The Influence of Plastic Barriers on Aerosol Infection Risk during Airport Security Checks

Plastic barriers physically separate queuing passengers in airport security check areas as a measure against aerosol transmission. However, this may create 'canyons';that interfere with the existing ventilation design: potentially inhibiting airflow, concentrating exhaled viruses, and exacerbating aerosol transmission risk. Accordingly, this study investigated the transmission implications of installing plastic barriers in a security check area with computational fluid dynamics (CFD). Two air distribution schemes were modeled: one with linear air supply diffusers aligned vertically to (Case 1) and another with diffusers parallel with (Case 2) the orientation of partitions. The drift-flux model was used to calculate the spread of viral bioaerosols with 5 µm in diameter;then the Wells–Riley equation was applied to assess aerosol transmission risk for SARS-CoV-2. According to simulation results, in Case 1, installing plastic barriers resulted in relatively small changes in volume with a high infection risk of 1% or greater in the breathing zone within the first 25 min. However, in Case 2, using plastic barriers resulted in the continuous increase in this volume within the first 25 min while this volume was near zero if without plastic barriers. In conclusion, installing plastic barriers needs careful consideration because they do not reduce the risk of airborne SARS-CoV-2 transmission and might even exacerbate it without localized ventilation and air cleaning.

Ceiling impact on air disinfection performance of Upper-Room Germicidal Ultraviolet (UR-GUV).

This study used Computational Fluid Dynamics (CFD) to investigate air disinfection for SARS-CoV-2 by the Upper-Room Germicidal Ultraviolet (UR-GUV), with focus on ceiling impact. The study includes three indoor settings, i.e., low (airport bus), medium (classroom) and high (rehearsal room) ceilings, which were ventilated with 100% clean air (CA case), 80% air-recirculation with a low filtration (LF case), and 80% air-recirculation with a high filtration (HF case). According to the results, using UR-GUV can offset the increased infection risk caused by air recirculation, with viral concentrations in near field (NF) and far field (FF) in the LF case similar to those in the CA case. In the CA case, fraction remaining (FR) was 0.48-0.73 with 25% occupancy rate (OR) and 0.49-0.91 with 45% OR in the bus, 0.41 in NF and 0.11 in FF in the classroom, and 0.18 in NF and 0.09 in FF in the rehearsal room. Obviously, UR-GUV performance in NF can be improved in a room with a high ceiling where FR has a power relationship with UV zone height. As using UR-GUV can only extend the exposure time to get infection risk of 1% (T 1% ) to 8 min in NF in the classroom, and 47 min in NF in the rehearsal room, it is necessary to abide by social distancing in the two rooms. In addition, T 1% in FF was calculated to be 18.3 min with 25% OR and 21.4% with 45% OR in the airport bus, showing the necessity to further wear a mask.

A Large-Scale Clinical Validation Study Using nCapp Cloud Plus Terminal by Frontline Doctors for the Rapid Diagnosis of COVID-19 and COVID-19 pneumonia in China

Background The outbreak of coronavirus disease 2019 (COVID-19) has become a global pandemic acute infectious disease, especially with the features of possible asymptomatic carriers and high contagiousness. Currently, it is difficult to quickly identify asymptomatic cases or COVID-19 patients with pneumonia due to limited access to reverse transcription-polymerase chain reaction (RT-PCR) nucleic acid tests and CT scans. Goal This study aimed to develop a scientific and rigorous clinical diagnostic tool for the rapid prediction of COVID-19 cases based on a COVID-19 clinical case database in China, and to assist doctors to efficiently and precisely diagnose asymptomatic COVID-19 patients and cases who had a false-negative RT-PCR test result. Methods With online consent, and the approval of the ethics committee of Zhongshan Hospital Fudan University (NCT04275947, B2020-032R) to ensure that patient privacy is protected, clinical information has been uploaded in real-time through the New Coronavirus Intelligent Auto-diagnostic Assistant Application of cloud plus terminal (nCapp) by doctors from different cities (Wuhan, Shanghai, Harbin, Dalian, Wuxi, Qingdao, Rizhao, and Bengbu) during the COVID-19 outbreak in China. By quality control and data anonymization on the platform, a total of 3,249 cases from COVID-19 high-risk groups were collected. The effects of different diagnostic factors were ranked based on the results from a single factor analysis, with 0.05 as the significance level for factor inclusion and 0.1 as the significance level for factor exclusion. Independent variables were selected by the step-forward multivariate logistic regression analysis to obtain the probability model. Findings We applied the statistical method of a multivariate regression model to the training dataset (1,624 cases) and developed a prediction model for COVID-19 with 9 clinical indicators that are accessible. The area under the receiver operating characteristic (ROC) curve (AUC) for the model was 0.88 (95% CI: 0.86, 0.89) in the training dataset and 0.84 (95% CI: 0.82, 0.86) in the validation dataset (1,625 cases). Discussion With the assistance of nCapp, a mobile-based diagnostic tool developed from a large database that we collected from COVID-19 high-risk groups in China, frontline doctors can rapidly identify asymptomatic patients and avoid misdiagnoses of cases with false-negative RT-PCR results.

