Smart Watch to Avoid Face Touching During Pandemic Condition

The most dangerous Coronavirus, COVID-19, is the source of this pandemic illness. This illness was initially identified in Wuhan, China, in December 2019, and currently sweeping the globe. The virus spreads quickly because it is so simple to transmit from one person to another. Fever is one of the obvious signs of COVID-19 and is one of its prevalent symptoms. The mucosal areas, such as the nose, eyes, and mouth, are among the most significant ways to catch this virus. In order to prevent and track the corona virus infection, this research suggests a face-touching detection and self-health report monitoring system. Their hygiene will immediately improve thanks to this system. In this pandemic circumstance, people use their hands in dirty environments like buses, trains, and other surfaces, where the virus can remain active for a very long time. With an accelerometer and a pulse oximeter sensor, this system alerts the user when they are carrying their hands close to their faces. © 2023 IEEE.

Efficient Centroid Based Distance Monitoring System Using Deep Learning

COVID-19 virus spread very rapidly if we come in contact to the other person who is infected, this was treated as acute pandemic. As per the data available at WHO more than 663 million infected cases reported and 6.7 million deaths are confirmed worldwide till Dec, 2022. On the basis of this big reported number, we can say that ignorance can cause harm to the people worldwide. Most of the people are vaccinated now but as per standard guideline of WHO social distancing is best practiced to avoid spreading of COVID-19 variants. This is difficult to monitor manually by analyzing the persons live cameras feed. Therefore, there is a need to develop an automated Artificial Intelligence based System that detects and track humans for monitoring. To accomplish this task, many deep learning models have been proposed to calculate distance among each pair of human objects detected in each frame. This paper presents an efficient deep learning monitoring system by considering distance as well as velocity of the object detected to avoid each frame processing to improve the computation complexity in term of frames/second. The detected human object closer to some allowed limit (1m) marked by red color and all other object marked with green color. The comparison of with and without direction consideration is presented and average efficiency found 20.08 FPS (frame/Second) and 22.98 FPS respectively, which is 14.44% faster as well as preserve the accuracy of detection. © 2023 IEEE.

Earth's magnetic field spatial-temporal changes in relation to the SARS-CoV-2 virus spreading

The results of a study on the possible connection between the spread of the SARS-CoV-2 virus and the Earth's magnetic field based on the analysis of a large array digital data for 95 countries of the world are presented. The dependence of the spatial SARS-CoV-2 virus spread on the magnitude of the BIGRF Earth's main magnetic field modular induction values was established. The maximum diseases number occurs in countries that are located in regions with reduced (25. 0-30. 0 μT) and increased (48. 0-55. 0 μT) values, with a higher correlation for the first case. The spatial dependence of the SARS-CoV-2 virus spreading on geomagnetic field dynamics over the past 70 years was revealed. The maximum diseases number refers to the areas with maximum changes in it, both in decrease direction (up to - 6500 nT) and increase (up to 2500 nT), with a more significant correlation for countries located in regions with increased geomagnetic field. © 2022 EAGE. All Rights Reserved.

The Impact of COVID-19 Routine Life - A Survey

The COVID-19 pandemic, caused by the novel coronavirus, has had a significant impact on daily life, education, business, and trade. The virus spreads quickly through direct contact with droplets, fecal-oral transmission, and water contamination. The consequences of the pandemic can be classified into three categories: health, economic, and social. The physical, mental, and psychological behaviors of individuals have also changed due to the pandemic. This study aimed to assess the impact of COVID-19 on the general population. A survey questionnaire with ten questions was distributed through an online portal, and the responses were analyzed using SPSS software. The results showed that healthcare workers were among the most affected, with the primary impact on their social and psychological well-being. Although previous research suggested that all fields were equally affected, this study found that healthcare workers were the most impacted group. The study concluded that the COVID-19 pandemic had a significant impact on the social and psychological well-being of the general population, with healthcare workers being the most affected. © 2023 IEEE.

