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.

翻译：COVID-19疫情凸显了升级传染病监测系统及感染传播预测与建模系统的必要性，两者共同为循证公共卫生指南与政策提供依据。本文基于美国COVID-19疫情的教训，借鉴美国境内及海外司法管辖区关于特定数据类型价值的经验，探讨了有效监测系统支持大流行期间决策制定的需求。本报告界定了需要监测数据支撑的决策范围、为决策提供信息及校准传播动力学模型输入输出所需的数据要素，以及州级、地方、部落卫生当局决策所需的数据类型。我们明确了确保此类数据可用性所需采取的行动，并考量了相关工作对促进健康公平的贡献。