ABSTRACT
COVID-19 has caused not only unprecedented health crises but also economic crises among individuals across the world. White-collar (salaried-class) employees with a fixed salary face financial insecurity due to job loss, pay cuts and uncertainty in retaining a job. This study examines the financial behavior of Indian white-collar salariedclass investors to their cognitive biases. In addition, the mediating effect of financial self-efficacy on cognitive biases and financial behavior is examined. Respondents were given structured questionnaires (google forms) through emails and WhatsApp for data collection. SPSS and R-PLS are used to analyze the data. Conservatism (r = -.603, p < 0.05) and herding bias (r = -.703, p < 0.05) have a significant negative correlation with financial behavior. Financial self-efficacy has a significant positive correlation (r =.621. p < 0.050). Conservatism and herding predicted 60.5% and 62.2% of the variance, respectively. The direct and indirect paths between conservatism bias, financial self-efficacy, and financial behavior are significant. The paths between herding, financial self-efficacy and financial behavior are also significant. © Ankita Mulasi, Jain Mathew, Kavitha Desai, 2022.
ABSTRACT
Coronavirus disease is an infectious disease caused by severe acute respiratory syndrome-coronavirus 2 virus. It predominantly affects respiratory system causing fever, cough, and breathlessness. But it can also affect central nervous system and peripheral nervous system. It is important that physicians keep a high index of suspicion for patients with neurologic symptoms following a recent or during COVID-19 infection. The low rate of initial diagnosis is alarming, as few of the percentage of patients with Guillain-Barre syndrome will develop respiratory muscle weakness requiring invasive ventilation. The ability to recognize the disease process could lead to life saving management. Furthermore, the initiation of therapy such as plasma exchange or intravenous immunoglobulin leads to an accelerated recovery time. [ FROM AUTHOR]
ABSTRACT
Background: Environmental surveillance (ES) of a pathogen is crucial for understanding the community load of disease. As an early warning system, ES for SARS-CoV-2 has complemented routine diagnostic surveillance by capturing near real-time virus circulation at a population level. Methods: In this longitudinal study conducted between January 2022 and June 2022 in 28 sewershed sites in Bengaluru city (â¼11 million inhabitants), we quantified weekly SARS-CoV-2 RNA concentrations to track infection dynamics and provide evidence of change in the relative abundance of emerging variants. Findings: We describe an early warning system using the exponentially weighted moving average control chart and demonstrate how SARS-CoV-2 RNA concentrations in wastewater correlated with clinically diagnosed new COVID-19 cases, with the trends appearing 8-14 days earlier in wastewater than in clinical data. This was further corroborated by showing that the estimated number of infections is strongly correlated with SARS-CoV-2 RNA copies detected in the wastewater. Using a deconvolution matrix, we detected emerging variants of concern up to two months earlier in wastewater samples. In addition, we found a huge diversity in variants detected in wastewater compared to clinical samples. The findings from this study have been discussed regularly with local authorities to inform policy-making decisions. Interpretation: Our study highlights that quantifying viral titre, correlating it with a known number of cases in the area, and combined with genomic surveillance helps in tracking variants of concern (VOC) over time and space, enabling timely and making informed policy decisions. Funding: This work has been supported by funding from the Rockefeller Foundation grant to National Centre for Biological Sciences, TIFR) and the Indian Council of Medical Research grant to (FI) Tata Institute for Genetics and Society and Tata Trusts.
ABSTRACT
Most of the Indian cities and towns have been facing serious traffic noise pollution due to urbanization, substantial growth of new vehicles, inadequate road network, etc. Automotive traffic, railroads, and air traffic are the most common sources of noise pollution in cities, with vehicular traffic accounting for around 55% of overall metropolitan noise. Prolonged exposure to such loud noise causes anger, stress, mental diseases, discomfort, hypertension, concentration problems, and sleeplessness. This study aims to investigate the effects of COVID-19 societal lockdown on changes in the noise pollution levels before, during, and after the lockdown period in various industrial, commercial, residential, and silence zones in Bengaluru, India, in light of the recent imposition of COVID-19 societal lockdown. According to data acquired from the KSPCB (Karnataka State Pollution Control Board) online portal, the average noise levels before and during lockdown were determined to be in the range of 59.4 dB to 70.9 dB and 58.2 dB to 62.7 dB for different zones. During the lockdown, all commercial, industrial and educational activities were closed to limit the spread of infection, resulting in usage of private and commercial transportation declining dramatically. Reduction in the noise level was observed during the lockdown in all monitoring stations of Bengaluru, except for Indira Gandhi Institute of Child Health, where the noise level didn???t decline because of a COVID emergency. Maximum reduction was observed in the commercial area (11.56%) followed by industrial areas (8.34%). The result further indicated that only the industrial area experienced an increase of 8.41% in noise level, while other areas experienced a reduction in a noise level during the early post-lockdown. During the mid and late post-lockdown periods, most locations experienced a rapid spike in the noise intensity.