How to Make COVID-19 Contact Tracing Apps work: Insights From Behavioral Economics

  1. Ian Ayres
  2. Alessandro Romano
  3. Chiara Sotis

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Abstract

Due to network effects, Contact Tracing Apps (CTAs) are only effective if many people download them. However, the response to CTAs has been tepid. For example, in France less than 2 million people (roughly 3% of the population) downloaded the CTA. Against this background, we carry out an online experiment to show that CTAs can still play a key role in containing the spread of COVID-19, provided that they are re-conceptualized to account for insights from behavioral science. We start by showing that carefully devised in-app notifications are effective in inducing prudent behavior like wearing a mask or staying home. In particular, people that are notified that they are taking too much risk and could become a superspreader engage in more prudent behavior. Building on this result, we suggest that CTAs should be re-framed as Behavioral Feedback Apps (BFAs). The main function of BFAs would be providing users with information on how to minimize the risk of contracting COVID-19, like how crowded a store is likely to be. Moreover, the BFA could have a rating system that allows users to flag stores that do not respect safety norms like wearing masks. These functions can inform the behavior of app users, thus playing a key role in containing the spread of the virus even if a small percentage of people download the BFA. While effective contact tracing is impossible when only 3% of the population downloads the app, less risk taking by small portions of the population can produce large benefits. BFAs can be programmed so that users can also activate a tracing function akin to the one currently carried out by CTAs. Making contact tracing an ancillary, opt-in function might facilitate a wider acceptance of BFAs.

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  1. ScreenIT

    SciScore for 10.1101/2020.09.09.20191320: (What is this?)

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

    Table 1: Rigor

    Institutional Review Board Statementnot detected.
    RandomizationIn Phase I respondents were randomly assigned to one of three different groups: Pros, Pros and Cons and Control I.
    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:
    One potential explanation for this finding is that people find more credible a communication that also touches on the weaknesses of the app. To put it differently, authorities might be more credible when they guarantee that CTAs protect privacy if they have been transparent about problems like false positive and false negatives that are likely to constitute an issue [19]. However, the most important finding of our study is that providing users with useful information, such as in-app notifications on their current level of risk-taking, can significantly alter people’s behaviors. Respondents that were included in the Superspreader group were significantly less likely to attend large and small gatherings and to see people at risk, while they were significantly more likely to stay home. This suggests that CTAs can play an important role in promoting pro-social behavior during the pandemic. While also this function is best carried out when many people download the app, it does not require a minimum number of downloads. For instance, consider the case of France. As roughly 3% of the population (about 2 million people) downloaded the CTA, the number of contacts that it can identify is likely to be minimal because the likelihood that two people (infected and potentially infected) that meet both have the app is extremely small. In fact, in the first three weeks of its existence the app only notified 18 people that they had been exposed to COVID-19 [12]. This number is too small to hav...

    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

    SciScore is an automated tool that is designed to assist expert reviewers by finding and presenting formulaic information scattered throughout a paper in a standard, easy to digest format. SciScore checks for the presence and correctness of RRIDs (research resource identifiers), and for rigor criteria such as sex and investigator blinding. For details on the theoretical underpinning of rigor criteria and the tools shown here, including references cited, please follow this link.

  2. Version published to 10.1101/2020.09.09.20191320v1 on medRxiv