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.

来自野生动物的Spilover的疾病对全世界的人类健康构成了日益威胁，但 预测人畜共患病原体如何扩散仍然是一个重大挑战。人畜共患病很复杂， 在空间和临时发展的系统上，其行为受社会，生态，遗传的影响， 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兴风险以及改善复杂生态系统的机械模型的重点。监测人畜共患病原体及其宿主物种的现场研究通常假设观察到较高的宿主患病率是（i）宿主是病原体的“储层”，将其保持在稳定的种群水平； （ii）宿主是Spilover持续进入其他物种的来源。然而，生态模型表明，储层状态可以很强地取决于上下文，这是由外在因素介导的，包括与其他物种的相互作用和栖息地碎片。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，以模拟人畜共患模型。该项目将在亚利桑那州凤凰城地区构建汉坦病毒肺综合征（HPS）和谷热（Coccidioyymycis）疾病的新型大型，多宿主机械模型，以调查疾病的疾病和确定未来数据的良好疾病，并确定良好的疾病的良好疾病，并确定了良好的疾病，并确定了良好的疾病，并确定了进一步的范围，并确定了良好的疾病，并确定了进一步的范围。两种疾病都是美国西南部的内在疾病，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（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 Net模型，这些模型是模块化的，可扩展的，并且很容易被视为机械网络图，这使它们成为探索如何添加或删除宿主以及预期不断变化的土地利用来改变疾病动态的宝贵工具。 对于培训和宣传，该项目将实施和评估倡议，以（i）在公共外展活动中传达结果； （ii）构建一个针对疾病建模的基于课程的本科研究经验（CURE）； （iii）建立研究山谷热和减轻暴发的能力。对于外展活动，该项目将创建一个免费的Web应用程序，用于与模块化疾病模型及其视觉输出的公共互动。除了介绍HPS和Valley Fever的项目结果外，Web应用程序还将链接到一个互动的教科书，该教科书介绍了该项目要开发的Petri网。对于治疗类别，学生将专注于综合人畜共患病数据以改善风险建模，为ASU的大约7,000个面对面和在线生物学专业提供急需的研究机会。最后，该项目将组织一个学术和公共卫生研究人员的能力建设研讨会，以将其介绍给Petri净资源，并确定提高预测的数据需求。