Characterization of aerosol plumes from singing and playing wind instruments associated with the risk of airborne virus transmission.

The exhalation of aerosols during musical performances or rehearsals posed a risk of airborne virus transmission in the COVID-19 pandemic. Previous research studied aerosol plumes by only focusing on one risk factor, either the source strength or convective transport capability. Furthermore, the source strength was characterized by the aerosol concentration and ignored the airflow rate needed for risk analysis in actual musical performances. This study characterizes aerosol plumes that account for both the source strength and convective transport capability by conducting experiments with 18 human subjects. The source strength was characterized by the source aerosol emission rate, defined as the source aerosol concentration multiplied by the source airflow rate (brass 383 particle/s, singing 408 particle/s, and woodwind 480 particle/s). The convective transport capability was characterized by the plume influence distance, defined as the sum of the horizontal jet length and horizontal instrument length (brass 0.6 m, singing 0.6 m and woodwind 0.8 m). Results indicate that woodwind instruments produced the highest risk with approximately 20% higher source aerosol emission rates and 30% higher plume influence distances compared with the average of the same risk indicators for singing and brass instruments. Interestingly, the clarinet performance produced moderate source aerosol concentrations at the instrument's bell, but had the highest source aerosol emission rates due to high source airflow rates. Flute performance generated plumes with the lowest source aerosol emission rates but the highest plume influence distances due to the highest source airflow rate. Notably, these comprehensive results show that the source airflow is a critical component of the risk of airborne disease transmission. The effectiveness of masking and bell covering in reducing aerosol transmission is due to the mitigation of both source aerosol concentrations and plume influence distances. This study also found a musician who generated approximately five times more source aerosol concentrations than those of the other musicians who played the same instrument. Despite voice and brass instruments producing measurably lower average risk, it is possible to have an individual musician produce aerosol plumes with high source strength, resulting in enhanced transmission risk; however, our sample size was too small to make generalizable conclusions regarding the broad musician population.

A Scoping Review of Emerging COVID-19 Health Communication Research in Communication and Media Journals.

This article reports a scoping review of emerging research on COVID-19 health communication. We reviewed and analyzed 206 articles published in 40 peer-reviewed communication journals between January 2020 to April 2021. Our review identified key study characteristics and overall themes and trends in this rapidly expanding field of research. Our review of health communication scholarship during the early stages of the COVID-19 pandemic suggests that health communication scholars have risen to the challenges and interrogated important issues in COVID-19 communication at the individual, group, organizational, and societal levels. We identified important gaps that warrant future research attention including experimental research that seeks to test the causal effects of communication, studies that evaluate communication interventions in under-served populations, research on mental health challenges imposed by the pandemic, and investigations on the promise of emerging communication technologies for supporting pandemic mitigation efforts.

Tradeoffs between ventilation, air mixing, and passenger density for the airborne transmission risk in airport transportation vehicles.

Airport transportation vehicles, such as buses, aerotrains, and shuttles, provide important passenger transfer services in airports. This study quantitatively investigated COVID-19 aerosol infection risk and identified acceptable operational conditions, such as passenger occupancy rates and duration of rides, given the performance of vehicle ventilation. The greatest risk to the largest number of passengers is from an index case whose exhaled breath would take the longest time to exit the vehicle. The study identified such a case based on ventilation patterns, and it tracked the spread of viral aerosols (5 µm) by using the Wells-Riley equation to predict aerosol infection risk distribution. These distributions allowed a definition of an imperfect mixing degree (δ) as the ratio of actual risk and the calculated risk under a perfect mixing condition, and further derived regression equations to predict δ in the far-field (FF) and near-field (NF) of each passenger. These results revealed an order of magnitude higher aerosol infection risk in NF than in FF. For example, with a ventilation rate of 58 ACH (air changes per hour) and a 45% occupancy rate, unmasked passengers should stay up to 15 min in the bus and 35 min in the shuttle to limit infection risk in NF within 10%. These also indicate that masking is an important and effective risk reduction measure in transportation vehicles, especially important in NF. Overall, the analysis of imperfect air mixing allows direct comparison of risks in different transportation vehicles and a structured approach to development of policy recommendations.

