A Closer Look at Spatial-Slice Features Learning for COVID-19 Detection (preprint)

Conventional Computed Tomography (CT) imaging recognition faces two significant challenges: (1) There is often considerable variability in the resolution and size of each CT scan, necessitating strict requirements for the input size and adaptability of models. (2) CT-scan contains large number of out-of-distribution (OOD) slices. The crucial features may only be present in specific spatial regions and slices of the entire CT scan. How can we effectively figure out where these are located? To deal with this, we introduce an enhanced Spatial-Slice Feature Learning (SSFL++) framework specifically designed for CT scan. It aim to filter out a OOD data within whole CT scan, enabling our to select crucial spatial-slice for analysis by reducing 70% redundancy totally. Meanwhile, we proposed Kernel-Density-based slice Sampling (KDS) method to improve the stability when training and inference stage, therefore speeding up the rate of convergence and boosting performance. As a result, the experiments demonstrate the promising performance of our model using a simple EfficientNet-2D (E2D) model, even with only 1% of the training data. The efficacy of our approach has been validated on the COVID-19-CT-DB datasets provided by the DEF-AI-MIA workshop, in conjunction with CVPR 2024. Our source code will be made available.

Simple 2D Convolutional Neural Network-based Approach for COVID-19 Detection (preprint)

This study explores the use of deep learning techniques for analyzing lung Computed Tomography (CT) images. Classic deep learning approaches face challenges with varying slice counts and resolutions in CT images, a diversity arising from the utilization of assorted scanning equipment. Typically, predictions are made on single slices which are then combined for a comprehensive outcome. Yet, this method does not incorporate learning features specific to each slice, leading to a compromise in effectiveness. To address these challenges, we propose an advanced Spatial-Slice Feature Learning (SSFL++) framework specifically tailored for CT scans. It aims to filter out out-of-distribution (OOD) data within the entire CT scan, allowing us to select essential spatial-slice features for analysis by reducing data redundancy by 70\%. Additionally, we introduce a Kernel-Density-based slice Sampling (KDS) method to enhance stability during training and inference phases, thereby accelerating convergence and enhancing overall performance. Remarkably, our experiments reveal that our model achieves promising results with a simple EfficientNet-2D (E2D) model. The effectiveness of our approach is confirmed on the COVID-19-CT-DB datasets provided by the DEF-AI-MIA workshop.

Combined cooling, heating, power and oxygen for hospital buildings employing photovoltaic power and liquefied methane

To enhance the energy and gas supply security and reduce the greenhouse gas emission simultaneously, this paper presents a new cryogen-based co-production concept of combined cooling, heating, power and oxygen (CCHPO) for hospital buildings. By integrating with local photovoltaic power generation, two cryogenic liquids of liquefied methane and liquefied oxygen are used to store and produce multiple energies and medical gas. Detailed system modeling and performance analysis are carried out regarding the actual energy and gas consumption data from hospital buildings. The results obtained show the proposed CCHPO solution can be expected to fulfill the simultaneous requirements of energy conservation during normal operation and sustainable energy and gas supply during emergency operations.

Exploring the mechanism of Platycodonis Radix-Licorice for prevention and treatment of COVID-19 based on network pharmacology and molecular docking

