Transmission of droplet aerosols in an elevator cabin: Effect of the ventilation mode.

The recent outbreak of COVID-19 has threatened public health. Owing to the relatively sealed environment and poor ventilation in elevator cabins, passengers are at risk of respiratory tract infection. However, the distribution and dispersion of droplet aerosols in elevator cabins remain unclear. This study investigated the transmission of droplet aerosols exhaled by a source patient under three ventilation modes. Droplet aerosols produced by nose breathing and mouth coughing were resolved using computational fluid dynamics (CFD) simulations. We adopted the verified renormalization group (RNG) k-ε turbulence model to simulate the flow field and the Lagrangian method to track the droplet aerosols. In addition, the influence of the ventilation mode on droplet transmission was evaluated. The results showed that droplet aerosols gathered in the elevator cabin and were difficult to discharge under the mixed and displacement ventilation modes with specific initial conditions. The inhalation proportion of droplet aerosols for air curtain was 0.016%, which was significantly lower than that for mixed ventilation (0.049%) and displacement ventilation (0.071%). The air curtain confined the transmission of droplet aerosols with the minimum ratios of inhalation, deposition, and suspension and is thus recommended to reduce the exposure risk.

Impacts of androgen deprivation therapy on the risks and outcomes of SARS-CoV-2 infection in patients with prostate cancer.

Studies have investigated the effects of androgen deprivation therapy (ADT) use on the incidence and clinical outcomes of coronavirus disease 2019 (COVID-19); however, the results have been inconsistent. We searched the PubMed, Medline, Cochrane, Scopus, and Web of Science databases from inception to March 2022; 13 studies covering 84 003 prostate cancer (PCa) patients with or without ADT met the eligibility criteria and were included in the meta-analysis. We calculated the pooled risk ratios (RRs) with 95% confidence intervals (CIs) to explore the association between ADT use and the infection risk of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and severity of COVID-19. After synthesizing the evidence, the pooled RR in the SARS-CoV-2 positive group was equal to 1.17, and the SARS-CoV-2 positive risk in PCa patients using ADT was not significantly different from that in those not using ADT (P = 0.544). Moreover, no significant results concerning the beneficial effect of ADT on the rate of intensive care unit admission (RR = 1.04, P = 0.872) or death risk (RR = 1.23, P = 0.53) were found. However, PCa patients with a history of ADT use had a markedly higher COVID-19 hospitalization rate (RR = 1.31, P = 0.015) than those with no history of ADT use. These findings indicate that ADT use by PCa patients is associated with a high risk of hospitalization during infection with SARS-CoV-2. A large number of high quality studies are needed to confirm these results.

Epi-DNNs: Epidemiological priors informed deep neural networks for modeling COVID-19 dynamics

Differential equations-based epidemic compartmental models and deep neural networks-based artificial intelligence (AI) models are powerful tools for analyzing and fighting the transmission of COVID-19. However, the capability of compartmental models is limited by the challenges of parameter estimation, while AI models fail to discover the evolutionary pattern of COVID-19 and lack explainability. This paper aims to provide a novel method (called Epi-DNNs) by integrating compartmental models and deep neural networks (DNNs) to model the complex dynamics of COVID-19. In the proposed Epi-DNNs method, the neural network is designed to express the unknown parameters in the compartmental model and the Runge–Kutta method is implemented to solve the ordinary differential equations (ODEs) so as to give the values of the ODEs at a given time. Specifically, the discrepancy between predictions and observations is incorporated into the loss function, then the defined loss is minimized and applied to identify the best-fitted parameters governing the compartmental model. Furthermore, we verify the performance of Epi-DNNs on the real-world reported COVID-19 data on the Omicron epidemic in Shanghai covering February 25 to May 27, 2022. The experimental findings on the synthesized data have revealed its effectiveness in COVID-19 transmission modeling. Moreover, the inferred parameters from the proposed Epi-DNNs method yield a predictive compartmental model, which can serve to forecast future dynamics.

