Zhang, D.; Pee, L.G.; Pan, S.L.; Wang, J.: Information practices in data analytics for supporting public health surveillance (2024)
0.01
0.0075969696 = product of:
0.030387878 = sum of:
0.030387878 = weight(_text_:science in 1197) [ClassicSimilarity], result of:
0.030387878 = score(doc=1197,freq=4.0), product of:
0.12305341 = queryWeight, product of:
2.6341193 = idf(docFreq=8627, maxDocs=44218)
0.0467152 = queryNorm
0.24694869 = fieldWeight in 1197, product of:
2.0 = tf(freq=4.0), with freq of:
4.0 = termFreq=4.0
2.6341193 = idf(docFreq=8627, maxDocs=44218)
0.046875 = fieldNorm(doc=1197)
0.25 = coord(1/4)
- 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.
- Source
- Journal of the Association for Information Science and Technology. 75(2023) no.1, S.79-93
