Use of Artificial Intelligence on spatio-temporal data to generate insights during COVID-19 pandemic: A Review

  1. Gihan Jayatilaka
  2. Jameel Hassan
  3. Umar Marikkar
  4. Rumali Perera
  5. Suren Sritharan
  6. Harshana Weligampola
  7. Mevan Ekanayake
  8. Roshan Godaliyadda
  9. Parakrama Ekanayake
  10. Vijitha Herath
  11. G M Dilshan Godaliyadda
  12. Anuruddhika Rathnayake
  13. Samath D. Dharmaratne
  14. Janaka Ekanayake

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Abstract

The COVID-19 pandemic, within a short time span, has had a significant impact on every aspect of life in almost every country on the planet. As it evolved from a local epidemic isolated to certain regions of China, to the deadliest pandemic since the influenza outbreak of 1918, scientists all over the world have only amplified their efforts to combat it. In that battle, Artificial Intelligence, or AI, with its wide ranging capabilities and versatility, has played a vital role and thus has had a sizable impact. In this review, we present a comprehensive analysis of the use of AI techniques for spatio-temporal modeling and forecasting and impact modeling on diverse populations as it relates to COVID-19. Furthermore, we catalogue the articles in these areas based on spatio-temporal modeling, intrinsic parameters, extrinsic parameters, dynamic parameters and multivariate inputs (to ascertain the penetration of AI usage in each sub area). The manner in which AI is used and the associated techniques utilized vary for each body of work. Majority of articles use deep learning models, compartment models, stochastic methods and numerous statistical methods. We conclude by listing potential paths of research for which AI based techniques can be used for greater impact in tackling the pandemic.

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  1. Version published to 10.1101/2020.11.22.20232959v5 on medRxiv
  2. Version published to 10.1101/2020.11.22.20232959v4 on medRxiv
  3. Version published to 10.1101/2020.11.22.20232959v3 on medRxiv
  4. Version published to 10.1101/2020.11.22.20232959v2 on medRxiv
  5. ScreenIT

    SciScore for 10.1101/2020.11.22.20232959: (What is this?)

    Please note, not all rigor criteria are appropriate for all manuscripts.

    Table 1: Rigor

    Institutional Review Board Statementnot detected.
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.
    Sex as a biological variablenot detected.

    Table 2: Resources

    No key resources detected.

    Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).

    Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:
    However, one major limitation is their inability to ingest a large volume of data for decision making and adapt accordingly.66 Due to the improvement in processing power of mobile devices, AI algorithms can be adapted to solving problems such as proximity identification and improve the accuracy and reliability of these systems through adaptation and real-time learning. Furthermore, classical techniques often fail to model the spatio-temporal relationships of the input data. Studies have been conducted to find the correlation between the traveling patterns of individuals and how it correlates to the spread of disease.65 However, individual-level tracking only focuses on geographical location and overlooks other factors such as social clusters, race and age. It would be interesting to consider these social clustering parameters for this study, as they may have significant correlation with the spread of pandemic. Graph Neural Networks (GNN) can be used to model and identify specific events in the spatial and/or temporal domain. The spatial information can be modelled through graphs and the information can be propagated within the nodes through message passing. In addition to GNNs, using state-of-the-art NLP techniques like Transformers,67 spatio-temporal information that pays specific attention to events (e.g. sudden spikes in patients) in the temporal and/or spatial domain can be efficiently modeled. Despite the large number of recent vision-based solutions for COVID-19,68 the ...

    Results from TrialIdentifier: No clinical trial numbers were referenced.

    Results from Barzooka: We did not find any issues relating to the usage of bar graphs.

    Results from JetFighter: We did not find any issues relating to colormaps.

    Results from rtransparent:
    • Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
    • Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
    • No protocol registration statement was detected.

    About SciScore

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  6. Version published to 10.1101/2020.11.22.20232959v1 on medRxiv