文章基本信息

标题：An analecta of visualizations for foodborne illness trends and seasonality
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作者：Ryan B. Simpson ; Bingjie Zhou ; Tania M. Alarcon Falconi 等
期刊名称：Scientific Data
电子版ISSN：2052-4463
出版年度：2020
卷号：7
期号：1
页码：1-15
DOI：10.1038/s41597-020-00677-x
语种：English
出版社：Nature Publishing Group
摘要：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鈥檚 (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.