Ventilation solutions for healthcare patient rooms to control respiratory infections

Building ventilation significantly impacts healthy and safe indoor conditions preventing airborne virus spread between people. Therefore, ventilation strategy is a globally essential and health-promoting research topic. Previous studies showed the importance of sufficient ventilation for diluting the virus concentration and reducing the infection risk. The present study investigates the probability of coronavirus infection in the typical room calculated with the Wells Riley proposes recommendations for further research of indoor airflow effect on the virus transmission. © 2022 17th International Conference on Indoor Air Quality and Climate, INDOOR AIR 2022. All rights reserved.

A Comprehensive Alert System Based on Social Distancing for Cautioning People Amidst the COVID-19 Pandemic Using Deep Neural Network Models

The World Health Organization (WHO) has suggested a successful social distancing strategy for reducing the COVID-19 virus spread in public places. All governments and national health bodies have mandated a 2-m physical distance between malls, schools, and congested areas. The existing algorithms proposed and developed for object detection are Simple Online and Real-time Tracking (SORT) and Convolutional Neural Networks (CNN). The YOLOv3 algorithm is used because YOLOv3 is an efficient and powerful real-time object detection algorithm in comparison with several other object detection algorithms. Video surveillance cameras are being used to implement this system. A model will be trained against the most comprehensive datasets, such as the COCO datasets, for this purpose. As a result, high-risk zones, or areas where virus spread is most likely, are identified. This may support authorities in enhancing the setup of a public space according to the precautionary measures to reduce hazardous zones. The developed framework is a comprehensive and precise solution for object detection that can be used in a variety of fields such as autonomous vehicles and human action recognition. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Smart Health Care Monitoring and Indoor Occupancy Control System using Arduino

The recent COVID-19 pandemic has necessitated the need to develop effective COVID-19 pandemic control strategies. One of the crucial steps for individual protection is to stop the virus spread by the wearing face masks. The proposed method is developed to monitor the infected people in the crowded public areas like shopping centers, wedding hall, workplace, school or college. The abnormal temperature is detected by using sensor and the obtained signal will then be sent to the Arduino device connected to the controller. In order to stop the spread of COVID 19 viruses, this study intends to design and develop a novel system to automatically limit the room capacity based on temperature. The proposed Atmega328 microcontroller-based body temperature detection and a room capacity measuring device is connected with the android smart phone of the user. © 2022 IEEE.

Model-Based Optimization of Vaccination Strategies in Different Phases of Pandemic Virus Spread

This paper aims at applying optimal control principles to investigate optimal vaccination strategies in different phases of a pandemic. Background of the study is that many countries have started their vaccination procedures against the COVID-19 disease in the beginning of 2021, but supply shortages for the vaccines prevented that everyone could be vaccinated immediately. At the beginning of 2022, in contrast, the vaccine supply was ample, but the effectiveness of different existing vaccines to avoid infection by new virus variants was in doubt, as well as the acceptance of booster doses decreased over time. To account for these effects, two formulations of optimization tasks based on different epidemic models are proposed in this paper. The solution of these tasks determines optimal distribution strategies for available vaccines, and optimized vaccination schemes to reduce the need of booster doses for later phase. Effectiveness of these strategies compared with other popular strategies (as applied in practice) is demonstrated through a series of simulations © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

COVID-19 and Minimizing Micro-Spatial Interactions

COVID-19, the novel coronavirus that has disrupted lives around the world, continues to challenge how humans interact in public and shared environments. Repopulating the micro-spatial setting of an office building, with virus spread and transmission mitigation measures, is critical for a return to normalcy. Advice from public health experts, such as maintaining physical distancing from others and well-ventilated spaces, are essential, yet there is a lack of sound guidance on configuring office usage that allows for a safe return of workers. This paper highlights the potential for decision-making and planning insights through location analytics, particularly within an office setting. Proposed is a spatial analytic framework addressing the need for physical distancing and limiting worker interaction, supported by geographic information systems, network science, and spatial optimization. The developed modeling approach addresses dispersion of assigned office spaces as well as associated movement within the office environment. This can be used to support the design and utilization of offices in a manner that minimizes the risk of COVID-19 transmission. Our proposed model produces two main findings: (1) that the consideration of minimizing potential interaction as an objective has implications for the safety of work environments, and (2) that current social distancing measures may be inadequate within office settings. Our results show that leveraging exploratory spatial data analyses through the integration of geographic information systems, network science, and spatial optimization, enables the identification of workspace allocation alternatives in support of office repopulation efforts. © 2022 held by the owner/author(s).

