Impact of wearing a surgical mask on respiratory function in view of a widespread use during COVID-19 outbreak. A case-series study

Background:Because of the COVID-19 outbreak, the widespread use of Respiratory Protective Devices (RPD) is recommended to prevent the spread of infection. This recommendation involves not only healthcare workers but other category of workers and the general population as well, in public places, especially where social distancing is difficult to maintain. The use of facemasks should not cause physical impairment to individuals, especially for people suffering from lung and heart diseases.Objectives:To evaluate the impact of RPDs on the respiratory function in healthy and asthmatic subjects, in order to identify the fitness for use mainly, but not only for, occupational purposes during COVID-19 outbreak.Methods:Ten individuals were included, three of which affected by asthma and three current smokers. A Respiratory Functional Test (RFT) was performed at three times: at the beginning of the work shift 1) without wearing and 2) wearing surgical masks, and 3) after 4 hours of usual working activities wearing the masks. Arterial Blood Gas (ABG) samples were also tested before the first test and the third test.Results:Observed RFTs and ABG parameters did not suffer significant variations, but for Maximal Voluntary Ventilation (P=0.002). Data on asthmatic subjects and smokers were comparable to healthy subjects.Discussion:Our results suggest that wearing a surgical mask does not produce significant respiratory impairment in healthy subjects nor in subjects with asthma. Four hours of continuing mask-wearing do not cause a reduction in breathing parameters. Fitness for use in subjects with more severe conditions has to be evaluated individually. Our adapted technique for RFTs could be adopted for the individual RPDs fitness evaluation.Key words: Respiratory protective devices, personal protective equipment, occupational health, surgical mask, COVID-19     

Response to the COVID-19 Outbreak in Urban Settings in China

The COVID-19 outbreak in China was devastating and spread throughout the country before being contained. Stringent physical distancing recommendations and shelter-in-place were first introduced in the hardest-hit provinces, and by March, these recommendations were uniform throughout the country. In the presence of an evolving and deadly pandemic, we sought to investigate the impact of this pandemic on individual well-being and prevention practices among Chinese urban residents. From March 2–11, 2020, 4607 individuals were recruited from 11 provinces with varying numbers of COVID-19 cases using the social networking app WeChat to complete a brief, anonymous, online survey. The analytical sample was restricted to 2551 urban residents. Standardized scales measured generalized anxiety disorder (GAD), the primary outcome. Multiple logistic regression was conducted to identify correlates of GAD alongside assessment of community practices in response to the COVID-19 pandemic. We found that during the COVID-19 pandemic, the recommended public health practices significantly (p < 0.001) increased, including wearing facial mask, practicing physical distancing, handwashing, decreased public spitting, and going outside in urban communities. Overall, 40.3% of participants met screening criteria for GAD and 49.3%, 62.6%, and 55.4% reported that their work, social life, and family life were interrupted by anxious feelings, respectively. Independent correlates of having anxiety symptoms included being a healthcare provider (aOR = 1.58, p < 0.01), living in regions with a higher density of COVID-19 cases (aOR = 2.13, p < 0.01), having completed college (aOR = 1.38, p = 0.03), meeting screening criteria for depression (aOR = 6.03, p < 0.01), and poorer perceived health status (aOR = 1.54, p < 0.01). COVID-19 had a profound impact on the health of urban dwellers throughout China. Not only did they markedly increase their self- and community-protective behaviors, but they also experienced high levels of anxiety associated with a heightened vulnerability like depression, having poor perceived health, and the potential of increased exposure to COVID-19 such as living closer to the epicenter of the pandemic.Supplementary InformationThe online version contains supplementary material available at 10.1007/s11524-020-00498-8.     

