ArticleFrontiers in public health2022
Internet search data with spatiotemporal analysis in infectious disease surveillance: Challenges and perspectives.
Article in Frontiers in public health, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Review
- Artificial intelligence in infection surveillance: Data integration, applications and future directions.Biomedical journal · 2026Review
- Infodemiology and Infoveillance of the Four Most Widespread Arbovirus Diseases in Italy.Epidemiologia (Basel, Switzerland) · 2024Article
- Sentiment analysis of epidemiological surveillance reports on COVID-19 in Greece using machine learning models.Frontiers in public health · 2023Review
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
With the rapid development of the internet, the application of internet search data has been seen as a novel data source to offer timely infectious disease surveillance intelligence. Moreover, the advancements in internet search data, which include rich information at both space and time scales, enable investigators to sufficiently consider the spatiotemporal uncertainty, which can benefit researchers to better monitor infectious diseases and epidemics. In the present study, we present the necessary groundwork and critical appraisal of the use of internet search data and spatiotemporal analysis approaches in infectious disease surveillance by updating the current stage of knowledge on them. The study also provides future directions for researchers to investigate the combination of internet search data with the spatiotemporal analysis in infectious disease surveillance. Internet search data demonstrate a promising potential to offer timely epidemic intelligence, which can be seen as the prerequisite for improving infectious disease surveillance.
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