COVID-19 is caused by a novel coronavirus which was first reported in Wuhan city, China. The pandemic has led to considerable mortality globally. India, at present has the second largest burden of COVID-19 cases globally. Clinical trials with new interventions, including new vaccine candidates are being explored in the scientific world. Countries like China and India, with a rich history of traditional medicine, are exploring the effectiveness of traditional medicines to treat COVID-19. This study included 725 patients from an Isolation center, of which 230 (31.7%) were excluded due to reasons like incorrect phone numbers, no response on phone, or denying consent to participate. Finally, 495 participants had responded, of which 367 (74.1%) had not used any Complementary and Alternative Medicine (CAM) product or home remedies while 128 (25.8%) people used 161 CAM products and home remedies during the treatment and even afterward. More than half of the participants (59.6%) among them had consumed Ayurvedic Kadha. Many respondents consumed more than one CAM products or home remedies but there were no reported acute or severe adverse effects with these products. However, it is essential to ensure the safety of these interventions on long-term use because patients with other comorbidities can have a detrimental effect due to these products or due to drug herb interaction with their ongoing medications. Hence, long-term follow-up studies of recovered patients are crucial in determining the effects of medications or CAM products on organ functions due to disease or interventions.The gendered implications of COVID-19, in particular in terms of gender-based violence and the gendered division of care work, have secured some prominence, and ignited discussion about prospects for a 'feminist recovery'. In international law terms, feminist calls for a response to the pandemic have privileged the United Nations Security Council (UNSC), conditioned-I argue-by two decades of the pursuit of the Women, Peace and Security (WPS) agenda through the UNSC. The deficiencies of the UNSC response, as characterised by the Resolution 2532 adopted to address the pandemic, manifest yet again the identified deficiencies of the WPS agenda at the UNSC, namely fragmentation, securitisation, efficacy and legitimacy. What Resolution 2532 does bring, however, is new clarity about the underlying reasons for the repeated and enduring nature of these deficiencies at the UNSC. Specifically, the COVID-19 'crisis' is powerful in exposing the deficiencies of the crisis framework in which the UNSC operates. My reflections draw on insights from Hilary Charlesworth's seminal contribution 'International Law A Discipline of Crisis' to argue that, instead of conceding the 'crisis' framework to the pandemic by prioritising the UNSC, a 'feminist recovery' must instead follow Charlesworth's exhortation to refocus on an international law of the everyday.We study Susceptible-Exposed-Asymptomatic-Infectious-Recovered (SEAIR) epidemic spreading model of COVID-19. It captures two important characteristics of the infectiousness of COVID-19 delayed start and its appearance before onset of symptoms, or even with total absence of them. The model is theoretically analyzed in continuous-time compartmental version and discrete-time version on random regular graphs and complex networks. We show analytically that there are relationships between the epidemic thresholds and the equations for the susceptible populations at the endemic equilibrium in all three versions, which hold when the epidemic is weak. We provide theoretical arguments that eigenvector centrality of a node approximately determines its risk to become infected.The coronavirus disease 2019 (Covid-19) outbreak led the world to an unprecedented health and economic crisis. In an attempt to respond to this emergency, researchers worldwide are intensively studying the dynamics of the Covid-19 pandemic. In this study, a Susceptible - Infected - Removed - Sick (SIRSi) compartmental model is proposed, which is a modification of the classical Susceptible - Infected - Removed (SIR) model. The proposed model considers the possibility of unreported or asymptomatic cases, and differences in the immunity within a population, i.e., the possibility that the acquired immunity may be temporary, which occurs when adopting one of the parameters ( γ ) other than zero. Local asymptotic stability and endemic equilibrium conditions are proved for the proposed model. The model is adjusted to the data from three major cities of the state of São Paulo in Brazil, namely, São Paulo, Santos, and Campinas, providing estimations of duration and peaks related to the disease propagation. This study reveals that temporary immunity favors a second wave of infection and it depends on the time interval for a recovered person to be susceptible again. It also indicates the possibility that a greater number of patients would get infected with decreased time for reinfection.Everyone, across borders, race and gender, is affected by the global COVID-19 pandemic-but not equally. In this paper, we examine a burgeoning new literature discussing the employment effects of COVID-19. We explore the extent to which COVID-19 will exacerbate gendered employment disparities, income generation gaps, and, ultimately, poverty gaps, using a simple microsimulation methodology. We test our approach in Colombia, which has implemented an unparalleled number of mitigation measures and has reopened its economy earlier than regional neighbors. We find that COVID-19 increases the poverty headcount to a daunting degree (between 3.0 and 9.1 pp increases). Mitigation measures vary considerably in their individual impact (up to 0.9 pp poverty reduction). A fiscally neutral Universal Basic Income program would cause larger poverty reductions. Importantly, both men and women report similar poverty impacts from the pandemic and mitigation policies, reflecting the magnitude of the downturn, the design of interventions and our own poverty measure.COVID-19 outbreak has become a global pandemic that affected more than 200 countries. Predicting the epidemiological behavior of this outbreak has a vital role to prevent its spreading. In this study, long short-term memory (LSTM) network as a robust deep learning model is proposed to forecast the number of total confirmed cases, total recovered cases, and total deaths in Saudi Arabia. The model was trained using the official reported data. The optimal values of the model's parameters that maximize the forecasting accuracy were determined. The forecasting accuracy of the model was assessed using seven statistical assessment criteria, namely, root mean square error (RMSE), coefficient of determination (R2), mean absolute error (MAE), efficiency coefficient (EC), overall index (OI), coefficient of variation (COV), and coefficient of residual mass (CRM). A reasonable forecasting accuracy was obtained.  https://www.selleckchem.com/products/hada-hydrochloride.html  The forecasting accuracy of the suggested model is compared with two other models. The first is a statistical based model called autoregressive integrated moving average (ARIMA).