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PublicationArticle A hybrid deep learning model for COVID-19 prediction and current status of clinical trials worldwide(Tech Science Press, 2020) Shwet Ketu; Pramod Kumar MishraInfections or virus-based diseases are a significant threat to human societies and could affect the whole world within a very short time-span. Corona Virus Disease-2019 (COVID-19), also known as novel coronavirus or SARSCoV- 2 (Severe Acute Respiratory Syndrome-Coronavirus-2), is a respiratory based touch contiguous disease. The catastrophic situation resulting from the COVID-19 pandemic posed a serious threat to societies globally. The whole world is making tremendous efforts to combat this life-threatening disease. For taking remedial action and planning preventive measures on time, there is an urgent need for efficient prediction models to confront the COVID-19 outbreak. A deep learning-based ARIMA-LSTM hybrid model is proposed in this article for predicting the COVID-19 outbreak by utilizing real-time information from the WHO's daily bulletin report as well as provides information regarding clinical trials across the world. To evaluate the suitability and performance of our proposed model compared to other well-established prediction models, an experimental study has been performed. To estimate the prediction results, the three performance measures, i.e., Root Mean Square Error (RMSE), Coefficient of determination (R2 Score), and Mean Absolute Percentage Error (MAPE) have been employed. The prediction results of fifty countries substantiated the fact that the proposed ARIMA-LSTM hybrid model performs very well as compared to other models. The proposed model archives the lowest RMSE, lowest MAPE, and highest R2 Score throughout the testing, under varied selection criteria (country- wise). This article aims to contribute a deep learning-based solution for the wellbeing of livings and to provide the current status of clinical trials across the globe. © 2021 Tech Science Press. All rights reserved.PublicationEditorial Implementation of Early Detection Services for Cancer in India During COVID-19 Pandemic(SAGE Publications Ltd, 2020) Divya Khanna; Naveen Chandrahas Khargekar; Ajay Kumar KhannaEarly detection of cancer greatly increases the chances of better survival. The emergence of COVID-19 pandemic has disrupted several essential health services globally and early detection of cancer services is one of them. The routine cancer screenings have plummeted in many developed countries since the crisis. India has highest estimated lip and oral cavity cancer cases worldwide (119,992, 33.8%) and the secondhighest number of breast (162,468, 17.8%) and cervix uteri (96,922,30.7%) cancers in Asian sub-continent. Not only India has high burden of cancer, but the majority (75-80%) of patients have advanced disease at the time of diagnosis. Hence is it imperative that early detection services should be kept functional at out-patient settings so that at least the patients coming to hospitals with early signs and symptoms can be diagnosed as early as possible. Strategies need to be adopted to continue early detection services and ensure safety of patients and health care workers from COVID-19 transmission. © The Author(s) 2020.PublicationArticle Domestic Tourism Growth in India, Post COVID-19: Perspectives of Tour Operators(Institute for Tourism, 2024) P.J. Shyju; V.G. Girish; Kaustav Chatterjee; Priyanka SinghDomestic tourism in India recorded impressive growth and supported the local economy significantly in the last phase of the COVID-19 pandemic. The extant research on this topic covers the impact of the pandemic and resilience strategies. Still, it leaves a gap in the discussion relating to the service providers' experience in the context of domestic tourism. The present study stems from the absence of empirical research on the trends associated with domestic tourism growth in India post-COVID-19. It examines the trends in domestic tourist behaviour from the tour operators’ perspective. It employs a qualitative research design to explore the experiences of tour operators in India. Phone interviews were conducted with 26 tour operators to collect data. A thematic-content analysis is performed to generate themes with the help of Atlas ti software. The findings present the trends, preferences, motivations, and patterns of domestic growth tourism in India. © 2024 The Author(s).PublicationArticle Mathematical model of COVID-19 with comorbidity and controlling using non-pharmaceutical interventions and vaccination(Springer Science and Business Media B.V., 2021) Parthasakha Das; Ranjit Kumar Upadhyay; Arvind Kumar Misra; Fathalla A. Rihan; Pritha Das; Dibakar GhoshPandemic is an unprecedented public health situation, especially for human beings with comorbidity. Vaccination and non-pharmaceutical interventions only remain extensive measures carrying a significant socioeconomic impact to defeating pandemic. Here, we formulate a mathematical model with comorbidity to study the transmission dynamics as well as an optimal control-based framework to diminish COVID-19. This encompasses modeling the dynamics of invaded population, parameter estimation of the model, study of qualitative dynamics, and optimal control problem for non-pharmaceutical interventions (NPIs) and vaccination events such that the cost of the combined measure is minimized. The investigation