Analysis of Cardiovascular Complications during Delivery Admissions Associated with COVID-19

BACKGROUND: Persons with Coronavirus Disease 2019 (COVID-19) infection have an increased risk of pregnancy-related complications. However, data on acute cardiovascular complications during delivery admissions remain limited. OBJECTIVE(S): To determine whether birthing individuals with COVID-19 have an increased risk of acute peripartum cardiovascular complications during their delivery admission. METHOD(S): This population-based retrospective cohort study used the National Inpatient Sample (2020) by utilizing ICD-10 codes to identify delivery admissions with a diagnosis of COVID-19. A multivariable logistic regression model was developed to report an adjusted odds ratio for the association between COVID-19 and acute peripartum cardiovascular complications. RESULT(S): A total of 3,458,691 weighted delivery admissions were identified, of which 1.3% were among persons with COVID-19 (n=46,375). Persons with COVID-19 were younger (median 28 vs. 29 years, p<0.01) and had a higher prevalence of gestational diabetes mellitus (GDM), preterm births and Cesarean delivery (p<0.01). After adjustment for age, race/ethnicity, comorbidities, insurance, and income, COVID-19 remained an independent predictor of peripartum cardiovascular complications including preeclampsia (aOR 1.33 [1.29-1.37]), peripartum cardiomyopathy (aOR 2.09 [1.54-2,84]), acute coronary syndrome (ACS) (aOR 12.94 [8.85-18.90]), and cardiac arrhythmias (aOR 1.55 [1.45-1.67]) compared with no COVID-19. Likewise, the risk of in-hospital mortality, AKI, stroke, pulmonary edema, and VTE was higher with COVID-19. For resource utilization, cost of hospitalization ($5,374 vs. $4,837, p<0.01) was higher for deliveries among persons with COVID-19. CONCLUSION(S): Persons with COVID-19 had a higher risk of preeclampsia, peripartum cardiomyopathy, ACS, arrhythmias, in-hospital mortality, pulmonary edema, AKI, stroke, and VTE during delivery hospitalizations. This was associated with an increased cost of hospitalization. Keywords: COVID-19, Pregnancy, GDM, PCOS, Preeclampsia, CVD, Cardiovascular Disease Abbreviations: COVID-19: Coronavirus disease-2019, GDM: Gestational Diabetes Mellitus, PCOS: Polycystic Ovary Syndrome, National Inpatient Sample: NIS, AHRQ: Agency for Healthcare Research and Quality, HCUP: the Healthcare Cost and Utilization Project Funding and Conflicts of Interest Dr. Michos reports Advisory Board participation for Amgen, AstraZeneca, Amarin, Bayer, Boehringer Ingelheim, Esperion, Novartis, Novo Nordisk, and Pfizer. The remaining authors have nothing to disclose.Copyright © 2023

A Quality Improvement Project to Improve the Efficiency of Pulmonary Nodule Service at a Tertiary Centre

Introduction: With the increasing detection of incidental pulmonary nodules (IPNs), there is a clinical need for a dedicated IPN service to ensure that growing PNs are managed in a timely manner. Pre COVID-19, our centre ran a virtual nodule service, delivered on an ad-hoc basis by the lung cancer physicians. We hypothesised that efficiency of the service would improve with a dedicated nodule team. We were awarded a pump priming grant by the Thames Valley Cancer Alliance to implement a nodule navigator run service. We report the initial outcomes of this project here. Objective(s): To evaluate the PN navigator service. Method(s): Retrospective data pre-service development was collected from patients presenting to the PN service between April and June 2022. The service was established in October 2022 and data from October and November 2022 collected. Student t-test was used to compare means. [Table presented] Results: 107 patients were included pre-service and 92 patients in the post-service development cohorts. Data for time to CT report and patient contact are summarised in Table 1. There was no reduction in mean time from CT scan date to CT report (Table 1;31vs 27;p=0.143) but a reduction was seen between CT report and patient contact (Table 1, 45 vs 20;p<0.001). Conclusion(s): This small cohort study shows an improvement in the time between CT scan and patient contact following the introduction of a dedicated PN service. This may reduce delays in the diagnosis of early-stage lung cancer. Whilst there was no significant difference between the CT scan date and CT report, these data highlight an area in the pathway that can improve. Further aims of the project are to collect patient satisfaction and IPN discharge. Disclosure: No significant relationships.Copyright © 2023 Elsevier B.V.

