Dórea Fernanda C, Vial Flavie, Revie Crawford W
Department of Disease Control and Epidemiology, National Veterinary Institute, Uppsala, Sweden.
Animal and Plant Health Agency, Sand Hutton, United Kingdom.
Front Vet Sci. 2023 Jan 27;10:1114800. doi: 10.3389/fvets.2023.1114800. eCollection 2023.
Syndromic surveillance has been an important driver for the incorporation of "big data analytics" into animal disease surveillance systems over the past decade. As the range of data sources to which automated data digitalization can be applied continues to grow, we discuss how to move beyond questions around the means to handle volume, variety and velocity, so as to ensure that the information generated is fit for disease surveillance purposes. We make the case that the value of data-driven surveillance depends on a "needs-driven" design approach to data digitalization and information delivery and highlight some of the current challenges and research frontiers in syndromic surveillance.
在过去十年中，症状监测一直是将“大数据分析”纳入动物疾病监测系统的重要推动因素。随着可应用自动数据数字化的数据源范围不断扩大，我们讨论了如何超越围绕处理数据量、多样性和速度的方法的问题，以确保生成的信息适用于疾病监测目的。我们认为，数据驱动监测的价值取决于数据数字化和信息传递的“需求驱动”设计方法，并强调了症状监测当前的一些挑战和研究前沿。