Transmission of droplet aerosols in an elevator cabin: Effect of the ventilation mode

The recent outbreak of COVID-19 has threatened public health. Owing to the relatively sealed environment and poor ventilation in elevator cabins, passengers are at risk of respiratory tract infection. However, the distribution and dispersion of droplet aerosols in elevator cabins remain unclear. This study investigated the transmission of droplet aerosols exhaled by a source patient under three ventilation modes. Droplet aerosols produced by nose breathing and mouth coughing were resolved using computational fluid dynamics (CFD) simulations. We adopted the verified renormalization group (RNG) k-ε turbulence model to simulate the flow field and the Lagrangian method to track the droplet aerosols. In addition, the influence of the ventilation mode on droplet transmission was evaluated. The results showed that droplet aerosols gathered in the elevator cabin and were difficult to discharge under the mixed and displacement ventilation modes with specific initial conditions. The inhalation proportion of droplet aerosols for air curtain was 0.016%, which was significantly lower than that for mixed ventilation (0.049%) and displacement ventilation (0.071%). The air curtain confined the transmission of droplet aerosols with the minimum ratios of inhalation, deposition, and suspension and is thus recommended to reduce the exposure risk.

Epi-DNNs: Epidemiological priors informed deep neural networks for modeling COVID-19 dynamics.

Differential equations-based epidemic compartmental models and deep neural networks-based artificial intelligence (AI) models are powerful tools for analyzing and fighting the transmission of COVID-19. However, the capability of compartmental models is limited by the challenges of parameter estimation, while AI models fail to discover the evolutionary pattern of COVID-19 and lack explainability. This paper aims to provide a novel method (called Epi-DNNs) by integrating compartmental models and deep neural networks (DNNs) to model the complex dynamics of COVID-19. In the proposed Epi-DNNs method, the neural network is designed to express the unknown parameters in the compartmental model and the Runge-Kutta method is implemented to solve the ordinary differential equations (ODEs) so as to give the values of the ODEs at a given time. Specifically, the discrepancy between predictions and observations is incorporated into the loss function, then the defined loss is minimized and applied to identify the best-fitted parameters governing the compartmental model. Furthermore, we verify the performance of Epi-DNNs on the real-world reported COVID-19 data on the Omicron epidemic in Shanghai covering February 25 to May 27, 2022. The experimental findings on the synthesized data have revealed its effectiveness in COVID-19 transmission modeling. Moreover, the inferred parameters from the proposed Epi-DNNs method yield a predictive compartmental model, which can serve to forecast future dynamics.

Viral contamination on the surfaces of the personal protective equipment among health care professionals working in COVID-19 wards: A single-center prospective, observational study.

OBJECTIVE: To evaluate potential viral contamination on the surfaces of personal protective equipment (PPE) in COVID-19 wards. METHODS: Face shields, gloves, the chest area of PPE and shoe soles were sampled at different time points. The samples were tested for the presence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) by PCR, and the cycle threshold (CT) values were recorded. RESULTS: The positive rate was 74.7% (239/320) for all PPE specimens. The CT values of the samples were ranked in the following order: face shields > chests > gloves > shoe soles (37.08±1.38, 35.48±2.02, 34.17±1.91 and 33.52±3.16, respectively; P for trend < .001). After disinfection, the CT values of shoe soles decreased compared with before disinfection (32.78±3.47 vs. 34.3±2.61, P = .037), whereas no significant effect of disinfection on the CT values of face shields, chests and gloves was observed. After disinfection, the CT values of specimens collected from shoe soles gradually increased; before disinfection, the CT values of shoe sole specimens were all less than 35. CONCLUSIONS: SARS-CoV-2 can attach to the surfaces of the PPE of healthcare professionals in COVID-19 wards, especially the shoe soles and undisinfected gloves. Shoe soles had the highest SARS-CoV-2 loads among all tested PPE items.

TRIM18 is a critical regulator of viral myocarditis and organ inflammation.