reveals that disease persists with the increase in exposed individuals having comorbidity in society. The extensive computational efforts show that mean fluctuations in the force of infection increase with corresponding entropy. This is a piece of evidence that the outbreak has reached a significant portion of the population. However, optimal control strategies with combined measures provide an assurance of effectively protecting our population from COVID-19 by minimizing social and economic costs. © 2021, The Author(s), under exclusive licence to Springer Nature B.V.PublicationArticle A qualitative exploration of stressors and coping strategies of transmen during the global crisis(Sapienza Universita Editrice, 2024) Akanksha Srivastava; Yogesh Kumar Arya; Shobhna Joshi; Tushar SinghIntroduction: The lockdown protocols with various restrictions were put in effect to stop the proliferation of coronavirus. It brought many challenges in the life of the mass community, but the challenges faced by transmen during the lockdown were unique and, in some cases, more challenging due to the high level of marginalization, ignorance, and lack of basic support for them even in normal times. This further led to the poor psychological and physical health of these individuals. The present paper aims to understand the subjective ordeal of transmen, the stressors they faced at the time of lockdown and the coping strategies opted to deal with the stressors. Method: Semi-structured interviews were conducted with 15 transmen of the age range 21 to 30 years residing in India. The interviews were conducted after the end of the pandemic during and after phase 4 of unlock in India when the restriction on movement was removed. The interviews were later analyzed using Thematic analysis. Result: The analysis shows several physical, psychological, and social stressors emerged during the pandemic including emotional and physical violence from unaccepting parents, unavailability of supportive organizations, inaccessibility of medical assistance and hormone treatment, suicidal ideation due to free time, feelings of isolation and use of wrong pronouns all affecting the mental health severely. To deal with these stressors, transmen adopted various strategies that include, cognitive appraisal, emotional support from friends, and self-help thought. Conclusion: These findings are discussed within the Indian context during the pandemic. The findings of the present work will help transmen in suggesting the ways of dealing with the stressors at uncertain times. © Author(s)PublicationRetracted India perspective: CNN-LSTM hybrid deep learning model-based COVID-19 prediction and current status of medical resource availability(Springer Science and Business Media Deutschland GmbH, 2022) Shwet Ketu; Pramod Kumar MishraThe epidemic situation may cause severe social and economic impacts on a country. So, there is a need for a trustworthy prediction model that can offer better prediction results. The forecasting result will help in making the prevention policies and remedial action in time, and thus, we can reduce the overall social and economic impacts on the country. This article introduces a CNN-LSTM hybrid deep learning prediction model, which can correctly forecast the COVID-19 epidemic across India. The proposed model uses convolutional layers, to extract meaningful information and learn from a given time series dataset. It is also enriched with the LSTM layer's capability, which means it can identify long-term and short-term dependencies. The experimental evaluation has been performed to gauge the performance and suitability of our proposed model among the other well-established time series forecasting models. From the empirical analysis, it is also clear that the use of extra convolutional layers with the LSTM layer may increase the forecasting model's performance. Apart from this, the deep insides of the current situation of medical resource availability across India have been discussed. © 2021, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.PublicationEditorial The kidney and COVID-19 patients – Important considerations(Elsevier USA, 2020) Shailesh Kumar Patel; Rohit Singh; Jigyasa Rana; Ruchi Tiwari; Senthilkumar Natesan; Harapan Harapan; Kovy Arteaga-Livias; D. Katterine Bonilla-Aldana; Alfonso J. Rodríguez-Morales; Kuldeep Dhama[No abstract available]PublicationReview Why airborne transmission hasn't been conclusive in case of COVID-19? An atmospheric science perspective(Elsevier B.V., 2021) Kirpa Ram; Roseline C. Thakur; Dharmendra Kumar Singh; Kimitaka Kawamura; Akito Shimouchi; Yoshika Sekine; Hidekazu Nishimura; Sunit K. Singh; Chandra Mouli Pavuluri; R.S. Singh; S.N. TripathiAirborne transmission is one of the routes for the spread of COVID-19 which is caused by inhalation of smaller droplets1 containing SARS-CoV-2 (i.e., either virus-laden particulate matter: PM and/or droplet nuclei) in an indoor environment. Notably, a significant fraction of the small droplets, along with respiratory droplets, is produced by both symptomatic and asymptomatic individuals during expiratory events such as breathing, sneezing, coughing and speaking. When these small droplets are exposed to the ambient environment, they may interact with PM and may remain suspended in the atmosphere even for several hours. Therefore, it is important to know the fate of these droplets and processes (e.g., physical and chemical) in