Projected COVID-19 epidemic in the United States in the context of the effectiveness of a potential vaccine and implications for social distancing and face mask use

BackgroundMultiple candidates of COVID-19 vaccines have entered Phase III clinical trials in the United States (US). There is growing optimism that social distancing restrictions and face mask requirements could be eased with widespread vaccine adoption soon.MethodsWe developed a dynamic compartmental model of COVID-19 transmission for the four most severely affected states (New York, Texas, Florida, and California). We evaluated the vaccine effectiveness and coverage required to suppress the COVID-19 epidemic in scenarios when social contact was to return to pre-pandemic levels and face mask use was reduced. Daily and cumulative COVID-19 infection and death cases from 26th January to 15th September 2020 were obtained from the Johns Hopkins University Coronavirus resource center and used for model calibration.ResultsWithout a vaccine (scenario 1), the spread of COVID-19 could be suppressed in these states by maintaining strict social distancing measures and face mask use levels. But relaxing social distancing restrictions to the pre-pandemic level without changing the current face mask use would lead to a new COVID-19 outbreak, resulting in 0.8–4 million infections and 15,000–240,000 deaths across these four states over the next 12 months. Under this circumstance, introducing a vaccine (scenario 2) would partially offset this negative impact even if the vaccine effectiveness and coverage are relatively low. However, if face mask use is reduced by 50% (scenario 3), a vaccine that is only 50% effective (weak vaccine) would require coverage of 55–94% to suppress the epidemic in these states. A vaccine that is 80% effective (moderate vaccine) would only require 32–57% coverage to suppress the epidemic. In contrast, if face mask usage stops completely (scenario 4), a weak vaccine would not suppress the epidemic, and further major outbreaks would occur. A moderate vaccine with coverage of 48–78% or a strong vaccine (100% effective) with coverage of 33–58% would be required to suppress the epidemic. Delaying vaccination rollout for 1–2 months would not substantially alter the epidemic trend if the current non-pharmaceutical interventions are maintained.ConclusionsThe degree to which the US population can relax social distancing restrictions and face mask use will depend greatly on the effectiveness and coverage of a potential COVID-19 vaccine if future epidemics are to be prevented. Only a highly effective vaccine will enable the US population to return to life as it was before the pandemic.     

The Influence of Social Distancing on COVID-19 Mortality in US Counties: Cross-sectional Study

BackgroundPrevious studies on the impact of social distancing on COVID-19 mortality in the United States have predominantly examined this relationship at the national level and have not separated COVID-19 deaths in nursing homes from total COVID-19 deaths. This approach may obscure differences in social distancing behaviors by county in addition to the actual effectiveness of social distancing in preventing COVID-19 deaths.ObjectiveThis study aimed to determine the influence of county-level social distancing behavior on COVID-19 mortality (deaths per 100,000 people) across US counties over the period of the implementation of stay-at-home orders in most US states (March-May 2020).MethodsUsing social distancing data from tracked mobile phones in all US counties, we estimated the relationship between social distancing (average proportion of mobile phone usage outside of home between March and May 2020) and COVID-19 mortality (when the state in which the county is located reported its first confirmed case of COVID-19 and up to May 31, 2020) with a mixed-effects negative binomial model while distinguishing COVID-19 deaths in nursing homes from total COVID-19 deaths and accounting for social distancing– and COVID-19–related factors (including the period between the report of the first confirmed case of COVID-19 and May 31, 2020; population density; social vulnerability; and hospital resource availability). Results from the mixed-effects negative binomial model were then used to generate marginal effects at the mean, which helped separate the influence of social distancing on COVID-19 deaths from other covariates while calculating COVID-19 deaths per 100,000 people.ResultsWe observed that a 1% increase in average mobile phone usage outside of home between March and May 2020 led to a significant increase in COVID-19 mortality by a factor of 1.18 (P<.001), while every 1% increase in the average proportion of mobile phone usage outside of home in February 2020 was found to significantly decrease COVID-19 mortality by a factor of 0.90 (P<.001).ConclusionsAs stay-at-home orders have been lifted in many US states, continued adherence to other social distancing measures, such as avoiding large gatherings and maintaining physical distance in public, are key to preventing additional COVID-19 deaths in counties across the country.     

