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1.
Journal of Clinical & Scientific Research ; 11(2):77-82, 2022.
Article in English | Academic Search Complete | ID: covidwho-1835177

ABSTRACT

Background: Severe acute respiratory syndrome Coronavirus2 (SARSCoV2) disease (COVID-19) has spread nationwide including union territory of Puducherry. Methods: Consecutive asymptomatic or mildly symptomatic COVID-19 patients admitted to the COVID-19 ward were included in the study. Demographic details, following of social norms, contact-exposure history, presence of co-morbidities, vital parameters, clinical symptoms and signs, development of new symptoms, progression and outcome of study patients are reported. Results: Six hundred and forty two patients were included for final analysis. Most of symptomatic patients did not use face mask (87%) and did not follow social distancing (84.1%) or hand hygiene (91.3%). Out of mildly symptomatic patients, 12 become moderately or severely symptomatic and were shifted to intensive care unit. All these patients were male, aged more than 50 years with co-morbidities. Conclusions: Wearing face mask, social distancing and hand hygiene can decrease disease severity. Male patients with co-morbidities and old age are at higher risk of progression to moderate or severe COVID-19 infection. [ FROM AUTHOR] Copyright of Journal of Clinical & Scientific Research is the property of Sri Venkateswara Institute of Medical Sciences and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)

2.
3rd International Conference on Electronic Communication and Artificial Intelligence, IWECAI 2022 ; : 507-511, 2022.
Article in English | Scopus | ID: covidwho-1831841

ABSTRACT

The use of computer vision to realize non-contact face mask wearing testing and identify the standardization of wearing can improve the efficiency of mask wearing inspection, which is of great significance to reduce the spread of COVID-19. Based on the YOLO v3 framework in deep learning, this project conducts an in-depth study on the accuracy and efficiency of face mask detection, introduces its structure and principle, and explores and experiments its application based on TensorFlow. The evaluation and identification rate of the experiment was 78∗, which verified the feasibility and practicability of the method. © 2022 IEEE.

3.
21st International Symposium INFOTEH-JAHORINA, INFOTEH 2022 ; 2022.
Article in English | Scopus | ID: covidwho-1831827

ABSTRACT

This paper describes research effort aimed at the use of machine learning, Internet of Things, and edge computing for a use case in health, mainly the prevention of the spread of infectious diseases. The main motivation for the research was the Covid-19 pandemic and the need to improve control of the prevention measures implementation. In the study, the experimentation was focused on the use of machine learning to create and utilize prediction models for face mask detection. The prediction model is then evaluated on the various platforms with a focus on the use on various edge devices equipped with a video camera sensor. Different platforms have been tested and evaluated such as standard laptop PC, Raspberry Pi3, and Jetson Nano AI edge platform. Finally, the paper discusses a possible approach to implement a solution that would utilize the face mask detection function and lays out the path for the future research steps. © 2022 IEEE.

4.
2021 International Conference on Computational Performance Evaluation, ComPE 2021 ; : 815-819, 2021.
Article in English | Scopus | ID: covidwho-1831747

ABSTRACT

The coronavirus pandemic (COVID-19) has unfolded hastily throughout the entire world. This pandemic disease can spread through droplets and can be airborne. Hence, the use of face masks in public places is crucial to stop its spread. The present study aims to develop a system that can identify masked or non-masked faces;whether it is a normal mask, transparent mask, or a face alike mask. The face mask detection system is developed with the help of Convolutional Neural Networks (CNN). The model compression technique of Knowledge Distillation has been used to make the machine lesser computation and memory intensive so that it is simple to install the model on a few embedded gadgets and cell computing platforms. Using the model compression technique and GPU systems will help boom the calculation velocity of the model and drop the storage space required for calculations. The experimental outcomes show that the developed detector is capable to classify diverse types of masks. Also, it can classify video images in real-time. Using the Knowledge Distillation on the baseline model can improve the testing accuracy from 88.79% to 90.13%. The proposed unique system can be implemented to assist in the prevention of COVID-19 spread and detect various mask types. © 2021 IEEE.

