Search

This paper deploys the containment principle by Della Croce and Nicole-Berva (2021) to adjudicate COVID-19 non-conformity in India. The paper argues that the containment principle offers a guide to evaluating pandemic legislation and outlines the duties of the state. It then evaluates the Indian Pandemic response and legislation against the containment principles and finds it arbitrary, arguing that the discretionary power vested in the state enables it to treat two instances of COVID-19 non-conformities, the Tablighi Jamaat and the Kumbh, differently. The non-conformity of the devotees and the deviance of the state can be read within a framework of exceptionalism, collective irrationality, and conspiracy theories that firstly enables the devotees to maintain a sense of invulnerability, and secondly, allows the state to blame the minority community for threatening its exceptional status. These acts of non-conformities constitute disobedience against containment principles that are unjustifiable and demonstrate a moral failure to not harm others and a failure of the state to equally distribute benefits and burdens of pandemic policies. [ABSTRACT FROM AUTHOR]

Mining is an important enterprise that will supply essential minerals for the country's infrastructural development. Mining is a key industry and a development engine in the Indian economy. The epidemic has had both immediate and long-term effects on the labour and industries in India. Short-term effects include the cessation of mineral production, salary loss due to absenteeism and pandemic limitations, unemployment, and the selling of minerals on the market. The reputation and economic health of industries, fresh mineral deposit exploration, and new mining complex development have all been impacted in long-term. Aside from the mining industry-specific impact of COVID-19, the social fabric of the mining workers has been impacted since they are living in an environment of fear and uncertainty. This paper attempts to analyse the effect of COVID-19 on extractive industries in India. It also focuses the steps which taken by the Indian government. The paper concluded with the negative effect of COVID-19 which disrupts the mining sector and the contribution declined during COVID-19. However, for instance from mineral rich-Odisha, the mining sector takes up by its growth in post COVID-19 period with remarkable increment from negative growth to high positive rate. [ABSTRACT FROM AUTHOR]

In response to the beginnings of the COVID-19 pandemic, India's government instituted a severe nationwide lockdown in late March 2020. In this paper, we analyze household survey data on income from an Indian state, Punjab. We analyze the impacts of the pandemic and lockdown on total household income, as well as its components, including wages, business income and transfers. This paper innovates in this area by using Poisson Pseudo Maximum Likelihood estimation to deal with the skewed distributions of various income components, as well as quantile regressions to examine differential impacts across the income distribution. We examine the differences between rural and urban households, differential impacts on female-headed and laborer households, and the time pattern of impacts. We find that rural households experienced more persistent income declines than urban households, and their wages suffered relatively more. Households with daily laborers were most badly affected, and this was especially true at the bottom of the income distribution. [ABSTRACT FROM AUTHOR]

Indoor air quality (IAQ) in the built environment is significantly influenced by particulate matter, volatile organic compounds, and air temperature. Recently, the Internet of Things (IoT) has been integrated to improve IAQ and safeguard human health, comfort, and productivity. This review seeks to highlight the potential of IoT integration for monitoring IAQ. Additionally, the paper details progress by researchers in developing IoT/mobile applications for IAQ monitoring, and their transformative impact in smart building, healthcare, predictive maintenance, and real-time data analysis systems. It also outlines the persistent challenges (e.g., data privacy, security, and user acceptability), hampering effective IoT implementation for IAQ monitoring. Lastly, the global developments and research landscape on IoT for IAQ monitoring were examined through bibliometric analysis (BA) of 106 publications indexed in Web of Science from 2015 to 2022. BA revealed the most significant contributing countries are India and Portugal, while the top productive institutions and researchers are Instituto Politecnico da Guarda (10.37% of TP) and Marques Goncalo (15.09% of TP), respectively. Keyword analysis revealed four major research themes: IoT, pollution, monitoring, and health. Overall, this paper provides significant insights for identifying prospective collaborators, benchmark publications, strategic funding, and institutions for future IoT-IAQ researchers. [ABSTRACT FROM AUTHOR]

