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Aim: The coronavirus disease 2019 (COVID‐19) pandemic affected cancer service delivery and the feasibility of following the standard treatment guidelines. The present paper describes the use of clinical care guidelines for cancer management in routine practice and the approach adopted towards cancer care during the COVID‐19 pandemic in India. Methods: A web‐based survey was done in 107 hospitals (including public and private health facilities) that hosted Hospital‐Based Cancer Registries under the National Cancer Registry Programme. The participants comprised Principal Investigators of these registries, who were also medical, surgical, and radiation oncology clinicians. The survey was done between May 1, 2021, and July 31, 2021. Participants were provided with a web link for the survey questionnaire, confidential login, and password. Results: The study found high utilization of Clinical Practice Guidelines (CPGs) during practice, with eight out of ten physicians constantly to referring them. The study reported lack of knowledge, skills, and training to administer the treatment based on the guidelines followed by organizational infrastructure and affordability of treatment by the patients as the factors hampering utilization. International clinical guidelines were preferred when compared to national guidelines. The COVID‐19 pandemic decreased the use of CPGs, wherein six out of ten clinicians reported their use. Conclusion: Stakeholders who formulate clinical guidelines must consider the practical aspects and feasibility of implementing such guidelines during a pandemic and similar situations. This should be coupled with adequate changes in care practice to ensure optimal care delivery and a continuum of cancer care in routine and pandemic‐imposed situations. [ABSTRACT FROM AUTHOR]

This paper analyses findings of the 'PROWELLMIGRANTS'2 project, which qualitatively investigated COVID‐19 impacts on migrants' well‐being and mental health in Kerala, India. It draws on a novel conceptual framework that combines assemblage‐thinking with theories of social contracts in disasters. The paper first explores how past development processes and contemporary migration policies in Kerala, and India more widely, generated conditions of vulnerability for migrant workers in Kerala prior to the pandemic. Next it shows that Government of Kerala interventions, in some cases supported by the central Government of India, temporarily addressed these vulnerabilities during the pandemic. In acknowledging the helpful response of the Kerala government, we problematise its stance on migrant workers during 'normal' times and speculate that permanently addressing these conditions of vulnerability would be a more logical approach. We acknowledge this involves overcoming many wider barriers. Thus, the paper also contains national‐level policy implications. [ABSTRACT FROM AUTHOR]

The first case of COVID‐19 in India detected on January 30, 2020, after its emergence in Wuhan, China, in December 2019. The lockdown was imposed as anemergency measure by the Indian government to prevent the spread of COVID‐19 but gradually eased out due to its vast economic consequences. Just 15 days after the relaxation of lockdown restrictions, Delhi became India's worst city in terms of COVID‐19 cases. In this paper, we propose a variable‐order fractional SIR (susceptible, infected, removed) model at state‐level scale. We introduce a algorithm that uses the differential evolution algorithm in combination with Adam–Bashforth–Moulton method to learn the parameters in a system of variable‐order fractional SIR model. The model can predict the confirm COVID‐19 cases in India considering the effects of nationwide lockdown and the possible estimate of the number of infliction inactive cases after the removal of lockdown on June 1, 2020. A new parameter p is introduced in the classical SIR model representing the fraction of infected people that get tested and are thereby quarantined. The COVID‐19 trajectory in Delhi, as per our model, predicts the slowing down of the spread between January and February 2021, touching a peak of around 5 lakh confirmed cases. [ABSTRACT FROM AUTHOR]

The outbreak of the SARS CoV2 virus, commonly referred to as the COVID‐19 pandemic, has impacted the social, economic, political, and cultural lives of citizens around the world. The sudden outbreak of the pandemic has exposed the legal preparedness, or lack thereof, of governments to reduce and contain its drastic impact. Strong legislative measures play a crucial role in any epidemic or pandemic situation. In this situation, the Indian Government has requested all state governments to invoke the Epidemic Disease Act (EDA) of 1897 to address the COVID‐19 emergency. The Central Government has also used the powers provided in the Disaster Management Act (DMA) of 2005. As the country is facing its first major health emergency since independence, the existing legislative measures to deal with a COVID‐19 like situation are lacking and require certain amendments to address such situations in the future. This paper aims to present the current constitutional and legislative response to health emergencies in India and attempts to identify gray areas in the statutory provisions. Based on the analysis, this paper suggests several recommendations for amending current legislation and suggests the promulgation of comprehensive public health law. This paper is largely based on primary sources such as the EDA and the DMA, regulations, guidelines, rules issued by the public authorities and court cases related to health and health emergencies along with secondary resources such as newspaper articles and published papers. [ABSTRACT FROM AUTHOR]

The COVID‐19 crisis has translated into an unprecedented humanitarian crisis for the poor and marginalized groups in society. The countrywide lockdowns, quarantine measures, and mobility restrictions across 200 countries of the world have resulted in a host of negative manifestations for women. There have been unprecedented losses in the informal economy, which is dominated by women. Some scholars also contend that the pandemic will translate into heightened burden of unpaid domestic work, loss of economic autonomy and disruption to maternal health services. Despite these factors a gendered perspective is absent in the policy response to this crisis. It is against this background that the present paper employed a feminist intersectionality lens to conduct participatory field based research on the lived experiences of women in informal employment in Indian Punjab during the COVID‐19 crisis. The research unearthed the specific pathways through which existing socio‐economic inequities rooted in caste, class and occupational entities magnify the vulnerabilities experienced by women during such a health crisis. The research offers a contextualized framework for understanding the gendered impacts of the crisis. It also highlights the urgency of taking account of gender specific constraints during the health crisis so as to institute robust, effective and equitable policy interventions. [ABSTRACT FROM AUTHOR]

This paper empirically investigates the impact of COVID‐19 on the volatility of stock prices in India with the help of a generalized autoregressive conditional heteroscedasticity model. Daily closing prices of stock indices, Nifty and Sensex from September 3, 2019 to July 10, 2020 has been used for the analysis. Further, the study has been attempted to make a comparison of stock price return in pre‐COVID‐19 and during COVID‐19 situation. Findings reveal that the stock market in India has experienced volatility during the pandemic period. While comparing the result during COVID period with that of the pre‐COVID, we found that the return on the indices is higher in the pre‐COVID‐19 period than during COVID‐19. [ABSTRACT FROM AUTHOR]

The outbreak of Coronavirus 2019 (COVID‐19) has impacted everyday lives globally. The number of positive cases is growing and India is now one of the most affected countries. This paper builds predictive models that can predict the number of positive cases with higher accuracy. Regression‐based, Decision tree‐based, and Random forest‐based models have been built on the data from China and are validated on India's sample. The model is found to be effective and will be able to predict the positive number of cases in the future with minimal error. The developed machine learning model can work in real‐time and can effectively predict the number of positive cases. Key measures and suggestions have been put forward considering the effect of lockdown. [ABSTRACT FROM AUTHOR]