Center for Forecasting and Outbreak Analytics, US Centers for Disease Control and Prevention, Atlanta, GA, United States.
Center for Communicable Disease Dynamics, Department of Epidemiology, Harvard T. H. Chan School of Public Health, Boston, MA, United States.
Front Public Health. 2024 Jul 15;12:1408193. doi: 10.3389/fpubh.2024.1408193. eCollection 2024.
The COVID-19 pandemic has highlighted the need to upgrade systems for infectious disease surveillance and forecasting and modeling of the spread of infection, both of which inform evidence-based public health guidance and policies. Here, we discuss requirements for an effective surveillance system to support decision making during a pandemic, drawing on the lessons of COVID-19 in the U.S., while looking to jurisdictions in the U.S. and beyond to learn lessons about the value of specific data types. In this report, we define the range of decisions for which surveillance data are required, the data elements needed to inform these decisions and to calibrate inputs and outputs of transmission-dynamic models, and the types of data needed to inform decisions by state, territorial, local, and tribal health authorities. We define actions needed to ensure that such data will be available and consider the contribution of such efforts to improving health equity.
新冠疫情凸显了升级传染病监测和预测系统以及感染传播建模的必要性，这两者为基于证据的公共卫生指导和政策提供了信息。在这里，我们借鉴了美国新冠疫情的经验教训，讨论了有效监测系统在大流行期间支持决策的要求，同时还研究了美国和其他司法管辖区的经验，以了解特定数据类型的价值。在本报告中，我们定义了需要监测数据的决策范围、为做出这些决策以及调整传染病传播动力学模型的输入和输出所需的数据要素，以及为州、领地、地方和部落卫生当局做出决策所需的数据类型。我们定义了确保此类数据可用所需的行动，并考虑了这些努力对改善健康公平的贡献。
