Detection of SARS-CoV-2 with RAPID: A prospective cohort study
Marcelo D.T. Torres,
Lucas F. de Lima,
André L. Ferreira,
William R. de Araujo,
Paul Callahan,
Antonio Dávila, Jr.,
Benjamin S. Abella,
Cesar de la Fuente-Nunez
Affiliations
Marcelo D.T. Torres
Machine Biology Group, Departments of Psychiatry and Microbiology, Institute for Biomedical Informatics, Institute for Translational Medicine and Therapeutics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA; Departments of Bioengineering and Chemical and Biomolecular Engineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, PA, USA; Penn Institute for Computational Science, University of Pennsylvania, Philadelphia, PA, USA
Lucas F. de Lima
Machine Biology Group, Departments of Psychiatry and Microbiology, Institute for Biomedical Informatics, Institute for Translational Medicine and Therapeutics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA; Departments of Bioengineering and Chemical and Biomolecular Engineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, PA, USA; Penn Institute for Computational Science, University of Pennsylvania, Philadelphia, PA, USA; Portable Chemical Sensors Lab, Department of Analytical Chemistry, Institute of Chemistry, State University of Campinas - UNICAMP, Campinas, Sao Paulo, Brazil
André L. Ferreira
Machine Biology Group, Departments of Psychiatry and Microbiology, Institute for Biomedical Informatics, Institute for Translational Medicine and Therapeutics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA; Departments of Bioengineering and Chemical and Biomolecular Engineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, PA, USA; Penn Institute for Computational Science, University of Pennsylvania, Philadelphia, PA, USA; Portable Chemical Sensors Lab, Department of Analytical Chemistry, Institute of Chemistry, State University of Campinas - UNICAMP, Campinas, Sao Paulo, Brazil
William R. de Araujo
Portable Chemical Sensors Lab, Department of Analytical Chemistry, Institute of Chemistry, State University of Campinas - UNICAMP, Campinas, Sao Paulo, Brazil
Paul Callahan
Penn Acute Research Collaboration, Department of Emergency Medicine, University of Pennsylvania, Philadelphia, PA, USA
Antonio Dávila, Jr.
Penn Acute Research Collaboration, Department of Emergency Medicine, University of Pennsylvania, Philadelphia, PA, USA
Benjamin S. Abella
Penn Acute Research Collaboration, Department of Emergency Medicine, University of Pennsylvania, Philadelphia, PA, USA
Cesar de la Fuente-Nunez
Machine Biology Group, Departments of Psychiatry and Microbiology, Institute for Biomedical Informatics, Institute for Translational Medicine and Therapeutics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA; Departments of Bioengineering and Chemical and Biomolecular Engineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, PA, USA; Penn Institute for Computational Science, University of Pennsylvania, Philadelphia, PA, USA; Corresponding author
Summary: COVID-19 has killed over 6 million people worldwide. Currently available methods to detect SARS-CoV-2 are limited by their cost and need for multistep sample preparation and trained personnel. Therefore, there is an urgent need to develop fast, inexpensive, and scalable point-of-care diagnostics that can be used for mass testing. Between January and March 2021, we obtained 321 anterior nare swab samples from individuals in Philadelphia (PA, USA). For the Real-time Accurate Portable Impedimetric Detection prototype 1.0 (RAPID) test, anterior nare samples were tested via an electrochemical impedance spectroscopy (EIS) approach. The overall sensitivity, specificity, and accuracy of RAPID in this cohort study were 80.6%, 89.0%, and 88.2%, respectively. We present a rapid, accurate, inexpensive (<$5.00 per unit), and scalable test for diagnosing COVID-19 at the point-of-care. We anticipate that further iterations of this approach will enable widespread deployment, large-scale testing, and population-level surveillance.
