Currently, only symptomatic cases of COVID-19, caused by the pathogen 2019-nCoV, are being identified and isolated. A large percentage of these cases lack the typical known symptoms of fever, fatigue, and dry cough. Furthermore, COVID-19 carriers can remain asymptomatic during the incubation period and thus facilitate community spread. In Canada, as of July 6, only 2,940,925 people (~7.82% of the population) have been tested for COVID-19, with more than 105,536 positive cases identified. Given that as many as 33%–41% of all COVID-19 cases lack the known symptoms, up to an estimated 34,826–43,269 cases could be asymptomatic and likely endangering public health. Early detection and isolation of COVID-19 cases, especially asymptomatic cases, remains an unmet challenge and is therefore crucial for controlling this outbreak and future hazards. In short, a novel method to identify asymptomatic cases is urgently needed. We aim to realize a rapid testing solution by developing a new sensing technology to identify asymptomatic and presymptomatic cases through early detection of a “hidden” symptom using the saliva sample. Our COVID-19-related research is supported by CMC and Mitacs Accelerate funding to develop a safe, low-complexity, rapid, and easy-to-use at-home sensing device (with mass-production potential) for the early detection of infection as a reliable symptom to isolate COVID-19 cases. The proposed technology—encompassing bioengineering, microelectronic open-JFET, computer vision, deep learning techniques—will allow accurate testing of saliva at home using a portable sensor communicated with cloud computational platform that can evaluate disease progress or treatment.

Industry Partner(s):CMC Microsystems

Academic Institution:York University

Academic Researcher: Gafar-Zadeh, Ebrahim

Focus Areas: COVID-19

Platforms: GPU