5.
2021 International Conference on Computational Performance Evaluation, ComPE 2021 ; : 803-809, 2021.
Article in English | Scopus | ID: covidwho-1831737

ABSTRACT

Covid-19 has brought various complications in our day-to-day life leading to a disruption in overall movements across the world. Although still researchers and scientists are working on finding more effective ways to deal with it, wearing a face is one of the most simplistic yet efficient ways to overcome this. Wearing a face mask all the time in public places has become a new normal. Therefore, face mask detection for monitoring of people in public places has become a crucial task. Deep learning has been used to make recent advances in the field of object detection. To accomplish this objective, this research employs three state-of-the-art object identification models, notably YOLOv4 and YOLOv4-tiny. The models were trained using a dataset that included photos of persons wearing and not wearing masks. Considering it for surveillance purposes, it can also be used for detection of face and mask in motion. The models employ an approach that involves drawing bounding boxes (red or green) around people's faces and determining whether or not they are wearing a face mask. Further, the performance of these models was compared using mAP, recall F1-score and FPS © 2021 IEEE.

6.
International Conference on Electrical and Electronics Engineering, ICEEE 2022 ; 894 LNEE:327-344, 2022.
Article in English | Scopus | ID: covidwho-1826337

ABSTRACT

The COVID-19 pandemic has reshaped everyone’s life as we know it. Many measures and proposals have been suggested to prevent the virus’s rapid spread since scientists detected it. In the midst of many of these, one major piece of advice was to “wear a mask!”. The mask or face coverings can protect the wearer from contracting the virus or spreading it to others. Hence, to ensure that the public is not wearing a mask, one of the applications we have developed is face mask detection, which uses biometrics to map the features of a human face, analyse the data, and determine whether the detected face is a positive image or a non-face or negative image by placing a box around it. The main ideology of studying and researching face mask detection is to use computer technology to directly mimic the action and manner of human facial features and develop a system that reduces the amount of effort required to identify whether or not the person is wearing a mask and notifying them with the assistance of the proposed model. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

7.
2nd International Conference on Intelligent and Cloud Computing, ICICC 2021 ; 286:317-325, 2022.
Article in English | Scopus | ID: covidwho-1826297

ABSTRACT

The spread of coronavirus can be prevented among the people in a crowded place by making face mask mandatory so that the droplets from the mouth and nose would not reach the masses nearby. The negligence of some people, i.e., by not wearing the mask, causes the spread of this pandemic. Therefore, persons who do not wear masks should be tracked at the entrance to public venues such as malls, institutions, and banks. The mechanism proposed warns if the individual is wearing or not wearing the mask. The proposed system is built in a small CNN model to integrate any low-end devices with minimal cost. The small CNN model like ShuffleNet and Mobilenetv2 are evaluated in Transfer Learning and Deep Learning but the Deep Learning model has better performance than the Transfer Learning. Again, the Deep Learning approach, i.e., mobilenetv2 plus Support Vector Machine achieved 99.5% accuracy, 99.01% sensitivity, 100% precision, 100% FPR, 99.51% F1 score, 99.01% MCC, and 99.01% kappa coefficient. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

8.
2nd International Conference on Intelligent and Cloud Computing, ICICC 2021 ; 286:295-303, 2022.
Article in English | Scopus | ID: covidwho-1826296

ABSTRACT

The whole world is passing through a very difficult time since the outbreak of Covid-19. Wave after wave of this pandemic hitting people very hard across the globe. We have lost around 3.8 million lives so far to this pandemic. Moreover, the impact of this pandemic and the pandemic-induced lockdown on the lives and livelihoods of the people in the developing world is very significant. Till now there is no one-shot remedy available to stop this pandemic. However, spread can be controlled by social distancing, frequent hand sanitization, and using a face mask in public places. So, in this paper, we proposed a model to detect face mask of people in public places. The proposed model uses OpenCv module to pre-process the input images, it then uses a deep learning classifier MobileNetV3 for face mask detection. The accuracy of the proposed model is almost 97%. The proposed model is very light and can be installed on any mobile or embedded system. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

9.
International Conference on Artificial Intelligence and Sustainable Engineering, AISE 2020 ; 837:367-379, 2022.
Article in English | Scopus | ID: covidwho-1826273

ABSTRACT

The deadliest COVID-19 (SARS-CoV-2) is expanding steadily and internationally due to which the nation economy almost come to a complete halt;citizens are locked up;activity is stagnant and this turn toward fear of government for the health predicament. Public healthcare organizations are mostly in despair need of decision-making emerging technologies to confront this virus and enable individuals to get quick and efficient feedback in real-time to prevent it from spreading. Therefore, it becomes necessary to establish auto-mechanisms as a preventative measure to protect humanity from SARS-CoV-2. Intelligence automation tools as well as techniques could indeed encourage educators and the medical community to understand dangerous COVID-19 and speed up treatment investigations by assessing huge amounts of research data quickly. The outcome of preventing approach has been used to help evaluate, measure, predict, and track current infected patients and potentially upcoming patients. In this work, we proposed two deep learning models to integrate and introduce the preventive sensible measures like face mask detection and image-based X-rays scanning for COVID-19 detection. Initially, face mask detection classifier is implemented using VGG19 which identifies those who did not wear a face mask in the whole crowd and obtained 99.26% accuracy with log loss score 0.04. Furthermore, COVID-19 detection technique is applied onto the X-ray images that used a Xception deep learning model which classifies whether such an individual is an ordinary patient or infected from COVID-19 and accomplished overall 91.83% accuracy with 0.00 log loss score. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