BACKGROUND: Infections by viruses including severe acute respiratory syndrome coronavirus 2 could cause organ inflammations such as myocarditis, pneumonia and encephalitis. Innate immunity to viral nucleic acids mediates antiviral immunity as well as inflammatory organ injury. However, the innate immune mechanisms that control viral induced organ inflammations are unclear. METHODS: To understand the role of the E3 ligase TRIM18 in controlling viral myocarditis and organ inflammation, wild-type and Trim18 knockout mice were infected with coxsackievirus B3 for inducing viral myocarditis, influenza A virus PR8 strain and human adenovirus for inducing viral pneumonia, and herpes simplex virus type I for inducing herpes simplex encephalitis. Mice survivals were monitored, and heart, lung and brain were harvested for histology and immunohistochemistry analysis. Real-time PCR, co-immunoprecipitation, immunoblot, enzyme-linked immunosorbent assay, luciferase assay, flow cytometry, over-expression and knockdown techniques were used to understand the molecular mechanisms of TRIM18 in regulating type I interferon (IFN) production after virus infection in this study. RESULTS: We find that knockdown or deletion of TRIM18 in human or mouse macrophages enhances production of type I IFN in response to double strand (ds) RNA and dsDNA or RNA and DNA virus infection. Importantly, deletion of TRIM18 protects mice from viral myocarditis, viral pneumonia, and herpes simplex encephalitis due to enhanced type I IFN production in vivo. Mechanistically, we show that TRIM18 recruits protein phosphatase 1A (PPM1A) to dephosphorylate TANK binding kinase 1 (TBK1), which inactivates TBK1 to block TBK1 from interacting with its upstream adaptors, mitochondrial antiviral signaling (MAVS) and stimulator of interferon genes (STING), thereby dampening antiviral signaling during viral infections. Moreover, TRIM18 stabilizes PPM1A by inducing K63-linked ubiquitination of PPM1A. CONCLUSIONS: Our results indicate that TRIM18 serves as a negative regulator of viral myocarditis, lung inflammation and brain damage by downregulating innate immune activation induced by both RNA and DNA viruses. Our data reveal that TRIM18 is a critical regulator of innate immunity in viral induced diseases, thereby identifying a potential therapeutic target for treatment.

A triple-RBD-based mucosal vaccine provides broad protection against SARS-CoV-2 variants of concern.

The rapid mutation and spread of SARS-CoV-2 variants urge the development of effective mucosal vaccines to provide broad-spectrum protection against the initial infection and thereby curb the transmission potential. Here, we designed a chimeric triple-RBD immunogen, 3Ro-NC, harboring one Delta RBD and two Omicron RBDs within a novel protein scaffold. 3Ro-NC elicits potent and broad RBD-specific neutralizing immunity against SARS-CoV-2 variants of concern. Notably, intranasal immunization with 3Ro-NC plus the mucosal adjuvant KFD (3Ro-NC + KFDi.n) elicits coordinated mucosal IgA and higher neutralizing antibody specificity (closer antigenic distance) against the Omicron variant. In Omicron-challenged human ACE2 transgenic mice, 3Ro-NC + KFDi.n immunization significantly reduces the tissue pathology in the lung and lowers the viral RNA copy numbers in both the lung (85.7-fold) and the nasal turbinate (13.6-fold). Nasal virologic control is highly correlated with RBD-specific secretory IgA antibodies. Our data show that 3Ro-NC plus KFD is a promising mucosal vaccine candidate for protection against SARS-CoV-2 Omicron infection, pathology and transmission potential.

Appropriate Strategies for Reducing the Negative Impact of Online Reports of Suicide and Public Opinion From Social Media in China.

Suicide events may have a negative impact on all of society. The media plays a significant role in suicide prevention. Therefore, the aims of this study are (a) to understand the association between characteristics of suicide events and characteristics of who committed suicide, and event impact indexes (EIIs) of suicide reported on the internet; (b) to analyze violation of recommendations for reporting suicide by Weibo, and (c) to investigate the effect of online reports of suicide on public opinion. We carried out a content analysis of online reports of suicide. This study analyzed 113 suicide events, 300 news reports of suicide, and 2,654 Weibo comments about suicide collected from the WeiboReach between 2015 and 2020. We used a t-test and analysis of variance (ANOVA) to explore the potential factors associated with the EIIs of suicide events. The results found that (a) The suicide events reported on the internet during COVID-19 and those related to celebrities and students tend to have higher EIIs; (b) suicide reports on Weibo frequently violated WHO recommendations for suicide reporting in the media; and (c) public opinion of suicide reporting in the online media was mostly emotional and irrational, which is not beneficial for public mental health and suicide prevention. In conclusion, first, the situation of many people working from home or studying from home and spreading more time online during COVID-19 may lead to suicide events obtain more public attention. Online media could further improve public responsible reporting and daily media-content surveillance, especially taking particular care in those suicide events during COVID-19, and related to celebrities and students, which may have a higher event impact on the internet. Second, health managers should regular assessment of observance of the WHO recommendations for suicide reporting by online social media to prevent suicide. Third, health communication managers should use big data to identify, assess, and manage harmful information about suicide; and track anyone affected by suicide-related reports on social media to reduce the negative impact of public opinion to intervene suicide in the early stage of suicide.

