CK22-008 Characterizing dynamics of pandemic and preparing for speedy and accurate response

CK22-008 描述流行病动态并为快速准确的应对做好准备

• 批准号: 10696114
• 负责人: Jing Huang
• 金额: $ 5.82万
• 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/10696114
• 关键词: CK22; 008; Characterizing; dynamics; pandemic

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.

项目摘要/摘要 疾病传播的特性可以在整个大流行中发展，并且可能受卫生政策的影响 决策，区域人口特征，社区行为，环境特征和 人口免疫。这项建议是我们遇到的重大挑战的动机，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 时间，以及由于不同的测试标准和报告不一致而不断发展的数据元素和数据质量 行为。该提案的总体目标是建立一个大流行预测智能框架 随着时间的流逝，可以使数据质量以及不断发展的行为和环境特征不断发展 影响疾病传播。提出的建模方法的主要优点是它适应时间 改变社区行为和流动性，环境状况，缓解策略， 种群免疫和病毒进化。通过三个项目，我们将开发模型至1） 通过增强模型鲁棒性（数据误差和模型假设的鲁棒性），预测准确性，2） 连接病毒演化和传播之间的点，3）通过 整合五个主要组成部分，包括病毒进化，可传播性，社会行为，人口免疫力 和公共卫生政策，以建立一种用于传染病预测建模的学习系统。确保 拟议的研究的更广泛影响，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 宾夕法尼亚大学美国卫生与公共服务系的县/城市级数据， 和其他公开可用的数据资源。拟议的工作将有助于所需的基础工作 高级大流行科学，其中包括大流行和证据辅助健康的预测建模 进行大流行预防和反应的决策。