10.
Lecture Notes on Data Engineering and Communications Technologies ; 113:60-67, 2022.
Article in English | Scopus | ID: covidwho-1826246

ABSTRACT

The ongoing COVID-19 has caused a great amount of serious troubles for people around the world. Even though vaccination has been proven to be safe and highly effective against the COVID-19, it is far away to prevent thoroughly the spread of the disease and truly halt the pandemic. Therefore, we need to apply additional methods aside from vaccine injection, such as keeping the distance between people and always using the face masks during the ordinary conversations, in efforts to further reduce the COVID-19 contagion rate. To implement such methods, this research aims to investigate an efficient approach to detect and warn people that they should wear mask whenever they go to public places. Our proposed system studies the benefits of Local Binary Pattern (LBP) and deep learning model to provide accurate face mask detection and classification system. After comprehensive testing, we found that our system provided the detection rate up to 90% with the Kaggle, Face-Mask-Net, and our own datasets. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

11.
6th International Conference on Advances in Biomedical Engineering (ICABME) ; : 147-150, 2021.
Article in English | Web of Science | ID: covidwho-1822019

ABSTRACT

The number of diseases has greatly increased in the last decades in addition to the global pandemic that affected everyone's life and killed a lot of humans. With this increase, the use of masks has greatly increased as well. Modern masks have various problems and can be inconvenient to keep all day long. One of its worst problems is that they cause the fogging up of eyeglasses. Many products have tried to solve this problem, but none was completely successful. This paper aims to design a new mask that tackles this problem in an innovative way by optimizing the airflow of warm breath to exit the mask from the sides instead of going upward. The mask is for human use and can be adopted by global standards.

12.
Journal of Global Operations and Strategic Sourcing ; : 27, 2022.
Article in English | Web of Science | ID: covidwho-1822014

ABSTRACT

Purpose COVID-19 pandemic has exposed that even the best of the developed nations have surrendered to the devastations imposed on the global supply chains. The purpose of this study is to explore how COVID-19 has exaggerated the supply chain of production and distribution of Taiwan-based face masks and also investigate the conscientious factors and subfactors for it. Design/methodology/approach In this study, an analytical hierarchy processes (AHP)-based approach has been used to assign the criterion weights and to prioritize the responsible factors. Initially, based on 26 decision-makers, successful factors were categorized into five main categories, and then main categories and their subcategories factors were prioritized through individual and group decision-maker's contexts by using the AHP approach. Findings The results of this AHP model suggest that "Safety" is the most important and top-ranked factor, followed by production, price, work environment and distribution. The key informers in this study are stakeholders which consist of managers, volunteers, associations and non-governmental organizations. The results showed that good behavior of the employees under the "Safety" category is the top positioned responsible factor for successful production and distribution of face masks to the other countries with the highest global percentage of 15.7% and using sanitizers to protect health is the second most successful factor with the global percentage of 11.7%. Research limitations/implications The limitations faced in this study were limited to only Taiwan-based mask manufacturing companies, and it was dependent on the decisions of the limited company's decision-makers. Originality/value The novelty of this study is that the empirical analysis of this study has been based on a successful Taiwan masks manufacturing company and evaluates the responsible factors for the production and distribution of Taiwan masks to other countries during COVID-19.

13.
Infectious Disease Modelling ; 2022.
Article in English | ScienceDirect | ID: covidwho-1819502

ABSTRACT

A novel coronavirus (COVID-19) has emerged as a global serious public health issue from December 2019. People having a weak immune system are more susceptible to coronavirus infection. It is a double challenge for people of any age with certain underlying medical conditions including cardiovascular disease, diabetes, high blood pressure and cancer etc. Co-morbidity increases the probability of COVID-19 complication. In this paper a deterministic compartmental model is formulated to understand the transmission dynamics of COVID-19. Rigorous mathematical analysis of the model shows that it exhibits backward bifurcation phenomenon when the basic reproduction number is less than unity. For the case of no re-infection it is shown that having the reproduction number less than one is necessary and sufficient for the effective control of COVID-19, that is, the disease free equilibrium is globally asymptotically stable when the reproduction threshold is less than unity. Furthermore, in the absence of reinfection, a unique endemic equilibrium of the model exists which is globally asymptotically stable whenever the reproduction number is greater than unity. Numerical simulations of the model, using data relevant to COVID-19 transmission dynamics, show that the use of efficacious face masks publicly could lead to the elimination of COVID-19 up to a satisfactory level. The study also shows that in the presence of co-morbidity, the disease increases significantly.