The objective of this paper is to provide an insight on effect of stringency in Covid-19 spread in India especially in Chennai, a city were more lockdown, and restrictions was imposed to control the infection. Even though the restriction was imposed in the country by the end of March 2020, the growth reduction was seen in the mid of June as the awareness was increased. The average Covid-19 case growth was got reduce from 3.43 to 2.62% by July mid. To analysis the impact of stringency, a detailed analysis was done on Chennai city which was imposed with more repeated lockdowns to flatten the curve. We tried to fit a regression line with three difference scenario of data. The results show a promising R-squared and p value, with a right skewed distribution normal probability plot. The impact of lockdown in people's lives in different sectors were also discussed in this paper. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic has left an indelible mark on societies worldwide, challenging governments to respond swiftly and effectively to mitigate its impact. India, with its vast population and complex healthcare landscape, faced unique challenges in formulating and implementing a pandemic response strategy. The article examines India's policy response to the COVID-19 pandemic and explores the valuable lessons it offers for shaping a more resilient and prepared society in a post-COVID world. It provides a comprehensive analysis of India's multifaceted approach to managing the pandemic, highlighting key elements such as lockdowns, testing and contact tracing, healthcare infrastructure, vaccination drives, and economic relief measures. By delving into both the successes and shortcomings of these policies, it seeks to extract valuable insights for policymakers and public health officials globally. As the world transitions into a post-COVID era, the lessons learned from India's experience offer a roadmap for building stronger healthcare systems, improving disaster preparedness, and enhancing social safety nets. The article underscores the importance of proactive governance, community engagement, data-driven decision-making, and international collaboration in the face of global health crises. The paper demonstrates that India's journey through the pandemic provides a wealth of knowledge that can inform policy development, foster greater resilience, and help societies better navigate the uncertainties of a post-COVID world. By reflecting on the successes and challenges of India's response, this article offers actionable insights for shaping a more equitable, sustainable, and prepared society in the wake of the COVID-19 pandemic. [ABSTRACT FROM AUTHOR]

Unexpected and sudden spread of the novel Coronavirus disease (COVID-19) has opened up many scopes for researchers in the fields of biotechnology, health care, educational sectors, agriculture, manufacturing, service sectors, marketing, finance, etc. Hence, the researchers are concerned to study, analyze and predict the impact of infection of COVID-19. The COVID-19 pandemic has affected many fields, particularly the stock markets in the financial sector. In this paper, we have proposed an econometric approach and stochastic approach to analyze the stochastic nature of stock price before and during a COVID-19-specific pandemic period. For our study, we considered the BSE SENSEX INDEX closing pricing data from the Bombay Stock Exchange for the period before and during COVID-19. We have applied the statistical tools, namely descriptive statistics for testing the normal distribution of data, unit root test for testing the stationarity, and GARCH and stochastic model for measuring the risk, also investigated drift and volatility (or diffusion) coefficients of the stock price SDE by using R Environment software and formulated the 95% confidence level bound with the help of 500 times simulations. Finally, the results have been obtained from these methods and simulations are discussed. [ABSTRACT FROM AUTHOR]

The COVID-19 has affected and threatened the world health system very critically throughout the globe. In order to take preventive actions by the agencies in dealing with such a pandemic situation, it becomes very necessary to develop a system to analyze the impact of environmental parameters on the spread of this virus. Machine learning algorithms and artificial Intelligence may play an important role in the detection and analysis of the spread of COVID-19. This paper proposed a twinned gradient boosting machine (GBM) to analyze the impact of environmental parameters on the spread, recovery, and mortality rate of this virus in India. The proposed paper exploited the four weather parameters (temperature, humidity, atmospheric pressure, and wind speed) and two air pollution parameters (PM2.5 and PM10) as input to predict the infection, recovery, and mortality rate of its spread. The algorithm of the GBM model has been optimized in its four distributions for best performance by tuning its parameters. The performance of the GBM is reported as excellent (where R2 = 0.99) in training for the combined dataset comprises all three outcomes i.e. infection, recovery and mortality rates. The proposed approach achieved the best prediction results for the state, which is worst affected and highest variation in the atmospheric factors and air pollution level. [ABSTRACT FROM AUTHOR]

