A Review of Filtration Performance of Protective Masks.

Masks are essential and effective small protective devices used to protect the general public against infections such as COVID-19. However, available systematic reviews and summaries on the filtration performance of masks are lacking. Therefore, in order to investigate the filtration performance of masks, filtration mechanisms, mask characteristics, and the relationships between influencing factors and protective performance were first analyzed through mask evaluations. The summary of filtration mechanisms and mask characteristics provides readers with a clear and easy-to-understand theoretical cognition. Then, a detailed analysis of influencing factors and the relationships between the influencing factors and filtration performance is presented in. The influence of the aerosol size and type on filtration performance is nonlinear and nonconstant, and filtration efficiency decreases with an increase in the gas flow rate; moreover, fitness plays a decisive role in the protective effects of masks. It is recommended that the public should wear surgical masks to prevent COVID-19 infection in low-risk and non-densely populated areas. Future research should focus on fitness tests, and the formulation of standards should also be accelerated. This paper provides a systematic review that will be helpful for the design of masks and public health in the future.

Love pets, love their owners? - the transmission effect of consumers' warmth perception of pet images on B & B

Due to changes in consumption trends, the pet economy(e.g., buying, feeding, and caring for pets) has emerged in recent years. Especially during the COVID-19 pandemic, consumers have become eager to alleviate stress and anxiety through interactions with animals. Consumer interest in adopting pets is growing, along with a surge in the adoption rate. The rapid rise of the pet economy has also fueled the development of related industries, such as the food industry, medical industry, and Internet industry. Therefore, integrating pets into consumer experience and marketing communication has received increasing attention. Pets can give people a warm feeling. In the hospitality industry, it is important for service personnel to make customers feel at home. Previous studies have mainly focused on how to improve the service quality of employees to make customers feel more warmth from the perspective of human characteristics(e.g., expression, appearance). However, in the current context of the pet economy, pets, cute, warm and therapeutic, have been applied to the hospitality industry. They bring customers a novel accommodation experience as housekeepers. What if these clues of warmth are shown to consumers in advance in the marketing process of B&B? Can they influence consumers' attitudes? In other words, in the online marketing of B&B, can pets be used as a carrier of warmth to transfer consumers' warm perception of pets to warm impressions of service personnel, and can pet images become a key clue to influence consumers' consumption decisions? If so, what is the mechanism?At present, academic research in this area is still insufficient. Against this background, three experiments were designed to verify the warmth transmission effect of pets, as well as its psychological mechanism(i.e., perceived social presence) and the boundary condition(i.e., an individual's attitude towards pets). In experiment 1, text material was used to conduct a simulation experiment. Differences in customers' perception of the warmth attribute of the B&B host(main service personnel of B&B) under two conditions(with and without cute pet images) were compared to verify the main effect that pet images have a positive impact on consumers' perception of the warmth of the B&B host. In experiment 2, a situational experiment was conducted with pictures of pet dogs to verify the main effect again. Meanwhile, the internal mechanism of the pet warmth transmission effect was also analyzed. The study revealed the mediating effect of perceived social presence. In experiment 3, it was found that the boundary condition of the pet warmth transmission effect is consumers' different attitudes towards pets after replacing pictures of pet dogs with those of cats. To conclude,firstly, compared with pictures of the B&B environment without pets, those with pets can enhance the customer's warmth perception of the host. Additionally, the increase in perceived social presence is the key psychological mechanism of the pet warmth transmission effect. Finally, the more positive the attitude of consumers toward pets, the more likely they can perceive the evidence of the social presence of the B&B host and improve their perception of the warmth impression of the host. Unlike previous studies from the perspective of individual characteristics of service personnel, this paper took pets, the appendage of service providers, as the research object to explore the influence of pets on customers' impression and perception of the B&B host. Also, it revealed the internal mechanism and boundary condition of the warmth transmission effect. These findings provide B&B operators with a fresh marketing perspective and have practical significance in promoting the integration of the pet economy and hospitality industry.

Pooled testing efficiency increases with test frequency.

