As the COVD-19 pandemic spreads, several new severe acute respiratory syndrome coronavirus 2 (SARS-COV-2) variants with various mutations across the genome have arisen, and they appear to be the greater risk to global public health. In this study, we have performed molecular characterization of SARS-COV-2 circulating in India between January 2020 and May 2021. Phylogenetic analysis of the SARS-COV-2 reported in the first and second waves of the outbreak showed the evolutionary hierarchy of SARS-COV-2 that was dispersed across the evolutionary tree of SARS-COV-2 with six major next strain clades: 19A (5.3%), 20A (29.9%), 20B (24.9%), 20I-Alpha, V1 (7.4%), 21A-Delta (17.2%), and 21B-Kappa (12.7%). Among the observed clades, 21A-Delta and 21B-Kappa belonging to the B.1.617 and its sublineages are the two notable clades that dominated approximately 78% of the total SARS-COV-2 genomes reported during April and May 2021. This study has also established a link between different SARS-COV-2 variants and risk of mortality during the COVID-19 epidemic using multivariable logistic regression model for patient-level data. The estimated model demonstrates that the risk of mortality of the COVID 19 patients infected by variant B.1.617 and/or its sublineages is much higher than the other preexisting SARS-COV-2 variants, especially among individuals over 45 years of age, regardless of gender. Considering the transmissibility of the B.1.617 and its sublineages and its potential impact to the public health, real-time analysis of COVID-19 cases coupled with stringent genomics surveillance are promising tools to develop and adapt stringent measures to contain and reduce the impact of SARS-COV-2.
Introduction Coronavirus disease 2019 (COVID-19) is an emerging infection and quickly disseminated around the world. This article studies the epidemiology and early transmission dynamics of COVID-19 in Karnataka, which would be useful for effective epidemic management and policy formulation.
Materials and Methods All COVID-19 cases reported in the state of Karnataka, India, till June 12, 2020, are included in the study. The epidemiology and transmission dynamics of COVID-19 in Karnataka is studied through descriptive statistical analysis.
Results The findings illustrate a gender-, age-, and region-based disparity in the susceptibility and fatality. There appears to be a male preponderance in the susceptibility, but a female preponderance in fatality. It is also found that the adults are more susceptible to the infection, while the elderly have the risk of high fatality. Further, infected individuals in the region with urbanization have a higher risk of fatality than other regions. The study shows that the chances of recovery for females are lower than males, and further, the chances of recovery are positively related to the age of the infected person. The chances of recovery are higher if the infected individual is younger and they diminish if the individual is older. The study also explores that the chances of recovery are affected by the patient’s geographical location. It is also noted that individuals who returned from foreign travel have better chances of recovery than the locally transmitted individuals.
Conclusion Though the risk of susceptibility to COVID-19 infection is equal to all, the burden of getting infected and the burden of fatality is unequally distributed among different demographic categories. To manage the contagious spread of epidemic, to reduce fatality, and to increase the chances of recovery, targeted policy actions are suggested to benefit the vulnerable demographic categories.
The present study has attempted to discuss the association between corporate hedging theories and the usage of foreign currency loans by companies listed in India. A total of 349 non-financial companies were selected, and the data for the financial year ending 31st March, 2018 were considered for the analysis. The descriptive statistics indicate that 55% of the sample companies had borrowed funds in foreign currency. The companies were highly levered and maintained adequate short-term assets to honor short-term obligations. A logit model was employed for analyzing the cross-sectional data. The dependent variable being binary (‘0’ for non-user of foreign currency loans and ‘1’ for foreign currency loan user), the study found the variable ‘industry type’ to have a significant association with usage of foreign currency loans. Companies from the manufacturing sector were likely to use foreign currency loans than companies from the services sector. Debt to net worth, export to sales, revenue (log of revenue) were the variables that significantly influenced the likelihood of companies raising foreign currency loans. Interest coverage ratio had a negative influence on the likelihood of companies opting for foreign currency loans. Hosmer and Lemeshow test showed that the model is a good fit indicating 73% accuracy in predicting the users of foreign currency loans as ‘foreign currency loan users’. Theories such as financial distress, size, and extent of international operations explain why companies raise foreign currency loans.
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