COVID-19 Detection Using Deep Learning Based Medical Image Segmentation

COVID-19 is a rapidly spreading illness around the globe, yet healthcare resources are limited. Timely screening of people who may have had COVID-19 is critical in reducing the virus's spread considering the lack of an effective treatment or medication. COVID-19 patients should be diagnosed as well as isolated as early as possible to avoid the infection from spreading and levelling the pandemic arc. To detect COVID-19, chest ultrasound tomography seems to be an option to the RT-PCR assay. The Ultrasound of the lung is a very precise, quick, relatively reliable surgical assay that can be used in conjunction with the RT PCR (Reverse Transcription Polymerase Chain Reaction) assay. Differential diagnosis is difficult due to large differences in structure, shape, and position of illnesses. The efficiency of conventional neural learning-based Computed tomography scans feature extraction is limited by discontinuous ground-glass and acquisitions, as well as clinical alterations. Deep learning-based techniques, primarily Convolutional Neural Networks (CNN), had successfully proved remarkable therapeutic outcomes. Moreover, CNNs are unable to capture complex features amongst images examples, necessitating the use of huge databases. In this paper semantic segmentation method is used. The semantic segmentation architecture U-Net is applied on COVID-19 CT images as well as another method is suggested based on prior semantic segmentation. The accuracy of U-Net is 87% and by using pre-trained U-Net with convolution layers gives accuracy of 89.07%. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Medical Assistance Robot with capabilities of Mask Detection with Automatic Sanitization and Social Distancing Detection/ Awareness

Healthcare sectors such as hospitals, nursing homes, medical offices, and hospice homes encountered several obstacles due to the outbreak of Covid-19. Wearing a mask, social distancing and sanitization are some of the most effective methods that have been proven to be essential to minimize the virus spread. Lately, medical executives have been appointed to monitor the virus spread and encourage the individuals to follow cautious instructions that have been provided to them. To solve the aforementioned challenges, this research study proposes an autonomous medical assistance robot. The proposed autonomous robot is completely service-based, which helps to monitor whether or not people are wearing a mask while entering any health care facility and sanitizes the people after sending a warning to wear a mask by using the image processing and computer vision technique. The robot not only monitors but also promotes social distancing by giving precautionary warnings to the people in healthcare facilities. The robot can assist the health care officials carrying the necessities of the patent while following them for maintaining a touchless environment. With thorough simulative testing and experiments, results have been finally validated. © 2022 IEEE.

Impact of Vaccination Policies for COVID-19 Using Hybrid Simulation

A stochastic model for individual immune response is developed. This model is then incorporated in a larger simulation model for the spread of COVID-19 in a population. The simulator allows random transitions between being susceptible, exposed, having mild or severe symptoms, as well as random non-exponential sojourn times in those states. The model is more efficient than others based on geographical location, where the virus spreads according to actual distance between individuals. We are able to simulate much larger populations and vary parameters such as time between vaccinations, probability of infection, and so on. We present an application to study the effects on healthcare as a function of vaccination policies. © 2022 IEEE.

