Collaborative Research: Urban Vector-Borne Disease Transmission Demands Advances in Spatiotemporal Statistical Inference

合作研究：城市媒介传播疾病传播需要时空统计推断的进步

• 批准号: 2414688
• 负责人: Mercedes Pascual
• 金额: $ 60.77万
• 依托单位: New York University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2024
• 资助国家: 美国
• 起止时间: 2024-02-15 至 2024-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2414688&HistoricalAwards=false
• 关键词: Collaborative; Research; Urban; Vector; Borne

Statistical analysis of partially-observed, nonlinear, stochastic spatiotemporal systems is a methodological challenge. Many existing inference algorithms suffer from a "curse of dimensionality" that prohibits their applicability to models describing interacting dynamic processes occurring within and between many spatial locations. New algorithms will be developed, and shown in theory and in practice to advance capabilities for spatiotemporal data analysis. This methodological research will be carried out in the context of addressing a public health concern, transmission of dengue virus. Global incidence of dengue has risen 30-fold over the past fifty years, with notable geographical expansion in South and Central America. The municipality of Rio de Janeiro is a focal point for dengue transmission in this region. Spatiotemporal data on dengue cases in Rio de Janeiro will be analyzed, together with data on human movement, temperature, and rainfall. Policy decisions for the detection, control, and potential eradication of infectious diseases are informed by model-based understanding of disease transmission. Improved understanding of the spatiotemporal dynamics of disease transmission will have implications for improvements in disease control. Mathematical models will be developed to describe spatiotemporal dynamics of dengue transmission, and the novel statistical methodology will be used to link these models to the data from Rio de Janeiro.Spatiotemporal partially-observed Markov process models provide a framework for formulating and answering questions relating spatiotemporal data to an underlying stochastic dynamic process. Statistically efficient inference involves integrating out over possible values of the latent process, a task known as filtering. Except when the system is approximately linear and Gaussian, filtering spatiotemporal models is challenging. One algorithm developed in this project will address the curse of dimensionality by guiding Monte Carlo particles toward important regions in the latent variable space. Another algorithm will combine many weak, independent filters to give a global filtering solution. Disease transmission systems, which are highly nonlinear and stochastic and are imperfectly observable, will be used to motivate and demonstrate the capabilities of the new algorithms. Specifically, models will be developed for the dynamics of dengue transmission in the major metropolis of Rio de Janeiro. Spatiotemporal stochastic epidemiological models will be used to examine the role of human mobility, host immunity, and climate variability in the context of a heterogeneous socioeconomic landscape. A particular goal is to identify locations that function as sources of infection critical to disease invasion and persistence as well as those that act as sinks incapable of sustained local transmission.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.

部分观察到的非线性，随机时空系统的统计分析是方法论挑战。许多现有的推理算法都遭受了“维度的诅咒”，该算法禁止其适用于描述许多空间位置内部和之间发生的相互作用动态过程的模型。将开发新的算法，并在理论和实践中显示以提高时空数据分析的能力。这项方法论研究将在解决公共卫生问题的背景下进行，即传播登革热病毒。在过去的五十年中，登革热的全球发病率已经上升了30倍，南美洲和中美洲的地理扩张显着扩展。里约热内卢市是该地区登革热传播的焦点。将分析有关里约热内卢的登革热病例的时空数据，以及有关人类运动，温度和降雨的数据。通过基于模型的疾病传播的理解，可以为检测，控制和潜在根除传染病的政策决策提供信息。对疾病传播的时空动力学的了解得以提高，将对改善疾病控制有影响。将开发数学模型来描述登革热传播的时空动力学，并将使用新型的统计方法将这些模型与Rio de Janeiro.Spatiotemporal部分观察到的Markov Process模型联系起来，提供了一个框架，该模型提供了一个框架，以配制和回答有关时空数据与下层稳定的动力学过程相关的问题。统计上有效的推理涉及整合潜在过程的可能值，该任务称为过滤。除非系统大致是线性和高斯的，否则过滤时空模型具有挑战性。该项目中开发的一种算法将通过将蒙特卡洛颗粒引导到潜在可变空间中的重要区域来解决维度的诅咒。另一种算法将结合许多弱的独立过滤器，以提供全局过滤解决方案。疾病传播系统高度非线性和随机性且无法完美地观察到，它将用于激励和证明新算法的能力。具体而言，将开发用于里约热内卢大都市登革热传播动力学的模型。 时空的随机流行病学模型将用于检查人类流动性，宿主免疫力和气候变异性在异质社会经济景观背景下的作用。 一个特定的目标是确定作为对疾病入侵和持久性至关重要的感染来源的位置，以及那些无能力持续的本地传播的位置。该奖项反映了NSF的法定任务，并被认为是通过基金会的知识分子优点和更广泛的审查标准通过评估来通过评估来支持的。