DMS/NIGMS 2: Spatial, Multi-Host Petri Net Models for Zoonotic Disease Forecasting

DMS/NIGMS 2：用于人畜共患疾病预测的空间、多主机 Petri 网络模型

• 批准号: 10797423
• 负责人: Beckett Sterner
• 金额: $ 27.34万
• 依托单位: ARIZONA STATE UNIVERSITY-TEMPE CAMPUS
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-09-01 至 2027-08-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10797423
• 关键词: Area; Arizona; Biological Models; Biology; Coccidioidomycosis; Communication; Complex; Data; Data Science; Data Sources; Disease; Disease Outbreaks; Disease Reservoirs; Disease model; Ecosystem; Education and Outreach; Educational workshop; Endemic Diseases; Event; Future; Genetic; Habitats; Hantavirus; Hantavirus Pulmonary Syndrome; Health; Human; Link; Mediating; Modeling; Monitor; National Institute of General Medical Sciences; Output; Persons; Prevalence; Public Health; Research; Research Personnel; Resources; Risk; Rodent; Rodent Model; Sampling; Source; Southwestern United States; Stable Populations; Students; System; Textbooks; Time; Virus; Visual; Visualization; Wild Animals; Zoonoses; behavior influence; burden of illness; desert fever; effective intervention; environmental change; field study; fungus; improved; interdisciplinary approach; land use; life history; novel; outbreak control; outreach; pathogen; simulation; social influence; tool; trait; trend; undergraduate research experience; web app; zoonotic spillover

Diseases that spillover from wild animals pose an increasing threat to human health worldwide, but forecasting how zoonotic pathogens spread remains a major challenge. Zoonotic diseases are complex, spatially and temporally evolving systems whose behaviors are influenced by social, ecological, genetic, and evolutionary factors. Understanding the contributions of biotic and abiotic factors to accurate disease forecasting is an urgent priority for managing the emerging risks of rapid environmental change and for improving mechanistic models of complex ecological systems. Field studies monitoring zoonotic pathogens and their host species have typically assumed that observing high host prevalence is strong evidence that (i) the host is a ‘reservoir’ of the pathogen, maintaining it at a stable population level; and (ii) the host is a persistent source of spillover into other species. However, ecological models have shown that reservoir status can be strongly context dependent, mediated by extrinsic factors including interactions with other species and habitat fragmentation. An interdisciplinary approach combining complex systems modeling, data science, and risk analysis is therefore needed to model zoonotic spillover dynamics. This project will construct novel largescale, multi-host mechanistic models of the Hantavirus Pulmonary Syndrome (HPS) and Valley Fever (coccidioidomycosis) diseases in the Phoenix, Arizona metro area to investigate the stability versus context-sensitivity of hosts as disease reservoirs and identify key future data sources required to improve forecasting and identify effective interventions aimed at reducing disease burden. Both diseases are endemic to the southwestern United States and are believed to be spread by rodent hosts, but they are caused by different types of pathogens (viruses and fungi, respectively) and show divergent case trends. The project will use Petri Net models, which are modular, scalable, and readily visualized as mechanistic network diagrams, which makes them a valuable tool for exploring how adding or removing hosts and changing land use are expected to change disease dynamics. For training and outreach, the project will implement and evaluate initiatives to (i) communicate results in public outreach events; (ii) construct a course-based undergraduate research experience (CURE) focused on disease modeling; and (iii) build capacity for researching Valley Fever and mitigating outbreaks. For outreach, the project will create a free web app for public interaction with modular disease models and their visual outputs. In addition to presenting project results for HPS and Valley Fever, the web app will be linked to an interactive textbook introducing Petri Nets to be developed by the project. For the CURE class, students will focus on synthesizing zoonotic disease data to improve risk modeling, providing urgently needed research opportunities for ASU’s approximately 7,000 in-person and online biology majors. Lastly, the project will organize a capacity-building workshop of academic and public health researchers to introduce them to Petri Net resources and to identify data needs for improved forecasting.

野生动物传播的疾病对全球人类健康构成越来越大的威胁， 预测人畜共患病原体如何传播仍然是一个重大挑战。人畜共患病很复杂， 在空间和时间上不断演化的系统，其行为受到社会、生态、遗传 和进化因素。了解生物和非生物因素对准确疾病预测的贡献是管理快速环境变化的新风险和改进复杂生态系统的机械模型的紧迫优先事项。监测人畜共患病病原体及其宿主物种的实地研究通常假设，观察到宿主的高流行率是强有力的证据，表明（i）宿主是病原体的“水库”，使其保持在稳定的种群水平;（ii）宿主是溢出到其他物种的持久来源。然而，生态模型表明，水库的地位可以强烈的上下文依赖，介导的外在因素，包括与其他物种和栖息地破碎化的相互作用。因此，需要一种结合复杂系统建模、数据科学和风险分析的跨学科方法来模拟人畜共患病溢出动态。该项目将在亚利桑那州凤凰城市区构建汉坦病毒肺综合征（HPS）和山谷热（球孢子菌病）疾病的新型大规模多宿主机制模型，以研究宿主作为疾病储存库的稳定性与环境敏感性，并确定未来改善预测所需的关键数据来源，并确定旨在减少疾病负担的有效干预措施。这两种疾病都是美国西南部的地方病，据信是由啮齿动物宿主传播的，但它们是由不同类型的病原体（分别是病毒和真菌）引起的，并显示出不同的病例趋势。该项目将使用Petri网模型，这些模型是模块化的，可扩展的，并且易于可视化为机械网络图，这使得它们成为探索如何添加或删除主机和改变土地使用的有价值的工具。 在培训和推广方面，该项目将实施和评估以下举措：（一）在公共宣传活动中传达结果;（二）构建以课程为基础的本科生研究经验（CURE），重点是疾病建模;以及（三）建立研究山谷热和缓解疫情的能力。在推广方面，该项目将创建一个免费的网络应用程序，供公众与模块化疾病模型及其视觉输出进行互动。除了展示HPS和Valley Fever的项目结果外，该网络应用程序还将链接到一本介绍该项目开发的Petri网的交互式教科书。对于治愈类，学生将专注于合成人畜共患病数据，以改善风险建模，提供亚利桑那州立大学的约7,000人在人和在线生物学专业急需的研究机会。最后，该项目将为学术界和公共卫生研究人员举办一次能力建设讲习班，向他们介绍Petri网资源，并确定改进预测所需的数据。