Characterizing dynamics of pandemic and preparing for speedy and accurate response

项目摘要/摘要 疾病传播的特性可以在整个大流行期间演变，并可能受到卫生政策的影响 决策、区域人口特征、社区行为、环境特征以及 群体免疫力。这项提议的动机是我们遇到的重大挑战，包括 病毒演变、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不一致的报告而不断变化的数据元素和数据质量 行为。这项提议的首要目标是开发一个大流行预测情报框架。 可以随着时间的推移适应不断变化的数据质量以及不断演变的行为和环境特征 影响疾病传播。所提出的建模方法的主要优点是它对时间的适应性 不同的社区行为和流动性暴露、环境条件、缓解策略、 群体免疫和病毒进化。通过三个项目，我们将开发模型来1)改善 通过增强模型稳健性(对数据误差和模型假设的稳健性)来提高预测精度，2) 将病毒进化和传播性联系起来，3)通过以下方式推进最先进的预测 整合五个主要组成部分，包括病毒进化、传播性、社会行为、群体免疫 和公共卫生政策，以建立传染病预测建模的学习系统。为了确保 更广泛的影响，我们将开发、验证和评估以下方法和软件 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、 和其他公开可用的数据资源。拟议工作将有助于开展以下工作所需的基础工作 先进的大流行科学，其中包括大流行的预测建模和证据辅助健康 制定预防和应对大流行的政策。

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.