Predicting Bank Failures: A Synthesis of Literature and Directions for Future Research

Risk management has been a topic of great interest to Michael McAleer. Even as recent as 2020, his paper on risk management for COVID-19 was published. In his memory, this article is focused on bankruptcy risk in financial firms. For financial institutions in particular, banks are considered special, given that they perform risk management functions that are unique. Risks in banking arise from both internal and external factors. The GFC underlined the need for comprehensive risk management, and researchers since then have been working towards fulfilling that need. Similarly, the central banks across the world have begun periodic stress-testing of banks’ ability to withstand shocks. This paper investigates the machine-learning and statistical techniques used in the literature on bank failure prediction. The study finds that though considerable progress has been made using advanced statistical and computational techniques, given the complex nature of banking risk, the ability of statistical techniques to predict bank failures is limited. Machine-learning-based models are increasingly becoming popular due to their significant predictive ability. The paper also suggests the directions for future research.

COVID-19 or treatment associated immunosuppression may trigger hepatitis B virus reactivation: A case report.

BACKGROUND: Since the initial recognition of coronavirus disease 2019 (COVID-19) in Wuhan, this infectious disease has spread to most areas of the world. The pathogenesis of COVID-19 is yet unclear. Hepatitis B virus (HBV) reactivation occurring in COVID-19 patients has not yet been reported. CASE SUMMARY: A 45-year-old hepatitis B man with long-term use of adefovir dipivoxil and entecavir for antiviral therapy had HBV reactivation after being treated with methylprednisolone for COVID-19 for 6 d. CONCLUSION: COVID-19 or treatment associated immunosuppression may trigger HBV reactivation.

Impact of different control policies for COVID-19 outbreak on the air transportation industry: A comparison between China, the U.S. and Singapore.

Many countries have been implementing various control measures with different strictness levels to prevent the coronavirus disease 2019 (COVID-19) from spreading. With the great reduction in human mobility and daily activities, considerable impacts have been imposed on the global air transportation industry. This study applies a hybrid SARIMA-based intervention model to measure the differences in the impacts of different control measures implemented in China, the U.S. and Singapore on air passenger and air freight traffic. To explore the effect of time span for the measures to be in force, two scenarios are invented, namely a long-term intervention and a short-term intervention, and predictions are made till the end of 2020 for all three countries under both scenarios. As a result, predictive patterns of the selected metrics for the three countries are rather different. China is predicted to have the mildest economic impact on the air transportation industry in this year in terms of air passenger revenue and air cargo traffic, provided that the control measures were prompt and effective. The U.S. would suffer from a far-reaching impact on the industry if the same control measures are maintained. More uncertainties are found for Singapore, as it is strongly associated with international travel demands. Suggestions are made for the three countries and the rest of the world on how to seek a balance between the strictness of control measures and the potential long-term industrial losses.

Association of Overlapped and Un-overlapped Comorbidities with COVID-19 Severity and Treatment Outcomes: A Retrospective Cohort Study from Nine Provinces in China.

