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The aim of this paper is first to establish a general prediction framework for turning (period) term structures in COVID-19 epidemic related to the implementation of emergency risk management in the practice, which allows us to conduct the reliable estimation for the peak period based on the new concept of “Turning Period” (instead of the traditional one with the focus on “Turning Point”) for infectious disease spreading such as the COVID-19 epidemic appeared early in year 2020. By a fact that emergency risk management is necessarily to implement emergency plans quickly, the identification of the Turning Period is a key element to emergency planning as it needs to provide a time line for effective actions and solutions to combat a pandemic by reducing as much unexpected risk as soon as possible. As applications, the paper also discusses how this “Turning Term (Period) Structure” is used to predict the peak phase for COVID-19 epidemic in Wuhan from January/2020 to early March/2020. Our study shows that the predication framework established in this paper is capable to provide the trajectory of COVID-19 cases dynamics for a few weeks starting from Feb.10/2020 to early March/2020, from which we successfully predicted that the turning period of COVID-19 epidemic in Wuhan would arrive within one week after Feb.14/2020, as verified by the true observation in the practice. The method established in this paper for the prediction of “Turning Term (Period) Structures” by applying COVID-19 epidemic in China happened early 2020 seems timely and accurate, providing adequate time for the government, hospitals, essential industry sectors and services to meet peak demands and to prepare aftermath planning, and associated criteria for the Turning Term Structure of COVID-19 epidemic is expected to be a useful and powerful tool to implement the so-called “dynamic zero-COVID-19 policy” ongoing basis in the practice. © 2022, Science Press (China). All rights reserved.
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Background: Pandemics harm mental health by inducing stressors such as frustration, boredom, financial loss, self-isolation, fear of infection, and stigmatization. Students are vulnerable and at risk of ill effects of these stressors. Aim: The objective of this study was to determine the mental health status and associated social risk factors among dental students in Malaysia during the coronavirus disease 2019 pandemic. Materials and Methods: This was an online cross-sectional study done using the Depression Anxiety Stress Scale-21 questionnaire. The study was carried among the undergraduate dental students in Malaysia, during the period of compulsory self-quarantine. The prevalence of depression, anxiety, and stress (DAS) and their median scores were computed and analyzed with sociodemographic factors using Mann-Whitney U test, Kruskal-Wallis test, odds ratio, and Chi-square test. Results: The prevalence of DAS was 33.5%, 28.7%, and 7.3%, respectively, with no gender differences. Depression increased with increasing age (P = 0.043) and year of study (P = 0.015). The prevalence of depression was the highest in the students of Indian ethnicity (44%;P = 0. 018). Students from public universities reported a higher prevalence of anxiety (34%;P = 0.019) and stress scores (P = 0.013). A family's financial crisis increased the risk of DAS (P < 0.05). Being quarantined with family increased the odds of anxiety by 2.8 times (P < 0.05). Conclusion: Students were found to be vulnerable to the negative psychological impact of self-quarantine as measured by their mental health status. The study also identified demographic and social risk factors contributing toward this vulnerability. © Medical Journal of Dr. D.Y. Patil Vidyapeeth 2022.
