Abstract:
Over the last few years, a large number of smartphone apps have been developed to
"flatten the curve" of the rising number of COVID-19 infections. Knowledge of potential
symptoms and their distribution enables the early identification of infected individuals. We
developed a mobile app-based crowdsourcing methodology to assess the COVID-19infection risk
through shopping habits at indoor retail stores. The app's goal is to instil trust in customers to visit
stores, which will assist small and medium businesses to survive their operations in the near term.
According to the literature, there are several implementations for COVID-19 infection risk
estimations for such scenarios. A mobile app prototype was developed, and the risk was calculated
using the COVID-19 Aerosol Transmission Estimator model established by the University of
Colorado Boulder. The developed prototype mobile app was tested with end users to gather their
feedback through a questionnaire. In comparison to the complex implementation associated with
AI-based alternatives, this solution could be delivered at a lower cost with adequate accuracy of
COVID-19 infection risk assessments.
