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Surveying Search Terms for COVID-19 Disease Surveillance

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Novel & Intelligent Digital Systems: Proceedings of the 2nd International Conference (NiDS 2022) (NiDS 2022)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 556))

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Abstract

Studies on disease surveillance for COVID-19 have utilized search interests such as from Google Trends. The selection of search terms can play a pivotal role and affect the validity of the results of such systems. The present study inventoried search terms from studies associated with outbreak detection or prevention with the intent of contributing to the process of deriving an optimal search strategy. The studies were retrieved from the Google Scholar database using the phrase coronavirus+ “relative search volume”. Seventy-nine (79) were found eligible for the period from 2020 to 2021. The collection of search terms obtained comprised of COVID-19 names, symptoms, public measures and protective measures. The network of search interests depicted disease-related terms and symptoms to be predominant. Further studies are directed to model search interests and incidence of the outbreak leading to the deployment of early warning systems geared for outbreak detection.

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Acknowledgements

This work was supported with the internally-funded research grant of the MSU-Iligan Institute of Technology.

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Authors and Affiliations

  1. MSU-Iligan Institute of Technology, 9200, Iligan City, Philippines

    Adrian Galido & Jerina Jean Ecleo

Authors
  1. Adrian Galido
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  2. Jerina Jean Ecleo
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Correspondence to Adrian Galido .

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Editors and Affiliations

  1. University of West Attica, Aigaleo, Greece

    Akrivi Krouska

  2. University of West Attica, Aigaleo, Greece

    Christos Troussas

  3. College of Engineering, University of the Philippines, Diliman, Philippines

    Jaime Caro

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Galido, A., Ecleo, J.J. (2023). Surveying Search Terms for COVID-19 Disease Surveillance. In: Krouska, A., Troussas, C., Caro, J. (eds) Novel & Intelligent Digital Systems: Proceedings of the 2nd International Conference (NiDS 2022). NiDS 2022. Lecture Notes in Networks and Systems, vol 556. Springer, Cham. https://doi.org/10.1007/978-3-031-17601-2_31

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