A novel approach to preventing SARS-CoV-2 transmission in classrooms: A numerical study

The education sector has suffered a catastrophic setback due to the ongoing COVID pandemic, with classrooms being closed indefinitely. The current study aims to solve the existing dilemma by examining COVID transmission inside a classroom and providing long-term sustainable solutions. In this work, a standard 5 × 3 × 5 m3 classroom is considered where 24 students are seated, accompanied by a teacher. A computational fluid dynamics simulation based on OpenFOAM is performed using a Eulerian-Lagrangian framework. Based on the stochastic dose-response framework, we have evaluated the infection risk in the classroom for two distinct cases: (i) certain students are infected and (ii) the teacher is infected. If the teacher is infected, the probability of infection could reach 100% for certain students. When certain students are infected, the maximum infection risk for a susceptible person reaches 30%. The commonly used cloth mask proves to be ineffective in providing protection against infection transmission, reducing the maximum infection probability by approximately 26% only. Another commonly used solution in the form of shields installed on desks has also failed to provide adequate protection against infection, reducing the infection risk only by 50%. Furthermore, the shields serve as a source of fomite mode of infection. Screens suspended from the ceiling, which entrap droplets, have been proposed as a novel solution that reduces the infection risk by 90% and 95% compared to the no screen scenario besides being completely devoid of fomite infection mode. The manifestation of infection risk in the domain was investigated, and it was found out that in the case of screens the maximum infection risk reached the value of only 0.2 (20% infection probability) in 1325 s. © 2023 Author(s).

COVID-19 pandemic in the Arctic and Subarctic

The pandemic caused by the deadly Coronavirus has spread across the entire world, impacting the lives and livelihood of billions of people living in different regions. Even the Arctic and Subarctic regions are also not exempted from the spread and effect of this pandemic. In this study, we emphasize the COVID-19 pandemic situation of the Arctic and Subarctic regions. Even though the population density of these regions is significantly less, the eminent impact due to COVID-19 remains the same, perhaps more, considering the harsh weather, less communication, and health facilities. We have analyzed seasonal pandemic scenarios, risks, governance responses, and resilience of the locals as well as governments in and around the Arctic and Subarctic regions of Canada, Finland, Greenland, Iceland, Norway, Russia, Sweden, and the United States (Alaska). Despite these regions being extreme, the results reveal that the devastating effect of the pandemic remains almost the same at par with the context of the significantly lower population density. However, the governance shows a silver lining during this period, proving that humankind can win any battle for its sustenance with proper governance and management actions. © 2022 Elsevier Inc. All rights reserved.

Effect of ventilation and climatic conditions on COVID-19 transmission through respiratory droplet transport via both airborne and fomite mode inside an elevator

A numerical analysis using OpenFOAM has been performed in this work to investigate the infection risk due to droplet dispersal in an enclosed environment resembling an elevator, since infection risk in such confined places is very high. The effect of two scenarios on droplet dispersal, namely, the quiescent and the fan-driven ventilation, both subjected to various climatic conditions (of temperature and humidity) ranging from cold-humid (15 °C, 70% relative humidity) to hot-dry (30 °C, 30% relative humidity) have been studied. A risk factor derived from a dose-response model constructed upon the temporally averaged pathogen quantity existing around the commuter's mouth is used to quantify the risk of infection through airborne mode. It is found that the hot, dry quiescent scenario poses the greatest threat of infection (spatio-averaged risk factor 42%), whereas the cold-humid condition poses the least risk of infection (spatio-averaged risk factor 30%). The proper fan speed is determined for the epidemiologically safe operation of the elevator. The fan ventilation scenario with 1100 RPM (having a spatio-averaged risk factor of 10%) decreases the risk of infection by 67% in a hot, dry climatic condition as compared to a quiescent scenario and significantly in other climatic ambiences as well. The deposition potential of aerosolized droplets in various parts of the respiratory tract, namely, the extrathoracic and the alveolar and bronchial regions, has been analyzed thoroughly because of the concomitant repercussions of infection in various depths of the respiratory region. In addition, the airborne mode of infection and the fomite mode of infection (infection through touch) have also been investigated for both the ventilation scenarios. © 2022 Author(s).

