据与参与性活动地图和民族流行病学研究的数据相结合，以捕捉构成人畜共患病溢出途径的人为因素；例如，通过改变环境、宿主体内的病原体动态和人-宿主界面的做法。这些局部尺度的分析提供了一套界面模型，可以帮助解开确定拉萨病毒和与景观动态相关的危险人类暴露的时空分布的过程。示范成果提供了在相互竞争的风险，例如贫穷和粮食不安全的背景下，对疾病控制干预措施进行参与性审查的情景。最后，来自实证研究的数据和来自界面模型的紧急模式被整合回现有的基于大规模回归的风险模型。因此，当地规模的研究和模型预测可以填补我们在理解风险如何跨规模传播方面的关键空白，并被用于为拉沙热和更广泛的人畜共患病溢出正在进行的疾病管理工作提供信息。这一奖项反映了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。

The transmission of animal diseases to humans has significant consequences for public health and economies globally, as evidenced by the COVID-19 pandemic. When evaluating disease risks, policy decisions often rely on data visualization techniques, such as risk maps. However, there is a limited understanding of how broad-scale risk maps relate to local-scale processes that affect disease transmission from animals to humans. For example, risk maps are often missing vital information on how broad associations between disease risk and factors such as climate, land-use, and poverty are driven by interactions between humans, animals, and the environment that affect probability of human infection. On the other hand, localized studies revealing fine-scale disease processes are often poorly situated to inform projections of risk at broader spatial scales. This project bridges this gap through a study of Lassa fever, a rodent-borne hemorrhagic fever of public health significance in West Africa and a global health priority. Field studies are conducted to examine human-environment interactions that support reservoir populations and behaviors that result in Lassa virus exposures. These data can improve our understanding of risk factors and inform public health policies. This study uses participatory methods that engage local communities at the forefront of global health challenges in the construction of knowledge and management strategies. It also contributes to public health via the production of robust data products for scientists and decision makers and improved epidemic preparedness. This project uses a fine-scale quantitative and participatory modelling approach that explicitly integrates results from local-scale field studies into broad-scale risk models to identify the patterns and processes that drive spillover of Lassa virus within human-driven ecosystems. Field studies sample across known broad-scale drivers of Lassa fever risk to understand how local-scale processes vary across scale (e.g., how reservoir population dynamics are impacted by human land-use, or how poverty translates to high-risk human behavior etc.). Data on rodent population dynamics, movement, and infection are combined with data from participatory activity mapping and ethno-epidemiological research to capture the anthropogenic factors that construct pathways for zoonotic spillover; for example, through practices that modify environments, pathogen dynamics within reservoir hosts, and the human-reservoir interface. These local-scale analyses inform a set of interface models that can help to unpack the processes that determine the spatial and temporal distribution of Lassa virus and risky human exposures in relation to landscape dynamics. Model outcomes provide scenarios for participatory examination of disease control interventions in the context of competing risks, e.g., poverty and food insecurity. Finally, data from empirical studies and emergent patterns from the interface models are integrated back into existing broad-scale regression-based risk models. Local-scale studies and model predictions can therefore fill key gaps in our understanding of how risk is propagated across scales and be used to inform ongoing disease management efforts for Lassa fever, and zoonotic spillover more generally.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.