Safely opening schools: artificial intelligence techniques to control transmission of COVID-19

Azza Sarfraz, Zouina Sarfraz, Donald Hathaway III, Sarabjot Singh Makkar, Trissa Paul, Alanna Barrios, Muzna Sarfraz, Gaurav Patel, Nazma Hanif, Hafiza Hussain, Zainab Nadeem, Michael Talalaev, Marcos A. Sanchez-Gonzalez


Artificial intelligence techniques and similar digital technologies are promising applications for surveillance systems, contact tracing, and pandemic planning amid the COVID-19 pandemic. With no long-term effective treatment or vaccinations available, it is highly important to scale intelligence solutions to promote detection, school-level screening, monitoring, reducing burden of staff, and prediction potential COVID-19 outbreaks at schools. The objectives of this paper were to present the artificial intelligence for safely opening schools model, and build a solidifying analysis of current literature for applications of the system. The applications are imminent to promoting school health by maximizing the potential of AI technologies. While the AISOS model is not a silver bullet, the improvement in school transmission will be particularly useful as an emergent temporary, potentially permanent, measure of transmission control and monitoring.


School health, Artificial intelligence, Machine learning, Disease transmission, COVID-19

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