An Analysis Review of Detection Coronavirus Disease 2019 (COVID-19) Based on Biosensor Application

• Main Authors: Bakr Ahmed Taha, Yousif Al Mashhadany, Mohd Hadri Hafiz Mokhtar, Mohd Saiful Dzulkefly Bin Zan, Norhana Arsad
• Format: Article
• Language: English
• Published: MDPI AG 2020-11-01
• Series: Sensors
• Subjects: COVID-19 detection; biosensor application; COVID-19 transmission styles; sensors interaction; artificial intelligence
• Online Access: https://www.mdpi.com/1424-8220/20/23/6764

Timely detection and diagnosis are essentially needed to guide outbreak measures and infection control. It is vital to improve healthcare quality in public places, markets, schools and airports and provide useful insights into the technological environment and help researchers acknowledge the choices and gaps available in this field. In this narrative review, the detection of coronavirus disease 2019 (COVID-19) technologies is summarized and discussed with a comparison between them from several aspects to arrive at an accurate decision on the feasibility of applying the best of these techniques in the biosensors that operate using laser detection technology. The collection of data in this analysis was done by using six reliable academic databases, namely, Science Direct, IEEE Xplore, Scopus, Web of Science, Google Scholar and PubMed. This review includes an analysis review of three highlights: evaluating the hazard of pandemic COVID-19 transmission styles and comparing them with Severe Acute Respiratory Syndrome (SARS) and Middle East Respiratory Syndrome (MERS) to identify the main causes of the virus spreading, a critical analysis to diagnose coronavirus disease 2019 (COVID-19) based on artificial intelligence using CT scans and CXR images and types of biosensors. Finally, we select the best methods that can potentially stop the propagation of the coronavirus pandemic.