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Background: The COVID-19 infections and deaths have largely been uneven within and between countries. With 17% of the world's population, India has so far had 13% of global COVID-19 infections and 8.5% of deaths. Maharashtra accounting for 9% of India's population, is the worst affected state, with 19% of infections and 33% of total deaths in the country until 23rd December 2020. Though a number of studies have examined the vulnerability to and spread of COVID-19 and its effect on mortality, no attempt has been made to understand its impact on mortality in the states of India.Method: Using data from multiple sources and under the assumption that COVID-19 deaths are additional deaths in the population, this paper examined the impact of the disease on premature mortality, loss of life expectancy, years of potential life lost (YPLL), and disability-adjusted life years (DALY) in Maharashtra. Descriptive statistics, a set of abridged life tables, YPLL, and DALY were used in the analysis. Estimates of mortality indices were compared pre- and during COVID-19.Result: COVID-19 attributable deaths account for 5.3% of total deaths in the state and have reduced the life expectancy at birth by 0.8 years, from 73.2 years in the pre-COVID-19 period to 72.4 years by the end of 2020. If COVID-19 attributable deaths increase to 10% of total deaths, life expectancy at birth will likely reduce by 1.4 years. The probability of death in 20-64 years of age (the prime working-age group) has increased from 0.15 to 0.16 due to COVID-19. There has been 1.06 million additional loss of years (YPLL) in the state, and DALY due to COVID-19 has been estimated to be 6 per thousand.Conclusion: COVID-19 has increased premature mortality, YPLL, and DALY and has reduced life expectancy at every age in Maharashtra. [ABSTRACT FROM AUTHOR]

This write-up is a brief reflection of a rural doctor couple, Dr. Himmatrao Saluba Bawaskar (HSB) and Dr. Pramodini Himmatrao Bawaskar (PHB), working in the remote area of Maharashtra state of India during COVID-19 pandemic. During the pandemic, rural doctors are routinely exposed to symptomatic COVID-positive cases in the outpatient as well as indoor setting. The authors, both husband and wife, were in compulsory quarantine for twice at home and experienced social stigmas attached to a positive case. Here is a report the details of COVID-19 pattern and its management learned from the published scientific papers on COVID-19, and severe acute respiratory syndrome due to SARS-CoV-2 from December 2019 and their own experience in rural setting and the current literature shared in the form of personal narration. Apart from the personal experience of patients experience regarding quarantine period, COVID-19 is discussed in detail for the benefit of rural practitioners. [ABSTRACT FROM AUTHOR]

[Display omitted] • Indian electricity demand reduced by 11 % vis-à-vis 2019 during the lockdown. • Sectoral demand reduction—73 % industrial, remaining 27 % commercial. • Pan India lights-off event—hydropower provides the bulk of the flexibility (60 %). • The plant load factor of coal dropped to a record low of 35 % during phase-1. • Power sector emissions reduced by 16 % (86 MtCO2) compared to 2019. This paper aims to understand the responsiveness of the power sector during the lockdown due to the COVID-19 pandemic beginning from March 25, 2020 and analyses its impact on the demand, operation and supply of electricity in the Indian power system. The role of the C&I load share in reducing the electricity demand of the different states in India has been examined using multi-sectoral regression analysis. The impact of the pan India lights-off event on the short-term operational flexibility response of the power system has also been analysed using high-temporal resolution data. The results indicate that there has been a reduction of nearly 70 TWh of electricity demand during the lockdown period, an 11 % reduction compared to 2019. The top three states recording the highest reduction were Maharashtra, Gujarat and Tamil Nadu at 11 TWh, 10.6 TWh and 8.4 TWh, respectively. Regression analysis revealed that out of the total drop in load demand — 73 % is owed to the industrial sector, while the remaining 27 % is to the commercial sector. This demand drop also impacted the upstream electricity supply mix where 96 % of the reduction in supply was borne by the coal generating units, recording its lowest national plant load factor at 35 % with a 16 % reduction in overall CO 2 emission compared to the same period in 2019. In conclusion, a case study of Maharashtra has been used to analyse the impact of this reduction in electricity demand on the supply mix, hourly load profile and cost of supply under the different lockdown phases. [ABSTRACT FROM AUTHOR]