ChikvInt: a Chikungunya virus-host protein-protein interaction database.

Chikungunya is a fast-mutating virus causing Chikungunya virus disease (ChikvD) with a significant load of disability-adjusted life years (DALY) around the world. The outbreak of this virus is significantly higher in the tropical countries. Several experiments have identified crucial viral-host protein-protein interactions (PPIs) between Chikungunya Virus (Chikv) and the human host. However, no standard database that catalogs this PPI information exists. Here we develop a Chikv-Human PPI database, ChikvInt, to facilitate understanding ChikvD disease pathogenesis and the progress of vaccine studies. ChikvInt consists of 109 interactions and is available at www.chikvint.com.

Virtual-reality-based digital twin of office spaces with social distance measurement feature

Background: Social distancing is an effective way to reduce the spread of the SARS-CoV-2 virus. Many students and researchers have already attempted to use computer vision technology to automatically detect human beings in the field of view of a camera and help enforce social distancing. However, because of the present lockdown measures in several countries, the validation of computer vision systems using large-scale datasets is a challenge. Methods: In this paper, a new method is proposed for generating customized datasets and validating deep-learning-based computer vision models using virtual reality (VR) technology. Using VR, we modeled a digital twin (DT) of an existing office space and used it to create a dataset of individuals in different postures, dresses, and locations. To test the proposed solution, we implemented a convolutional neural network (CNN) model for detecting people in a limited-sized dataset of real humans and a simulated dataset of humanoid figures. Results: We detected the number of persons in both the real and synthetic datasets with more than 90% accuracy, and the actual and measured distances were significantly correlated (r=0.99). Finally, we used intermittent-layer- and heatmap-based data visualization techniques to explain the failure modes of a CNN. Conclusions: A new application of DTs is proposed to enhance workplace safety by measuring the social distance between individuals. The use of our proposed pipeline along with a DT of the shared space for visualizing both environmental and human behavior aspects preserves the privacy of individuals and improves the latency of such monitoring systems because only the extracted information is streamed. © 2021 Beijing Zhongke Journal Publishing Co. Ltd

ENDOPHTHALMITIS AFTER INTRAVITREAL INJECTIONS DURING THE COVID-19 PANDEMIC WITH IMPLEMENTATION OF UNIVERSAL MASKING

Purpose: To evaluate the rates of postintravitreal injection-related endophthalmitis during the COVID-19 pandemic with institution of both physician and patient face masking. Methods: All eyes receiving intravitreal injections of any kind from a single large tertiary retina practice in Houston, TX before (August 2017-March 22, 2020) and after (March 23, 2020-September 2020) COVID-19 pandemic universal masking protocols. The total number of injections and cases of acute injection-related endophthalmitis were determined from billing records and subsequent retrospective chart review. The primary outcome was the rate of endophthalmitis after intravitreal injection. Secondary outcomes included visual acuity, time until initial presentation, patient age, and differences in the overall number of injections performed monthly pre-COVID-19 and post-COVID-19. Results: A total of 134, 097 intravitreal injections were performed during the study period (111,679 pre-COVID-19 and 22,418 post-COVID-19 masking protocols). A total of 41 cases of acute endophthalmitis occurred in the pre-COVID group (0.04%, one in 2,500) and 7 cases in the post-COVID group (0.03%, one in 3,333) P = 0.85. Conclusion: In this single center, retrospective study, the implementation of universal patient and physician masking as practiced during the COVID-19 pandemic did not significantly affect the rate of postintravitreal injection endophthalmitis.