Pooled testing increases efficiency by grouping individual samples and testing the combined sample, such that many individuals can be cleared with one negative test. This short paper demonstrates that pooled testing is particularly advantageous in the setting of pandemics, given repeated testing, rapid spread, and uncertain risk. Repeated testing mechanically lowers the infection probability at the time of the next test by removing positives from the population. This effect alone means that increasing frequency by x times only increases expected tests by around [Formula: see text] However, this calculation omits a further benefit of frequent testing: Removing infections from the population lowers intragroup transmission, which lowers infection probability and generates further efficiency. For this reason, increasing testing frequency can paradoxically reduce total testing cost. Our calculations are based on the assumption that infection rates are known, but predicting these rates is challenging in a fast-moving pandemic. However, given that frequent testing naturally suppresses the mean and variance of infection rates, we show that our results are very robust to uncertainty and misprediction. Finally, we note that efficiency further increases given natural sampling pools (e.g., workplaces, classrooms) that induce correlated risk via local transmission. We conclude that frequent pooled testing using natural groupings is a cost-effective way to provide consistent testing of a population to suppress infection risk in a pandemic.

Group Testing in a Pandemic: The Role of Frequent Testing, Correlated Risk, and Machine Learning

Group testing increases efficiency by pooling patient specimens and clearing the entire group with one negative test. Optimal grouping strategy is well studied in one-off testing scenarios with reasonably well-known prevalence rates and no correlations in risk. We discuss how the strategy changes in a pandemic environment with repeated testing, rapid local infection spread, and highly uncertain risk. First, repeated testing mechanically lowers prevalence at the time of the next test. This increases testing efficiency, such that increasing frequency by x times only increases expected tests by around vx rather than x. However, this calculation omits a further benefit of frequent testing: infected people are quickly removed from the population, which lowers prevalence and generates further e?ciency. Accounting for this decline in intra-group spread, we show that increasing frequency can paradoxically reduce the total testing cost. Second, we show that group size and e?ciency increases with intra-group risk correlation, which is expected in natural test groupings based on proximity. Third, because optimal groupings depend on uncertain risk and correlation, we show how better estimates from machine learning can drive large efficiency gains. We conclude that frequent group testing, aided by machine learning, is a promising and inexpensive surveillance strategy.

Prediction of COVID-19 Patients at High Risk of Progression to Severe Disease.

In order to develop a novel scoring model for the prediction of coronavirus disease-19 (COVID-19) patients at high risk of severe disease, we retrospectively studied 419 patients from five hospitals in Shanghai, Hubei, and Jiangsu Provinces from January 22 to March 30, 2020. Multivariate Cox regression and orthogonal projections to latent structures discriminant analysis (OPLS-DA) were both used to identify high-risk factors for disease severity in COVID-19 patients. The prediction model was developed based on four high-risk factors. Multivariate analysis showed that comorbidity [hazard ratio (HR) 3.17, 95% confidence interval (CI) 1.96-5.11], albumin (ALB) level (HR 3.67, 95% CI 1.91-7.02), C-reactive protein (CRP) level (HR 3.16, 95% CI 1.68-5.96), and age ≥60 years (HR 2.31, 95% CI 1.43-3.73) were independent risk factors for disease severity in COVID-19 patients. OPLS-DA identified that the top five influencing parameters for COVID-19 severity were CRP, ALB, age ≥60 years, comorbidity, and lactate dehydrogenase (LDH) level. When incorporating the above four factors, the nomogram had a good concordance index of 0.86 (95% CI 0.83-0.89) and had an optimal agreement between the predictive nomogram and the actual observation with a slope of 0.95 (R2 = 0.89) in the 7-day prediction and 0.96 (R2 = 0.92) in the 14-day prediction after 1,000 bootstrap sampling. The area under the receiver operating characteristic curve of the COVID-19-American Association for Clinical Chemistry (AACC) model was 0.85 (95% CI 0.81-0.90). According to the probability of severity, the model divided the patients into three groups: low risk, intermediate risk, and high risk. The COVID-19-AACC model is an effective method for clinicians to screen patients at high risk of severe disease.