Investigating public behavior with artificial intelligence-assisted detection of face mask wearing during the COVID-19 pandemic.
PLoS One
; 18(4): e0281841, 2023.
Article
in English
| MEDLINE | ID: covidwho-2303408
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.
Full text:
Available
Collection:
International databases
Database:
MEDLINE
Main subject:
COVID-19
Type of study:
Prognostic study
Limits:
Humans
Country/Region as subject:
Asia
Language:
English
Journal:
PLoS One
Journal subject:
Science
/
Medicine
Year:
2023
Document Type:
Article
Affiliation country:
Journal.pone.0281841
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