Validating social distancing through deep learning and VR-based digital twins

The Covid-19 pandemic resulted in a catastrophic loss to global economies, and social distancing was consistently found to be an effective means to curb the virus’s spread. However, it is only as effective when every individual partakes in it with equal alacrity. Past literature outlined scenarios where computer vision was used to detect people and to enforce social distancing automatically. We have created a Digital Twin (DT) of an existing laboratory space for remote monitoring of room occupancy and automatically detecting violation of social distancing. To evaluate the proposed solution, we have implemented a Convolutional Neural Network (CNN) model for detecting people, both in a limited-sized dataset of real humans, and a synthetic dataset of humanoid figures. Our proposed computer vision models are validated for both real and synthetic data in terms of accurately detecting persons, posture, and intermediate distances among people. © 2021 Copyright held by the owner/author(s).

Generating Synthetic Data for Deep Learning using VR Digital Twin

The ongoing Covid-19 pandemic has made it challenging for large scale data collection, in particular for Convolutional Neural Network (CNN)-based computer vision systems. Additionally, there are numerous circumstances where security, privacy, and limitations pertaining to the accessibility of the required equipment make it arduous to validate computer vision systems with real-world datasets. In this paper, we investigated the possibilities of using synthetic datasets, generated from Virtual Environments (VE) for training and validation of CNN models. We present two use cases where the above-mentioned circumstances play a vital role in preparing the datasets and validating the model with large-scale datasets. By developing and leveraging a three-dimensional Digital Twin (DT), we produce large scale datasets for validating social distancing in workspaces;and in the context of semi-autonomous vehicles, we evaluate how a CNN-based object detection model would perform in an Indian road scenario. © 2021 ACM.

Eye gaze controlled adaptive virtual keyboard for users with SSMI

BACKGROUND: Users with Severe Speech and Motor Impairment (SSMI) often use a communication chart through their eye gaze or limited hand movement and care takers interpret their communication intent. There is already significant research conducted to automate this communication through electronic means. Developing electronic user interface and interaction techniques for users with SSMI poses significant challenges as research on their ocular parameters found that such users suffer from Nystagmus and Strabismus limiting number of elements in a computer screen. This paper presents an optimized eye gaze controlled virtual keyboard for English language with an adaptive dwell time feature for users with SSMI. OBJECTIVE: Present an optimized eye gaze controlled English virtual keyboard that follows both static and dynamic adaptation process. The virtual keyboard can automatically adapt to reduce eye gaze movement distance and dwell time for selection and help users with SSMI type better without any intervention of an assistant. METHODS: Before designing the virtual keyboard, we undertook a pilot study to optimize screen region which would be most comfortable for SSMI users to operate. We then proposed an optimized two-level English virtual keyboard layout through Genetic algorithm using static adaptation process;followed by dynamic adaptation process which tracks users' interaction and reduces dwell time based on a Markov model-based algorithm. Further, we integrated the virtual keyboard for a web-based interactive dashboard that visualizes real-time Covid data. RESULTS: Using our proposed virtual keyboard layout for English language, the average task completion time for users with SSMI was 39.44 seconds in adaptive condition and 29.52 seconds in non-adaptive condition. Overall typing speed was 16.9 lpm (letters per minute) for able-bodied users and 6.6 lpm for users with SSMI without using any word completion or prediction features. A case study with an elderly participant with SSMI found a typing speed of 2.70 wpm (words per minute) and 14.88 lpm (letters per minute) after 6 months of practice. CONCLUSIONS: With the proposed layout for English virtual keyboard, the adaptive system increased typing speed statistically significantly for able bodied users than a non-adaptive version while for 6 users with SSMI, task completion time reduced by 8.8% in adaptive version than nonadaptive one. Additionally, the proposed layout was successfully integrated to a web-based interactive visualization dashboard thereby making it accessible for users with SSMI. © 2021-IOS Press. All rights reserved.

