Coronavirus: a comparative analysis of detection technologies in the wake of emerging variants

Abstract

An outbreak of the coronavirus disease caused by a novel pathogen created havoc and continues to affect the entire world. As the pandemic progressed, the scientific community was faced by the limitations of existing diagnostic methods. In this review, we have compared the existing diagnostic techniques such as reverse transcription polymerase chain reaction (RT-PCR), antigen and antibody detection, computed tomography scan, etc. and techniques in the research phase like microarray, artificial intelligence, and detection using novel materials; on the prospect of sample preparation, detection procedure (qualitative/quantitative), detection time, screening efficiency, cost-effectiveness, and ability to detect different variants. A detailed comparison of different techniques showed that RT-PCR is still the most widely used and accepted coronavirus detection method despite certain limitations (single gene targeting- in context to mutations). New methods with similar efficiency that could overcome the limitations of RT-PCR may increase the speed, simplicity, and affordability of diagnosis. In addition to existing devices, we have also discussed diagnostic devices in the research phase showing high potential for clinical use. Our approach would be of enormous benefit in selecting a diagnostic device under a given scenario, which would ultimately help in controlling the current pandemic caused by the coronavirus, which is still far from over with new variants emerging.

Keywords: COVID-19; Diagnostic devices; Infectious disease; Point-of-care; SARS-CoV-2; Variants.

Fig. 1

An overview of the procedure of RT-PCR with sequences of some probes [7]

Fig. 2

a CT-scan of lungs for a healthy patient [32]. b CT-scan image of COVID infected patient with arrows showing ground-glass opacities [32]

Fig. 3

a Block diagram for identifying infection from CT scans. b Illustration of the framework used for modeling. c Corona score calculated from the model for patients at different levels of infections [89]

Fig. 4

Variants and its effects on diagnosis [100, 101]

Fig. 5

A comparative analysis of different methods used for the diagnosis of coronavirus