The Impact of COVID-19 Management Policies Tailored to Airborne SARS-CoV-2 Transmission: Policy Analysis

BackgroundDaily new COVID-19 cases from January to April 2020 demonstrate varying patterns of SARS-CoV-2 transmission across different geographical regions. Constant infection rates were observed in some countries, whereas China and South Korea had a very low number of daily new cases. In fact, China and South Korea successfully and quickly flattened their COVID-19 curve. To understand why this was the case, this paper investigated possible aerosol-forming patterns in the atmosphere and their relationship to the policy measures adopted by select countries.ObjectiveThe main research objective was to compare the outcomes of policies adopted by countries between January and April 2020. Policies included physical distancing measures that in some cases were associated with mask use and city disinfection. We investigated whether the type of social distancing framework adopted by some countries (ie, without mask use and city disinfection) led to the continual dissemination of SARS-CoV-2 (daily new cases) in the community during the study period.MethodsWe examined the policies used as a preventive framework for virus community transmission in some countries and compared them to the policies adopted by China and South Korea. Countries that used a policy of social distancing by 1-2 m were divided into two groups. The first group consisted of countries that implemented social distancing (1-2 m) only, and the second comprised China and South Korea, which implemented distancing with additional transmission/isolation measures using masks and city disinfection. Global daily case maps from Johns Hopkins University were used to provide time-series data for the analysis.ResultsThe results showed that virus transmission was reduced due to policies affecting SARS-CoV-2 propagation over time. Remarkably, China and South Korea obtained substantially better results than other countries at the beginning of the epidemic due to their adoption of social distancing (1-2 m) with the additional use of masks and sanitization (city disinfection). These measures proved to be effective due to the atmosphere carrier potential of SARS-CoV-2 transmission.ConclusionsOur findings confirm that social distancing by 1-2 m with mask use and city disinfection yields positive outcomes. These strategies should be incorporated into prevention and control policies and be adopted both globally and by individuals as a method to fight the COVID-19 pandemic.     

The impact of non-pharmaceutical interventions on COVID-19 epidemic growth in the 37 OECD member states

We estimated the impact of a comprehensive set of non-pharmeceutical interventions on the COVID-19 epidemic growth rate across the 37 member states of the Organisation for Economic Co-operation and Development during the early phase of the COVID-19 pandemic and between October and December 2020. For this task, we conducted a data-driven, longitudinal analysis using a multilevel modelling approach with both maximum likelihood and Bayesian estimation. We found that during the early phase of the epidemic: implementing restrictions on gatherings of more than 100 people, between 11 and 100 people, and 10 people or less was associated with a respective average reduction of 2.58%, 2.78% and 2.81% in the daily growth rate in weekly confirmed cases; requiring closing for some sectors or for all but essential workplaces with an average reduction of 1.51% and 1.78%; requiring closing of some school levels or all school levels with an average reduction of 1.12% or 1.65%; recommending mask wearing with an average reduction of 0.45%, requiring mask wearing country-wide in specific public spaces or in specific geographical areas within the country with an average reduction of 0.44%, requiring mask-wearing country-wide in all public places or all public places where social distancing is not possible with an average reduction of 0.96%; and number of tests per thousand population with an average reduction of 0.02% per unit increase. Between October and December 2020 work closing requirements and testing policy were significant predictors of the epidemic growth rate. These findings provide evidence to support policy decision-making regarding which NPIs to implement to control the spread of the COVID-19 pandemic.

     

The Effectiveness of Community-based Social Distancing for Mitigating the Spread of the COVID-19 Pandemic in Turkey

ObjectivesThe objective of this study was to demonstrate the effects of community-based social distancing interventions after the first coronavirus disease 2019 (COVID-19) case in Turkey on the course of the pandemic and to determine the number of prevented cases.MethodsIn this ecological study, the interventions implemented in response to the first COVID-19 cases in Turkey were evaluated and the effect of the interventions was demonstrated by calculating the effective reproduction number (R_t) of severe acute respiratory syndrome coro navirus 2 (SARS-CoV-2) when people complied with community-based social distancing rules.ResultsGoogle mobility scores decreased by an average of 36.33±22.41 points (range, 2.60 to 84.80) and a median of 43.80 points (interquartile range [IQR], 24.90 to 50.25). The interventions caused the calculated R_t to decrease to 1.88 (95% confidence interval, 1.87 to 1.89). The median growth rate was 19.90% (IQR, 10.90 to 53.90). A positive correlation was found between Google mobility data and R_t (r=0.783; p<0.001). The expected number of cases if the growth rate had not changed was predicted according to Google mobility categories, and it was estimated to be 1 381 922 in total. Thus, community-based interventions were estimated to have prevented 1 299 593 people from being infected.ConclusionsCommunity-based social distancing interventions significantly decreased the R_t of COVID-19 by reducing human mobility, and thereby prevented many people from becoming infected. Another important result of this study is that it shows health policy-makers that data on human mobility in the community obtained via mobile phones can be a guide for measures to be taken.     

