The COVID-19 pandemic has impacted the most short-term migrants who returned to their villages in desperation resulting in despondency and distress. In this context, the paper explores the factors of distress migration by analysing the data from a quick survey of 323 return migrants carried out in June 2020 to understand their employment and livelihood profile, reasons for their return to native places, coping mechanism and future plans. The findings reveal that due to lack of livelihood opportunities in their place of origin, most of them would eventually like to return to their destination places in the future to eke out their living. From a policy point of view, enhancing the economic base and livelihood opportunities by focusing on niche activities with improved provisioning of educational and health infrastructure and services can eventually help restrict out-migration from Uttarakhand.Migration and mobilities are vastly underestimated in India. In particular, circular migration remains poorly captured as circular migrants move back and forth between source and destination regions. Based on survey data from rural Bihar, an important source region of migration in India, this paper finds that a vast majority of migrants work and live in precarity in predominantly urban and prosperous destinations across India. However, those at the lowest rungs of the social and economic ladder in source regions-the scheduled castes and scheduled tribes, other backward classes I and the labouring class-are the worst off at destination; they are part of the most precarious shorter-term migration streams, earn the lowest incomes, have the poorest conditions of work, and live in the harshest circumstances. The paper shows that social and economic hierarchies, and in turn, precarity in source region is reproduced at destination, and, thus, there is little evidence that spatial mobility is associated with social mobility. Focusing on migrants' location, work, employment, income, housing, and access to basic services at destination, the paper foregrounds migrant precarity and adds to a small body of empirical literature that is significant in understanding the spatial and structural elements of circular migration in India and in turn, the migration crisis that emerged as a result of the economic shock of the COVID 19 pandemic.An estimated 3.5 million interstate migrant workers have become an indispensable part of Kerala's economy. The state also offers the highest wages for migrant workers for jobs in the unorganised sector in the entire Indian subcontinent. https://www.selleckchem.com/products/CP-690550.html Further, the state has evolved several measures for the inclusion of the workers and was able to effectively respond to their distress during the national lockdown. This paper examines labour migration to Kerala, key measures by the government to promote the social security of the workers and the state's response to the distress of migrant workers during lockdown, by synthesising the available secondary evidence. The welfare measures as well as interventions initiated by the state are exemplary and promising given the intent and provisions. However, some of them do not appear to have consideration of the grassroots requirements and implementation mechanisms to enhance access. As a result, the policy intent and substantial investments have not yielded the expected results. The state's effective response to the distress of workers during the lockdown emanates from its overall disaster preparedness and resilience achieved from confronting with two consecutive state-wide natural disasters and a public health emergency in the immediate past. While the government has played a strategic role through policy imperative and ensuring a synergistic response, the data presented by the state indicate a much larger but invisible role played by the employers and civil society in providing food and shelter to workers.COVID-19 is a new member of the Coronaviridae family that has serious effects on respiratory, gastrointestinal, and neurological systems. COVID-19 spreads quickly worldwide and affects more than 41.5 million persons (till 23 October 2020). It has a high hazard to the safety and health of people all over the world. COVID-19 has been declared as a global pandemic by the World Health Organization (WHO). Therefore, strict special policies and plans should be made to face this pandemic. Forecasting COVID-19 cases in hotspot regions is a critical issue, as it helps the policymakers to develop their future plans. In this paper, we propose a new short term forecasting model using an enhanced version of the adaptive neuro-fuzzy inference system (ANFIS). An improved marine predators algorithm (MPA), called chaotic MPA (CMPA), is applied to enhance the ANFIS and to avoid its shortcomings. More so, we compared the proposed CMPA with three artificial intelligence-based models include the original ANFIS, and two modified versions of ANFIS model using both of the original marine predators algorithm (MPA) and particle swarm optimization (PSO). The forecasting accuracy of the models was compared using different statistical assessment criteria. CMPA significantly outperformed all other investigated models.The Directorate General for Competition at the European Commission enforces competition law in the areas of antitrust, merger control, and State aid. After providing a general presentation of the role of the Chief Competition Economist's team, this article surveys some of the main developments at the Directorate General for Competition over 2019/2020. In particular, the article reviews the economic analysis in the Qualcomm predation case, recent developments in the assessment of vertical mergers, as well as the new "Temporary Framework" that has been developed in the wake of the COVID pandemic.We are currently living in a state of uncertainty due to the pandemic caused by the SARS-CoV-2 virus. There are several factors involved in the epidemic spreading, such as the individual characteristics of each city/country. The true shape of the epidemic dynamics is a large, complex system, considerably hard to predict. In this context, Complex networks are a great candidate for analyzing these systems due to their ability to tackle structural and dynamic properties. Therefore, this study presents a new approach to model the COVID-19 epidemic using a multi-layer complex network, where nodes represent people, edges are social contacts, and layers represent different social activities. The model improves the traditional SIR, and it is applied to study the Brazilian epidemic considering data up to 05/26/2020, and analyzing possible future actions and their consequences. The network is characterized using statistics of infection, death, and hospitalization time. To simulate isolation, social distancing, or precautionary measures, we remove layers and reduce social contact's intensity.