Assessment of challenges and problems in supply chain among retailers during COVID-19 epidemic through AHP-TOPSIS hybrid MCDM technique

Framework and objectives: COVID-19 epidemic has sparked concern and has elevated the need for therapeutic tools, health equipment's, and day-to-day necessities for healthcare workers' well-being. The goal of this study is to uncover the operational problems that suppliers encounter when it comes to offering effective services. The research also intends to offer an Industry 4.0 strategy for reducing COVID-19's effect. The problems are weighed and priority is assigned by multi-criteria decision making to identify the most essential parameter which impacts the suppliers. Methods: A comprehensive literature assessment on the rampant eruption of COVID 19 and supply chain is conducted with the aid of literatures available on SCOPUS, Science Direct, and Google Scholar using appropriate keywords. To get further insights, certain pertinent and applicable industry reports and blogs are also used. Problems were analysed with AHP method and priority was assigned by technique for order performance by similarity to ideal solution (TOPSIS). Weights are calculated by AHP method and assigned to each criteria attribute. Results: We recognized eleven key problems that serve as an operational obstacle in the retail industry and proposed the use of Industry 4.0 technology to address them. The contemporary study is accomplished by using hybrid combination of two Multi Criteria Estimators methods- Analytical Hierarchical Process (AHP) and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS). Further, the most significant problem comes out to be Maintenance of an appropriate balance among supply and demand followed by Lack of Viability. Key findings: Prioritization of supply chain problems are arranged in descending order Maintenance of an appropriate balance among supply and demand ​> ​Lack of Viability ​> ​Absence of government funding ​> ​Lack of access ​> ​Absence of Confidence ​> ​Scarcity of work force ​> ​Lack of security and safety ​> ​Deficiency of surplus medical amenities ​> ​Consumer attitude ​> Absence of Supply Chain flexibility ​> ​Communication problems. Conclusion: In order to combat the pandemic, Industry 4.0 can play a key role in lowering the effect of identified issues on retailers. For the successful administration of healthcare basics, trust and openness are required. To enhance services, suppliers, distributors and policy makers should make informed decisions during COVID-19 and other comparable events. Therefore, suggested guidelines and framework will offer upcoming directions for research in fields of pandemic check, business logistics management, and catastrophe administration. Balance in supply and demand is the most significant attribute as its percentage contribution is the maximum (27.52%) followed by Safety of employees (26.51%). Furthermore, the research then ranks these models on the basis of their attributes with the aid of TOPSIS. Among all these problems, Maintenance of an appropriate balance among supply and demand and lack of viability are identified as the prime most and common concern for retailers in supply chain management during the COVID-19 pandemic. © 2022 The Authors

SRC: Superior Robustness of COVID-19 Detection from Noisy Cough Data Using GFCC

This research is focused on a highly effective and untapped feature called gammatone frequency cepstral coefficients (GFCC) for the detection of COVID-19 by using the nature-inspired meta-heuristic algorithm of deer hunting optimization and artificial neural network (DHO-ANN). The noisy crowdsourced cough datasets were collected from the public domain. This research work claimed that the GFCC yielded better results in terms of COVID-19 detection as compared to the widely used Mel-frequency cepstral coefficient in noisy crowdsourced speech corpora. The proposed algorithm's performance for detecting COVID-19 disease is rigorously validated using statistical measures, F1 score, confusion matrix, specificity, and sensitivity parameters. Besides, it is found that the proposed algorithm using GFCC performs well in terms of detecting the COVID-19 disease from the noisy crowdsourced cough dataset, COUGHVID. Moreover, the proposed algorithm and undertaken feature parameters have improved the detection of COVID-19 by 5% compared to the existing methods. © 2023 CRL Publishing. All rights reserved.

Mental Distress, Perceived Stress and Resilience among Special Students during Covid-19 Pandemic

Background: Almost everyone in society has been affected by the Covid epidemic.However, it has a different impact on individuals who have disabilities or special needs. People with special needs are more prone to this. Aim(s): To explore relationship between mental distress, perceived stress and resilience among special students during Covid-19 Method:Cross-sectional correlational research design was used to investigate the relationship between study variables. The total sample of n=250 special students consisted of 100(40%) females and 150(60%) with having age range from 15 to 30 years. The purposive sampling technique was employed to collect the data by using different research instruments Results:Results were drawn using correlation and multiple regression analyses. To explore gender differences, an independent t-test was computed. It was concluded that mental distress is negatively correlated with resilience and positively correlated with perceived stress. PraccticalImplication: This research would be helpfulfor the teachers of the special education centers, they train their how they develop resilience hence they may become emotionally stable and deal effectively with any worrisome circumstance well. It will help school administration of the special students'to conduct workshops on the resilience so that students could deal with their mental distress and perceived stress effectively. Conclusion(s): There was little published research on the variables examined in this study, particularly in Pakistan. To increase the body of knowledge, research on this subject must be done in Pakistan. Copyright © 2022 Lahore Medical And Dental College. All rights reserved.

