Activity

This analysis makes use of economic forecasts for 2020 issued by the European Commission in Autumn 2019 and Spring 2020, and of a counterfactual under a no-policy change assumption, to analyse the impact of the COVID-19 crisis on EU households´ income. Additionally, our analysis assesses the cushioning effect of discretionary fiscal policy measures taken by the EU Member States. We find that the COVID-19 pandemic is likely to affect significantly households’ disposable income in the EU, with lower income households being more severely hit. However, our results show that due to policy intervention, the impact of the crisis is expected to be similar to the one experienced during the 2008-2009 financial crisis. In detail, our results indicate that discretionary fiscal policy measures will play a significant cushioning role, reducing the size of the income loss (from -9.3% to -4.3% for the average equivalised disposable income), its regressivity and mitigating the poverty impact of the pandemic. We conclude that policy interventions are therefore instrumental in cushioning against the impact of the crisis on inequality and poverty.

The online version contains supplementary material available at 10.1007/s10888-021-09485-8.

The online version contains supplementary material available at 10.1007/s10888-021-09485-8.Medicinal plants are one of the most important sources of antiviral agents and lead compounds. Lignans are a large class of natural compounds comprising two phenyl propane units. Many of them have demonstrated biological activities, and some of them have even been developed as therapeutic drugs. In this review, 630 lignans, including those obtained from medicinal plants and their chemical derivatives, were systematically reviewed for their antiviral activity and mechanism of action. The compounds discussed herein were published in articles between 1998 and 2020. The articles were identified using both database searches (e.g., Web of Science, Pub Med and Scifinder) using key words such as antiviral activity, antiviral effects, lignans, HBV, HCV, HIV, HPV, HSV, JEV, SARS-CoV, RSV and influenza A virus, and directed searches of scholarly publisher’s websites including ACS, Elsevier, Springer, Thieme, and Wiley. The compounds were classified on their structural characteristics as 1) arylnaphthalene lignans, 2) aryltetralin lignans, 3) dibenzylbutyrolactone lignans, 4) dibenzylbutane lignans, 5) tetrahydrofuranoid and tetrahydrofurofuranoid lignans, 6) benzofuran lignans, 7) neolignans, 8) dibenzocyclooctadiene lignans and homolignans, and 9) norlignans and other lignoids. Details on isolation and antiviral activities of the most active compounds within each class of lignan are discussed in detail, as are studies of synthetic lignans that provide structure-activity relationship information.The outbreak of COVID-19 had already shown its harmful impact on mankind, especially on health sectors, global economy, education systems, cultures, politics, and other important fields. Like most of the affected countries in the globe, India is now facing serious crisis due to COVID-19 in the recent times. The evaluation of the present status of the provinces affected by COVID-19 is very much essential to the government authorities to impose preventive strategies in controlling the spread of COVID-19 and to take necessary measures. In this article, a computational methodology is developed to estimate the present status of states and provinces which are affected due to COVID-19 using a fuzzy inference system. The factors such as population density, number of COVID-19 tests, confirmed cases of COVID-19, recovery rate, and mortality rate are considered as the input parameters of the proposed methodology. Considering positive and negative factors of the input parameters, the rule base is developed using triangular fuzzy numbers to capture uncertainties associated with the model. The application potentiality is validated by evaluating Pearson’s correlation coefficient. A sensitivity analysis is also performed to observe the changes of final output by varying the tolerance ranges of the inputs. The results of the proposed method show that some of the provinces have very poor performance in controlling the spread of COVID-19 in India. So, the government needs to take serious attention to deal with the pandemic situation of COVID-19 in those provinces.The sudden increase in coronavirus disease 2019 (COVID-19) cases puts high pressure on healthcare services worldwide. At this stage, fast, accurate, and early clinical assessment of the disease severity is vital. PEG400 In general, there are two issues to overcome (1) Current deep learning-based works suffer from multimodal data adequacy issues; (2) In this scenario, multimodal (e.g., text, image) information should be taken into account together to make accurate inferences. To address these challenges, we propose a multi-modal knowledge graph attention embedding for COVID-19 diagnosis. Our method not only learns the relational embedding from nodes in a constituted knowledge graph but also has access to medical knowledge, aiming at improving the performance of the classifier through the mechanism of medical knowledge attention. The experimental results show that our approach significantly improves classification performance compared to other state-of-the-art techniques and possesses robustness for each modality from multi-modal data. Moreover, we construct a new COVID-19 multi-modal dataset based on text mining, consisting of 1393 doctor-patient dialogues and their 3706 images (347 X-ray + 2598 CT + 761 ultrasound) about COVID-19 patients and 607 non-COVID-19 patient dialogues and their 10754 images (9658 X-ray + 494 CT + 761 ultrasound), and the fine-grained labels of all. We hope this work can provide insights to the researchers working in this area to shift the attention from only medical images to the doctor-patient dialogue and its corresponding medical images.In the context of a recent outbreak of the coronavirus disease (COVID-19), the present study investigated the buffering effect of grit on the relationship between fear of COVID-19 and psychological distress. The data were collected from 224 Japanese participants (98 females; mean age = 46.56, SD = 13.41) in July 2020. The measures used in this study included the Fear of COVID-19 Scale (FCV-19S), Short Grit Scale, and Depression, Anxiety, and Stress Scale 21 (DASS). The results of mediation analyses revealed significant indirect effects of consistency of interest, a major component of grit, on psychological distress (depression estimate = .042; 95% CI [.008, .088], anxiety estimate = .021; 95% CI [.001, .050], and stress estimate = .030; 95% CI [.004, .066]); we also found non-significant indirect effects of perseverance of effort, another major component of grit, on psychological distress. These results suggest that consistency of interest buffers the psychological distress induced by fear of COVID-19. Based on these results, it can be concluded that individuals with higher consistency of interest are less likely to experience worsening of their mental health, even if they experience fear of COVID-19 during the pandemic.