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The lockdown measures to contain the coronavirus disease 2019 (COVID‐19) pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS‐CoV‐2) in China in early 2020 was considered effective by the World Health Organization and many academics around the world. At the same time, the Western media labelled these measures as draconian. This research examined the reasons why the Chinese people were willing to support such measures. Based on an online survey of 528 Chinese participants, conducted in April 2020, the results revealed that the participants' instrumental attitudes (e.g., the outcome of the lockdown), but not experiential attitudes (i.e., the experience of the lockdown), were predictive of their support for a lockdown policy. Further analysis showed that those with higher communitarian worldviews had favourable instrumental attitudes and strong support regardless of the level of the perceived severity of the virus, whereas those with lower communitarian worldviews had more favourable instrumental attitudes and policy support when perceived severity was high than when it was low. Both theoretical and practical implications are discussed. [ABSTRACT FROM AUTHOR]

Since the infectious coronavirus disease (COVID-19) was first reported in Wuhan, it has become a public health problem in China and even around the world. This pandemic is having devastating effects on societies and economies around the world. The increase in the number of COVID-19 tests gives more information about the epidemic spread, which may lead to the possibility of surrounding it to prevent further infections. However, wearing a face mask that prevents the transmission of droplets in the air and maintaining an appropriate physical distance between people, and reducing close contact with each other can still be beneficial in combating this pandemic. Therefore, this research paper focuses on implementing a Face Mask and Social Distancing Detection model as an embedded vision system. The pretrained models such as the MobileNet, ResNet Classifier, and VGG are used in our context. People violating social distancing or not wearing masks were detected. After implementing and deploying the models, the selected one achieved a confidence score of 100%. This paper also provides a comparative study of different face detection and face mask classification models. The system performance is evaluated in terms of precision, recall, F1-score, support, sensitivity, specificity, and accuracy that demonstrate the practical applicability. The system performs with F1-score of 99%, sensitivity of 99%, specificity of 99%, and an accuracy of 100%. Hence, this solution tracks the people with or without masks in a real-time scenario and ensures social distancing by generating an alarm if there is a violation in the scene or in public places. This can be used with the existing embedded camera infrastructure to enable these analytics which can be applied to various verticals, as well as in an office building or at airport terminals/gates. [ABSTRACT FROM AUTHOR]