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Designing to Fight Pandemics: A Review of Literature and Identifying Design Patterns for COVID-19 Tracing Apps

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Human-Computer Interaction. User Experience and Behavior (HCII 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13304))

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Abstract

Throughout the pandemic, digital contact tracing using smartphone applications (or apps) has been endorsed by many authorities across the globe as a tool to limit the spread of COVID-19. Consequently, to deploy contact tracing in large populations, multiple contact tracing apps have been developed and deployed globally. However, due to the relative recency of the COVID-19 pandemic as well as the suddenness of the need for contact tracing at this scale, app designers are often left with no systematic guidelines. Designers today lack guidelines on what factors might affect perceptions and adoption of their apps. They also lack a knowledgebase of features that could be appropriate to include in their app for a given context. To address this gap, we conducted a review of the academic literature on attitudes towards and adoption of COVID-19 response apps, as well as a feature review of a diverse set of international tracing apps. Our investigation yielded a set of design patterns which can be used readily by designers of contact tracing apps. Our work lays the foundation to identify opportunities for new contextual feature design and use.

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Authors and Affiliations

  1. Brigham Young University, Provo, UT, 84602, USA

    Isaac Criddle, Amanda Hardy, Garrett Smith, Thomas Ranck & Xinru Page

  2. Indian Institute of Technology, Kharagpur, India

    Mainack Mondal

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  1. Isaac Criddle
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  2. Amanda Hardy
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Correspondence to Xinru Page .

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  1. The Open University of Japan, Chiba, Japan

    Masaaki Kurosu

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Criddle, I., Hardy, A., Smith, G., Ranck, T., Mondal, M., Page, X. (2022). Designing to Fight Pandemics: A Review of Literature and Identifying Design Patterns for COVID-19 Tracing Apps. In: Kurosu, M. (eds) Human-Computer Interaction. User Experience and Behavior. HCII 2022. Lecture Notes in Computer Science, vol 13304. Springer, Cham. https://doi.org/10.1007/978-3-031-05412-9_3

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  • DOI: https://doi.org/10.1007/978-3-031-05412-9_3

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