Objective: To investigate the possible mechanism of Platycodonis Radix-Licorice drug pair in the intervention of COVID-19 by using network pharmacology and molecular docking technique. Methods The database TCMSP was retrieved for the chemical constituents and targets of Platycodonis Radix-Licorice drug pair. Coronavirus disease targets were screened by the Gene Cards, OMIM,TTD, PharmGkb and DrugBank database. Cytoscape 3.7.2 software was used to construct the drug-component-target network. The PPI(protein-protein interaction) network was obtained by drug-disease intersection targets, and the core genes were found through CytoNCA plug-in. Meanwhile, GO(gene ontology) analysis and KEGG(Kyoto encyclopedia of genes and genomes) pathway analysis were performed by using Bioconductor database to predict the mechanism. AutoDock Tools 1.5.6 software was used to simulate the molecular docking of the main active ingredients with the novel coronavirus key binding site protein [SARS-CoV-2 main protease(severe acute respiratory syndrome coronavirus 2 main protease, Mpro) and ACE2(angiotensin converting enzyme 2)]. Results A total of 7 active ingredients of Platycodonis Radix,92 active ingredients of Licorice,2766 drug targets, and 674 disease targets were obtained, and 67 drug-disease common targets were excavated. The key targets involved RELA,STAT1,MAPK3,TP53,MAPK1,MAPK8,STAT3,MAPK14,IL1 B and TNF by the database STRING and CytoNCA plug-in.Go enrichment analysis showed that the main functions of Platycodonis Radix-Licorice drug pair on the intervention of COVID-19 were antioxidant reaction, cell respond to chemical stress, regulation of apoptotic signaling pathways, reaction to lipopolysaccharides and reaction to bacteria-derived molecules, etc.. KEGG pathways involved Coronavirus disease-COVID-19 pathway, IL-17 signaling pathway and so on, were mainly associated with immune response, inflammation-related pathways, inhibition of viral infection, and other inhibition of cancer. The molecular docking results showed that glepidotin A,quercetin, licochalcone a and luteolin had good binding ability with Mpro and ACE2. Conclusion Platycodonis Radix-Licorice drug pair act on SARS-CoV-2 through multiple components, multiple targets, and multiple channel combination. And the main active ingredients have a fine binding ability with Mpro and ACE2. The method can provide theoretical support for the possibility of traditional Chinese medicine(TCM) against COVID-19.

Multistate Models for the Recovery Process in the Covid-19 Context: An Empirical Study of Chinese Enterprises

The Covid-19 pandemic has severely affected enterprises worldwide. It is thus of practical significance to study the process of enterprise recovery from Covid-19. However, the research on the effects of relevant determinants of business recovery is limited. This article presents a multistate modeling framework that considers the determinants, recovery time, and transition likelihood of Chinese enterprises by the state of those enterprises as a result of the pandemic (recovery state), with the help of an accelerated failure time model. Empirical data from 750 enterprises were used to evaluate the recovery process. The results indicate that the main problems facing non-manufacturing industries are supply shortages and order cancellations. With the increase of supplies and orders, the probability of transition between different recovery states gradually increases, and the recovery time of enterprises becomes shorter. For manufacturing industries, the factors that hinder recovery are more complex. The main problems are employee panic and order cancellations in the initial stage, employee shortages in the middle stage, and raw material shortages in the full recovery stage. This study can provide a reference for enterprise recovery in the current pandemic context and help policymakers and business managers take necessary measures to accelerate recovery.

Minimizing Fleet Size and Improving Bike Allocation of Bike Sharing under Future Uncertainty (preprint)

As a rapidly expanding service, bike sharing is facing severe problems of bike over-supply and demand fluctuation in many Chinese cities. This study develops a large-scale method to determine the minimum fleet size under uncertainty, based on the bike sharing data of millions of trips in Nanjing. It is found that the algorithm of minimizing fleet size under the incomplete-information scenario is effective in handling future uncertainty. For a dockless bike sharing system, supplying 14.5% of the original fleet could meet 96.8% of trip demands. Meanwhile, the results suggest that providing a integrated service platform that integrates multiple companies can significantly reduce the total fleet size by 44.6%. Moreover, in view of the COVID-19 pandemic, this study proposes a social distancing policy that maintains a suitable usage interval. These findings provide useful insights for improving the resource efficiency and operational service of bike sharing and shared mobility.