Based on the data from the Periodic Labour Force Survey (PLFS) and multiple rounds of the Consumer Pyramids Household Survey from Centre for Monitoring Indian Economy (CMIE), the paper summarises evidence on changes in the employment–unemployment scenario since the onset of the COVID-19 pandemic and related curbs on economic activity since April 2020. The paper presents evidence on how the pandemic affected pre-existing gender, caste gaps and patterns of employment by sector of production (agricultural, manufacturing, and services). The paper situates these shifts in a longer arc, comparing them to the pre-COVID employment scenario, to understand the long-term impact of the pandemic on the Indian labour market. The data show that the pandemic reinforced and deepened many of the pre-existing schisms. As economic activity is recovering, employment is increasing, but is yet to return to the pre-pandemic levels. The employment challenge is bigger than just achieving the pre-pandemic levels: there is an urgent need to boost job creation and rectify pre-pandemic deep-rooted inequalities. [ABSTRACT FROM AUTHOR]

COVID-19 has laid an impact on every sector of the world. Howsoever severe, vaccines have acted as the sole source of a protective guard to prevent the further spread of the COVID-19 pandemic. In this research, the authors broadly focus on the trends in the vaccination drive of India. The paper revolves around a prediction and evaluation approach, which depending on the past and the current trends of daily vaccinations, obtain comparable results using a self-built recurrent neural network of LSTM layers for this study on time series evaluation. Through the neural network, the study predicts the exact vaccination figures likely to be achieved 1 year after vaccine introduction in the Indian subcontinent. The gathered data from January 16, 2021, until September 30, 2021, follow effective visualization of how the model outputs resemble the vaccination numbers for October 2021 and the predictions until January 16, 2022. Finally, the paper follows an extensive data analysis keeping in mind, the analogy of the number of COVID-19 cases and deaths before and after the vaccination system was centralized, to prove how sustainable the framework has been so far. [ABSTRACT FROM AUTHOR]

Even after scavenging the existence of mankind for the past year, the wrath of CoVID-19 is yet to die down. Countries like India are still getting haunted by the devastating conundrum, with coronavirus ripping through its citizens in the concurrent second wave. The surge of cases has prompted rapid intervention, with medical authorities pushing it to the limit to curve a roadblock to its aggressive growth. But, even after effortless work, human intervention remains slow and insufficient. Furthermore, relevant testing methodologies have shown weakness while detecting threats, with the recent growth of post-Covid complexities, thereby leaving a painful mark. This as such created a major requirement for technological advancements, which can cater to the mass. The growth of computational prowess in the past decade made the field of Deep Learning a major contributor in curving out algorithms to solve this. Adding to the excellent foundation of Deep Learning, this paper, proposes a novel CoWarriorNet model for rapid detection of CoVID-19, via chest X-ray images, which adds in an extra layer of precision and confirmation in the detection of cases in both pre-Covid and post-Covid conditions. The proposed classification model curves out an excellent accuracy of 97.8%, with the major eye-candy being the sensitivity rate of 0.99 when detecting CoVID-19 cases. This model introduces a new concept of Alpha Trimmed Average Pooling, which along with the novel architecture adds a subtle touch to its high efficiency, thereby giving a much-needed solution to the medical experts. The two-mouthed architecture provides the added benefit of a confidence score, deducing human aid in case of discrepancy. [ABSTRACT FROM AUTHOR]

The horrifying and fast spreading COVID-19 pandemic has shocked India and in fact the entire world to its core. Indian Government has taken all the possible preventive steps to contain the wider spread of this highly contagious disease but the second wave in the month of April, 2021 has turned this strong country in a helpless position. In this paper, the effect of environmental factors like temperature and air quality index on the new confirmed cases along with recovered cases has been seen in Bengaluru Urban district of Karnataka State of India. Regression analysis has been carried out with the help of SPSS software. The outcomes from the paper will definitely give some valuable insights for the researchers around the world in their future combat measures. [ABSTRACT FROM AUTHOR]

