Integrated System for On-Site Rapid and Safe Screening of COVID-19

Since
the outbreak of coronavirus disease 2019 (COVID-19), the
epidemic has been spreading around the world for more than 2 years.
Rapid, safe, and on-site detection methods of COVID-19 are in urgent
demand for the control of the epidemic. Here, we established an integrated
system, which incorporates a machine-learning-based Fourier transform
infrared spectroscopy technique for rapid COVID-19 screening and air-plasma-based
disinfection modules to prevent potential secondary infections. A
partial least-squares discrimination analysis and a convolutional
neural network model were built using the collected infrared spectral
dataset containing 857 training serum samples. Furthermore, the sensitivity,
specificity, and prediction accuracy could all reach over 94% from
the results of the field test regarding 968 blind testing samples.
Additionally, the disinfection modules achieved an inactivation efficiency
of 99.9% for surface and airborne tested bacteria. The proposed system
is conducive and promising for point-of-care and on-site COVID-19
screening in the mass population.