Influence of Co-morbidities During SARS-CoV-2 Infection in an Indian Population.

Background: Since the outbreak of COVID-19 pandemic the interindividual variability in the course of the disease has been reported, indicating a wide range of factors influencing it. Factors which were the most often associated with increased COVID-19 severity include higher age, obesity and diabetes. The influence of cytokine storm is complex, reflecting the complexity of the immunological processes triggered by SARS-CoV-2 infection. A modern challenge such as a worldwide pandemic requires modern solutions, which in this case is harnessing the machine learning for the purpose of analysing the differences in the clinical properties of the populations affected by the disease, followed by grading its significance, consequently leading to creation of tool applicable for assessing the individual risk of SARS-CoV-2 infection. Methods: Biochemical and morphological parameters values of 5,000 patients (Curisin Healthcare (India) were gathered and used for calculation of eGFR, SII index and N/L ratio. Spearman's rank correlation coefficient formula was used for assessment of correlations between each of the features in the population and the presence of the SARS-CoV-2 infection. Feature importance was evaluated by fitting a Random Forest machine learning model to the data and examining their predictive value. Its accuracy was measured as the F1 Score. Results: The parameters which showed the highest correlation coefficient were age, random serum glucose, serum urea, gender and serum cholesterol, whereas the highest inverse correlation coefficient was assessed for alanine transaminase, red blood cells count and serum creatinine. The accuracy of created model for differentiating positive from negative SARS-CoV-2 cases was 97%. Features of highest importance were age, alanine transaminase, random serum glucose and red blood cells count. Conclusion: The current analysis indicates a number of parameters available for a routine screening in clinical setting. It also presents a tool created on the basis of these parameters, useful for assessing the individual risk of developing COVID-19 in patients. The limitation of the study is the demographic specificity of the studied population, which might restrict its general applicability.

Economic resiliency and recovery, lessons from the financial crisis for the COVID-19 pandemic: A regional perspective from Central and Eastern Europe.

In this paper we examine resiliency, the ability to absorb and recover from economic shocks, in 199 Nuts-3 regions in Central and Eastern Europe (CEE) following the 2008 global financial crisis. We find evidence of strong positive regional spillovers, meaning that regions tend to form clusters of high-performing and low-performing areas, a process that exacerbates regional income disparities. Using the experience of the recovery from the 2008 financial crisis, we simulate the effects of the COVID-19 pandemic on the ability of these, and, by extension, other upper-middle-income countries to recover from a shock to employment caused by the incidence of COVID-19. Using our recoverability equation estimates, we find that employment in no more than 31 of the 199 regions will have fully recovered in 2 years after the onset of the recovery from the crisis. Policy implications of the findings are discussed.

Short-run and long-run effects of unemployment on suicides: does welfare regime matter?

Background: Disentangling the immediate effects of an unemployment shock from the long-run relationship has a strong theoretical rationale. Different economic and psychological forces are at play in the first moment and after prolonged unemployment. This study suggests a diverse impact of short- and long-run unemployment on suicides in liberal and social-democratic countries. Methods: We take a macro-level perspective and simultaneously estimate the short- and long-run relationships between unemployment and suicide, along with the speed of convergence towards the long-run relationship after a shock, in a panel of 10 high-income countries. We also account for unemployment benefit spending, the share of the population aged 15-34, and the crisis effects. Results: In the liberal group of countries, only a long-run impact of unemployment on suicides is found to be significant (P = 0.010). In social-democratic countries, suicides are associated with initial changes in unemployment (P = 0.028), but the positive link fades over time and becomes insignificant in the long run. Further, crisis effects are a much stronger determinant of suicides in social-democratic countries. Once the broad welfare regime is controlled for, changes in unemployment-related spending do not matter for preventing suicides. Conclusions: A generous welfare system seems efficient at preventing unemployment-related suicides in the long run, but societies in social-democratic countries might be less psychologically immune to sudden negative changes in their professional lives compared with people in liberal countries. Accounting for the different short- and long-run effects could thus improve our understanding of the unemployment-suicide link.