Abstract
Coronavirus disease (COVID-19) primarily spreads through imbalanced social distancing practices. Automatic analysis is possible through deep learning-based methods to understand and control COVID-19. Healthcare analysis and prediction are best made in the situation of a pandemic such as COVID-19. This analysis can be used to classify the COVID-19 and non-COVID-19 groups and social distancing measures with good estimation by preventing immense dissemination. Postpreventive measures require parallel reinforcement to analyse current, upcoming, and uncertain situations of COVID-19 prevalence, which are effectively handled by implementing multicriteria decision-making methods. Herein, we estimate and measure the social distance by deep learning technique usage (You Only Look Once, Version 3 is a real-time object detection algorithm) in the proposed model for the analytic network process. The multicriteria decision making increases the evaluation of the risk factors. The modification of the pandemic model increases the application of social distancing and preventive measures. This model will alert us when the number of people exceeds in some area from the experimented barrier. RISTECB simulation is used in the preventive measures of the social distance among the sample population to see the initiators, infectors, suspicious, expirer, survivor, and transmitters. Postpreventive criteria used those results to set the barriers that are the critical points for prevention in uncertain situations. Therefore, this paper aimed to develop a framework, including social distancing and distance estimation, by using deep learning-based techniques through multicriteria decision-making methods such as the analytical network process. For simulation for statistical information of inclusive information of preventive measures and postpreventive measures, an automatic resonant transfer learning-based practice is used. General proportional analyses illustrate that the projected model helps in postpandemic COVID-19 preventive measures by amalgamating multiple techniques.
Highlights
Coronavirus disease 2019 (COVID-19) has become a global pandemic, and due to the prior unavailability of vaccines, detection of COVID-19 at the early stage is necessary to avoid its further spreading. e natural source of this virus is unidentified [1]
COVID-19 was publicised as a pandemic on February 11, 2020, by the World Health Organization (WHO)
Estimation, and COVID-19 classification with limited resources during a pandemic is the primary concern of the current situation
Summary
Coronavirus disease 2019 (COVID-19) has become a global pandemic, and due to the prior unavailability of vaccines, detection of COVID-19 at the early stage is necessary to avoid its further spreading. e natural source of this virus is unidentified [1]. Coronavirus disease 2019 (COVID-19) has become a global pandemic, and due to the prior unavailability of vaccines, detection of COVID-19 at the early stage is necessary to avoid its further spreading. In Wuhan City, Hubei Province, China, the COVID-19 firstly broke out on December 31, 2019. It spread like a fire in China and infected a group of children and juveniles and frequently originated in the old across the entire world [2]. It was identified that the respiratory disease has common symptoms: cough, fever, body aching, and breathing strain [3]. Direct or indirect contact with a disease-ridden person causes the infection by the scattering of the virus through the droplets on coughing and sneezing [6]. Vaccines or drugs for this disease are unavailable, so isolation and social distancing are the only solutions to this infection
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