14.
5th International Conference on Computational Vision and Bio Inspired Computing (ICCVBIC) ; 1420:239-251, 2021.
Article in English | Web of Science | ID: covidwho-1819414

ABSTRACT

Coronavirus disease 2019 (COVID-19) is an infectious disease caused by severe acute respiratory syndrome coronavirus2. COVID-19 has created the worldwide pandemic situation and it is causing a greater health crisis and deaths of the millions of humans all over the world. All the socio-economic activities are very much affected and there is a huge loss over the world in many aspects. If safety measures are not followed strictly in the public places, then there is a rapid spread of the disese at a very faster rate. Hence, this paper provides a thorough survey of the existing computer vision and machine learning-based technological solutions for controlling the spread of the disease. It also discusses some challenges and future perspectives in developing systems for monitoring the COVID-19 safety violations.

15.
Microbiology and Biotechnology Letters ; 50(1):95-101, 2022.
Article in Korean | EMBASE | ID: covidwho-1819165

ABSTRACT

Due to the pandemic caused by COVID-19, the demand for face masks is soaring and has often caused a shortage. The aim of this study was to evaluate the effect of ultraviolet (UV) and drying treatments on microbial contaminants in facial masks. To conduct this study, standard procedures were designed to develop samples contaminated by the control bacteria Escherichia coli, Staphylococcus aureus, and Pseudomonas aeruginosa. The contamination level of the standard samples was approximately 6.30 × 106 CFU/ml, and the UV light treatment was performed 1, 3, 5, and 7 times. To evaluate the effect of the UV and drying treatments, the masks were first treated with UV 1, 2, and 3 times, followed by the drying process. As a result, the mask contaminated with E. coli and P. aeruginosa showed a bacterial rate of approximately 99.9% after 1 UV irradiation, and in the case of the S. aureus-contaminated mask, it exhibited a bactericidal rate of approximately 99.9% after 7 UV irradiations. However, when the drying process was included after UV irradiation, all the samples contaminated with E. coli, S. aureus, and P. aeruginosa showed a bactericidal rate of 99.9% or more. The results of this study suggest that UV and drying treatments can effectively reduce the bacterial contaminants in facial masks. In addition, these results provide fundamental data and appropriate sterilization methods for reusing masks.

16.
International Journal of Environmental Research and Public Health ; 19(9), 2022.
Article in English | EMBASE | ID: covidwho-1818124

ABSTRACT

Certified disposable respirators afford important protection from hazardous aerosols but lose performance as they are worn. This study examines the effect of wear time on filtration efficiency. Disposable respirators were worn by CSIRO staff over a period of 4 weeks in early 2020. Participants wore the respirator masks for given times up to eight hours whilst working in laboratory/office environments. At that time COVID-19 precautions required staff to wear surgical (or other) masks and increase use of hand sanitizer from dispenser stations. Results obtained from a test group of ten individuals without health preconditions show an increasing number of masks failing with wear time, while the remainder continue to perform nearly unaffected for up to 8 h. Some masks were found to retain filtration performance better than others, possibly due to the type of challenge they were subjected to by the wearer. However, the rate and extent of decay are expected to differ between environments since there are many contributing factors and properties of the aerosol challenge cannot be controlled in a live trial. Penetration and variability increased during wear;the longer the wear time, the more deleterious to particle removal, particularly after approximately 2 h of wear. This behavior is captured in a descriptive statistical model based on results from a trial with this test group. The effectiveness of the masks in preventing the penetration of KCl particles was determined before and after wearing, with the analysis focusing on the most penetrating particles in a size range of 0.3–0.5 µm diameter where respirator masks are most vulnerable. The basic elements of the study, including the approach to filter testing and sample sanitization, are broadly applicable. Conclusions also have applicability to typical commercially available single-use respirator masks manufactured from melt blown polypropylene as they are reliant on the same physical principles for particle capture and electrostatic enhancement was comparable for the particle size range used for detection.

