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Although the emphasis on behaviors of hand-washing and mask-wearing was repeated during the pandemic of Coronavirus Disease 2019 (COVID-19), not everyone paid enough attention to this. A descriptive statistic was used to make sense of the status of hand hygiene and mask-wearing among primary school students in Wuhan, China. A binary logistic regression analysis was conducted to identify the risk factors affecting the behaviors of hand-washing and mask-wearing. p < 0.05 (two-sides) was considered as significant at statistics. 42.05% of the primary school students showed a good behavior of hand-washing, while 51.60% had a good behavior of mask-wearing. Gender, grade, out-going history, father’s occupation, mother’s educational background, and the time filling out the survey were significantly associated with hand hygiene, whereas grade, mother’s educational background, and residence were associated with mask-wearing. The behaviors of hand-washing and mask-wearing among primary school students were influenced by gender, grade, and other factors, therefore, parents should make efforts of behavior guidance whereas governments should enlarge medium publicity.

This study aims to portray the policy compliance landscape among Chinese citizens, focusing on their adherence to strict public health measures during the COVID-19 pandemic. Utilizing data from the 2021 Chinese General Social Survey, we employed ordinary least squares and ordered Logit models to explore factors influencing compliance with government epidemic prevention policies. Findings indicate that individuals with higher education levels, chronic illnesses, high trust in government, and strong sense of social responsibility tend to demonstrate higher policy compliance, while married individuals and those with high trust in the healthcare system exhibit lower policy compliance. Based on a dual framework of normative and calculative motivation, this study reveals multiple mechanisms underlying policy compliance. Under the normative pathway, trust, sense of responsibility, and education may promote internalized compliance through reinforcing policy identification, while married individuals may show weaker response due to family responsibilities. Under calculative motivation, those with higher education levels and poor health conditions may develop stronger protective motivation due to threat perception. In contrast, married individuals may prioritize household interests, reducing compliance. Meanwhile, high trust in the healthcare system may trigger a “trust paradox.”

Holding conspiracy beliefs regarding the COVID-19 pandemic in the United States has been associated with reductions in both actions to prevent the spread of the infection (eg, mask wearing) and intentions to accept a vaccine when one becomes available. Patterns of media use have also been associated with acceptance of COVID-19 conspiracy beliefs. Here we ask whether the type of media on which a person relies increased, decreased, or had no additional effect on that person's COVID-19 conspiracy beliefs over a 4-month period. We used panel data to explore whether use of conservative and social media in the United States, which were previously found to be positively related to holding conspiracy beliefs about the origins and prevention of COVID-19, were associated with a net increase in the strength of those beliefs from March to July of 2020. We also asked whether mainstream news sources, which were previously found to be negatively related to belief in pandemic-related conspiracies, were associated with a net decrease in the strength of such beliefs over the study period. Additionally, we asked whether subsequent changes in pandemic conspiracy beliefs related to the use of media were also related to subsequent mask wearing and vaccination intentions. A survey that we conducted with a national US probability sample in March of 2020 and again in July with the same 840 respondents assessed belief in pandemic-related conspiracies, use of various types of media information sources, actions taken to prevent the spread of the disease and intentions to vaccinate, and various demographic characteristics. Change across the two waves was analyzed using path analytic techniques. We found that conservative media use predicted an increase in conspiracy beliefs (β=.17, 99% CI .10-.25) and that reliance on mainstream print predicted a decrease in their belief (β=-.08, 99% CI -.14 to -.02). Although many social media platforms reported downgrading or removing false or misleading content, ongoing use of such platforms by respondents predicted growth in conspiracy beliefs as well (β=.072, 99% CI .018-.123). Importantly, conspiracy belief changes related to media use between the two waves of the study were associated with the uptake of mask wearing and changes in vaccination intentions in July. Unlike other media, use of mainstream broadcast television predicted greater mask wearing (β=.17, 99% CI .09-.26) and vaccination intention (β=.08, 95% CI .02-.14), independent of conspiracy beliefs. The findings point to the need for greater efforts on the part of commentators, reporters, and guests on conservative media to report verifiable information about the pandemic. The results also suggest that social media platforms need to be more aggressive in downgrading, blocking, and counteracting claims about COVID-19 vaccines, claims about mask wearing, and conspiracy beliefs that have been judged problematic by public health authorities.