Study shows that COVID-19 cases, deaths and recoveries vary in macro level. Geographical phenomena may act as potential controlling factor. The present paper investigates spatial pattern of COVID-19 cases and deaths in West Bengal (WB), India and assumes Kolkata is the source region of this disease in WB. Thematic maps on COVID related issues are prepared with the help of QGIS 3.10 software. As on 15th January 2021, WB has 564032 number of COVID-19 cases which is 0.618% to the total population of the state. However, the COVID-19 case for India is 0.843% and for world is 1.341% to its total population. Lorenz Curve shows skewed distribution of the COVID-19 cases in WB. 17 (90%) districts hold 84.11% of the total population and carry 56.30% of the total COVID-19 cases. However, the remaining two districts—Kolkata and North 24 Parganas—hold remaining 43.70% COVID-19 cases. Correlation coefficient with COVID-19 cases and Population Density, Urban Population and Concrete Roof of their house are significant at 1% level of significance. [ABSTRACT FROM AUTHOR]

The paper investigates the spread pattern and dynamics of Covid-19 propagation based on SIR model. Using the model dynamics, an analytical estimation has been obtained for virus span, its longevity, growing pattern, etc. Experimental simulations are carried out on the data of four regions of India over a period of two months of country-wide lockdown. The analysis illustrates the effect of lockdown on the contact rate and its implication. Simulation results illustrate that there is a cut-down in effective contact rate by a considerable factor ranging from 2 to 4 for the selected regions. Further, the estimates for the vaccines to be developed, maximum range and span of the disease can be also estimated. Results portray that the SIR model is a significant tool to cast the dynamics and predictions of Covid-19 outbreak in comparison to other epidemic models. The study demonstrates the progression of real time data in accordance with the SIR model with high accuracy. [ABSTRACT FROM AUTHOR]

When analysing the Covid-19 pandemic potential consequences on education, it is evident that it had adverse effects on the existing educational inequalities worldwide. However, little is known about how the digital divide have worsened the conventional educational system and reinforced pre-existing gender inequalities among the historically marginalised communities. This research paper explores how the pandemic, along with digital divide, deteriorated the educational system among the socially deprived groups (i.e. tribals also known as indigenous or Adivasis) and place them in a disadvantageous position. The paper reflects on how the Covid-19 pandemic re-configured the pre-existing issues of educational inequalities and how the digital dived have been manifested in a way that has particularly affected the young tribal girls. For this study, semi-structured interviews with tribal students, their parents and teachers residing in a remote area of Jharkhand, India, were conducted to understand their experiences of shifting to online education mode. Other than accessibility and infrastructure issue, the findings reveal that the elements of cultural and social issues (related to perceived benefits of education for girls and mindset or beliefs parents and teachers towards effectiveness of digital mode of education delivery) create and reinforce the digital divide for the tribal girls in the hinterlands. Based on the information collected from the interview, the study emphasises on re-thinking the digital learning ecosystem and provide policy recommendations to counteract the worsening digital divide and educational inequalities among socially deprived groups. [ABSTRACT FROM AUTHOR]

India is a hotspot of the COVID-19 crisis. During the first wave, several lockdowns (L) and gradual unlock (UL) phases were implemented by the government of India (GOI) to curb the virus spread. These phases witnessed many challenges and various day-to-day developments such as virus spread and resource management. Twitter, a social media platform, was extensively used by citizens to react to these events and related topics that varied temporally and geographically. Analyzing these variations can be a potent tool for informed decision-making. This paper attempts to capture these spatiotemporal variations of citizen reactions by predicting and analyzing the sentiments of geotagged tweets during L and UL phases. Various sentiment analysis based studies on the related subject have been done; however, its integration with location intelligence for decision making remains a research gap. The sentiments were predicted through a proposed hybrid Deep Learning (DL) model which leverages the strengths of BiLSTM and CNN model classes. The model was trained on a freely available Sentiment140 dataset and was tested over manually annotated COVID-19 related tweets from India. The model classified the tweets with high accuracy of around 90%, and analysis of geotagged tweets during L and UL phases reveal significant geographical variations. The findings as a decision support system can aid in analyzing citizen reactions toward the resources and events during an ongoing pandemic. The system can have various applications such as resource planning, crowd management, policy formulation, vaccination, prompt response, etc. [ABSTRACT FROM AUTHOR]