Optimization of extreme learning machine model with biological heuristic algorithms to estimate daily reference crop evapotranspiration in different climatic regions of China

• Hybrid ELM models (PSO-ELM, GA-ELM and ABC-ELM) were proposed for estimating ET 0 in different climate zones of China. • PSO-ELM model had the highest accuracy, followed by GA-ELM and ABC-ELM. • Hybrid ELM models outperformed standalone ELM and empirical models in different climate zones. • PSO-ELM model with T max , T min and RH obtained accurate ET 0 estimates in TCZ, SMZ and TMZ. • PSO-ELM model with only T max and T min was better performance on ET 0 estimates in MPZ. Accurate prediction of reference crop evapotranspiration (ET 0) is important for regional water resources management and optimal design of agricultural irrigation system. In this study, three hybrid models (PSO-ELM, GA-ELM and ABC-ELM) integrating the extreme learning machine model (ELM) with three biological heuristic algorithms, i.e., PSO, GA and ABC, were proposed for predicting daily ET 0 based on daily meteorological data from 2000 to 2019 at twelve representative stations in different climatic zones of China. The performances of the three hybrid ELM models were further compared with the standalone ELM model and three empirical models (Hargreaves, Priestley-Talor and Makkink models). The results showed that the hybrid ELM models (R 2 = 0.973–0.999) all performed better than the standalone ELM model (R 2 = 0.955–0.989) in four climatic regions in China. The estimation accuracy of the empirical models was relatively lower, with R2 of 0.822–0.887 and RMSE of 0.381–1.951 mm/d. The R 2 values of PSO-ELM, GA-ELM and ABC-ELM models were 0.993, 0.986 and 0.981 and the RMSE values were 0.266 mm/d, 0.306 mm/d and 0.404 mm/d, respectively, indicating that the PSO-ELM model had the best performance. When setting T max , T min and RH as the model inputs, the PSO-ELM model presented better performance in the temperate continental zone (TCZ), subtropical monsoon region (SMZ) and temperate monsoon zone (TMZ) climate zones, with R 2 of 0.892, 0866 and 0.870 and RMSE of 0.773 mm/d, 0.597 mm/d and 0.832 mm/d, respectively. The PSO-ELM model also performed in the mountain plateau region (MPZ) when only T max and T min data were available, with R2 of 0.808 and RMSE of 0.651 mm/d. All the three biological heuristic algorithms effectively improved the performance of the ELM model. Particularly, the PSO-ELM was recommended as a promising model realizing the high-precision estimation of daily ET 0 with fewer meteorological parameters in different climatic zones of China. [ FROM AUTHOR] Copyright of Journal of Hydrology is the property of Elsevier B.V. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)

The Roles of Tissue-Resident Memory T Cells in Lung Diseases.

The unique environment of the lungs is protected by complex immune interactions. Human lung tissue-resident memory T cells (TRM) have been shown to position at the pathogen entry points and play an essential role in fighting against viral and bacterial pathogens at the frontline through direct mechanisms and also by orchestrating the adaptive immune system through crosstalk. Recent evidence suggests that TRM cells also play a vital part in slowing down carcinogenesis and preventing the spread of solid tumors. Less beneficially, lung TRM cells can promote pathologic inflammation, causing chronic airway inflammatory changes such as asthma and fibrosis. TRM cells from infiltrating recipient T cells may also mediate allograft immunopathology, hence lung damage in patients after lung transplantations. Several therapeutic strategies targeting TRM cells have been developed. This review will summarize recent advances in understanding the establishment and maintenance of TRM cells in the lung, describe their roles in different lung diseases, and discuss how the TRM cells may guide future immunotherapies targeting infectious diseases, cancers and pathologic immune responses.

Investigation and precautions of SARS-CoV-2 infection among healthcare workers in Emergency Center

Objective: To analyze the causes of SARS-CoV-2 nosocomial infection among healthcare workers (HCWs) and explore the effective precaution strategies in Emergency Center.

