Abstract
Background: The COVID-19 pandemic has caused immense health and financial loss globally. Prior to mass vaccination, the only way to limit its spread was with standard health precautions, including mask-wearing, hand washing and social distancing. Compliance with these measures vary between countries, depending on the culture, the public awareness and the policies enacted by the government. A higher rate of mask-wearing is thought to be more effective in limiting the spread of the disease and methods of monitoring mask-wearing would be useful for campaigns to increase mask-wearing. Although the mask-wearing rate has been estimated with questionnaires and episodic counting studies, there are very few reports visually documenting the rate of mask wearing sequentially over time. In this study we developed an artificial intelligence (AI) system to analyze images from public CCTV’s to document the rate of mask wearing in Bangkok, Thailand, and correlate the rate with public health policies and COVID-19 events. Methodology: Public CCTV cameras in the Bangkok Metropolitan Area (BMA) were selected in view of their location and camera viewpoints. Every week, 9 hours of video clips from each of these cameras were downloaded and sent for analysis by an AI that had been trained to detect mask-wearing using pictures with and without masks from public databases. Images were divided for analysis into 2 parts, one part containing the mobile population and the other the non-mobile/vendor population. A number of the images was also visually checked for accuracy. The rate of mask-wearing was analyzed and correlated to public health measures and COVID-19 infections. The images of faces without masks were reviewed to understand factors related to not wearing masks. An in-person questionnaire was also performed on pedestrians in Bangkok to ascertain the factors for mask-wearing, or not, and to try to ascertain the attitudes, understanding and behavior of the general population in Bangkok. The mask-wearing rate seen on CCTV was compared with the mask-wearing rate obtained by a governmental online questionnaire.