Psychobehavioral Responses and Likelihood of Receiving COVID-19 Vaccines during the Pandemic,Hong Kong

To access temporal changes in psychobehavioral responses to the coronavirus disease (COVID-19) pandemic, we conducted a 5-round (R1–R5) longitudinal population-based online survey in Hong Kong during January–September 2020. Most respondents reported wearing masks (R1 99.0% to R5 99.8%) and performing hand hygiene (R1 95.8% to R5 97.7%). Perceived COVID-19 severity decreased significantly, from 97.4% (R1) to 77.2% (R5), but perceived self-susceptibility remained high (87.2%–92.8%). Female sex and anxiety were associated with greater adoption of social distancing. Intention to receive COVID-19 vaccines decreased significantly (R4 48.7% to R5 37.6%). Greater anxiety, confidence in vaccine, and collective responsibility and weaker complacency were associated with higher tendency to receive COVID-19 vaccines. Although its generalizability should be assumed with caution, this study helps to formulate health communication strategies and foretells the initial low uptake rate of COVID-19 vaccines, suggesting that social distancing should be maintained in the medium term.     

Cognitive predictors of COVID-19 mitigation behaviors in vaccinated and unvaccinated general population members

BackgroundGiven the long-term threat posed by COVID-19, predictors of mitigation behaviors are critical to identify. Prior studies have found that cognitive factors are associated with some COVID-19 mitigation behaviors, but few studies employ representative samples and no prior studies have examined cognitive predictors of vaccination status. The purpose of the present study was to examine associations between cognitive variables (executive function, delay discounting, and future orientation) and COVID-19 mitigation behaviors (mask wearing, social distancing, hand hygiene and vaccination) in a population representative sample.MethodsA population representative sample of 2,002 adults completed validated measures of delay discounting, future orientation, and executive function. Participants also reported frequency of mitigation behaviors, vaccination status, and demographics.ResultsFuture orientation was associated with more mask wearing (β = 0.160, 95 % CI [0.090, 0.220], p < 0.001), social distancing (β = 0.150, 95 % CI [0.070, 0.240], p < 0.001), hand hygiene behaviors (β = 0.090, 95 % CI [0.000, 0.190], p = 0.054), and a higher likelihood of being fully vaccinated (OR = 0.80, 95 % CI [0.670, 0.970], p = 0.020). Lower delay discounting predicted more consistent mask wearing (β = −0.060, 95 % CI[−0.120, −0.010], p = 0.032) and being fully vaccinated (OR = 1.28, 95 % CI [1.13, 1.44], p < 0.001), while more symptoms of executive dysfunction predicted less mask wearing (β = −0.240, 95 % CI [−0.320, −0.150] p < 0.001) and hand hygiene (β = −0.220, 95 % CI [−0.320, −0.130], p < 0.001), but not vaccination status (OR = 0.96, 95 % CI [0.80, 1.16], p = 0.690) or social distancing behaviors (β = −0.080, 95 % CI [−0.180, 0.020], p = 0.097). Overall, social distancing was the least well-predicted outcome from cognitive factors, while mask wearing was most well-predicted. Vaccination status was not a significant moderator of these effects of cognitive predictors on mitigation behaviors.ConclusionsCognitive variables predict significant variability in mitigation behaviors. regardless of vaccination status. In particular, thinking about the future and discounting it less may encourage more consistent implementation of mitigating behaviors.     