An Implementation of Real-time Activity Sensing using Wi-Fi: Identifying Optimal Machine Learning Techniques for Performance Evaluation

The elderly population is growing, and the health care system is experiencing a strain on services provided to the elderly. The recent COVID-19 pandemic has increased this strain and has resulted in an increased risk of exposure during visits to elderly homes. Increasing the desire to provide technological solutions to counteract this. Currently, there lack reliable real-time non-invasive sensing systems. This paper makes use of Radio Frequency sensing, where signal propagation is observed in Channel State Information (CSI) reports on Activities of Daily Living (ADLs). Real-time data has been collected for three classifications, “movement”, “empty room”, and “no activity”. A filter is applied to reduce the noise of the CSI data. Then the mean, max, min, kurtosis, skew and standard deviation features are extracted from the CSI data. A machine learning model provides classification for the real-time monitoring system allowing detection of abnormalities in the expected ADLs of the elderly. The timing of classifications gives insight into the real-time capabilities of the system. The Random Forest algorithm is chosen to create the machine learning model based on accuracy and timing capabilities. The model was able to achieve an accuracy of 100 % on new unseen testing data with an average classification time of 7.31 milliseconds. IEEE

COVID-19 SOP's Violations Detection in Terms of Face Mask Using Deep Learning

Facemask detection is a need of time as we are suffering in a pandemic situation of COVID-19, and facemask is considered the best preventive measure to stop the rapid spread. The vast majority of the world population is still unvaccinated, especially young and kids. Moreover, despite the vaccination, people are still getting Covid positive, and the majority are due to the Delta variant. So, we still need to have strict SOP implementation. The best way is to have some autonomous system to monitor SOP compliance and alert the authority to take countermeasures. Many people wear the mask, but the mask is usually on the chin and does not serve the purpose because the facemask must cover the mouth and nose to stop the spread. This study has proposed the improved version of the YOLOv4 model for the robust detection of face masks and checks whether the mask is worn in the recommended way. 2D convolutions of Yolov4 are replaced with the spatially separable convolutional in YOLOv4 to reduce the parameters so that the model can work in real-time. We have achieved an accuracy of 86.61% in terms of proper mask-wearing. Unlike other proposed approaches, our model is not only detecting the mask but also determines that whether the mask is worn in the recommended manner.

Stress, Anxiety and Depression Levels Associated with COVID 19 among the Oral Health Care Workers in North India: A Cross-Sectional Study

The COVID-19 outbreak has caused an impervious financial and psychological burden. Health care professionals, including oral health care workers, have been risking fighting the pandemic. The chief objective of the current study was to estimate the rates of prevalence of depression, stress, and anxiety among the oral health care professionals in Jammu and Udaipur city. The study was delineated as an online cross-sectional questionnaire-based research. It was mailed to different practitioners between May and July 2020, particularly those offered their services in COVID centers. The participants were to fill the self report questionnaires. Then, the parameters were measured using depression, anxiety, and stress scale 21(DASS 21) and Hamilton anxiety rating scale (HARS) to measure the degrees of depression, stress, and fear among the volunteers. The target population was divided into age groups between 23 to 28 years and over 28 years. Four hundred ninety responses were received and were considered for the study. The acquired data were analysed using IBM SPSSsoftware (windows version 23). The mean and standard deviations were calculated for stress, anxiety, depression using mentioned scale. The results were compared based on gender and age group. A statistically significant variance in stress level was found between male and female groups (p=0.002) and for the two age groups (p=0.001). Using the Hamilton anxiety rating scale, no statistically significant divergence could be seen among male and female participants. The current study showed stress, anxiety, and depressions were prevalent among health care workers working in COVID pandemic situations. Therefore, mental health status must be addressed, and issues must be resolved. © 2022, DESIDOC.

