The epidemiological impact of the Canadian COVID Alert app

  1. Shuo Sun
  2. Mairead Shaw
  3. Erica E.M. Moodie
  4. Derek Ruths

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Abstract

Objectives

We analyzed the effectiveness of the Canadian COVID Alert app on reducing COVID-19 infections and deaths due to the COVID-19 virus.

Methods

Two separate but complementary approaches were taken. First, we undertook a comparative study to assess how the adoption and usage of the COVID Alert app compared to those of similar apps deployed in other regions. Next, we used data from the COVID Alert server and a range of plausible parameter values to estimate the numbers of infections and deaths averted in Canada using a model that combines information on number of notifications, secondary attack rate, expected fraction of transmissions that could be prevented, quarantine effectiveness, and expected size of the full transmission chain in the absence of exposure notification.

Results

The comparative analysis revealed that the COVID Alert app had among the lowest adoption levels among apps that reported usage. Our model indicates that use of the COVID Alert app averted between 6284 and 10,894 infections across the six Canadian provinces where app usage was highest during the March–July 2021 period. This range is equivalent to 1.6–2.9% of the total recorded infections across Canada in that time. Using province-specific case fatality rates, 57–101 deaths were averted during the same period. The number of cases and deaths averted was greatest in Ontario, whereas the proportion of cases and deaths averted was greatest in Newfoundland and Labrador. App impact measures were reported so rarely and so inconsistently by other regions that the relative assessment of impact is inconclusive.

Conclusion

While the nationwide rates are low, provinces with widespread adoption of the app showed high ratios of averted cases and deaths (upper bound was greater than 60% of averted cases). This finding suggests that the COVID Alert app, when adopted at sufficient levels, can be an effective public health tool for combatting a pandemic such as COVID-19.

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  1. Version published to 10.17269/s41997-022-00632-w
  2. ScreenIT

    SciScore for 10.1101/2022.01.04.21268588: (What is this?)

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

    Table 1: Rigor

    NIH rigor criteria are not applicable to paper type.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    We conducted a search for reports related to app uptake and efficacy first using Google Scholar, then regular Google search results and news sources.
    Google Scholar
    suggested: (Google Scholar, RRID:SCR_008878)
    Google
    suggested: (Google, RRID:SCR_017097)

    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:
    An important limitation of our comparative analysis is that there were insufficient data available to provide reliable comparisons of the relative impact of exposure notification apps across countries. Because app deployment analyses and reporting periods differ across time (see Table 1), any direct comparisons across countries are not well defined, particularly given the fluctuating nature of COVID cases or “waves.” Reports span varying time periods, and it is not possible to assess if a given report provides accurate or representative overall usage and efficacy of an app or whether instead it captures a period of particular efficacy or lack thereof. Additionally, different apps had different purposes and resources dedicated to their development. For example, COVID Alert was developed and used as an exposure notification app, while the NHS COVID-19 App had additional functionality (e.g., checking local alert level, checking symptoms, booking COVID-19 tests) in addition to exposure notification. The paucity of data currently available from other countries makes meaningful comparisons of efficacy impossible. Further, given different app purposes and associated budgets, combined with the scarcity of published or public data, comparing costs of development and deployment was not possible. The main limitation of our modelling analysis is the inability to estimate key parameters of the model due to the high level of data aggregation employed to preserve privacy. Rather than being ...

    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.

    Results from scite Reference Check: We found no unreliable references.

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  3. Version published to 10.1101/2022.01.04.21268588v1 on medRxiv