Effects of Social Distancing Measures during the First Epidemic Wave of Severe Acute Respiratory Syndrome Infection,Greece

Greece imposed a nationwide lockdown in March 2020 to mitigate transmission of severe acute respiratory syndrome coronavirus 2 during the first epidemic wave. We conducted a survey on age-specific social contact patterns to assess effects of physical distancing measures and used a susceptible-exposed-infectious-recovered model to simulate the epidemic. Because multiple distancing measures were implemented simultaneously, we assessed their overall effects and the contribution of each measure. Before measures were implemented, the estimated basic reproduction number (R₀) was 2.38 (95% CI 2.01–2.80). During lockdown, daily contacts decreased by 86.9% and R₀ decreased by 81.0% (95% credible interval [CrI] 71.8%–86.0%); each distancing measure decreased R₀ by 10%–24%. By April 26, the attack rate in Greece was 0.12% (95% CrI 0.06%–0.26%), one of the lowest in Europe, and the infection fatality ratio was 1.12% (95% CrI 0.55%–2.31%). Multiple social distancing measures contained the first epidemic wave in Greece.     

Enhancing COVID-19 Epidemic Forecasting Accuracy by Combining Real-time and Historical Data From Multiple Internet-Based Sources: Analysis of Social Media Data,Online News Articles,and Search Queries

BackgroundThe SARS-COV-2 virus and its variants pose extraordinary challenges for public health worldwide. Timely and accurate forecasting of the COVID-19 epidemic is key to sustaining interventions and policies and efficient resource allocation. Internet-based data sources have shown great potential to supplement traditional infectious disease surveillance, and the combination of different Internet-based data sources has shown greater power to enhance epidemic forecasting accuracy than using a single Internet-based data source. However, existing methods incorporating multiple Internet-based data sources only used real-time data from these sources as exogenous inputs but did not take all the historical data into account. Moreover, the predictive power of different Internet-based data sources in providing early warning for COVID-19 outbreaks has not been fully explored.ObjectiveThe main aim of our study is to explore whether combining real-time and historical data from multiple Internet-based sources could improve the COVID-19 forecasting accuracy over the existing baseline models. A secondary aim is to explore the COVID-19 forecasting timeliness based on different Internet-based data sources.MethodsWe first used core terms and symptom-related keyword-based methods to extract COVID-19–related Internet-based data from December 21, 2019, to February 29, 2020. The Internet-based data we explored included 90,493,912 online news articles, 37,401,900 microblogs, and all the Baidu search query data during that period. We then proposed an autoregressive model with exogenous inputs, incorporating real-time and historical data from multiple Internet-based sources. Our proposed model was compared with baseline models, and all the models were tested during the first wave of COVID-19 epidemics in Hubei province and the rest of mainland China separately. We also used lagged Pearson correlations for COVID-19 forecasting timeliness analysis.ResultsOur proposed model achieved the highest accuracy in all 5 accuracy measures, compared with all the baseline models of both Hubei province and the rest of mainland China. In mainland China, except for Hubei, the COVID-19 epidemic forecasting accuracy differences between our proposed model (model i) and all the other baseline models were statistically significant (model 1, t₁₉₈=–8.722, P<.001; model 2, t₁₉₈=–5.000, P<.001, model 3, t₁₉₈=–1.882, P=.06; model 4, t₁₉₈=–4.644, P<.001; model 5, t₁₉₈=–4.488, P<.001). In Hubei province, our proposed model''s forecasting accuracy improved significantly compared with the baseline model using historical new confirmed COVID-19 case counts only (model 1, t₁₉₈=–1.732, P=.09). Our results also showed that Internet-based sources could provide a 2- to 6-day earlier warning for COVID-19 outbreaks.ConclusionsOur approach incorporating real-time and historical data from multiple Internet-based sources could improve forecasting accuracy for epidemics of COVID-19 and its variants, which may help improve public health agencies'' interventions and resource allocation in mitigating and controlling new waves of COVID-19 or other relevant epidemics.     

Factors Affecting Public Non-compliance With Large-scale Social Restrictions to Control COVID-19 Transmission in Greater Jakarta,Indonesia