Real-Time Face Mask Detection from CCTV Video Frames using Deep Neural Networks

The coronavirus, commonly known as SARS COVID-19, is causing a pandemic that is affecting individuals all over the world. The spread of the virus compelled the authorities to impose a rigorous lockdown on its citizens. Every person in society may experience a variety of issues as a result of this. According to WHO (World Health Organization) regulations, the sole method to halt the virus's spread is to wear a face mask. Therefore, the suggested approach makes sure that everyone appropriately wears a face mask in public locations. The objective of this approach is to detect people without face masks and people who wear facemasks incorrectly in social environments. This system consists of multiple face detection modules to find the area of interest within the video frames. In the next level, using the trained Deep Learning model, the presence of a mask is detected and faces without mask and faces wearing masks incorrectly are highlighted. The dataset for face mask identification comprises of 8190 photos with unique facial annotations from the Kaggle and RMFD datasets that come into two categories: "with mask” and "without mask”. © 2022 IEEE

Tracking of Travel Log and Salesforce of Covid-19 Perspective

The purpose of this analysis is to double-check the phrasing in order to make it simpler for experts and scientists to comprehend the impact of the coronavirus on the tourism industry and develop solutions to this problem. This study looks at how and why the pandemic has affected people's freedoms by analyzing the events and worries that have arisen as a result of the virus's spread. By doing so, the study pinpoints personality traits, societal norms, and unproven assumptions that the tourist sector needs to challenge and change. The report also examines the severe difficulties encountered by the travel industry throughout the Coronavirus phases and analyses some issues with the proposed remedy based on Salesforce technology. This document summarizes the nature and scope of the coronavirus, its effects on the tourism sector, and suggestions for analyzing those effects and mitigating some of them through the use of Salesforce's 'Travel Log Analysis.' © 2022 IEEE.

Intelligent Surface-Enhanced Raman Scattering Sensor System for Virus Identification

COVID-19 has devastated the entire world for the past couple of years. Timely and efficient detection and identification of a virus are crucial in preventing the wider virus spread. By using intelligent sensors based on Surface-Enhanced Raman Scattering (SERS), it is possible to detect and identify virus automatically. In this study, we successfully applied the XGBoost Algorithm (Supervised Machine Learning) to classify the type of the virus using the SERS sensor data. The supervised approach has a limitation when a new type of virus arises, whose shape is different from the previously known samples. To tackle this problem, we investigated the unsupervised learning approaches that can cluster the virus data into different groups without labeled data. The unsupervised approach presented in this paper is called k-Shape Clustering. This technique compares the cross-correlation between different samples and then clusters them into similar or different groups. If a subvariant of a virus emerges, it would be clustered into the existing virus groups;if a new type of virus is found, it would be clustered into a new group. Both of the approaches have shown very promising results based on extensive evaluations. © 2022 IEEE.

Al-Biruni Earth Radius Optimization for COVID-19 Forecasting

Several instances of pneumonia with no clear etiology were recorded in Wuhan, China, on December 31, 2019. The world health organization (WHO) called it COVID-19 that stands for "Coronavirus Disease 2019," which is the second version of the previously known severe acute respiratory syndrome (SARS) Coronavirus and identified in short as (SARSCoV-2). There have been regular restrictions to avoid the infection spread in all countries, including Saudi Arabia. The prediction of new cases of infections is crucial for authorities to get ready for early handling of the virus spread. Methodology: Analysis and forecasting of epidemic patterns in new SARSCoV-2 positive patients are presented in this research using metaheuristic optimization and long short-term memory (LSTM). The optimization method employed for optimizing the parameters of LSTM is Al-Biruni Earth Radius (BER) algorithm. Results: To evaluate the effectiveness of the proposed methodology, a dataset is collected based on the recorded cases in Saudi Arabia between March 7th, 2020 and July 13th, 2022. In addition, six regression models were included in the conducted experiments to show the effectiveness and superiority of the proposed approach. The achieved results show that the proposed approach could reduce the mean square error (MSE), mean absolute error (MAE), and R2 by 5.92%, 3.66%, and 39.44%, respectively, when compared with the six base models. On the other hand, a statistical analysis is performed to measure the significance of the proposed approach. Conclusions: The achieved results confirm the effectiveness, superiority, and significance of the proposed approach in predicting the infection cases of COVID-19. © 2023 CRL Publishing. All rights reserved.