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Background: RA has been associated with poor COVID-19 outcomes, but few studies have investigated outcomes in RA features such as interstitial lung disease. Objectives: To assess COVID-19 outcomes in patients with RA overall, and those with and without ILD, compared to general population comparators. Methods: A multicenter, retrospective cohort study was conducted at Mayo Clinic (19 hospitals and affiliated outpatient centers in 4 states) and Mass General Brigham (14 hospitals and affiliated outpatient centers in New England). Consecutive patients with RA meeting ACR/EULAR criteria and a positive COVID-19 test from March 1, 2020 through June 6, 2021 were matched 1:5 on age, sex, race, and COVID-19 test date with general population comparators without RA. RA features assessed included: RA-ILD per Bongartz criteria [1], duration, rheumatoid factor (RF), cyclic citrullinated peptide antibody (CCP), bone erosions, and treatments. The primary outcome was a composite of hospitalization or death following COVID-19 diagnosis. We used multivariable Cox regression to investigate the association of RA, and features such as ILD, with COVID-19 outcomes compared to matched comparators. Results: We analyzed 582 patients with RA and 2892 comparators without RA, all with COVID-19. Mean age was 62 years, 51% were female, and 79% were White. Mean RA duration was 11 years, 67% were seropositive (52% RF+ and 54% CCP+), 27% had bone erosions, 28% were on steroids, and 79% were on DMARDs. 50/582 (9%) patients with RA had ILD. The COVID-19 hospitalization or death rate for RA patients was higher than comparators (3.0 per 1,000 days [95% CI 2.5-3.6] vs. 1.9 per 1,000 days [95% CI 1.7-2.1], respectively). Overall, RA patients had a 53% higher risk of hospitalization or death than comparators after adjustment (95% CI 1.20-1.94). Among those with RA-ILD, the hospitalization or death rate was signifcantly higher than comparators (10.9 [95% CI 6.7-15.2] vs. 2.5 per 1,000 days [1.8-3.2], respectively). RA-ILD was associated with nearly 3-fold higher risk for hospitalization or death than comparators (multivariable HR 2.84 [95% CI 1.64-4.91], Table 1). There was a signifcant interaction between RA/comparator status and presence/absence of ILD for risk of severe COVID-19 (p<0.001, Figure 1). The elevated risk for severe COVID-19 was similar for RA subgroups defned by serostatus or bone erosions. Conclusion: We confrmed that RA was associated with severe COVID-19 outcomes compared to the general population. We found evidence that ILD may be an effect modifer for the relationship between RA and severe COVID-19 outcomes, but RA subgroups defned by serostatus and bone erosions had similarly elevated risk. These fndings suggest that ILD or its treatment may be a major contributor to severe COVID-19 outcomes in RA.
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Background: Systemic autoimmune rheumatic disease (SARD) patients may be at risk for disease fare and prolonged symptom duration after COVID-19, perhaps related to DMARD disruption and immune activation. Objectives: To describe DMARD disruption and identify differences in SARD activity among patients with and without prolonged COVID-19 symptom duration. Methods: We identifed all SARD patients with confrmed COVID-19 at the Mass General Brigham healthcare system in Boston, USA;prospective recruitment is ongoing. Surveys were used to collect demographics, clinical characteristics, DMARD disruption, COVID-19 course, and SARD disease activity before and after COVID-19. The survey included validated instruments measuring disease activity, pain, fatigue, functional status, and respiratory quality of life. Prolonged symptom duration was defned as COVID-19 symptoms lasting ≥28 days. We compared differences in patient-reported measures between those with and without prolonged symptoms. Results: We analyzed survey responses from 174 COVID-19 survivors with SARDs (mean age 52±16 years, 81% female, 80% White). The most common SARDs were RA (40%) and SLE (14%). Fifty-one percent of the 127 respondents on any DMARD reported a disruption to their regimen at COVID-19 onset (Figure 1). Among individual DMARDs, 56-77% were reported to have any change, except for hydroxychloroquine (23%) and rituximab (46%). SARD fare after COVID-19 was reported by 41% of respondents (Table 1). Patient global assessment of SARD activity was worse after COVID-19 (mean 7.6±2.3 before vs. 6.6±2.9 after COVID-19, p<0.001). Prolonged symptom duration was reported by 45% of participants. Those with prolonged symptoms had a higher initial COVID-19 symptom count (median 7 vs. 4, p<0.001) and were more likely to be hospitalized for COVID-19 (28% vs. 17%, p=0.001). Respondents experiencing prolonged symptom duration had higher disease activity on RAPID3 (p=0.007) as well as more pain (p<0.001) and fatigue (p=0.03) compared to those without prolonged symptoms. Conclusion: DMARD disruption, SARD fare, and prolonged symptoms were common in this prospective study of COVID-19 survivors with SARDs. Those with prolonged COVID-19 symptom duration, defned as ≥28 days, had higher SARD activity, more pain, and more fatigue compared to those without prolonged symptoms. These fndings suggest that post-acute sequelae of COVID-19 may have a large impact on underlying SARD activity and quality of life.
