JMIR Publications

ABSTRACT

Background:

To reduce transmission of SARS-CoV-2 and the associated spread of coronavirus disease 2019 (COVID-19), many jurisdictions around the world imposed mandatory or recommended social or physical distancing (hereafter “distancing.”) Explanations of the science underlying these mandates or recommendations were either highly technical or highly simplified.

Objective:

This study aimed to present the epidemiological justifications behind distancing recommendations in understandable ways to the general public. Our overall goal was to help people understand the dynamics of COVID-19 spread in their community, along with the implications of their own behavior for themselves, their circle, the health system, and society.

Methods:

We used Scrum, an agile framework, JavaScript (Vue.js framework), and code already developed for risk communication in another context of infectious disease transmission, we rapidly developed a new personalized web application. In our application, people make avatars that represent themselves and the people around them. These avatars are integrated into a 3-minute animation illustrating an epidemiological model for COVID-19 transmission, showing the differences in transmission with and without distancing. During the animation, the narration explains the science of how distancing reduces transmission of COVID-19 in plain language in English or French. The application offers full captions to complement the narration and a descriptive transcript for people using screen readers. We used Google Analytics to collect standard usage statistics. A brief, anonymous, optional survey also collected self-reported distancing behaviors and intentions in the previous and coming weeks, respectively. We launched and disseminated the application on Twitter and Facebook on April 8–9, 2020.

Results:

After 26 days, the application received >3600 unique hits from 82 countries. The optional survey at the end of the application collected 182 responses. Among this small subsample of users, survey respondents were nearly all (96.0%) already practicing distancing and (97.2%) indicated that they intended to practice distancing in the coming week. Among the small minority of people (n=7) who indicated that they had not been previously practicing distancing, 2 (29%) reported that they would practice distancing in the week to come.

Conclusions:

The developed web application used personalized risk communication to help people understand the relationship between individual-level behavior and population-level effects in the context of an infectious disease. The non-randomized design of this rapid study prevents us from concluding the application’s effectiveness; however, results thus far suggest that avatar-based visualizations may help people understand their role in infectious disease transmission.