Background The COVID‐19 pandemic brought about a new normal, necessitating the use of personal protective equipment (PPE) like face shields, surgical masks, gloves, and goggles. However, prolonged mask‐wearing introduced skin‐related issues due to changes in the skin's microenvironment, including increased humidity and temperature, as well as pressure on the skin. These factors led to skin deformation, vascular issues, edema, and inflammation, resulting in discomfort and cosmetic concerns. Clinical reports quickly highlighted the consequences of long‐term mask use, including increased cases of “maskne” (mask‐related acne) or mask‐wearing related disorders such as rosacea flare‐ups, skin‐barrier defects, itching, erythema, redness, hyperpigmentation, and lichenification. Some of these issues, like inflammation, oxidative stress, and poor wound healing, could be directly linked to acne‐related disorders or skin hypoxia. Aim To address these problems, researchers turned to rutin, a well‐known flavonoid with antioxidant, vasoactive, and anti‐inflammatory properties. However, rutin's poor water solubility presented a challenge for cosmetic formulations. To overcome this limitation, a highly water‐soluble form of rutin was developed, making it suitable for use at higher concentrations. Methods In vitro and ex vivo tests were conducted, as well as an innovative clinical trial including volunteers wearing surgical masks for at least 2 h, to evaluate the biological activity of this soluble rutin on the main skin concerns associated with mask‐wearing (inflammation, oxidative stress, skin repair, hyperpigmentation, and skin redness). Results The in vitro results showed that the active ingredient significantly reduced oxidative stress, improved wound healing, and reduced inflammation. In dark skin explants, the active ingredient significantly reduced melanin content, indicating its lightening activity. This effect was confirmed in the clinical study, where brown spots decreased significantly after 4 days of application. Moreover, measurements on volunteers demonstrated a decrease in skin redness and vascularization after the active ingredient application, indicating inflammation and erythema reduction. Volunteers reported improved skin comfort. Conclusion In summary, the COVID‐19 pandemic led to various skin issues associated with mask‐wearing. A highly soluble form of rutin was developed, which effectively addressed these concerns by reducing inflammation, oxidative stress, and hyperpigmentation while promoting wound healing. This soluble rutin offers a promising solution for the rapid treatment of maskne‐related disorders and other skin problems caused by prolonged mask use.

SignificanceThis paper examines whether the opening of K–12 schools may lead to the spread of COVID-19. Analyzing how an increase of COVID-19 cases is related to the timing of opening K–12 schools in the United States, we find that counties that opened K–12 schools with in-person learning experienced an increase in the growth rate of cases by 5 percentage points on average, controlling for a variety of policies, past infection rates, and other factors. This association of K–12 school visits with case growth is stronger when mask wearing is not mandated for staff at school. These findings support policies that promote masking and other precautionary measures at schools and giving vaccine priority to education workers. This paper empirically examines how the opening of K–12 schools is associated with the spread of COVID-19 using county-level panel data in the United States. As preliminary evidence, our event-study analysis indicates that cases and deaths in counties with in-person or hybrid opening relative to those with remote opening substantially increased after the school opening date, especially for counties without any mask mandate for staff. Our main analysis uses a dynamic panel data model for case and death growth rates, where we control for dynamically evolving mitigation policies, past infection levels, and additive county-level and state-week “fixed” effects. This analysis shows that an increase in visits to both K–12 schools and colleges is associated with a subsequent increase in case and death growth rates. The estimates indicate that fully opening K–12 schools with in-person learning is associated with a 5 (SE = 2) percentage points increase in the growth rate of cases. We also find that the association of K–12 school visits or in-person school openings with case growth is stronger for counties that do not require staff to wear masks at schools. These findings support policies that promote masking and other precautionary measures at schools and giving vaccine priority to education workers.

PurposeThis paper proposes a solution for recognizing human faces under mask-wearing. The lower part of human face is occluded and could not be used in the learning process of face recognition. So, the proposed solution is developed to recognize human faces on any available facial components which could be varied depending on wearing or not wearing a mask.Design/methodology/approachThe proposed solution is developed based on the FaceNet framework, aiming to modify the existing facial recognition model to improve the performance of both scenarios of mask-wearing and without mask-wearing. Then, simulated masked-face images are computed on top of the original face images, to be used in the learning process of face recognition. In addition, feature heatmaps are also drawn out to visualize majority of parts of facial images that are significant in recognizing faces under mask-wearing.FindingsThe proposed method is validated using several scenarios of experiments. The result shows an outstanding accuracy of 99.2% on a scenario of mask-wearing faces. The feature heatmaps also show that non-occluded components including eyes and nose become more significant for recognizing human faces, when compared with the lower part of human faces which could be occluded under masks.Originality/valueThe convolutional neural network based solution is tuned up for recognizing human faces under a scenario of mask-wearing. The simulated masks on original face images are augmented for training the face recognition model. The heatmaps are then computed to prove that features generated from the top half of face images are correctly chosen for the face recognition.