Subject(s)
Coronavirus Infections/epidemiology , Pandemics , Pneumonia, Viral/epidemiology , COVID-19 , HumansABSTRACT
The world today is being hit by COVID-19. As opposed to fingerprints and ID cards, facial recognition technology can effectively prevent the spread of viruses in public places because it does not require contact with specific sensors. However, people also need to wear masks when entering public places, and masks will greatly affect the accuracy of facial recognition. Accurately performing facial recognition while people wear masks is a great challenge. In order to solve the problem of low facial recognition accuracy with mask wearers during the COVID-19 epidemic, we propose a masked-face recognition algorithm based on large margin cosine loss (MFCosface). Due to insufficient masked-face data for training, we designed a masked-face image generation algorithm based on the detection of the detection of key facial features. The face is detected and aligned through a multi-task cascaded convolutional network;and then we detect the key features of the face and select the mask template for coverage according to the positional information of the key features. Finally, we generate the corresponding masked-face image. Through analysis of the masked-face images, we found that triplet loss is not applicable to our datasets, because the results of online triplet selection contain fewer mask changes, making it difficult for the model to learn the relationship between mask occlusion and feature mapping. We use a large margin cosine loss as the loss function for training, which can map all the feature samples in a feature space with a smaller intra-class distance and a larger inter-class distance. In order to make the model pay more attention to the area that is not covered by the mask, we designed an Att-inception module that combines the Inception-Resnet module and the convolutional block attention module, which increases the weight of any unoccluded area in the feature map, thereby enlarging the unoccluded area’s contribution to the identification process. Experiments on several masked-face datasets have proved that our algorithm greatly improves the accuracy of masked-face recognition, and can accurately perform facial recognition with masked subjects. © 2021 by the authors. Licensee MDPI, Basel, Switzerland.
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This paper aims to study the social effects of new media of People's Daily, one of the world's top 10 newspapers, on its coverage of COVID-19 epidemic. Due to its unique advantages in news reporting, new media plays an irreplaceable role in the reporting of public health emergencies. In the face of COVID-19 epidemic, the three major new media platforms of People's Daily (Microblog, WeChat and Tik Tok) have differentiated their reports according to their platform characteristics and audience preferences, thus effectively fulfilling their media functions. By collecting and analyzing relevant data of COVID-19 coverage by the three media platforms of People's Daily, this paper explores the change trend of the number of reports and their content, and points out the organic integration of media platforms can optimize the communication effect in Chinese society.
Subject(s)
Coronavirus Infections , Coronavirus , Eosinophils , Pandemics , Pneumonia, Viral , Betacoronavirus , COVID-19 , Humans , Leukocyte Count , SARS-CoV-2ABSTRACT
BACKGROUND: Recent studies have focused on initial clinical and epidemiological characteristics of the coronavirus disease 2019 (COVID-19), which is the mainly revealing situation in Wuhan, Hubei. AIM: This study aims to reveal more data on the epidemiological and clinical characteristics of COVID-19 patients outside of Wuhan, Zhejiang, China. DESIGN: This study was a retrospective case series. METHODS: Eighty-eight cases of laboratory-confirmed and three cases of clinically confirmed COVID-19 were admitted to five hospitals in Zhejiang province, China. Data were collected from 20 January 2020 to 11 February 2020. RESULTS AND DISCUSSION: Of all 91 patients, 88 (96.70%) were laboratory-confirmed COVID-19 with throat swab samples that tested positive for SARS-Cov-2, three (3.30%) cases were clinically diagnosed. The median age of the patients was 50 (36.5-57) years, and female accounted for 59.34%. In this sample, 40 (43.96%) patients had contracted the disease from local cases, 31 (34.07%) patients had been to Wuhan/Hubei, eight (8.79%) patients had contacted with people from Wuhan, and 11 (12.09%) patients were diagnosed after having flown together in the same flight with no passenger that could later be identified as the source of infection. In particular within the city of Ningbo, 60.52% cases can be traced back to an event held in a temple. The most common symptoms were fever (71.43%), cough (60.44%) and fatigue (43.96%). The median of incubation period was 6 (interquartile range 3-8) days and the median time from the first visit to a doctor to the confirmed diagnosis was 1 (1-2) days. According to the chest computed tomography scans, 67.03% cases had bilateral pneumonia. CONCLUSIONS: Social activity cluster, family cluster and flying alongside with persons already infected with COVID-19 were how people got infected with COVID-19 in Zhejiang.