Large-Scale Identification of T-Cell Epitopes Derived From Severe Acute Respiratory Syndrome Coronavirus 2 for the Development of Peptide Vaccines Against Coronavirus Disease 2019.

BACKGROUND: Coronavirus disease 2019 (COVID-19) continues to be a major public health challenge globally. The identification of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)-derived T-cell epitopes is of critical importance for peptide vaccines or diagnostic tools of COVID-19. METHODS: In this study, several SARS-CoV-2-derived human leukocyte antigen (HLA)-I binding peptides were predicted by NetMHCpan-4.1 and selected by Popcover to achieve pancoverage of the Chinese population. The top 5 ranked peptides derived from each protein of SARS-CoV-2 were then evaluated using peripheral blood mononuclear cells from unexposed individuals (negative for SARS-CoV-2 immunoglobulin G). RESULTS: Seven epitopes derived from 4 SARS-CoV-2 proteins were identified. It is interesting to note that most (5 of 7) of the SARS-CoV-2-derived peptides with predicted affinities for HLA-I molecules were identified as HLA-II-restricted epitopes and induced CD4+ T cell-dependent responses. These results complete missing pieces of pre-existing SARS-CoV-2-specific T cells and suggest that pre-existing T cells targeting all SARS-CoV-2-encoded proteins can be discovered in unexposed populations. CONCLUSIONS: In summary, in the current study, we present an alternative and effective strategy for the identification of T-cell epitopes of SARS-CoV-2 in healthy subjects, which may indicate an important role in the development of peptide vaccines for COVID-19.

A Large-Scale Clinical Validation Study Using nCapp Cloud Plus Terminal by Frontline Doctors for the Rapid Diagnosis of COVID-19 and COVID-19 pneumonia in China (preprint)

Background The outbreak of coronavirus disease 2019 (COVID-19) has become a global pandemic acute infectious disease, especially with the features of possible asymptomatic carriers and high contagiousness. It causes acute respiratory distress syndrome and results in a high mortality rate if pneumonia is involved. Currently, it is difficult to quickly identify asymptomatic cases or COVID-19 patients with pneumonia due to limited access to reverse transcription-polymerase chain reaction (RT-PCR) nucleic acid tests and CT scans, which facilitates the spread of the disease at the community level, and contributes to the overwhelming of medical resources in intensive care units. Goal This study aimed to develop a scientific and rigorous clinical diagnostic tool for the rapid prediction of COVID-19 cases based on a COVID-19 clinical case database in China, and to assist global frontline doctors to efficiently and precisely diagnose asymptomatic COVID-19 patients and cases who had a false-negative RT-PCR test result. Methods With online consent, and the approval of the ethics committee of Zhongshan Hospital Fudan Unversity (approval number B2020-032R) to ensure that patient privacy is protected, clinical information has been uploaded in real-time through the New Coronavirus Intelligent Auto-diagnostic Assistant Application of cloud plus terminal (nCapp) by doctors from different cities (Wuhan, Shanghai, Harbin, Dalian, Wuxi, Qingdao, Rizhao, and Bengbu) during the COVID-19 outbreak in China. By quality control and data anonymization on the platform, a total of 3,249 cases from COVID-19 high-risk groups were collected. These patients had SARS-CoV-2 RT-PCR test results and chest CT scans, both of which were used as the gold standard for the diagnosis of COVID-19 and COVID-19 pneumonia. In particular, the dataset included 137 indeterminate cases who initially did not have RT-PCR tests and subsequently had positive RT-PCR results, 62 suspected cases who initially had false-negative RT-PCR test results and subsequently had positive RT-PCR results, and 122 asymptomatic cases who had positive RT-PCR test results, amongst whom 31 cases were diagnosed. We also integrated the function of a survey in nCapp to collect user feedback from frontline doctors. Findings We applied the statistical method of a multi-factor regression model to the training dataset (1,624 cases) and developed a prediction model for COVID-19 with 9 clinical indicators that are fast and accessible: 'Residing or visiting history in epidemic regions', 'Exposure history to COVID-19 patient', 'Dry cough', 'Fatigue', 'Breathlessness', 'No body temperature decrease after antibiotic treatment', 'Fingertip blood oxygen saturation<=93%', 'Lymphopenia', and 'C-reactive protein (CRP) increased'. The area under the receiver operating characteristic (ROC) curve (AUC) for the model was 0.88 (95% CI: 0.86, 0.89) in the training dataset and 0.84 (95% CI: 0.82, 0.86) in the validation dataset (1,625 cases). To ensure the sensitivity of the model, we used a cutoff value of 0.09. The sensitivity and specificity of the model were 98.0% (95% CI: 96.9%, 99.1%) and 17.3% (95% CI: 15.0%, 19.6%), respectively, in the training dataset, and 96.5% (95% CI: 95.1%, 98.0%) and 18.8% (95% CI: 16.4%, 21.2%), respectively, in the validation dataset. In the subset of the 137 indeterminate cases who initially did not have RT-PCR tests and subsequently had positive RT-PCR results, the model predicted 132 cases, accounting for 96.4% (95% CI: 91.7%, 98.8%) of the cases. In the subset of the 62 suspected cases who initially had false-negative RT-PCR test results and subsequently had positive RT-PCR results, the model predicted 59 cases, accounting for 95.2% (95% CI: 86.5%, 99.0%) of the cases. Considering the specificity of the model, we used a cutoff value of 0.32. The sensitivity and specificity of the model were 83.5% (95% CI: 80.5%, 86.4%) and 83.2% (95% CI: 80.9%, 85.5%), respectively, in the training dataset, and 79.6% (95% CI: 76.4%, 82.8%) and 81.3% (95% CI: 78.9%, 83.7%), respectively, in the validation dataset, which is very close to the published AI model. The results of the online survey 'Questionnaire Star' showed that 90.9% of nCapp users in WeChat mini programs were 'satisfied' or 'very satisfied' with the tool. The WeChat mini program received a significantly higher satisfaction rate than other platforms, especially for 'availability and sharing convenience of the App' and 'fast speed of log-in and data entry'. Discussion With the assistance of nCapp, a mobile-based diagnostic tool developed from a large database that we collected from COVID-19 high-risk groups in China, frontline doctors can rapidly identify asymptomatic patients and avoid misdiagnoses of cases with false-negative RT-PCR results. These patients require timely isolation or close medical supervision. By applying the model, medical resources can be allocated more reasonably, and missed diagnoses can be reduced. In addition, further education and interaction among medical professionals can improve the diagnostic efficiency for COVID-19, thus avoiding the transmission of the disease from asymptomatic patients at the community level.

