Novel coronavirus disease 2019 (COVID-19) caused by severe acute respiratory syndrome coronavirus-2 (SARS-COV-2) is spreading rapidly around the world and has become a global pandemic. Meteorological factors have been recognized as one of the critical factors that influence the epidemiology and transmission of infectious diseases. In this context, the World Meteorological Organization and scholars at home and abroad have paid extensive attention to the relationships of environment and meteorology with COVID-19. This paper systematically collected and sorted out relevant domestic and foreign studies, and reviewed the latest research progress on the impact of environmental and meteorological factors on COVID-19, classifying them into typical meteorological factors (such as temperature, humidity, and wind speed), local environmental factors (such as indoor enclosed environment, ventilation, disinfection, and air conditioning), and air pollution. Current research evidence suggests that typical meteorological factors, local environmental factors, and air pollutants are closely related to the transmission of COVID-19. However, the results of different studies are still divergent due to uncertainty about the influencing mechanism, and differences in research areas and methods. This review elucidated the importance of environmental and meteorological factors to the spread of COVID-19, and provided useful implications for the control of further large-scale transmission of COVID-19 and the development of prevention and control strategies under different environmental and meteorological conditions.Copyright © 2022, Shanghai Municipal Center for Disease Control and Prevention. All rights reserved.
The outbreak of coronavirus disease 2019 (COVID-19) occurred at the end of 2019, and it has continued to be a source of misery for millions of people and companies well into 2020. There is a surge of concern among all persons, especially those who wish to resume in-person activities, as the globe recovers from the epidemic and intends to return to a level of normalcy. Wearing a face mask greatly decreases the likelihood of viral transmission and gives a sense of security, according to studies. However, manually tracking the execution of this regulation is not possible. The key to this is technology. We present a deep learning-based system that can detect instances of improper use of face masks. A dual-stage convolutional neural network architecture is used in our system to recognize masked and unmasked faces. This will aid in the tracking of safety breaches, the promotion of face mask use, and the maintenance of a safe working environment. In this paper, we propose a variant of a multi-face detection model which has the potential to target and identify a group of people whether they are wearing masks or not.