A Simulation Model for Predicting the Spread of COVID-19 Virus

COVID-19 has shown a high potential of transmission within the last two years. To interrupt the chain of transmission, it is estimated that 85% of the population must be immune. Since not all society wants to take vaccinations, it is very important to predict how the current precautions will impact the virus development. This paper presents a simulation model framework that can be used to predict the development of SARS-CoV-2 virus. The model was based on SEIR (Susceptible-Exposed-Infectious-Removed) model but was significantly extended by adding a set of additional changeable parameters and a new layer responsible for modelling the virus spread patterns. To test the capability of the model to predict the virus spread in a hermetic group of people, we run the 28-days simulation of the spread of the 4-th wave of COVID-19 in a shopping mall visited by 6500 agents. The simulation results showed a remarkable relation to the real development of the 4th wave of COVID-19 in a small hermetic community (the gryfiński district in West Pomeranian Voivodeship of Poland). © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

The transformation from Pandemic to Endemic of Covid-19: Spatio-temporal Analysis of Citizen Mobility in Asia Countries

Covid-19 began in 2019 until now in 2022. It is still taking place in various countries, including Asia. It is necessary to lock down or limit citizens' activities to prevent the virus's spread. The imposition of lockdowns for several countries causes the mobility of citizens to be modified in many sectors. However, with vaccination in stages starting in 2021, people will become more resistant to the risk of being exposed to or dying from Covid-19. The process of transforming the return of activity before the pandemic needs to be observed, especially how countries' readiness, especially in Asia, to accept Covid-19 as endemic. The data used in this study are data on Covid-19 cases and Vaccinations by WHO (World Health Organization) and also Mobility Data from Google. This data will be used to conduct observations and Spatio-temporal analysis to see the development of cases, vaccinations, and mobility of citizens for various sectors. This research will include several stages of study, such as descriptive statistics, correlation analysis, and the last is trend analysis. The results of this study for countries in Asia are 21.87% have mobility above the baseline since 2021. Next, 46.87% of countries in Asia have mobility, with an increasing trend in 2022. Only 18.75% of countries in Asia have stable mobility. Below the baseline, there are also about 12.5% of countries in Asia that are around the baseline. In general, countries in Asia have an excellent response to the imposition of an endemic status for Covid-19. © 2022 IEEE.

An Approach to Strengthen Pandemic Resilience of the IT Industry Considering Employees Perception—A Systematic Survey

The whole world is presently battling against the Coronavirus pandemic, which has tested every aspect of life. The virus spread has severe implications on the global economy. In the face of a deteriorating economic environment brought on by globalisation, Indian industries must bear the significant economic brunt and suffer severe consequences. One among the seriously affected industry is the well-reputed Indian IT industry. During COVID 19 era, employees from the IT sector have faced various troublesome situations. In the present study, a web-based survey consisting of questions concerning WFH during pandemic has been conducted to visualise the repercussions caused due to COVID 19 pandemic. This paper studies the benefits, challenges, and implications for IT firms moving forward while also analysing employees’ perceptions of the current work situation to improve the current systems. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

ST-COVID: a Deep Multi-View Spatio-temporal Model for COVID-19 Forecasting

The outbreak of COVID-19 has caused a dramatic loss of human life worldwide. Reliable prediction results are crucial on pandemic prevention and control in the early stage. However, it is a very challenging task due to insufficient data and dynamic virus spread pattern. Unlike most existing works only considering local data for a given region, we propose a spatio-temporal prediction model (ST-COVID) for COVID-19 forecasting to borrow experience from historical observations of other regions. Specifically, our proposed model consists of two views: spatial view (modeling global spatial connectivity with neighbor regions in geography and semantic space via GCNs), temporal view (extracting local and global latent temporal trend via CNNs and GRU). Extensive experiments on two real-world datasets at state and county level in US indicate that the proposed model outperforms over nine baselines in both short-term and long-term prediction. © 2022 IEEE.