Numerous studies emphasize accuracy in machine learning regression models, yet scalability and execution efficiency are often overlooked, critical for large datasets or extensive computations. This paper introduces a scalable, distributed Spark MLlib regression model through the best subset selection approach to predict Covid-19 statistics in India, demonstrating high accuracy, scalability, and execution efficiency. Notably, limited research focuses on tree-based regression, particularly gradient boost regression, in the context of the Covid-19 dataset. The proposed work optimizes regression models for accuracy and execution time on Spark clusters of varying sizes using the best subset selection approach. Evaluation encompasses Root Mean Square Error (RMSE), Mean Absolute Error (MAE), R2 Error for accuracy, and execution time analysis. Results indicate superior prediction accuracy in tree-based regression, with Gradient Boosted Tree Regression (GBTR) leading, and Random Forest Regression (RFR) surpassing Decision Tree Regression (DTR). Accuracy remains consistent across Python library, Spark MLlib on a single machine, and clusters of varying sizes, with Spark MLlib displaying lower execution times than Python's machine learning library on a single machine. Furthermore, execution times decrease substantially within Spark clusters, particularly for the iterative GBTR. This research uncovers scalability and execution efficiency aspects, highlighting tree-based regression's accuracy and advocating for Spark MLlib's efficacy in enhancing execution efficiency, especially across multi-node clusters. [ABSTRACT FROM AUTHOR]

Social lockdowns improved air quality during the COVID-19 pandemic. Governments had previously spent a lot of money addressing air pollution without success. This bibliometric study measured the influence of COVID-19 social lockdowns on air pollution, identified emerging issues, and discussed future perspectives. The researchers examined the contributions of countries, authors, and most productive journals to COVID-19 and air pollution research from January 1, 2020, to September 12, 2022, from the Web of Sciences Core Collection (WoS). The results showed that (a) publications on the COVID-19 pandemic and air pollution were 504 (research articles) with 7495 citations, (b) China ranked first in the number of publications (n = 151; 29.96% of the global output) and was the main country in international cooperation network, followed by India (n = 101; 20.04% of the total articles) and the USA (n = 41; 8.13% of the global output). Air pollution plagues China, India, and the USA, calling for many studies. After a high spike in 2020, research published in 2021 declined in 2022. The author's keywords have focused on "COVID-19," "air pollution," "lockdown," and "PM25." These keywords suggest that research in this area is focused on understanding the health impacts of air pollution, developing policies to address air pollution, and improving air quality monitoring. The COVID-19 social lockdown served as a specified procedure to reduce air pollution in these countries. However, this paper provides practical recommendations for future research and a model for environmental and health scientists to examine the likely impact of COVID-19 social lockdowns on urban air pollution. [ABSTRACT FROM AUTHOR]

