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[Display omitted] • SARS-CoV-2 detection and monitoring by different electrochemical sensors. • Sensors are based on targets viz., nucleocapsid, spike antigen, immuno-globulin and viral nucleic acids. • Voltammetric, impedimetric, and amperometric techniques are the best practices. • High sensitivity and low limit of detection are observed with electrochemical sensors. Coronavirus (COVID-19), a deadly pandemic has spread worldwide and created many global health issues. Though methods of its detection are being continuously developed for the early detection and monitoring of COVID-19, still there is need for more novel methods. The presently used methods include rapid antigen tests, serological surveys, reverse transcription-polymerase chain reaction (RT-PCR), artificial intelligence-based techniques, and assays based on sensors/biosensors. Of all these, RT-PCR test has high sensitivity and specificity though it requires more time for testing and need for skilled technicians. Recently, electrochemical sensors have been developed for rapid monitoring and detection of SARS-CoV-2 from the patient's biological fluid samples. This review covers the recently developed electrochemical sensors that are focused on the detection of viral nucleic acid, immunoglobulin, antigen, and the entire viral particles. In addition, we also compare and assess their detection limits, sensitivities and specificities for the identification and monitoring of COVID-19. Furthermore, this review will address the best practices for the development of electrochemical sensors such as electrode fouling, limit of detection/limit of quantification determination and verification. [ABSTRACT FROM AUTHOR]

[Display omitted] • MIPs-based electrochemical sensors for COVID-19 diagnostic are cost-effective, exhibit high selectivity and specificity. • MIPs nano-sensors are stable at extreme conditions. • MIPs nano-sensors can be easily fabricated and integrated with IoT. The importance of early diagnosis of infectious disease has been revealed well by the COVID-19 pandemic. The current methods for testing SARS-CoV-2 mainly utilize biorecognition elements. The process of production of these biorecognition elements is not only tedious, time-consuming but also costly. The molecularly imprinted polymers recently have gained considerable attention as they are stable and also offer high selectivity and specificity than conventional labels. The present review discussed the MIPs-based electrochemical nano-sensors diagnostic of SARS-CoV-2. [ABSTRACT FROM AUTHOR]

The proliferating outbreak of COVID-19 raises global health concerns and has brought many countries to a standstill. Several restrain strategies are imposed to suppress and flatten the mortality curve, such as lockdowns, quarantines, etc. Artificial Intelligence (AI) techniques could be a promising solution to leverage these restraint strategies. However, real-time decision-making necessitates a cloud-oriented AI solution to control the pandemic. Though many cloud-oriented solutions exist, they have not been fully exploited for real-time data accessibility and high prediction accuracy. Motivated by these facts, this paper proposes a cloud-oriented AI-based scheme referred to as D-espy (i.e., Disease-espy) for disease detection and prevention. The proposed D-espy scheme performs a comparative analysis between Autoregressive Integrated Moving Average (ARIMA), Vanilla Long Short Term Memory (LSTM), and Stacked LSTM techniques, which signify the dominance of Stacked LSTM in terms of prediction accuracy. Then, a Medical Resource Distribution (MRD) mechanism is proposed for the optimal distribution of medical resources. Next, a three-phase analysis of the COVID-19 spread is presented, which can benefit the governing bodies in deciding lockdown relaxation. Results show the efficacy of the D-espy scheme concerning 96.2% of prediction accuracy compared to the existing approaches. [Display omitted] • An AI-based disease prediction model that predicts the COVID-19 cases. • A medical resource distribution (MRD) scheme that help to allocate medical resources. • A three-phase analysis of the proposed scheme is done in the Indian context. [ABSTRACT FROM AUTHOR]