OBJECTIVE: Several COVID-19 patients have overlapping comorbidities. The independent role of each component contributing to the risk of COVID-19 is unknown, and how some non-cardiometabolic comorbidities affect the risk of COVID-19 remains unclear. METHODS: A retrospective follow-up design was adopted. A total of 1,160 laboratory-confirmed patients were enrolled from nine provinces in China. Data on comorbidities were obtained from the patients' medical records. Multivariable logistic regression models were used to estimate the odds ratio ( OR) and 95% confidence interval (95% CI) of the associations between comorbidities (cardiometabolic or non-cardiometabolic diseases), clinical severity, and treatment outcomes of COVID-19. RESULTS: Overall, 158 (13.6%) patients were diagnosed with severe illness and 32 (2.7%) had unfavorable outcomes. Hypertension (2.87, 1.30-6.32), type 2 diabetes (T2DM) (3.57, 2.32-5.49), cardiovascular disease (CVD) (3.78, 1.81-7.89), fatty liver disease (7.53, 1.96-28.96), hyperlipidemia (2.15, 1.26-3.67), other lung diseases (6.00, 3.01-11.96), and electrolyte imbalance (10.40, 3.00-26.10) were independently linked to increased odds of being severely ill. T2DM (6.07, 2.89-12.75), CVD (8.47, 6.03-11.89), and electrolyte imbalance (19.44, 11.47-32.96) were also strong predictors of unfavorable outcomes. Women with comorbidities were more likely to have severe disease on admission (5.46, 3.25-9.19), while men with comorbidities were more likely to have unfavorable treatment outcomes (6.58, 1.46-29.64) within two weeks. CONCLUSION: Besides hypertension, diabetes, and CVD, fatty liver disease, hyperlipidemia, other lung diseases, and electrolyte imbalance were independent risk factors for COVID-19 severity and poor treatment outcome. Women with comorbidities were more likely to have severe disease, while men with comorbidities were more likely to have unfavorable treatment outcomes.

Beyond the emergency department: Effects of COVID-19 on emergency medicine resident education.

BACKGROUND: In December 2019, a novel coronavirus (COVID-19) caused widespread clinical disease, triggering limited in-person gatherings and social-distancing guidelines to minimize transmission. These regulations led most emergency medicine (EM) residency training programs to rapidly transition to virtual didactics. We sought to evaluate EM resident perceptions of the effects of COVID-19 on their didactic and clinical education. METHODS: We performed a cross-sectional survey study at seven EM residency programs using a mixed-methods approach designed to understand resident perceptions regarding the impact of COVID-19 on their educational experience. Quantitative data were presented as percentages with comparison of subgroups, while open-ended responses were analyzed using qualitative methodology. RESULTS: We achieved a 59% response rate (187/313). The majority of respondents (119/182, 65.4%) reported that the COVID-19 pandemic had a negative impact on their residency education with junior residents disproportionately affected. A total of 81 of 182 (44.5%) participants reported that one or more of their clinical rotations were partially or completely canceled due to the pandemic. Additionally, we identified four themes and 34 subthemes highlighting the contextual effects of the pandemic, which were then divided into positive and negative influences on the residency experience. The four themes include systems experience, clinical experience, didactic experience, and wellness. CONCLUSION: Our study examined the impact of COVID-19 on residents' educational experiences. We found overall mixed responses with a slightly negative impact on residency education, wellness, and clinical rotations, while satisfaction with EM as a career choice was increased. Factors influencing this included systems, clinical, and didactic experiences as well as overall wellness.

Pathways of the COVID-19 Pandemic with Human Mobility across Countries (preprint)

This study develops a holistic view of the novel coronavirus (COVID-19) transmission worldwide through a spatial-temporal model with network dynamics. By using a unique human mobility dataset containing 547,166 flights with a total capacity of 101,455,913 passengers among 22 countries during the past three months, we analyze the associations between international travel movement in six continents and the new infections in these countries. Results show that policymakers should move away from the previous practices that focus only on restricting hotspot areas with high transmission rates. Instead, they should develop a new holistic view of global human mobility to adjust the international movement restriction. The study highlights that international human mobility is the key to understand the transmission pathways and the small world phenomenon in the global network of COVID-19 pandemic.

COVID-19 Pandemic with Human Mobility Across Countries

This study develops a holistic view of the novel coronavirus (COVID-19) spread worldwide through a spatial-temporal model with network dynamics. By using a unique human mobility dataset containing 547 166 flights with a total capacity of 101 455 913 passengers from January 22 to April 24, 2020, we analyze the epidemic correlations across 22 countries in six continents and particularly the changes in such correlations before and after implementing the international travel restriction policies targeting different countries. Results show that policymakers should move away from the previous practices that focus only on restricting hotspot areas with high infection rates. Instead, they should develop a new holistic view of global human mobility to impose the international movement restriction. The study further highlights potential correlations between international human mobility and focal countries' epidemic situations in the global network of COVID-19 pandemic.