ABSTRACT
This novel coronavirus (CoV) is known as 'SARS-CoV2' or '2019 novel coronavirus' or 'COVID-19' by the WHO. It is started at the end of 2019 in China. It is the outbreak of pneumonia related to chest issues. COVID-19 is an infectious virus. As COVID-19 is a contiguous disease, early detection is more important. It can be incurable if the virus is detected later. The identification of COVID-19 disease is done by collecting samples from the throat and nose. Sometimes when the patient is more severe, that time they are asked to take a chest X-Ray. This research proposed a system for the identification of the virus utilizing X-Ray images. Dataset used consists of both Covid and Normal X-ray images. In this research, we used the ResNet50 model to predict the disease. It contains 48 convolutional layers, one MaxPool, and Average Pool layers. 'RELM' is a suitable classifier, and it gave better accuracy than other classifiers. This research can be practically helpful to the physicians with the usage of datasets for the successful diagnosis of pandemic disease (COVID-19) in the healthcare field. We built the RELM classifiers with convolution Neural Network as our contribution in this research. © 2022 IEEE.
ABSTRACT
Background: The Coronavirus disease 2019 (COVID-19) pandemic poses a serious public health concern worldwide. Certain regions of the globe were severely affected in terms of prevalence and mortality than other. Although the cause for this pattern is not clearly understood, lessons learned from previous epidemics and emerging evidences suggest the major role of ecological factors like ambient air pollutants (AAP) and meteorological parameters in increased COVID-19 incidence. The present study aimed to understand the impact of these factors on SARS-CoV-2 transmission and their associated mortality in major cities of India.Methods: This study used secondary AAP, meteorological and COVID-19 data from official websites for the period January-November 2020, which were divided into Pre-lockdown (January-March 2020), Phase I (April to June 2020) and Phase II (July to November 2020). After comprehensive screening, five major cities that includes 48 CPCB monitoring stations collecting daily data of ambient temperature, particulate matter PM2.5 and 10 were analysed. Spearman and Kendall’s rank correlation test was performed to understand the association between SARS-CoV-2 transmission and AAP and, meteorological variables. Similarly, case fatality rate (CFR) was determined to compute the correlation between AAP and COVID-19 related morality.Results: The level of air pollutants in major cities were significantly reduced during Phase I compared to Pre-lock down and increased upon Phase II in all the cities. During the Phase II in Delhi, the strong significant positive correlation was observed between the AAP and SARS-CoV-2 transmission. However, in Bengaluru, Hyderabad, Kolkata and Mumbai AAP levels were moderate and no correlation was noticed. The relation between AT and SARS-CoV-2 transmission was inconclusive as both positive and negative correlation observed. In addition, Delhi and Kolkata showed a positive association between long-term exposure to the AAP and COVID-19 CFR. Conclusion: Our findings support the hypothesis that the particulate matter upon exceeding the satisfactory level serves as an important cofactor in increasing the risk of SARS-CoV-2 transmission and related mortality. These findings would help public health experts to understand the SARS-CoV-2 transmission against ecological variables in India and provides supporting evidence to healthcare policymakers and government agencies for formulating strategies to combat the COVID-19.