Objectives:The Indonesian government issued large-scale social restrictions (called Pembatasan Sosial Berskala Besar, or PSBB) at the beginning of the coronavirus disease 2019 (COVID-19) pandemic to control the spread of COVID-19 in Jakarta, Bogor, Depok, Tangerang, and Bekasi (Greater Jakarta). Public compliance poses a challenge when implementing large-scale social restrictions, and various factors have contributed to public non-compliance with the regulation. This study aimed to determine the degree of non-compliance and identify the factors that contributed to public non-compliance with the PSBB in Greater Jakarta, Indonesia.Methods:This was a quantitative study with a cross-sectional design. A total of 839 residents of Greater Jakarta participated in this study. Data were collected online using a Google Form, and convenience sampling was undertaken. Univariate and multivariate analyses were performed to explore the relationships between public non-compliance with the PSBB regulation and socio-demographic variables, respondents’ opinion of the PSBB, and social capital.Results:A total of 22.6% of subjects reported participating in activities that did not comply with the PSBB. The variables that most affected non-compliance with the PSBB were age, gender, income, opinion of the PSBB, and social capital.Conclusions:Strengthening social capital and providing information about COVID-19 prevention measures, such as washing one’s hands with soap, wearing masks properly, and maintaining social distancing, is essential. Robust public understanding will foster trust and cooperation with regard to COVID-19 prevention efforts and provide a basis for mutual agreement regarding rules/penalties.     

Public Discourse Against Masks in the COVID-19 Era: Infodemiology Study of Twitter Data

BackgroundDespite scientific evidence supporting the importance of wearing masks to curtail the spread of COVID-19, wearing masks has stirred up a significant debate particularly on social media.ObjectiveThis study aimed to investigate the topics associated with the public discourse against wearing masks in the United States. We also studied the relationship between the anti-mask discourse on social media and the number of new COVID-19 cases.MethodsWe collected a total of 51,170 English tweets between January 1, 2020, and October 27, 2020, by searching for hashtags against wearing masks. We used machine learning techniques to analyze the data collected. We investigated the relationship between the volume of tweets against mask-wearing and the daily volume of new COVID-19 cases using a Pearson correlation analysis between the two-time series.ResultsThe results and analysis showed that social media could help identify important insights related to wearing masks. The results of topic mining identified 10 categories or themes of user concerns dominated by (1) constitutional rights and freedom of choice; (2) conspiracy theory, population control, and big pharma; and (3) fake news, fake numbers, and fake pandemic. Altogether, these three categories represent almost 65% of the volume of tweets against wearing masks. The relationship between the volume of tweets against wearing masks and newly reported COVID-19 cases depicted a strong correlation wherein the rise in the volume of negative tweets led the rise in the number of new cases by 9 days.ConclusionsThese findings demonstrated the potential of mining social media for understanding the public discourse about public health issues such as wearing masks during the COVID-19 pandemic. The results emphasized the relationship between the discourse on social media and the potential impact on real events such as changing the course of the pandemic. Policy makers are advised to proactively address public perception and work on shaping this perception through raising awareness, debunking negative sentiments, and prioritizing early policy intervention toward the most prevalent topics.     

The use of face masks during vaccine roll-out in New YorkCity and impact on epidemic control

Face masks were mandated in New York during the first wave in 2020, and in 2021 the first vaccine programs have commenced. We aimed to examine the impact of face mask and other NPIs use with a gradual roll out of vaccines in NYC on the epidemic trajectory.A SEIR mathematical model of SARS-CoV-2 transmission was developed for New York City (NYC), which accounted for decreased mobility for lockdown, testing and tracing. Varied mask’s usage and efficacy were tested, along with a gradual increase in vaccine uptake over five months. The model has been calibrated using notification data in NYC from March first to June 29.Masks and other NPIs result in immediate impact on the epidemic, while vaccination has a delayed impact, especially when implemented over a long period of time. A pre-emptive, early mandate for masks is more effective than late mask use, but even late mask mandates will reduce cases and deaths by over 20%. The epidemic curve is suppressed by at least 50% of people wearing a mask from the start of the outbreak but surges when mask wearing drops to 30% or less. With a slow roll out of vaccines over five months at uptake levels of 20–70%, NPIs use will still be needed and has a greater impact on epidemic control.When vaccine roll out is slow or partial in cities experiencing local transmission of COVID-19, masks and other NPIs will be necessary to mitigate transmission until vaccine coverage is high and complete. Vaccine alone cannot rapidly control an epidemic because of the time lag to two-dose immunity. Even after high coverage, the ongoing need for NPIs is unknown and will depend on long-term duration of vaccine efficacy, the use of boosters and optimized dosage scheduling and variants of concern.     

