Tufts University Friedman School of Nutrition Science and Policy, Boston, USA.
Sci Data. 2020 Oct 13;7(1):346. doi: 10.1038/s41597-020-00677-x.
Disease surveillance systems worldwide face increasing pressure to maintain and distribute data in usable formats supplemented with effective visualizations to enable actionable policy and programming responses. Annual reports and interactive portals provide access to surveillance data and visualizations depicting temporal trends and seasonal patterns of diseases. Analyses and visuals are typically limited to reporting the annual time series and the month with the highest number of cases per year. Yet, detecting potential disease outbreaks and supporting public health interventions requires detailed spatiotemporal comparisons to characterize spatiotemporal patterns of illness across diseases and locations. The Centers for Disease Control and Prevention's (CDC) FoodNet Fast provides population-based foodborne-disease surveillance records and visualizations for select counties across the US. We offer suggestions on how current FoodNet Fast data organization and visual analytics can be improved to facilitate data interpretation, decision-making, and communication of features related to trend and seasonality. The resulting compilation, or analecta, of 436 visualizations of records and codes are openly available online.
全球疾病监测系统面临越来越大的压力,需要以可用格式维护和分发数据,并辅以有效的可视化效果,以支持可行的政策和规划应对措施。年度报告和交互式门户提供了对监测数据和可视化效果的访问,这些数据和可视化效果描绘了疾病的时间趋势和季节性模式。分析和可视化效果通常仅限于报告年度时间序列以及每年病例数量最多的月份。然而,要发现潜在的疾病爆发并支持公共卫生干预措施,需要进行详细的时空比较,以描述疾病和地点的时空模式。疾病控制与预防中心 (CDC) 的 FoodNet Fast 提供了基于人群的食源性疾病监测记录和美国部分县的可视化效果。我们就如何改进当前的 FoodNet Fast 数据组织和可视化分析提出了建议,以促进数据解释、决策制定以及与趋势和季节性相关特征的沟通。记录和代码的 436 个可视化效果的汇编或选集在线上公开提供。