Detection of SARS-CoV-2 in Environment: Current Surveillance and Effective Data Management of COVID-19.
Sh NadzirahNoraziah Mohamad ZinArif KhalidNur Faizah Abu BakarSiti Syafiqah KamarudinSiti Shahara ZulfakarKen Wong KonNor Azila Muhammad AzamiTeck Yew LowRoharsyafinaz RoslanM Nizar Hadi M NassirAnis Amirah AlimP Susthitha MenonNorhayati SoinSubash C B GopinathHuda AbdullahJahariah SampeHafzaliza Erny Zainal AbidinSiti Nurfadhlina Mohd NoorAhmad Ghadafi IsmailChang Fu DeeAzrul Azlan HamzahPublished in: Critical reviews in analytical chemistry (2023)
Since diagnostic laboratories handle large COVID-19 samples, researchers have established laboratory-based assays and developed biosensor prototypes. Both share the same purpose; to ascertain the occurrence of air and surface contaminations by the SARS-CoV-2 virus. However, the biosensors further utilize internet-of-things (IoT) technology to monitor COVID-19 virus contamination, specifically in the diagnostic laboratory setting. The IoT-capable biosensors have great potential to monitor for possible virus contamination. Numerous studies have been done on COVID-19 virus air and surface contamination in the hospital setting. Through reviews, there are abundant reports on the viral transmission of SARS-CoV-2 through droplet infections, person-to-person close contact and fecal-oral transmission. However, studies on environmental conditions need to be better reported. Therefore, this review covers the detection of SARS-CoV-2 in airborne and wastewater samples using biosensors with comprehensive studies in methods and techniques of sampling and sensing (2020 until 2023). Furthermore, the review exposes sensing cases in public health settings. Then, the integration of data management together with biosensors is well explained. Last, the review ended with challenges to having a practical COVID-19 biosensor applied for environmental surveillance samples.
Keyphrases
- sars cov
- public health
- label free
- respiratory syndrome coronavirus
- risk assessment
- human health
- coronavirus disease
- drinking water
- health risk
- healthcare
- case control
- high throughput
- emergency department
- particulate matter
- quantum dots
- sensitive detection
- big data
- social media
- health information
- single cell
- deep learning