Maqola

Information practices in data analytics for supporting public health surveillance

Dan ZhangDepartment of Information Resources Management, Business School Nankai University Tianjin ChinaLoo Geok PeeWee Kim Wee School of Communication and Information Nanyang Technological University SingaporeShan L. PanSchool of Information Systems and Technology Management, UNSW Australia Business School The University of New South Wales Sydney New South Wales AustraliaJingyuan WangPengcheng Laboratory Shenzhen China

2023en

ABI

Annotatsiya

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.

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Identifikatorlar

DOI: 10.1002/asi.24841

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