17.
Clinical Cancer Research ; 27(6 SUPPL 1), 2021.
Article in English | EMBASE | ID: covidwho-1816922

ABSTRACT

Purpose: The COVID-19 pandemic has disrupted many facets of life for rural and urban patients with cancer. Here, we characterize the impact of the pandemic on social and health behaviors of rural and urban cancer patients. Methods: N=1,326 adult cancer patients, who visited HCI in the last 4 years and enrolled in either Total Cancer Care or Precision Exercise Prescription studies, completed a COVID-19 survey. The survey was administered between Aug and Sept 2020 and included questions on demographic and clinical information as well as employment status, health behaviors, and COVID-19 prevention measures. Results: The mean age was 61 (19-92) years, with 54% female, 97% non-Hispanic White, 80% stage I-III, 42% employed full or part-time, 25% living in rural counties, and 85% reporting good to excellent overall health. Cancer patients in rural compared to urban counties were more likely to be older (rural=63 vs. urban=60 years;p=0.01), retired or not employed (rural=63% vs. urban=56%;p=0.04), not have health insurance coverage (rural=4% vs. urban=2%;p=0.01), and have ever smoked (rural=35% vs. urban=24%;p=0.001). However, urban patients reported “somewhat” to “a lot” of change in their daily lives more frequently than rural patients (urban=86% vs. rural=77%;p<0.001), but there were no differences in change in social interaction or feeling lonely between populations. Changes in health behaviors namely exercise habits due to the pandemic were more common in patients residing in urban vs. rural counties (urban=51% vs. rural=39%;p<0.001), with more urban patients either exercising less (urban=23% vs. rural=17%) or more frequently (urban=12% vs. rural=8%);however, there were no significant differences with respect to changes in alcohol consumption between these groups. In terms of prevention measures, urban patients compared to rural patients were more likely to use face masks “fairly” or “very often” (urban=94% vs. rural=83%;p<0.001) and also felt they were more likely to contract a COVID-19 infection (22% vs. 14%;p=0.003), but there were no differences for other risk mitigation behaviors, such as hand sanitizer use. Conclusion: These findings suggest that the first 6 months of the COVID-19 pandemic had disparate effects on cancer patients living in rural and urban counties. Rural patients were more likely to have risk factors associated with poor health outcomes, such as not having health insurance coverage and having a history of smoking. However, urban patients were more likely to experience larger changes in their daily lives and exercise habits. Urban patients were more likely to follow preventive measures (e.g., wearing face masks) and felt they were at a greater risk of contracting the virus. Further research is needed to better characterize the pandemic's short- and long-term effects on cancer patients in rural and urban settings and appropriate interventions.

18.
IEEE International Symposium on Technology and Society (ISTAS) ; : 501-501, 2020.
Article in English | Web of Science | ID: covidwho-1816462

ABSTRACT

Taiwan is known for its effective responses to COVID-19, with only 799 confirmed cases by the end of 2020. Based on the previous research, this article identifies three major technical approaches used in Taiwan to prevent the community spread of COVID-19: (1) Digital fence and entry quarantine system to track close contacts and force 14 days of in-home quarantine;(2) Evolving face mask distribution policy and system to ensure fair allocation of the limited face mask resources;and (3) Open-source software co-developed by the government and tech community to share real-time COVID-19 related information and conduct location history based contact tracing. The combat against COVID-19 in Taiwan is a success in digital governance, with great synergy between the government and every citizen.

19.
Vaccine ; 40(17):2473, 2022.
Article in English | EMBASE | ID: covidwho-1815243
20.
Safety and Health at Work ; 2022.
Article in English | EMBASE | ID: covidwho-1815168

ABSTRACT

Background: On the basis of its role for the development of occupational health research, information, good practices, the International Commission on Occupational Health (ICOH) launched the present survey to collect information on public health and prevention policies put in place by the governments of the countries in the world to contain the pandemic. Methods: A cross-sectional study was conducted through an online questionnaire focused on COVID-19 data, public health policies, prevention measures, support measures for economy, work, and education, personal protective equipment, intensive care units, contact tracing, return to work, and the role of ICOH against COVID-19. The questionnaire was administered to 113 ICOH National Secretaries and senior OSH experts. Collected data refer to the period ranging from the beginning of the pandemic in each country to June 30, 2020. Results: A total of 73 questionnaires from 73 countries around the world were considered valid, with a 64.6% response rate. Most of the respondents (71.2%) reported that the state of emergency was declared in their country, and 86.1% reported lockdown measures. Most of the respondents (66.7%) affirmed that the use of face masks was compulsory in their country. As for containment measures, 97.2% indicated that mass gatherings (meetings) were limited. Regarding workplace closing, the most affected sector was entertainment (90.1%). Conclusion: The results of this survey are useful to gain a global view on COVID-19 policy responses at country level.

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