The world is currently facing a pandemic called COVID-19 which has drastically changed our human lifestyle, affecting it badly. The lifestyle and the thought processes of every individual have changed with the current situation. This situation was unpredictable, and it contains a lot of uncertainties. In this paper, the authors have attempted to predict and analyze the disease along with its related issues to determine the maximum number of infected people, the speed of spread, and most importantly, its evaluation using a model-based parameter estimation method. In this research the Susceptible-Infectious-Recovered model with different conditions has been used for the analysis of COVID-19. The effects of lockdown, the light switch method, and parameter variations like contact ratio and reproduction number are also analyzed. The authors have attempted to study and predict the lockdown effect, particularly in India in terms of infected and recovered numbers, which show substantial improvement. A disease-free endemic stability analysis using Lyapunov and LaSalle's method is presented, and novel methods such as the convalescent plasma method and the Who Acquires Infection From Whom method are also discussed, as they are considered to be useful in flattening the curve of COVID-19. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic has exacerbated the already existing gender inequalities with substantial implications on women. With the closure of offices and educational institutions, and the emerging norm of work from home and online education, along with the lack of services of domestic worker, the need to perform unpaid chores in the household has increased. Simultaneously, the requirements of social distancing and sanitization have created new unpaid chores. Owing to the sexual division of labour, and gendered roles and social norms of performing domestic and care work, the burden of unpaid work falls disproportionately on women. In this context, the objective of the paper is to study the impact of COVID-19 on time spent on unpaid work and the underlying gender differences in the urban centres in India. Specifically, the paper will do a comparative analysis of the gender differences in time spent on unpaid work before and during the lockdown, and analyse the reasons for the same. [ABSTRACT FROM AUTHOR]

In India, the major drivers of both internal and international migration are the prevailing unemployment, competitive labour market and enhanced livelihood prospects in the destination state or country. However, the nationwide lockdown and the sealing of inter-state and international borders to control the COVID-19 pandemic triggered the reverse migration of informal migrant workers. This requires the central and state governments to collectively forge strategies to enable their reverse migration and smooth reintegration in the post-COVID economy. In this paper, we have focused on the inter-state migrants in India and returnee migrants only from Gulf countries as they account for two-thirds of Indian migrants living abroad. This study conducted a comparative analysis of the Indian government's varied approach towards its internal and international migrants during their reverse migration, repatriation and reintegration after the announcement of the lockdown. Firstly, the paper compares the challenges faced by internal and international migrant workers during these stages with the help of in-depth interview data collected from migrants and social workers. Secondly, the varied governmental responses towards their repatriation are discussed. Thirdly, it analyses the obstacles in their economic reintegration to help frame suitable welfare policies for the Indian migrant community. [ABSTRACT FROM AUTHOR]

The corona pandemic altered many traditional and historical norms of society and law. COVID-19 created a humanitarian crisis in some parts of globe, while pandemic privacy and civil liberties were under threat all over world. To combat the deadly virus, individual liberty and equality were compromised. This paper focuses on how India's health problem has compromised people's right to privacy. It will highlight how strict executive policies led to the creation of a massive surveillance system in the name of combating the COVID-19 pandemic, as well as how the absence of any policy or legal framework led to the exclusion of individuals and their families who were suspected of having the virus or caring for those who were infected with the deadly virus. The paper uses case studies and data collected from primary as well as secondary sources. The authors will also point out how the absence of privacy regulation puts millions of citizens' private information at risk of being compromised or exploited against their will. [ABSTRACT FROM AUTHOR]

Although the narrative of modernity has been rejected by numerous scientists, it remains a powerful paradigm. Several Western countries have, during the COVID-19 pandemic, witnessed renewed interest in some archaic practices and beliefs. Based mostly on media analysis, this paper provides an insight into religious responses to the COVID-19 pandemic in Slovakia and India, two completely different cultural areas. Simultaneously, it challenges the self-identification of the West as the epicentre of rational thought, in contrast to the so-called non-Western world. The self-image of superiority adopted by the modern West in terms of religious relevance has proved to be distorted, as the tendency to turn to spiritual practices in times of crisis is not exclusively a feature of non-Western societies. [ABSTRACT FROM AUTHOR]

