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Introduction: As historically verified, countries with comprehensive, well designed social protection systems in place are better prepared to cope with large scale catastrophes of all kinds, always in such situation there is still a need for government interventions other than social protection and larger scale discretionary social protection or related interventions. Objective: The article presents the actions of countries to minimize the negative social effects of the COVID-19 coronavirus pandemic. The text is an attempt to answer how social security systems should be adapted to aforementioned crisis? Materials and methods: The text uses research methods such as: literature criticism and statistical analysis of data and revision of implemented state intervention policies based on reports of Organisation for Economic Cooperation and Development, International Labour Organizaton, European Foundation for the Improvement of Living and Working Conditions and International Monetary Fund. Results: 1) For social security institutions of key importance to ensure continuity of operations of all services – of contributory social insurance as well of social assistance - was to ensure continuous payment of all benefits due and quick response to the new entitlement emerging. It is also necessary to ensure that all citizens are fully insured, regardless of the form of contract under which they perform work. 2) In many countries, special emergency measures that extended coverage and increased benefits were taken by governments without clearly identifying the sources of funding and very often burdening social security funds with non-statutory expenses and affecting their long-term financial sustainability. 3) In the longer run, there is a need to ensure universal health care coverage of the adequate quality, there is a need to develop policies which will secure at least minimum income security to all – independently of their labour market status, forms of employment, sex, ethnicity or nationality.
The ongoing coronavirus disease 2019 (COVID-19) pandemic threatens global health thereby causing unprecedented social, economic, and political disruptions. One way to prevent such a pandemic is through interventions at the human-animal-environment interface by using an integrated One Health (OH) approach. This systematic literature review documented the three coronavirus outbreaks, i.e. SARS, MERS, COVID-19, to evaluate the evolution of the OH approach, including the identification of key OH actions taken for prevention, response, and control.
The OH understandings identified were categorized into three distinct patterns: institutional coordination and collaboration, OH in action/implementation, and extended OH (i.e. a clear involvement of the environmental domain). Across all studies, OH was most often framed as OH in action/implementation and least often in its extended meaning. Utilizing OH as institutional coordination and collaboration and the extended OH both increased over time. OH actions were classified into twelve sub-groups and further categorized as classical OH actions (i.e. at the human-animal interface), classical OH actions with outcomes to the environment, and extended OH actions.
The majority of studies focused on human-animal interaction, giving less attention to the natural and built environment. Different understandings of the OH approach in practice and several practical limitations might hinder current efforts to achieve the operationalization of OH by combining institutional coordination and collaboration with specific OH actions. The actions identified here are a valuable starting point for evaluating the stage of OH development in different settings. This study showed that by moving beyond the classical OH approach and its actions towards a more extended understanding, OH can unfold its entire capacity thereby improving preparedness and mitigating the impacts of the next outbreak.
Background There is a lack of cardiac magnetic resonance (CMR) data regarding mid- to long-term myocardial damage due to Covid-19 in elite athletes. Objective This study investigated mid-to long-term consequences of myocardial involvement after a Covid-19 infection in elite athletes.
Methods Between January 2020 and October 2021, 27 athletes of the German Olympic centre Rhineland with confirmed Covid-19 infection were analyzed. 9 healthy non-athlete volunteers served as control. CMR was performed in mean 182 days (SD 99) after initial positive test result.
Results CMR did not reveal any signs of acute myocarditis in regard to the current Lake Louise criteria or myocardial damage in any of the 26 elite athletes with previous Covid-19 infection. Nevertheless, 92 % of the athletes experienced a symptomatic course and 54 % reported lasting symptoms for more than 4 weeks. In one male athlete CMR revealed an arrhythmogenic right ventricular cardiomyopathy (ARVC) and this athlete was excluded from the study. Athletes had significantly enlarged left and right ventricle volumes and increased left ventricular myocardial mass in comparison to the healthy control group (LVEDVi 103.4 vs. 91.1 ml/m 2 p=0.031; RVEDVi 104.1 vs. 86.6 ml/m 2 p=0.007; and LVMi 59.0 vs. 46.2 g/m 2 p=0.002).
Conclusion Our findings suggest that the risk for mid-to long-term myocardial damage seems to be very low to negligible in elite athletes. No conclusions can be drawn regarding myocardial injury in the acute phase of infection nor about possible long-term myocardial effects in the general population.
There are several recent works which had proposed an automatic computer-aided diagnosis (CAD) deep learning (DL) model to diagnose coronavirus disease 2019 (COVID-19) using chest x-ray images (CXR) to propose a high-accuracy CAD method to detect COVID-19 automatically. In this study, seven different models including Convolutional Neural Network (CNN) models such as VGG-16 and vision transformer (ViT) models, are proposed. The different proposed models are trained with a three-class balanced dataset consisting of 3,000 CXR images consisting of 1,000 CXR images for each class of COVID-19, Normal, and Lung-Opacity. A publicly available dataset to train and test the models is used from Kaggle-COVID-19-Radiography-Dataset. From the experiments, the accuracy of the VGG16 model is 93.44% and ViT's is 92.33%. Besides, the binary classification between two classes of COVID-19 and Normal CXR with a limited number of just 100 images for each class, using a transfer learning technique, with a validation accuracy of 97.5% is proposed.