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.

项目总结