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.