Investigating public behavior with artificial intelligence-assisted detection of face mask wearing during the COVID-19 pandemic
Issued Date
2023-04-01
Resource Type
eISSN
19326203
Scopus ID
2-s2.0-85152244365
Pubmed ID
37040359
Journal Title
PLoS ONE
Volume
18
Issue
4 April
Rights Holder(s)
SCOPUS
Bibliographic Citation
PLoS ONE Vol.18 No.4 April (2023)
Suggested Citation
Seresirikachorn K., Ruamviboonsuk P., Soonthornworasiri N., Singhanetr P., Prakayaphun T., Kaothanthong N., Somwangthanaroj S., Theeramunkong T. Investigating public behavior with artificial intelligence-assisted detection of face mask wearing during the COVID-19 pandemic. PLoS ONE Vol.18 No.4 April (2023). doi:10.1371/journal.pone.0281841 Retrieved from: https://repository.li.mahidol.ac.th/handle/20.500.14594/82135
Title
Investigating public behavior with artificial intelligence-assisted detection of face mask wearing during the COVID-19 pandemic
Other Contributor(s)
Abstract
Objectives Face masks are low-cost, but effective in preventing transmission of COVID-19. To visualize public’s practice of protection during the outbreak, we reported the rate of face mask wearing using artificial intelligence-assisted face mask detector, AiMASK. Methods After validation, AiMASK collected data from 32 districts in Bangkok. We analyzed the association between factors affecting the unprotected group (incorrect or non-mask wearing) using univariate logistic regression analysis. Results AiMASK was validated before data collection with accuracy of 97.83% and 91% during internal and external validation, respectively. AiMASK detected a total of 1,124,524 people. The unprotected group consisted of 2.06% of incorrect mask-wearing group and 1.96% of non-mask wearing group. Moderate negative correlation was found between the number of COVID-19 patients and the proportion of unprotected people (r = -0.507, p<0.001). People were 1.15 times more likely to be unprotected during the holidays and in the evening, than on working days and in the morning (OR = 1.15, 95% CI 1.13–1.17, p<0.001). Conclusions AiMASK was as effective as human graders in detecting face mask wearing. The prevailing number of COVID-19 infections affected people’s mask-wearing behavior. Higher tendencies towards no protection were found in the evenings, during holidays, and in city centers.