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The current study compared air pollution levels during the Covid-19 pandemic years (14 Nov. 2020, 04 Nov. 2021) and the previous year's Diwali celebrations (19 October 2017, 07 November 2018, 27 October 2019) in Delhi. PM2.5, NH3, SO2, PM10, NO2, CO, and O3 concentrations were substantially higher in 2020 Diwali than in 2017, 2018, 2019, and 2021 Diwali. PM2.5, PM10, and CO concentrations were always above the permissible limits (Very poor and Sever AQI Category);however, except on Diwali days, NO2 concentrations were within allowable limit (Good and Satisfactory AQI Category), and other pollutants such as SO2, NH3, and O3 concentrations were determined to be within permissible limits (Good AQI Category) throughout the year in Delhi. This data suggests that during the pandemic, people were following the guideline given by honorable Supreme Court of India and use less amount of firecrackers than in previous years. But the stubble burning contribution in 2020 was higher than last year and the meteorological condition was also unfavorable in that year. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.
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Objective: The objective of the study was to find out any peculiarities in the epidemiological and clinical profiles of COVID-19 cases, admitted in the hospital;which may be useful in management of health services in future. Method(s): Retrospective analysis of hospital records of COVID-19 cases admitted from March to May 2021 in our COVID hospital. A total of 1332 hospital case records were analyzed. Result(s): Out of 1332 admitted COVID-19 cases, 50% were in age group 40-60 years. About 60% cases were male. Symptoms were fever (88.29%), sore throat (70.64%), breathlessness (58.84%), loss of smell (58.82%), pain in abdomen (53%), loss of taste (35.29%), and diarrhea (29.43%). Most cases had multiple symptoms. About 60% cases came in serious condition. About 65% cases needed intensive care unit admission. About 50% cases expired. Conclusion(s): Only peculiarity noticed in clinical profile was loss of taste and sense of smell in few cases. Preponderance of males in the age group of 40-60 years and high mortality among the admitted cases was only peculiar epidemiological feature. Copyright © 2022 The Authors. Published by Innovare Academic Sciences Pvt Ltd. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/)
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In early 2020, WHO declared COVID-19, a pandemic disease, which severely infected human inhabitant and health. Researchers, doctors, etc., are finding ways to combat the disease. RT-PCR testing is the initial type of testing that was used to detect whether a patient is COVID (+) or COVID (−).This test kit is costly and the result takes around 6hours. So testing a heavy chunk of the population with RT-PCR is a difficult task. To counter this, X-rays/CT scan-based testing can be used to detect COVID (+) cases to control its spread. X-rays are preferable to CT as they are cheaper and even produce low radiations. The second issue that was noticed during this pandemic period was the availability of doctors. To resolve this issue, a robust automated system for early prediction is essential. Automated systems using machine learning (ML), deep learning (DL) approaches are giving promising results in the detection of COVID (+) cases. In this chapter, we propose a framework for automatic recognition of COVID (+), normal, and pneumonia cases (i.e., multiclassification) over X-ray images. In the proposed method, a dataset of COVID (+), normal, and pneumonia images is used. Initially, the dataset is preprocessed, followed by feature extraction using gray level cooccurrence matrix (GLCM), gray level difference method (GLDM), wavelet transform (WT), and fast Fourier transform (FFT) methods. Features extracted are concatenated to construct a feature pool and these features are used for multiclassification using ML algorithms: support vector machines (SVM) and XG Boost. XG Boost performs better than SVM. © 2022 Elsevier Inc. All rights reserved.
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In wireless communications several imaging methods are developed. During this pandemic situation in worldwide due to COVID 19, telemedicine plays a vital role in maintaining social distancing. In telemedicine, there is demand for high compression and encryption approaches to overcome bandwidth and security issues. In this work, a joint compression and encryption (JCE) approach for images is proposed. The proposed model is more secure with faster computational time. For compression Absolute Moment Truncation Coding approach is used and furthermore for encryption two chaotic maps i.e., Arnold’s Cat and Henon Map are applied on the compressed image. Further, the experimental results were studied to measure the performance of the model for brute force, differential and statistical attack. The proposed schemes have shown good results for compression and even for security i.e., NPCR 99.86, UCAI=30.29, MSE= 108.91, PSNR=28.34, Correlation Coefficient = -0.0054 and Entropy 7.97 against the various other approaches. © 2021 Taru Publications.
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In this report, we propose an alternative way of detecting the Covid-19 disease using Convolutional Neural Networks based deep learning models which will classify between Covid-19 and other similar respiratory diseases such as other types of Viral Pneumonia or Bacterial Pneumonia, using images of Chest X-Rays. The models developed as part of this project achieved approximately 98% accuracy on a test dataset of 300 images. © 2021 IEEE.
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The COVID-19 pandemic, emerging at the end of 2019, hit hard countries all over the world – including Central Europe. Nevertheless, both infection rates and fatalities have significant spatial differences. The aim of this paper is to reveal the spatial patterns of COVID-19 in Central Europe on various spatial scales. During the first wave in the spring of 2020 some of the countries encountered relatively low levels of infections and fatalities. The second wave of the pandemic caused significant health and health care problems in the whole region. The first wave hit Austria, Germany and Switzerland harder, and the dense urban agglomerations had the most outstanding concentrations of coronavirus infections. The spatiality of the pandemic changed during the second wave;the number of infections and fatalities grew in the Eastern countries of the region as well as in rural areas. By this token the second wave had a more severe effect on areas which were less affected before. The factors shaping these spatial processes are diverse and fluctuating over space and time, creating a complex spatial system of causes and consequences. © 2021