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The study aims to explore and review the scientific documents published in the pandemic covid-19 and tourism sector. The bibliometric analysis was used to collect and analyze scientific documents in the Web of Science database. The analysis used the R program to get information and map the main idea related to contributors in different objects, including the authors, institution, country and publication source, thematic mapping of the paper in covid-19 and tourism sector. The finding discovers 791 scientific documents and 320 sources. The finding emphasizes that the research topic in pandemic covid-19 and tourism sector is generally given information and benefit.
ABSTRACT
Background A vaccine against coronavirus disease 2019 (COVID-19) with highly effective protection is urgently needed. The anti-severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) antibody response and duration after vaccination are crucial predictive indicators. Objectives To evaluate the response and duration for 5 subsets of anti-SARS-CoV-2 antibodies after vaccination and their predictive value for protection. Methods We determined the response and duration for 5 subsets of anti-SARS-CoV-2 antibodies (neutralizing antibody, anti-RBD total antibody, anti-Spike IgG, anti-Spike IgM, and anti-Spike IgA) in 61 volunteers within 160 days after the CoronaVac vaccine. A logistic regression model was used to determine the predictors of the persistence of neutralizing antibody persistence. Results The seropositivity rates of neutralizing antibody, anti-RBD total antibody, anti-Spike IgG, anti-Spike IgM, and anti-Spike IgA were only 4.92%, 27.87%, 21.31%, 3.28% and 0.00%, respectively, at the end of the first dose (28 days). After the second dose, the seropositivity rates reached peaks of 95.08%, 100.00%, 100.00%, 59.02% and 31.15% in two weeks (42 days). Their decay was obvious and the seropositivity rate remained at 19.67%, 54.10%, 50.82%, 3.28% and 0.00% on day 160, respectively. The level of neutralizing antibody reached a peak of 149.40 (101.00–244.60) IU/mL two weeks after the second dose (42 days) and dropped to 14.23 (7.62–30.73) IU/mL at 160 days, with a half-life of 35.61(95% CI, 32.68 to 39.12) days. Younger participants (≤31 years) had 6.179 times more persistent neutralizing antibodies than older participants (>31 years) (P<0.05). Participants with anti-Spike IgA seropositivity had 4.314 times greater persistence of neutralizing antibodies than participants without anti-Spike IgA seroconversion (P<0.05). Conclusions Antibody response for the CoronaVac vaccine was intense and comprehensive with 95.08% neutralizing seropositivity rate, while decay was also obvious after 160 days. Therefore, booster doses should be considered in the vaccine strategies.
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Abstract The exponential spread of COVID-19 worldwide is evident, with devastating outbreaks primarily in the United States, Spain, Italy, the United Kingdom, France, Germany, Turkey and Russia. As of 1 May 2020, a total of 3,308,386 confirmed cases have been reported worldwide, with an accumulative mortality of 233,093. Due to the complexity and uncertainty of the pathology of COVID-19, it is not easy for front-line doctors to categorise severity levels of clinical COVID-19 that are general and severe/critical cases, with consistency. The more than 300 laboratory features, coupled with underlying disease, all combine to complicate proper and rapid patient diagnosis. However, such screening is necessary for early triage, diagnosis, assignment of appropriate level of care facility, and institution of timely intervention. A machine learning analysis was carried out with confirmed COVID-19 patient data from 10 January to 18 February 2020, who were admitted to Tongji Hospital, in Wuhan, China. A softmax neural network-based machine learning model was established to categorise patient severity levels. According to the analysis of 2662 cases using clinical and laboratory data, the present model can be used to reveal the top 30 of more than 300 laboratory features, yielding 86.30% blind test accuracy, 0.8195 F1-score, and 100% consistency using a two-way patient classification of severe/critical to general. For severe/critical cases, F1-score is 0.9081 (i.e. recall is 0.9050, and precision is 0.9113). This model for classification can be accomplished at a mini-second-level computational cost (in contrast to minute-level manual). Based on available COVID-19 patient diagnosis and therapy, an artificial intelligence model paradigm can help doctors quickly classify patients with a high degree of accuracy and 100% consistency to significantly improve diagnostic and classification efficiency. The discovered top 30 laboratory features can be used for greater differentiation to serve as an essential supplement to current guidelines, thus creating a more comprehensive assessment of COVID-19 cases during the early stages of infection. Such early differentiation will help the assignment of the appropriate level of care for individual patients.
ABSTRACT
BACKGROUND: In March 2020, the World Health Organization (WHO) declared COVID-19 a public health emergency of international concern. A small proportion of patients infected with COVID-19 go on to develop pneumonia. We speculated that COVID-19 may be likely to result in psychological disorders such as anxiety and depression. In this study, we conducted an investigation of anxiety and depression in patients with COVID-19. METHODS: Sixty-five COVID-19 patients were randomly enrolled into this study. Anxiety and depression among participants were measured through the completion of anonymous Chinese-language Zung self-rating anxiety scale and self-rating depression scale questionnaires. Data were analyzed using independent samples t-tests, Mann-Whitney U-tests, and χ2 tests. RESULTS: The questionnaire results showed that 26.15% and 41.54% of participants suffered from anxiety and depression, respectively, although there was no significantly statistical difference between the proportions of COVID-19 patients with anxiety and depression. Statistically significant differences in employment status, partial pressure of oxygen, and corticosteroid application existed between moderate- and severe COVID-19 patients (P<0.05). In particular, the partial pressure of oxygen was significantly lower in severe COVID-19 patients than in their moderate counter parts (71.31±23.54 vs. 101.06±34.43, U=156, P=0.006). Total lymphocytes was lower in severe group than in moderate group [1.659±0.643 vs. 0.745 (0.645, 0.928), U=109, P=0.000]. Also, a higher proportion of female than male patients had anxiety (χ2=5.388, P=0.02). COVID-19 patients who received antiviral medications also displayed a higher rate of anxiety (χ2=4.481, P=0.034). Total lymphocytes between the non-anxiety and anxiety had statistical difference (U=321, P=0.019). Meanwhile, total lymphocytes between the non-depression and depression also had statistical difference (U=389.5, P=0.01). CONCLUSIONS: Among patients with COVID-19, females and those treated with antiviral medications were more likely to experience anxiety. In addition, our findings reflected the effect of anxiety and depression on immune system.