Grant: Alliance Grants

The rapid spread of COVID-19 has resulted in taking drastic measures in isolating individuals suspected of infection to ensure community safety. Currently, suspected patients are advised to self-quarantine at home, which can be impossible to track and confirm compliance. Although some countries have ensured enforcement through smartphone apps, such measures can be circumvented by simply leaving the smartphones at home. Researchers from Ryerson University and the University of Toronto, in partnership with Dapasoft Inc., will develop a hybrid system (a lightweight non-detachable wristband with accompanying mobile app) for voluntary or authorized assigned tracking of persons who are advised or subject to a self-quarantine period. The geolocation feature of smartphones, combined with consistent connection with the wristband, will create a monitoring system that is difficult to break. The system will also have options to collect biomedical data such as heart rate, temperature, and self-reported data such as respiratory symptoms, time and duration of contact with others. The collected data (with appropriate ethics approval from participating institutions) will be analyzed using novel artificial intelligence algorithms to measure primary and secondary outcomes. The primary outcome of this project is measuring compliance, which will be calculated through identifying the movement patterns of individuals. Secondary outcomes include risk assessment of outbreaks, and visualization maps of clusters of outbreaks. We will employ novel information fusion mechanisms, combined with unsupervised clustering approaches that we have recently proposed to generate intuitive visualization of clustering maps, that will be highly beneficial for non-technical domain experts (e.g. healthcare practitioners) to identify trends within the data. We plan to package the algorithms in an easily deployable analytics and visualization tool that can potentially be utilized in case of a similar future outbreak.