Multiple Parallel Activity Detection and Recognition to Avoid COVID-19 Spread-Out

COVID-19 has been affecting people around the globe. It is affecting almost every country currently, according to the World Health Organization (WHO). This virus is transmitted to another person if an asymptomatic person makes close contact with the everyday person. There is no cure for this virus, and the only solution is social distancing and avoids the people doing these activities. In this paper, we proposed a system for detecting and recognizing the activities that violate social distancing. These activities involve handshakes and hugging. We implement a system that is capable of detecting and identifying multiple parallel activities. Temporal features are extracted for similar activities in 16 frames. We use the two models for this purpose: Faster RCNN for the detection and ResNet-50 to recognize the activities. First, Faster RCNN detects the group of people and that region of interest ROI saved and passes to the ResNet-50 to recognize the activities. We also generated our dataset on the local environment in multiple parallel activities. This system achieves the accuracy of 95.03% for the detection of testing dataset and recognition of multiple parallel activities 92.88% accuracy accomplished. The system used different public datasets and generated some local datasets for handshake and hugging activities. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

GRAVE DANGER: RARE CASE OF COMPLETE HEART BLOCK SECONDARY TO GRAVES DISEASE

Background: Complete heart block (CHB) is a cardiac conduction disorder commonly due to age-related degeneration of the conduction system. Other etiologies include hypothyroidism, Lyme disease or COVID-19, infiltrative cardiomyopathy, myocarditis, and atrioventricular (AV) nodal blocking agents. Hyperthyroidism is an extremely rare cause of CHB. Case: We present the case of a 40-year-old previously healthy male who presented after two syncopal episodes. He denied any home medications, recreational drug use, or prior syncopal episodes. He did endorse worsening palpitations, heat intolerance, anxiety, insomnia and diarrhea for one month. Initial EKG was normal. Labs revealed an undetectable thyroid stimulating hormone (TSH), and high T4 of 3.26 ng/dL. Potassium was 3.1 mMol/L which was replaced to normal levels. In the emergency department, he had another syncopal episode. Telemetry showed a 20 second episode of CHB. Patient was admitted and started on methimazole. Decision-making: Labs showed positive TSH receptor antibodies and thyroid stimulating immunoglobulins, confirming a diagnosis of Graves’ disease. COVID-19 IgG antibodies were positive with negative COVID-19 PCR, indicative of remote COVID 19 infection. Cardiac MRI did not show any myocarditis or infiltrative disease, and otherwise revealed a structurally normal heart. Lyme disease antibodies were negative. Toxicology screen was negative. Thyroid ultrasound showed diffuse heterogeneity of the gland. 72 hour telemetry monitoring revealed no further conduction abnormalities. At this point, CHB wes attributed to hyperthyroidism. As this was reversible, and CHB resolved after initiation of methimazole, a permanent pacemaker was not placed. He was discharged with a 30-day event monitor which did not show any conduction abnormalities. Conclusion: This case highlights a rare sequela of hyperthyroidism induced CHB. Although the pathophysiology is not well understood, a proposed mechanism is the direct toxic effect of T3 leading to focal inflammation of the AV node. Further studies are needed to evaluate the pathophysiology and chronicity of this process, which will assist in the decision to implant a permanent pacemaker.

Impact of copper and Emerging Technologies against Novel Coronavirus

Coronavirus belongs to the novel virulent strains of the respiratory viruses. It is an invisible enemy having significant threats to human health. The major trouble is that the spread of coronavirus is not limited to its transmission from human to human (by contact, fomites, and droplets) but also continue to transmit from contaminated surfaces to humans. These infectious viruses can survive in different non-biocidal materials for a long time. Copper holds a significant position in different biological and biochemical processes because its ions Cu+2 and Cu+ can carry out oxidation, dioxygen transportation, and electron transference. It is a redox-active metal. It can convert into Cu+2 or Cu+ state by accepting or donating electrons. Reactive Oxygen Species (ROS) are generated on alloy surfaces. The redox reaction of copper (Cu+2 ↔ Cu+) along with the generation of ROS results in enhanced inactivation of the virus. In this review, the effectiveness of copper against coronaviruses has been explained. The denaturing of specific proteins of coronavirus by the interaction of copper and its ions has also been reported. Hence, the copper coated surfaces could be used in public areas. Furthermore, the review represents the different techniques used for the coating of copper on conductive and non-conductive surfaces. Copyright © 2021 Textile Bioengineering and Informatics Society.