Association between severity of COVID-19 and clinical and biochemical characteristics: a cross-sectional study (preprint)

Background: Coronavirus Disease 2019 (COVID-19) is a novel infectious disease caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) emerged in Wuhan and has quickly spread across the world. The mortality rate in critically ill patients with COVID-19 is high. This study analyzed clinical and biochemical parameters between mild and severe patients, helping to identify severe or critical patients early.Methods: In this single center, cross-sectional study, 143 patients were included and divided to mild/moderate and sever/critical groups. Correlation between the disease criticality and clinical features and peripheral blood biochemical markers was analyzed. Cut-off values for critically ill patients were speculated through the ROC curve.Results： Significantly, disease severity was associated with age (r = 0.458, P < 0.001) , comorbidities (r = 0.445, P < 0.001) , white cell counts (r = 0.229, P = 0.006) , neutrophil count (r = 0.238, P = 0.004) , lymphocyte count (r = -0.295, P < 0.001) , albumin (r = -0.603, P < 0.001) , high-density lipoprotein cholesterol (r= -0.362, P < 0.001) , serum potassium (r = -0.237, P = 0.004) , plasma glucose (r = 0.383, P < 0.001) , total bilirubin (r = 0.340, P < 0.001) , serum amyloid A (r = 0.58, P < 0.001) , procalcitonin (r = 0.345, P < 0.001) , C-reactive protein ( r = 0.477, P < 0.001) , lactate dehydrogenase (r = 0.548, P < 0.001) , aspartate aminotransferase (r = 0.342, P < 0.001) , alanine aminotransferase (r = 0.264, P = 0.001) , erythrocyte sedimentation rate (r = 0.284, P = 0.001) and D-dimer (r = 0.477, P ＜ 0.001) .Conclusion: With following parameters such as age > 52 years, C-reactive protein > 64.79 mg/L, lactate dehydrogenase > 245 U/L, D-dimer > 0.96 ug/mL, serum amyloid A > 100.02 mg/L, or albumin ＜ 36 g/L, the progress of COVID-19 to critical stage should be closely observed and possibly prevented. Lymphocyte count, serum potassium and procalcitonin may also be a prognostic indicator.