the atmosphere to better understand airborne transmission. Therefore, we reviewed existing literature focussed on the transmission of SARS-CoV-2 in the spread of COVID-19 and present an environmental perspective on why airborne transmission hasn't been very conclusive so far. In addition, we discuss various environmental factors (e.g., temperature, humidity, etc.) and sampling difficulties, which affect the conclusions of the studies focussed on airborne transmission. One of the reasons for reduced emphasis on airborne transmission could be that the smaller droplets have less number of viruses as compared to larger droplets. Further, smaller droplets can evaporate faster, exposing SARS-CoV-2 within the small droplets to the environment, whose viability may further reduce. For example, these small droplets containing SARS-CoV-2 might also physically combine with or attach to pre-existing PM so that their behaviour and fate may be governed by PM composition. Thus, the measurement of their infectivity and viability is highly uncertain due to a lack of robust sampling system to separately collect virions in the atmosphere. We believe that the present review will help to minimize the gap in our understanding of the current pandemic and develop a robust epidemiological method for mortality assessment. © 2021 Elsevier B.V.PublicationArticle Effect of Lockdown Amid COVID-19 on Ambient Air Quality in 16 Indian Cities(Frontiers Media S.A., 2021) Amit Kumar Mishra; Prashant Rajput; Amit Singh; Chander Kumar Singh; Rajesh Kumar MallThe COVID-19 pandemic has affected severely the economic structure and health care system, among others, of India and the rest of the world. The magnitude of its aftermath is exceptionally devastating in India, with the first case reported in January 2020, and the number has risen to ~31.3 million as of July 23, 2021. India imposed a complete lockdown on March 25, which severely impacted migrant population, industrial sector, tourism industry, and overall economic growth. Herein, the impacts of lockdown and unlock phases on ambient atmospheric air quality variables have been assessed across 16 major cities of India covering the north-to-south stretch of the country. In general, all assessed air pollutants showed a substantial decrease in AQI values during the lockdown compared with the reference period (2017–2019) for almost all the reported cities across India. On an average, about 30–50% reduction in AQI has been observed for PM2.5, PM10, and CO, and maximum reduction of 40–60% of NO2 has been observed herein, while the data was average for northern, western, and southern India. SO2 and O3 showed an increase over a few cities as well as a decrease over the other cities. Maximum reduction (49%) in PM2.5 was observed over north India during the lockdown period. Furthermore, the changes in pollution levels showed a significant reduction in the first three phases of lockdown and a steady increase during subsequent phase of lockdown and unlock period. Our results show the substantial effect of lockdown on reduction in atmospheric loading of key anthropogenic pollutants due to less-to-no impact from industrial activities and vehicular emissions, and relatively clean transport of air masses from the upwind region. These results indicate that by adopting cleaner fuel technology and avoiding poor combustion activities across the urban agglomerations in India could bring down ambient levels of air pollution at least by 30%. Copyright © 2021 Mishra, Rajput, Singh, Singh and Mall.PublicationArticle Outbreak of coronavirus disease 2019 (COVID-19) in india and consideration of preventive aspects by ayurveda(J. K. Welfare and Pharmascope Foundation, 2020) Srivastava Niraj; Saxena Varsha; Gehlot Sangeeta; B.M. SinghIn December 2019, an outbreak of severe acute respiratory syndrome Coron-avirus (SARSCoV-2) infection occurred in Wuhan city, Hubei Province, China (East Asia) furthermore worldwide including India. On 30 January 2020, the first case of the COVID-19 pandemic was reported in India. India has reached more than 1.5 lakh confirmed cases including more than 4000 fatalities by dreadful COVID-19 infection. At present, there is no vaccine for prevention or medicine for treatment. Only preventive measures like frequently hand-wash by soap and water, or hand sanitizers along with social distancing are effective to avoid the exposure of this virus. Ayurveda is the oldest acknowl-edged organized medicine on the earth. Immunity has an important role in maintaining health and prevention of diseases. In Ayurveda, Rasayana drugs are known for their immunomodulation and rejuvenation properties. On March 31, 2020, Ministry of AYUSH has issued advisory for enhancing immunity through lifestyle modification, dietary management, prophylactic interventions and simple remedies based on the symptoms. After that successful implementation, Government of India has planned to conduct clinical trials on three herbal nootropic and immunomodulatory drugs viz. Ashwagandha, Guduchi and Mulethi and AYUSH-64 (Ayurvedic anti-malaria drug) for their preventive properties against Covid-19 infections. This review article covers summary of the COVID-19 i.e. transmission, clinical presentation, investiga-tion and prevention along with preventive measures in according to Ayurveda that can be adopted for future clinical trial. © International Journal of Research in Pharmaceutical Sciences.