Information practices in data analytics for supporting public health surveillance
通过甲型流感和COVID-19疫情监测的案例研究，识别并详细描述了数据分析中的探索性信息实践（如探查、综合、交流）和利用性信息实践（如搜寻、调整、拓展），丰富了数据分析支持公共卫生监测的实证理解。
Abstract Public health surveillance based on data analytics plays a crucial role in detecting and responding to public health crises, such as infectious disease outbreaks. Previous information science research on the topic has focused on developing analytical algorithms and visualization tools. This study seeks to extend the research by investigating information practices in data analytics for public health surveillance. Through a case study of how data analytics was conducted for surveilling Influenza A and COVID‐19 outbreaks, both exploration information practices (i.e., probing, synthesizing, exchanging) and exploitation information practices (i.e., scavenging, adapting, outreaching) were identified and detailed. These findings enrich our empirical understanding of how data analytics can be implemented to support public health surveillance.