So far, there have been no published population studies on the relationship between a COVID-19 infection and public risk perception, information source, knowledge, attitude, and behaviors during the COVID-19 outbreak in China. This study aims to understand the relationships between COVID-19 infection; four personal nonpharmaceutical interventions (NPIs; handwashing, proper coughing habits, social distancing, and mask wearing); and public risk perception, knowledge, attitude, and other social demographic variables. An online survey of 8158 Chinese adults between February 22 and March 5, 2020, was conducted. Bivariate associations between categorical variables were examined using Fisher exact test. We also explored the determinants of four NPIs as well as their association with COVID-19 infection using logistic regression. Of 8158 adults included, 57 (0.73%) were infected with COVID-19. The overwhelming majority of respondents showed a positive attitude (n=8094, 99.2%), positive risk perception (n=8146, 99.9%), and high knowledge levels that were among the strongest predictors of the four adopted NPIs (handwashing: n=7895, 96.8%; proper coughing: 5997/6444, 93.1%; social distancing: n=7104/8158, 87.1%; and mask wearing: 5011/5120, 97.9%). There was an increased risk of COVID-19 infection for those who did not wash their hands (2.28% vs 0.65%; risk ratio [RR] 3.53, 95% CI 1.53-8.15; P=.009), did not practice proper coughing (1.79% vs 0.73%; RR 2.44, 95% CI 1.15-5.15; P=.03), did not practice social distancing (1.52% vs 0.58%; RR 2.63, 95% CI 1.48-4.67; P=.002), and did not wear a mask (7.41% vs 0.6%; RR 12.38, 95% CI 5.81-26.36; P<.001). For those who did practice all other three NPIs, wearing a mask was associated with a significantly reduced risk of infection compared to those who did not wear a mask (0.6% vs 16.7%; P=.04). Similarly, for those who did not practice all or part of the other three NPIs, wearing a mask was also associated with a significantly reduced risk of infection. In a penalized logistic regression model including all four NPIs, wearing a mask was the only significant predictor of COVID-19 infection among the four NPIs (odds ratio 7.20, 95% CI 2.24-23.11; P<.001). We found high levels of risk perception, positive attitude, desirable knowledge, as well as a high level of adopting the four NPIs. The relevant knowledge, risk perception, and attitude were strong predictors of adapting the four NPIs. Mask wearing, among the four personal NPIs, was the most effective protective measure against COVID-19 infection, with added preventive effect among those who practiced all or part of the other three NPIs.

To reduce the spread of COVID-19, mask wearing has become ubiquitous in much of the world. We studied the extent to which masks impair emotion recognition and dampen the perceived intensity of facial expressions by naturalistically inducing positive, neutral, and negative emotions in individuals while they were masked and unmasked. Two groups of online participants rated the emotional intensity of each presented image. One group rated full faces (N=104); the other (N=102) rated cropped images where only the upper face was visible. We found that masks impaired the recognition of and rated intensity of positive emotions. This happened even when the faces were cropped and the lower part of the face was not visible. Masks may thus reduce positive emotion and/or expressivity of positive emotion. However, perception of negativity was unaffected by masking, perhaps because unlike positive emotions like happiness which are signaled more in the mouth, negative emotions like anger rely more on the upper face.

The world has lived an exceptional time period caused by the Coronavirus pandemic. To limit Covid-19 propagation, governments required people to wear a facial mask outside. In facial data analysis, mask-wearing on the human face creates predominant occlusion hiding the important oral region and causing more challenges for human face recognition and categorisation. The appropriation of existing solutions by taking into consideration the masked context is indispensable for researchers. In this paper, we propose an approach for mask-wearing prediction and adaptive facial human-gender classification. The proposed approach is based on convolutional neural networks (CNNs). Both mask-wearing and gender information are crucial for various possible applications. Experimentation shows that mask-wearing is very well detectable by using CNNs and justifies its use as a prepossessing step. It also shows that retraining with masked faces is indispensable to keep up gender classification performances. In addition, experimentation proclaims that in a controlled face-pose with acceptable image quality' context, the gender attribute remains well detectable. Finally, we show empirically that the adaptive proposed approach improves global performance for gender prediction in a mixed context.

Background Mask wearing has been shown to be an effective strategy for slowing the spread of COVID-19. While early studies have uncovered some evidence of racial and ethnic differences in mask-wearing behavior, critical gaps remain. We begin to address these gaps by (1) more comprehensively investigating the role of race and ethnicity on mask wearing during the COVID-19 pandemic and (2) examining whether gender intersects with race and ethnicity to differently influence mask-wearing patterns. Methods Data were drawn from the COVID-19 Impact Survey, a cross-sectional, nationally representative survey of adults living in the U.S. Data were pooled from three time points that ranged from late April 2020 to early June 2020. The final analytic sample consisted of 4688 non-institutionalized adults living in the U.S. A series of logistic regression models with robust standard errors were used to estimate differences in mask-wearing patterns. Results Compared with White respondents, results revealed Black, Latina/o, and Asian respondents were more likely to report wearing a mask in response to the coronavirus. Moreover, results show White men were least likely to wear a mask from late April 2020 to early June 2020. Conclusion Overall, findings demonstrate mask-wearing patterns during the COVID-19 pandemic are differently shaped by racial and ethnic background and gender. Findings from this study can inform targeted strategies designed to increase mask-wearing adherence among U.S. adults.