Effectiveness of copper and emerging technologies against novel coronavirus

Coronavirus belongs to the novel virulent strains of respiratory viruses. It is an invisible enemy that is a significant threat to human health. The major problem in the spread of coronavirus is that transmission is not limited to human to human (by contact, fomites, and droplets), it can also be transmitted from contaminated surfaces to humans. These infectious viruses can survive in different non-biocidal materials for a long time. Copper holds a significant position in different biological and biochemical processes because its ions Cu+2 and Cu+ can carry out oxidation, dioxygen transportation, and electron transference. It is a redox-active metal. It can convert into Cu+2 or Cu+ state by accepting or donating electrons. Reactive Oxygen Species (ROS) are generated on alloy surfaces. The redox reaction of copper (Cu+2 ↔ Cu+) along with the generation of ROS results in enhanced inactivation of the virus. In this review, the effectiveness of copper against coronaviruses is explained. The denaturing of specific proteins of coronavirus by the interaction of copper and its ions has also been reported. Hence, copper coated surfaces can be used in public areas to inhibit the spread of the virus. Furthermore, the review represents the different techniques used for the coating of copper on conductive and non-conductive surfaces. © 2019 Textile Bioengineering and Informatics Symposium Proceedings 2021 - 14th Textile Bioengineering and Informatics Symposium, TBIS 2021. All rights reserved.

An ICU Admission Predictive Model for COVID-19 Patients in Saudi Arabia

Globally, COVID-19 already emerged in around 170 million confirmed cases of infected people and, as of May 31, 2021, affected more than 3.54 million deaths. This pandemic has given rise to numerous public health and socioeconomic issues, emphasizing the significance of unraveling the epidemic’s history and forecasting the disease’s potential dynamics. A variety of mathematical models have been proposed to obtain a deeper understanding of disease transmission mechanisms. Machine Learning (ML) models have been used in the last decade to identify patterns and enhance prediction efficiency in healthcare applications. This paper proposes a model to predict COVID-19 patients admission to the intensive care unit (ICU). The model is built upon robust known classification algorithms, including classic Machine Learning Classifiers (MLCs), an Artificial Neural Network (ANN) and ensemble learning. This model’s strength in predicting COVID-19 infected patients is shown by performance analysis of various MLCs and error metrics. Among other used ML models, the ANN model resulted in the highest accuracy, 97.9% over other models. Mean Squared Error showed that the ANN method had the lowest error (0.0809). In conclusion, this paper could be beneficial to ICU staff to predict ICU admission based on COVID-19 patients’ clinical characteristics. © 2021. All Rights Reserved.

Covid-19: From Pandemic to Epidemic an Ostrich Syndrome Causing Desensitization among Urban and Suburban Masses

Background: After declaration of the Covid-19 as pandemic by WHO, like every country of the world Pakistan also took exceptional precautionary measure to control the spread and transmission of this virus. These measures included strict lockdowns, shutting down educational institutions, markets, shopping malls, airports and masjids. The study primarily aimed to assess the individuals' knowledge, attitude, behavioral practices to prevent coronavirus and its psychological impacts on their mental health but later researcher also conducted another post study. Factually, the economic crisis, holy month of Ramadan and Eid-ul-Fitar forced the authorities soften the lockdown. Consequently, after this relaxation, the patients of Covid-19 as well as the death rates increased exponentially in Punjab. Methods: A non-equivalent quasi-experimental research designed was used to conduct pre and post study. An online survey having standardized questions about the peoples' knowledge regarding Covid-19, attitudes, their behavioral practices to prevent it and in what manner covid effected their mental health was conducted. Moreover, it also includes the information for demographics of sample. The link was sent to the participants through email, WhatsApp, Social media, Twitter, Facebook and LinkedIn. Similarly, same procedure was followed in post-study, with the same participants, but after a gap of three weeks. Results: Data was analyzed by SPSS and t-test was applied to draw comparisons among the responses of pre and post study. Results revealed that peoples' knowledge and attitude towards Covid-19 remained same in both studies, however, their behavioral practices to prevent Covid-19 and psychological impacts on their mental health (i.e., stress, depression, fear) greatly reduced in post study as compared to the prestudy. Conclusion: The authorities should initiate the health education programs to improve the knowledge, attitude and behavioral practices regarding Covid-19 and to combat its psychological impact on mental health. Besides, it is highly recommended that authorities should take bold steps to enforce formal, or smart lockdown to take control over the situation or else get ready to filled up the graveyards. The situation can also be controlled by enforcing precautionary measures and re-inviting awareness.