Quantifying the Effect of Public Activity Intervention Policies on COVID-19 Pandemic Containment Using Epidemiologic Data From 145 Countries

ObjectivesMost countries have adopted public activity intervention policies to control the coronavirus disease 2019 (COVID-19) pandemic. Nevertheless, empirical evidence of the effectiveness of different interventions on the containment of the epidemic was inconsistent.MethodsWe retrieved time-series intervention policy data for 145 countries from the Oxford COVID-19 Government Response Tracker from December 31, 2019, to July 1, 2020, which included 8 containment and closure policies. We investigated the association of timeliness, stringency, and duration of intervention with cumulative infections per million population on July 1, 2020. We introduced a novel counterfactual estimator to estimate the effects of these interventions on COVID-19 time-varying reproduction number (R_t).ResultsThere is some evidence that earlier implementation, longer durations, and more strictness of intervention policies at the early but not middle stage were associated with reduced infections of COVID-19. The counterfactual model proved to have controlled for unobserved time-varying confounders and established a valid causal relationship between policy intervention and R_t reduction. The average intervention effect revealed that all interventions significantly decrease R_t after their implementation. R_t decreased by 30% (22%-41%) in 25 to 32 days after policy intervention. Among the 8 interventions, school closing, workplace closing, and public events cancellation demonstrated the strongest and most consistent evidence of associations.ConclusionsOur study provides more reliable evidence of the quantitative effects of policy interventions on the COVID-19 epidemic and suggested that stricter public activity interventions should be implemented at the early stage of the epidemic for improved containment.     

Natural Language Processing Insight into LGBTQ+ Youth Mental Health During the COVID-19 Pandemic: Longitudinal Content Analysis of Anxiety-Provoking Topics and Trends in Emotion in LGBTeens Microcommunity Subreddit

BackgroundWidespread fear surrounding COVID-19, coupled with physical and social distancing orders, has caused severe adverse mental health outcomes. Little is known, however, about how the COVID-19 crisis has impacted LGBTQ+ youth, who disproportionately experienced a high rate of adverse mental health outcomes before the COVID-19 pandemic.ObjectiveWe aimed to address this knowledge gap by harnessing natural language processing methodologies to investigate the evolution of conversation topics in the most popular subreddit for LGBTQ+ youth.MethodsWe generated a data set of all r/LGBTeens subreddit posts (n=39,389) between January 1, 2020 and February 1, 2021 and analyzed meaningful trends in anxiety, anger, and sadness in the posts. Because the distribution of anxiety before widespread social distancing orders was meaningfully different from the distribution after (P<.001), we employed latent Dirichlet allocation to examine topics that provoked this shift in anxiety.ResultsWe did not find any differences in LGBTQ+ youth anger and sadness before and after government-mandated social distancing; however, anxiety increased significantly (P<.001). Further analysis revealed a list of 10 anxiety-provoking topics discussed during the pandemic: attraction to a friend, coming out, coming out to family, discrimination, education, exploring sexuality, gender pronouns, love and relationship advice, starting a new relationship, and struggling with mental health.ConclusionsDuring the COVID-19 pandemic, LGBTQ+ teens increased their reliance on anonymous discussion forums when discussing anxiety-provoking topics. LGBTQ+ teens likely perceived anonymous forums as safe spaces for discussing lifestyle stressors during COVID-19 disruptions (eg, school closures). The list of prevalent anxiety-provoking topics in LGBTQ+ teens’ anonymous discussions can inform future mental health interventions in LGBTQ+ youth.     

我国非医用口罩标准及防护性能介绍

 

新型冠状病毒肺炎（COVID-19）主要通过近距离飞沫传播和接触传播,口罩可以降低感染风险和传播概率,但因医用口罩产能有限,各地政府鼓励企业生产非医用口罩,以满足公众新冠肺炎疫情防控需要。本文介绍我国非医用口罩相关标准,对比分析其细菌过滤效率、颗粒物过滤效率等防护性能要求,提出非医用口罩防护新型冠状病毒的重要指标。同时,分析2020年1月27日-2月25日我国各省公示的102份非医用口罩企业标准,提出疫情防控非医用口罩标准化的建议,以期为相关部门、生产企业及普通人群提供帮助。