#StayHome or #Marathon? Social Media Enhanced Pandemic Surveillance on Spatial-temporal Dynamic Graphs (preprint)

COVID-19 has caused lasting damage to almost every domain in public health, society, and economy. To monitor the pandemic trend, existing studies rely on the aggregation of traditional statistical models and epidemic spread theory. In other words, historical statistics of COVID-19, as well as the population mobility data, become the essential knowledge for monitoring the pandemic trend. However, these solutions can barely provide precise prediction and satisfactory explanations on the long-term disease surveillance while the ubiquitous social media resources can be the key enabler for solving this problem. For example, serious discussions may occur on social media before and after some breaking events take place. These events, such as marathon and parade, may impact the spread of the virus. To take advantage of the social media data, we propose a novel framework, Social Media enhAnced pandemic suRveillance Technique (SMART), which is composed of two modules: (i) information extraction module to construct heterogeneous knowledge graphs based on the extracted events and relationships among them; (ii) time series prediction module to provide both short-term and long-term forecasts of the confirmed cases and fatality at the state-level in the United States and to discover risk factors for COVID-19 interventions. Extensive experiments show that our method largely outperforms the state-of-the-art baselines by 7.3% and 7.4% in confirmed case/fatality prediction, respectively.

Intelligent disinfection robots assist medical institutions in controlling environmental surface disinfection

In recent years, noncontact crewless operations have become prominent in the field of environmental disinfection. Robots that automatically disinfect the air and surfaces of hospital environments can help reduce the human resources spent on environmental cleaning and disinfection and minimize the risk of occupational exposure for staff. These robots also facilitate informatized management of environmental disinfection, reduce costs, and increase the efficiency of disinfection efforts.

Prevalence and Predictors of PTSD during the Containment Stage of COVID-19 Epidemic among College Female Students in China (preprint)

Background: College students are a uniquely vulnerable group and may experience high stress levels due to COVID-19, especially for girls. This study aims to identify the post-traumatic stress disorder (PTSD) symptoms and related factors among the target population during the initial phases of the COVID-19 pandemic. Methods: A cross-sectional online survey was conducted during the initial phases of the COVID-19 pandemic in China. A total of 2205 college female students from six provinces enrolled in this study and completed the questions about cognitive status of COVID-19, the Impact of Event Scale-6 (IES-6), the Multidimensional Perceived Social Support Scale (MPSSS) and a self-developed 10-item Perceived threat scale. Univariate and multivariate logistic regression were performed by SPSS software to explore the determinants of PTSD symptoms. Results: PTSD symptoms were prevalent in this sample of college female students, and 34.20% met the cut-off for PTSD. Self-reported fair or poor health (AOR=1.78, 95%CI: 1.22-2.59), high concern about COVID-19 (AOR=1.66, 95%CI: 1.35-2.03), beliefs that 'COVID-19 can cause a global outbreak' (AOR=1.26, 95%CI: 1.02-1.56), the perception of ‘risk of infection’ (AOR=2.46, 95%CI: 2.16-2.81), beliefs that ‘closed management’ and ‘COVID-19 as a public health emergency of international concern’ would have an impact, and the fear of ‘impact on life planning’ were all positively associated with PTSD (AOR=1.37, 1.22 and 1.29, respectively), whereas perceived social support from family (AOR=0.81, 95%CI: 0.70-0.93) was negatively associated with PTSD. Among the significant variables at the bivariate level, multivariate logistic regression revealed that the greatest protector for PTSD was the high knowledge score (AOR=0.73, 95%CI: 0.60-0.90), while had confirmed cases among relatives and friends (AOR=7.70, 95%CI: 1.28-46.25) was the strongest predictor of PTSD. Conclusions: In summary, PTSD symptoms were prevalent among college female students in China during the COVID-19 epidemic. Targeting vulnerable populations to improve their knowledge of COVID-19 and create an atmosphere of social support would be beneficial to improve the mental health of the female students during the COVID-19 epidemic.

Effects of an Online Mindful Living With Challenge (MLWC) Intervention on Mental Health and Quality of Life Among COVID-19 Patients in China: A Protocol of a Randomized Controlled Trial (preprint)

