Situating the Covid Aware app: Challenges of designing with predictive models

Design has long been described as an interdisciplinary, integrative discipline (Friedman, 2003). Buchanan (1992) stressed that “without integrative disciplines of understanding and action, there is little of extending knowledge beyond the library or laboratory in order to serve the purpose of enriching human life." As such, design-led approaches can be key to mobilising efforts towards goals that require knowledge from several disciplines, as in the case of emergency responses to Covid-19. In this chapter, we describe the design process of a rapid-response project that combined knowledge from several disciplines to design and implement a mobile phone application to raise awareness of the risks of Covid-19 in Jamaica. The project’s international team included mathematicians, computer scientists, bioinformaticians, designers, human geographers, social scientists, social workers, and anthropologists who worked together to engage communities, define concepts and scenarios, produce data simulations, and create interfaces that could mobilise, inform and ultimately make an impact on reducing the spread of the virus. The design-led process provided concrete ways for the team to apply their academic expertise towards the production of a tangible outcome. We discuss the framing of the project, the challenges that emerged during the design and implementation of a pilot application, and the process by which members of the team discussed and compromised on recommendations in order to achieve a concrete outcome within the proposed time frame. In light of feedback received from participants who trialled a pilot application, we reflect on what could have been improved in our process, stressing key considerations for related projects. Overall, the process demonstrates the potential of hands-on design approaches to quickly mobilise multidisciplinary collaboration while also warning about the risks of combining disparate approaches and applying high-end technologies under strict constraints.