Characterizing dynamics of pandemic and preparing for speedy and accurate response

描述大流行的动态并为快速、准确的应对做好准备

• 批准号: 10617938
• 负责人: Jing Huang
• 金额: $ 29.05万
• 依托单位: CHILDREN'S HOSP OF PHILADELPHIA
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-09-30 至 2025-09-29
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10617938
• 关键词: Characterizing; dynamics; pandemic; preparing; speedy

Project Summary/Abstract Properties of disease transmission can evolve throughout the pandemic and may be influenced by health policy decisions, regional demographic characteristics, community behaviors, environmental characteristics, and population immunity. This proposal is motivated by significant challenges we have encountered, including dynamic connections between virus evolution, health policy, population behavior and degree of immunity over time, and evolving data elements and data quality due to varying testing criteria and inconsistent reporting behavior. The overarching goal of this proposal is to develop a framework of pandemic predictive intelligence that can adapt over time to changing data quality and evolving behavioral and environmental characteristics that influence disease transmission. The key advantage of the proposed modeling approach is its adaptation to time varying exposures of community behavior and mobility, environmental conditions, mitigation strategies, population immunity, and viral evolution. Through three projects, we will develop models to 1) improve the forecasting accuracy by enhancing model robustness (robustness to data error and model assumptions), 2) connect the dots between viral evolution and transmissibility, and 3) advance the state-of-the-art forecasting by integrating five major components, including viral evolution, transmissibility, social behavior, population immunity and public health policy, to build a learning system for predictive modeling for infectious disease. To ensure the broader impact of the proposed research, we will develop, validate, and evaluate methodology and software for pandemic forecasting, real-time monitoring, mitigation, and prevention of the spread of pathogens using national county/city-level data from the US Department of Health and Human Services, the University of Pennsylvania, and other publicly available data resources. The proposed work will contribute to foundational work needed to advance pandemic science, which includes predictive modelling of pandemic and evidence-assisted health policymaking for pandemic prevention and response.

项目总结/摘要 在整个大流行期间，疾病传播的特性会发生变化，并可能受到卫生政策的影响 决策，区域人口特征，社区行为，环境特征， 群体免疫力。提出这项建议的动机是我们遇到的重大挑战，包括 病毒进化、卫生政策、人口行为和免疫程度之间的动态联系， 由于不同的测试标准和不一致的报告， 行为该提案的总体目标是建立一个流行病预测情报框架 它可以随着时间的推移适应不断变化的数据质量和不断发展的行为和环境特征， 影响疾病传播。所提出的建模方法的主要优点是其适应时间 不同的社区行为和流动性，环境条件，缓解策略， 群体免疫和病毒进化。通过三个项目，我们将开发模型，以1）改善 通过增强模型鲁棒性（对数据误差和模型假设的鲁棒性）提高预测精度，2） 连接病毒进化和传播性之间的点，和3）推进最先进的预测， 整合了五个主要组成部分，包括病毒进化，传播性，社会行为，群体免疫 和公共卫生政策，建立一个传染病预测模型的学习系统。确保 更广泛的影响，我们将开发，验证和评估方法和软件， 大流行预测、实时监测、缓解和预防病原体的传播， 来自美国卫生与公众服务部、宾夕法尼亚大学、 以及其他公开可用的数据资源。拟议的工作将有助于基础工作， 推进流行病科学，包括流行病预测建模和循证卫生 制定预防和应对大流行病的政策。