Background COVID-19 can lead to increased psychological symptoms such as post-traumatic stress disorder (PTSD), depression, and anxiety, especially for patients with COVID-19. Studies suggest that mindfulness-based intervention is an effective, easily delivered and non-aggressive online therapy for patients with mental disorders. This study aims to explore the efficacy and possible mechanism of a Mindful Living With Challenge (MLWC) intervention designed for Chinese COVID-19 survivors in alleviating their psychological problems caused by both the disease and the pandemic.Methods This study is a protocol for a randomized controlled trial. More than 1600 eligible participants will be assigned 1:1 to an online MLWC intervention group or a waitlist control group. All participants will be asked to complete online questionnaires at baseline , post-program, and 3-month follow-up. The primary outcome is mental health status which includes PTSD and other psychological symptoms (i.e. depression, anxiety). The secondary outcomes are related physical symptoms including fatigue and sleeplessness assessed by verified scales such as the Fatigue Scale-14, Pittsburgh Sleep Quality Index. In addition, Five Facets Mindfulness Questionnaire, the Nonattachment Scale, the Stillness Scale, the Resilience Style Questionnaire and the Social Support Scale will be used to assess the mindfulness, stillness, nonattachment level, resilience and perceived social support before and after the intervention, which may be the possible mediators and moderators of the link between the MLWC intervention and target outcomes. Data will be analyzed based on an intention-to-treat approach, and SPSS software will be used to perform statistical analysis.Discussion This study will provide scientific evidence on the efficacy and possible mechanism of the MLWC intervention in improving the quality of life and psychological status among COVID-19 survivors in China. Findings from this study will contribute to a growing research field that assesses the effectiveness of mobile-based and theoretically guided interventions for improving the psychological status of the COVID-19 survivors. Moreover, findings from this study will also contribute to the prevention and management of the psychological complications patients face during such public health emergencies.Trial registration Chinese Clinical Trial Registry (ChiCTR), ChiCTR2000037524; Registered on August 29, 2020, http://www.chictr.org.cn/showproj.aspx?proj=60034. 

Successive Passage In Vitro Led to Lower Virulence and Higher Titer of A Variant Porcine Epidemic Diarrhea Virus.

A highly virulent porcine epidemic diarrhea virus (PEDV) appeared in China and spread rapidly to neighbor countries, which have led to great economic losses to the pig industry. In the present study, we isolated a PEDV using Vero cells and serially propagated 100 passages. PEDV SDSX16 was characterized in vitro and in vivo. The viral titers increased to 107.6 TCID50/mL (100th) by serial passages. The spike (S) gene and the whole gene of the SDSX16 virus was fully sequenced to assess the genetic stability and relatedness to previously identified PEDV. Along with successive passage in vitro, there were 18 nucleotides (nt) deletion occurred in the spike (S) gene resulting in a deletion of six amino acids when the SDSX16 strain was passaged to the 64th generation, and this deletion was stable until the P100. However, the ORF1a/b, M, N, E, and ORF3 genes had only a few point mutations in amino acids and no deletions. According to growth kinetics experiments, the SDSX16 deletion strain significantly enhanced its replication in Vero cells since it was passaged to the 64th generation. The animal studies showed that PEDV SDSX16-P10 caused more severe diarrhea and vomiting, fecal shedding, and acute atrophic enteritis than SDSX16-P75, indicating that SDSX16-P10 is enteropathogenic in the natural host, and the pathogenicity of SDSX16 decreased with successive passage in vitro. However, SDSX16-P10 was found to cause lower levels of cytokine expression than SDSX16-P75 using real-time PCR and flow cytometry, such as IL1ß, IL6, IFN-ß, TNF-α, indicating that SDSX16-P10 might inhibit the expression of cytokines. Our data indicated that successive passage in vitro resulted in virulent attenuation in vivo of the PEDV variant strain SDSX16.

Psychological Impact and Associated Factors During the Containment Stage of Covid-19 Epidemic Among College Students in China (preprint)

