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

项目总结 公共卫生不断面临来自多种病原体的威胁，但病原体 与不同疾病相关的疾病通常根据监测被划分， 管理和研究。这种划分的方法忽略了许多方面，即 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的(例如，交叉反应免疫)、行为的 (例如，促使遵守良好的卫生习惯)或临床(例如，误诊)。现代、数据- 数学建模的驱动型方法有可能解决联合建模的动态 通过解释这些相互作用来传播病原体。在这样做的同时，建模还具有 通过借用信息来改进病原体特定疾病预测的可能性 不同疾病的监测数据。到目前为止，这种潜力在很大程度上仍未得到开发。在这 项目中，我将开发一个通用的框架来模拟联合循环的动态 病原体。该框架的第一个组件将使用贝叶斯分层建模 病原体传播动力学的机制描述与统计描述的融合 监控流程，允许最大限度地利用异类数据流 告知生物学推论。该框架的第二个组件将涉及验证 通过对未来疾病动态的预测进行模型推论。这两个组件都是 框架将涉及多个模型的使用，这些模型代表关于以下方面的相互竞争的假设 病原体相互作用，以及其他形式的模型不确定性。这一框架将是 应用于两个环境：巴西的蚊媒病毒和印第安纳州的呼吸道病原体。在……里面 在这两个背景下，新近出现的病原体和地方性病原体的共同循环构成了新的 监视和控制活动的挑战，使新的建模工具的开发 应对这些挑战，尤其是及时。

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.