Investigation on COVID-19 by using Machine Learning Techniques

The COVID-19 pandemic had brought about a standstill to many activities across the world. Health experts, doctors and academic investigators across the globe have been attempting to come to terms with the trying demands posed on the human population due to the pandemic. This paper attempts to develop a precise model for examination of and forecasting effective measures to be implemented during different situations to limit the impact of COVID-19. It also addresses various trials and tests faced while using machine learning algorithms. For the experimental analysis different parameters such as countries (China, America, India, South Africa and Italy), month, types of measures to be undertaken (awareness campaigns, economic measures, domestic travel restrictions, health screening at airports and psychological assistance involving medical social work) and date of implementation details are considered. COVID-19 epidemic determent procedures by recognizing, evaluating danger situation and probable paths of epidemic using a machine-learning technique have been explored. A proposed methodology to forecast extension of lockdown in order to exterminate COVID-19 is presented wherein SVM regression technique is used for prediction of actual extension of lockdown during the pandemic situation. © 2021 IEEE.

Detecting When Pre-trained nnU-Net Models Fail Silently for Covid-19 Lung Lesion Segmentation

Automatic segmentation of lung lesions in computer tomography has the potential to ease the burden of clinicians during the Covid-19 pandemic. Yet predictive deep learning models are not trusted in the clinical routine due to failing silently in out-of-distribution (OOD) data. We propose a lightweight OOD detection method that exploits the Mahalanobis distance in the feature space. The proposed approach can be seamlessly integrated into state-of-the-art segmentation pipelines without requiring changes in model architecture or training procedure, and can therefore be used to assess the suitability of pre-trained models to new data. We validate our method with a patch-based nnU-Net architecture trained with a multi-institutional dataset and find that it effectively detects samples that the model segments incorrectly. © 2021, Springer Nature Switzerland AG.

EDITH - A robust framework for prevention of cyber attacks in the covid era

In the era of cloud technology, all the emails sent from one person to another needs to be secure. Security attacks like email phishing, EBomb, DNS spoofing and so on have become a trend in this digital era. Among the said attacks, email phishing has been observed to be most common to be used by attackers to trap victims using fake email with embedded malwares, which is not made aware of non-cyber professionals. With the current pandemic that is Covid-19, which is storming the whole world and enforcing people all over to stay indoors, thus indirectly increasing the online digital footprints of all, so there's an increase in SMB(Server message block) port all over the world which is leading attackers to find their victims easily by unique, dynamic and various other vulnerabilities which no standard virus, malware detection software which was before being provided by the IT companies to its employees and for general internet users;have proven to be not that effective. so we propose a different approach along with a tool kit that will work in identifying the embedded malware files and fake websites more dynamically and effectively. © 2021 IEEE.

COVID-19 associated stroke — A single centre experience

Background and Purpose: Various neurological complications have been reported in association with COVID-19. We report our experience of COVID-19 with stroke at a single center over a period of eight months spanning 1 March to 31 October 2020. Methods: We recruited all patients admitted to Internal Medicine with an acute stroke, who also tested positive for COVID-19 on RTPCR. We included all stroke cases in our analysis for prediction of in-hospital mortality, and separately analyzed arterial infarcts for vascular territory of ischemic strokes. Results: There were 62 stroke cases among 3923 COVID-19 admissions (incidence 1.6%). Data was available for 58 patients {mean age 52.6 years;age range 17–91;F/M=20/38;24% (14/58) aged ≤40;51% (30/58) hypertensive;36% (21/58) diabetic;41% (24/58) with O2 saturation <95% at admission;32/58 (55.17 %) in-hospital mortality}. Among 58 strokes, there were 44 arterial infarcts, seven bleeds, three arterial infarcts with associated cerebral venous sinus thrombosis, two combined infarct and bleed, and two of indeterminate type. Among the total 49 infarcts, Carotid territory was the commonest affected (36/49;73.5%), followed by vertebrobasilar (7/49;14.3%) and both (6/49;12.2%). Concordant arterial block was seen in 61% (19 of 31 infarcts with angiography done). ‘Early stroke’ (within 48 hours of respiratory symptoms) was seen in 82.7% (48/58) patients. Patients with poor saturation at admission were older (58 vs 49 years) and had more comorbidities and higher mortality (79% vs 38%). Mortality was similar in young strokes and older patients, although the latter required more intense respiratory support. Logistic regression analysis showed that low Glasgow coma score (GCS) and requirement for increasing intensity of respiratory support predicted in-hospital mortality. Conclusions: We had a 1.6% incidence of COVID-19 related stroke of which the majority were carotid territory infarcts. In-hospital mortality was 55.17%, predicted by low GCS at admission. © 2021 Journal of Association of Physicians of India. All rights reserved.