Background College students are a uniquely vulnerable group and may experience high stress levels due to COVID-19. This study aims to identify the the psychological state and related factors on Chinese college students during the initial phases of the COVID-19 pandemic. Methods From February 23 to March 5, 2020, a cross-sectional online survey was conducted among 3606 college students from seven provinces in China using standard questionnaires measuring adverse psychological outcomes and related factors including Impact of Event Scale-6 (IES-6), Depression, Anxiety and Stress Scale (DASS), Perceived Social Support Scale (PSSS) and Simplified Coping Style Questionnaire (SCSQ). Exploratory factor analysis (EFA) were used to determine underlying constructs of the perceived threat items. Multivariate regression was used to explore the determinants of adverse psychological impact. Results Posttraumatic stress (PTS) were prevalent in this sample of college students, and 34.22% met the cut-off for posttraumatic stress disorder (PTSD). The proportion of having mild to extremely severe symptoms of depression, anxiety and stress were 15.70%, 13.31% and 7.10%, respectively. The impact of closed-off management on life, perceived threat and passive coping strategies were positively correlated to PTS and DASS scores, while knowledge score, perceived social support and active coping strategies were negatively correlated to DASS scores. Conclusions In summary, adverse psychological symptoms were prevalent among college students in China during the COVID-19 epidemic. Identifying vulnerable populations and formulating correspondingly psychological interventions would be beneficial to improve the mental health during the COVID-19 epidemic.

The Impact of Psychology Interventions on Changing Mental Health Status and Sleep Quality in University Students during the COVID-19 Pandemic (preprint)

Abstract Objective We evaluated the change in mental health and sleep quality of college students at four time periods. Methods Mental health status and sleep quality were using the Pittsburgh Sleep Quality Index (PSQI) and Symptom Checklist-90-Revised (SCL-90-R) questionnaire across four time periods. Psychology interventions were carried out from the third period. Results Students in the third period had higher PSQI total scores [mean (SD), 6.01 (3.27)] than those in the first period [5.60 (3.11)], second period [4.17 (2.10)] and fourth period [4.09 (2.80)]. After adjustment for covariates there was a decline of 1.89 points in the PSQI in the fourth period compared with the highest period. The SCL-90-R scores were highest in the second period [121.19 (47.83)], and were higher than the scores in the first [107.60 (52.21)] and second period [107.79 (27.20)] and lowest in the fourth period [97.82 (17.12)]. The decline in scores was 23.38 points after adjustment for covariates. The prevalence of psychological distress and sleep disturbances respectively decreased from 28.6% to 11.7% and from 10.4% to 2.6% comparing to the highest period. Sleep quality showed a significant positive correlation with mental health status. Conclusions The pattern of change in mental health status was different to that of sleep quality. The implementation of comprehensive psychology intervention may improve mental health and sleep quality. These findings may inform public health policy during the reopening of schools in other regions.

Epidemiological and Clinical Characteristics of COVID-19 Patients with Cancers: A Systematic Review and Meta-Analysis of Global Data (preprint)

Background Data on the prevalence of cancer in coronavirus disease 2019 (COVID-19)-infected patients and the severe illness incidence and mortality of COVID-19 patients with cancers remains unclear. Methods We systematically searched PubMed, Embase, Cochrane Library, and Web of Science, from database inception to July 15, 2020, for studies of patients with COVID-19 infection that had available comorbidity information on cancer. The primary endpoint was the pooled prevalence of cancer in COVID-19 patients and the secondary endpoint was the outcomes of COVID-19-infected cancer patients with incidence of severe illness and death rate. We calculated the pooled prevalence and corresponding 95% confidence intervals (95% CIs) using a random-effects model, and performed meta-regression analyses to explore heterogeneity. Subgroup analyses were conducted based on continent, country, age, sample size and study design. Findings A total of 107 eligible global studies were included in the systematic review. 90 studies with 94,845 COVID-19 patients in which 4,106 patients with cancer morbidity were included in the meta-analysis for prevalence of cancer morbidity among COVID-19 patients. 21 studies with 70,969 COVID-19 patients in which 3,351 patients with cancer morbidity who had severe illness or death during the studies. The overall prevalence of cancer among the COVID-19 patients was 0.07 (95% CI 0.05~0.09). The cancer prevalence in COVID-19 patients of Europe (0.22, 95% CI 0.17~0.28) was higher than that in Asia Pacific (0.04, 95% CI 0.03~0.06) and North America (0.05, 95% CI 0.04~0.06). The prevalence of COVID-19-infected cancer patients over 60 years old was 0.10 (95% CI 0.07~0.14), higher than that of patients equal and less than 60 years old (0.05, 95% CI 0.03~0.06). The pooled prevalence of severe illness among COVID-19 patients with cancers was 0.35 (95% CI 0.27~0.43) and the pooled death rate of COVID-19 patients with cancers was 0.18 (95% CI 0.14~0.18). The pooled incidence of severe illness of COVID-19 patients with cancers from Asia Pacific, Europe, and North America were 0.38(0.24, 0.52), 0.36(0.17, 0.55), and 0.26(0.20, 0.31), respectively; and the pooled death rate from Asia Pacific, Europe, and North America were 0.17(0.10, 0.24), 0.26(0.13, 0.39), and 0.19(0.13, 0.25), respectively. Interpretation To our knowledge, this study is the most comprehensive and up-to-date meta-analysis assessing the prevalence of cancer among COVID-19 patients, severe illness incidence and mortality rate. The prevalence of cancer varied significantly in geographical continents and ages. The COVID-19 patients with cancer were at-risk for severe illness and a high death rate. The European COVID-19 patients had the highest cancer prevalence among the three continents examined and were also the most likely to progress to severe illness and death. Although the Asia Pacific COVID-19 patients had the lowest cancer prevalence, their severe illness rate was similar to that of European.