Hot-Spot Zone Detection to Tackle Covid19 Spread by Fusing the Traditional Machine Learning and Deep Learning Approaches of Computer Vision

Corona Virus is a pandemic, and the whole world is affected due to it. Apart from the vaccine, the only cure for this drastic disease is to follow the rules and regulations that avoid further spread. There are different mechanisms like (Social Distancing, Mask Detection, Human occupancy etc.) through which we can able to stop the spread of the coronavirus. In this paper, we proposed hotspot zone detection using the computer vision techniques of deep learning. We have defined the hotspot area as the particular region on which the person touches more than some specified threshold. We further mark that area to some specific color to help the authority take necessary action and disinfect that particular place. To implement this algorithm, we have utilized the human-object interaction concept. We have extracted the dataset of person classes from the publicly available dataset for the person detection and the self-generated dataset to train the algorithm. Different experiments on object detection algorithms (YOLO-v3, Faster RCNN, SSD) for person detection have been performed in this work. We achieved the maximum accuracy in real-time on the YOLO-v3 for person detection. Whereas we have marked the specific area using the template matching algorithm of computer vision techniques. Our proposed algorithm detects the persons and extracts the region of interest points on which the user draws the rectangle. Then we find the intersection over union ratio between the detected person and the region of interest of the marked area to make the decision. We have achieved 88.72% accuracy on person detection in the local environment. Whereas, for the whole system of human-object interaction for detecting the hotspot area zone, we have achieved 86.7% accuracy using the confusion matrix.

Crowd Size Estimation Using CommSense Instrument for COVID-19 Echo Period

The period after the COVID-19 wave is called the Echo-period. Estimation of crowd size in an outdoor environment is essential in the Echo-period. Making a simple and flexible working system for the same is the need of the hour. This article proposes and evaluates a nonintrusive, passive, and cost-effective solution for crowd size estimation in an outdoor environment. We call the proposed system as LTE communication infrastructure based environment sensing or LTE-CommSense. This system does not need any active signal transmission as it uses LTE transmitted signal. So, this is a power-efficient, simple, low-footprint device. Importantly, the personal identity of the people in the crowd cannot be obtained using this method. First, the system uses practical data to determine whether the outdoor environment is empty or not. If not, it tries to estimate the number of people occupying the near range locality. Performance evaluation with practical data confirms the feasibility of this proposed approach.

Crowd Size using CommSense Instrument for COVID-19 Echo Period

The period after the COVID-19 wave is called the Echo-period. Estimation of crowd size in an outdoor environment is essential in the Echo-period. Making a simple and flexible working system for the same is the need of the hour. This article proposes and evaluates a non-intrusive, passive, and cost-effective solution for crowd size estimation in an outdoor environment. We call the proposed system as LTE communication infrastructure based environment sensing or LTE-CommSense. This system does not need any active signal transmission as it uses LTE transmitted signal. So, this is a power-efficient, simple low footprint device. Importantly, the personal identity of the people in the crowd can not be obtained using this method. First, the system uses practical data to determine whether the outdoor environment is empty or not. If not, it tries to estimate the number of people occupying the near range locality. Performance evaluation with practical data confirms the feasibility of this proposed approach. IEEE

Ultrasensitive detection of pathogenic viruses with electrochemical biosensor: State of the art.

Last few decades, viruses are a real menace to human safety. Therefore, the rapid identification of viruses should be one of the best ways to prevent an outbreak and important implications for medical healthcare. The recent outbreak of coronavirus disease (COVID-19) is an infectious disease caused by a newly discovered coronavirus which belongs to the single-stranded, positive-strand RNA viruses. The pandemic dimension spread of COVID-19 poses a severe threat to the health and lives of seven billion people worldwide. There is a growing urgency worldwide to establish a point-of-care device for the rapid detection of COVID-19 to prevent subsequent secondary spread. Therefore, the need for sensitive, selective, and rapid diagnostic devices plays a vital role in selecting appropriate treatments and to prevent the epidemics. During the last decade, electrochemical biosensors have emerged as reliable analytical devices and represent a new promising tool for the detection of different pathogenic viruses. This review summarizes the state of the art of different virus detection with currently available electrochemical detection methods. Moreover, this review discusses different fabrication techniques, detection principles, and applications of various virus biosensors. Future research also looks at the use of electrochemical biosensors regarding a potential detection kit for the rapid identification of the COVID-19.