Production of cheap hand sanitizer with herbal ingredients

Novel Corona Virus has spread to 188 countries around the world which made the people infected, facing moderate respiratory illness. Currently one of the major strategies to deal with COVID-19 and reduce community transmission of infections is the frequent use of hand sanitizers. However, a large section of common mass is unable to buy them due to higher price. Therefore, an approach has been presented here to produce cheaper sanitizers with easily available herbal ingredients like Aloe Vera gel, boiled water, surgical spirit, Glycerine etc. The estimated making cost of 100 ml of sanitizer was 16 rupees. The mass production of this sanitizer can be very effective for large scale use of sanitizers by common people. © 2021 Institute of Physics Publishing. All rights reserved.

E-commerce Trade and Data Localization: A Developing Country Perspective

This article explores the issue of data localization by capturing all relevant debates and discussion around it. It investigates issues related to data management, storage, and ownership, followed by the data safety and security concerns of developing countries in a rapidly changing digital world. Storing data locally can be an effective way to tackle these concerns. Data localization can bring the data storing market price down. It can inject sufficient incentive to spur technological innovation in the system. If workable templates of data safety and privacy frameworks can be built locally, consumers’ rights will also be protected. Data localization also has the potential to positively contribute to effective redressal of damages in developing countries related to data leakage. The COVID-19 pandemic has considerably sharpened existing conflicts in the e-commerce ecosystem. Treating this crisis as an opportunity and pushing for digital data safety and security by means of data localization is the ideal strategy for developing and emerging economies to adopt. © 2020

A Comparative Analysis of Different Regression Models on Predicting the Spread of Covid-19 in India

According to the World Health Organization (WHO) Situation Reports of Corona Virus Disease(Covid-19), as on 15th May 2020, India has 81,970 totals confirmed cases, 2649 total deaths and is still within the limit of community transmission phase. In this study, we analyze the spread of the disease and the fatalities caused up to 15th May 2020, as per the data obtained. A granular computing based regression model, namely Granular Box Regression is used along with Linear Regression for comparative analysis to study the increase in the number of confirmed cases and deaths based on days and an increase in the number of samples tested per day. A separate analysis is also conducted to evaluate the performance of Polynomial Regression on the same dataset. The performance of the different models has been evaluated using R-squared, Mean Absolute Error, Root Mean Squared Error, and Mean Squared Error values. © 2020 IEEE.

Social network analysis of COVID-19 transmission in Karnataka, India.

We used social network analysis (SNA) to study the novel coronavirus (COVID-19) outbreak in Karnataka, India, and to assess the potential of SNA as a tool for outbreak monitoring and control. We analysed contact tracing data of 1147 COVID-19 positive cases (mean age 34.91 years, 61.99% aged 11-40, 742 males), anonymised and made public by the Karnataka government. Software tools, Cytoscape and Gephi, were used to create SNA graphics and determine network attributes of nodes (cases) and edges (directed links from source to target patients). Outdegree was 1-47 for 199 (17.35%) nodes, and betweenness, 0.5-87 for 89 (7.76%) nodes. Men had higher mean outdegree and women, higher mean betweenness. Delhi was the exogenous source of 17.44% cases. Bangalore city had the highest caseload in the state (229, 20%), but comparatively low cluster formation. Thirty-four (2.96%) 'super-spreaders' (outdegree ⩾ 5) caused 60% of the transmissions. Real-time social network visualisation can allow healthcare administrators to flag evolving hotspots and pinpoint key actors in transmission. Prioritising these areas and individuals for rigorous containment could help minimise resource outlay and potentially achieve a significant reduction in COVID-19 transmission.