Model-based inference and forecasting of co-circulating pathogen dynamics

基于模型的共循环病原体动态的推理和预测

• 批准号: 10276759
• 负责人: Alex Perkins
• 金额: $ 39.13万
• 依托单位: UNIVERSITY OF NOTRE DAME
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-09-24 至 2026-06-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10276759
• 关键词: Accounting; Address; Adherence; Affect; Bayesian Modeling; Behavioral; Biological; Blood Circulation; Brazil; COVID-19; Clinical; Data; Development; Disease; Disease Outbreaks; Epidemiology; Face; Future; Hygiene; Immunity; Indiana; Joints; Modeling; Modernization; Process; Public Health; Research; Shapes; Time; Uncertainty; Virus; base; cross reactivity; data streams; heterogenous data; improved; mathematical model; mosquito-borne; mosquito-borne pathogen; neglect; pathogen; respiratory pathogen; surveillance data; tool; transmission process

PROJECT SUMMARY Public health faces threats from a multitude of pathogens on an ongoing basis, yet pathogens associated with different diseases are typically compartmentalized with respect to surveillance, management, and research. This compartmentalized approach ignores the many ways that pathogens interact, in some cases leading to the exacerbation of their collective burden on public health. These interactions can be biological (e.g., cross-reactive immunity), behavioral (e.g., prompting adherence to good hygiene), or clinical (e.g., misdiagnosis). Modern, data- driven approaches to mathematical modeling have the potential to resolve the dynamics of co- circulating pathogens by accounting for these interactions. In doing so, modeling also has the potential to improve pathogen-specific disease forecasts by borrowing information across surveillance data for different diseases. To date, this potential remains largely untapped. In this project, I will develop a generalizable framework for modeling the dynamics of co-circulating pathogens. The first component of this framework will use Bayesian hierarchical modeling to fuse mechanistic descriptions of pathogen transmission dynamics with statistical descriptions of surveillance processes, allowing for maximal leveraging of heterogeneous data streams to inform biological inferences. The second component of this framework will involve validating model inferences through forecasts of future disease dynamics. Both components of this framework will involve the use of multiple models that represent competing hypotheses about pathogen interaction, as well as other forms of model uncertainty. This framework will be applied in two settings: mosquito-borne viruses in Brazil and respiratory pathogens in Indiana. In both of these settings, co-circulation of recently emerged and endemic pathogens poses new challenges for surveillance and control activities, making the development of new modeling tools to address these challenges especially timely.

项目摘要 公共卫生面临着众多病原体的威胁，但病原体 与不同疾病有关 管理和研究。这种分隔的方法忽略了多种方式 病原体相互作用，在某些情况下导致其集体负担加剧 公共卫生。这些相互作用可以是生物学的（例如，交叉反应性免疫），行为性 （例如，提示遵守良好的卫生）或临床（例如误诊）。现代数据 - 驱动的数学建模方法具有解决共同动力学的潜力 通过考虑这些相互作用来循环病原体。在此过程中，建模还具有 通过借入信息来改善病原体特异性疾病预测的潜力 不同疾病的监视数据。迄今为止，这种潜力在很大程度上尚未开发。在这个 项目，我将开发一个可推广的框架，用于建模共同循环的动态 病原体。该框架的第一个组成部分将使用贝叶斯分层建模 病原体传播动力学的保险丝机械描述与统计描述 监视过程，允许最大程度地利用异质数据流 告知生物学推断。该框架的第二个组成部分将涉及验证 通过对未来疾病动态的预测来推断。这两个组成部分 框架将涉及多种模型的使用，这些模型代表竞争的假设 病原体相互作用以及其他形式的模型不确定性。这个框架将是 在两种环境中应用：巴西蚊子传播的病毒和印第安纳州的呼吸道病原体。在 这两种设置，最近出现的以及流行病原体的共同循环构成了新的 监视和控制活动的挑战，开发新建模工具 特别及时解决这些挑战。