The psychological impact of COVID-19 on medical care workers in China (preprint)

Background: Medical care workers experienced unprecedented levels of workload and pressure since the outbreak of COVID-19 started from the end of 2019. Little is known about its exact impact on medical care workers and related factors in China. This study aims to identify the psychological impact of COVID-19 on medical care workers in China. Methods: From February 23 th to March 5 th , 2020, a cross-sectional survey was conducted among 863 medical care workers from 7 provinces in China using standard questionnaires measuring adverse psychological outcomes including Impact of Event Scale-6 (IES-6), Depression, Anxiety and Stress Scale（DASS）and related psychosocial factors like perceived threat, social support and coping strategies. Exploratory Factor analysis was performed to identify the dimensions of perceived threat by study participants. Multivariate regression was used to examine the determinants of adverse psychological outcomes. Results: Posttraumatic stress (PTS) were prevalent in this sample of health care professionals, and 40.2% indicated positive screens for significant posttraumatic stress disorder (PTSD) symptoms. The proportion of having mild to extremely severe symptoms of depression, anxiety and stress were 13.6%, 13.9% and 8.6%, respectively. Perceived threat and passive coping strategies were positively correlated to PTS and DASS scores, while perceived social support and active coping strategies were negatively correlated to DASS scores. Nurses were more likely to be anxious than others among medical care workers during the COVID-19 epidemic. Conclusions: Adverse psychological symptoms were prevalent among medical care workers in China during the COVID-19 epidemic. Screening for adverse psychological outcomes and developing corresponding preventive measures would be beneficial in decreasing negative psychological outcomes.

Distinctive trajectories of COVID-19 epidemic by age and gender: a retrospective modeling of the epidemic in South Korea (preprint)

Objectives: Elderly people had suffered disproportional burden of COVID-19. We hypothesized that males and females in different age groups might have different epidemic trajectories. Methods: Using publicly available data from South Korea, daily new COVID-19 cases were fitted with generalized additive models, assuming Poisson and negative binomial distributions. Epidemic dynamics by age and gender groups were explored with interactions between smoothed time terms and age and gender. Results: A negative binomial distribution fitted the daily case counts best. Interaction between the dynamic patterns of daily new cases and age groups was statistically significant (p<0.001), but not with gender group. People aged 20-39 years led the epidemic processes in the society with two peaks: one major peak around March 1 and a smaller peak around April 7, 2020. The epidemic process among people aged 60 or above was trailing behind that of younger people with smaller magnitude. After March 15, there was a consistent decline of daily new cases among elderly people, despite large fluctuations of case counts among young adults. Conclusions: Although young people drove the COVID-19 epidemic in the whole society with multiple rebounds, elderly people could still be protected from virus infection after the peak of epidemic.