The World Health Organization announced on March 11, 2020 that COVID-19 could become a pandemic. COVID-19 is a contagious disease caused by the coronavirus that causes severe acute respiratory syndrome (SARS-CoV-2). Viruses usually enter the body through the mouth or nose. The virus then enters the alveoli, which are small air sacs inside the lungs. Cough, fatigue, fever, shortness of breath or breathing difficulties, and loss of smell and taste are all symptoms of COVID-19. Anosmia, also known as smell blindness, is a condition in which the ability to detect one or more smells is lost. Olfaction uses chemoreceptors to create signals that are processed in the brain and form the sense of smell in anosmia. Anosmia is recognised as a COVID-19 symptom in many countries, and some have developed "smell tests" as potential screening tools. The first level of screening, which is currently used in India, is primarily based on temperature and can result in false positives and negatives (fever as a symptom has not yet been developed although infection). One of the methods for detecting COVID-19 is an intermediate level of screening based on assessing an olfactory function, depending on the usage. This paper provides an overview of COVID-19 and its effects on the human body, as well as an overview of anosmia and how it contributes to one of the symptoms of COVID-19. [ABSTRACT FROM AUTHOR]

Corona Virus Disease 2019 (COVID-19) is a continuing extensive incident globally affecting several million people's health and sometimes leading to death. The outbreak prediction and making cautious steps is the only way to prevent the spread of COVID-19. This paper presents an Adaptive Neuro-fuzzy Inference System (ANFIS)-based machine learning technique to predict the possible outbreak in India. The proposed ANFIS-based prediction system tracks the growth of epidemic based on the previous data sets fetched from cloud computing. The proposed ANFIS technique predicts the epidemic peak and COVID-19 infected cases through the cloud data sets. The ANFIS is chosen for this study as it has both numerical and linguistic knowledge, and also has ability to classify data and identify patterns. The proposed technique not only predicts the outbreak but also tracks the disease and suggests a measurable policy to manage the COVID-19 epidemic. The obtained prediction shows that the proposed technique very effectively tracks the growth of the COVID-19 epidemic. The result shows the growth of infection rate decreases at end of 2020 and also has delay epidemic peak by 40–60 days. The prediction result using the proposed ANFIS technique shows a low Mean Square Error (MSE) of 1.184 × 10–3 with an accuracy of 86%. The study provides important information for public health providers and the government to control the COVID-19 epidemic. [ABSTRACT FROM AUTHOR]

This paper examines whether the Covid-19 pandemic has had a homogeneous or heterogeneous effect on stock returns in India. We consider panel data by using 1,318 companies that are listed on the National Stock Exchange of India. We find that the daily growth rate in Covid-19 cases and Covid-19 deaths are negatively associated with stock returns. Further, we observe that the average stock returns during Lockdown 2 are positive and highly significant, while the returns during Lockdowns 3 and 4 are negative. Moreover, our results show that the chemical, technology, and food and beverage industries earn higher returns. In contrast, the banking and finance, automotive, services, and cement and construction industries yield lower returns for the overall period. Interestingly, all industry groupings in this study earn a positive return during the lockdown period. In particular, the chemical, technology, automotive, metals and mining, and food and beverage industries provide higher returns during the lockdown period. Finally, this study supports the claim that the Covid-19 pandemic has had a heterogeneous effect in the Indian stock markets. [ABSTRACT FROM AUTHOR]

The rapid spread of the novel corona virus disease (COVID-19) has disrupted the traditional clinical services all over the world. Hospitals and healthcare centers have taken extreme care to minimize the risk of exposure to the virus by restricting the visitors and relatives of the patients. The dramatic changes happened in the healthcare norms have made it hard for the deaf patients to communicate and receive appropriate care. This paper reports a work on automatic sign language recognition that can mitigate the communication barrier between the deaf patients and the healthcare workers in India. Since hand gestures are the most expressive components of a sign language vocabulary, a novel dataset of dynamic hand gestures for the Indian sign language (ISL) words commonly used for emergency communication by deaf COVID-19 positive patients is proposed. A hybrid model of deep convolutional long short-term memory network has been utilized for the recognition of the proposed hand gestures and achieved an average accuracy of 83.36%. The model performance has been further validated on an alternative ISL dataset as well as a benchmarking hand gesture dataset and obtained average accuracies of 97 % and 99.34 ± 0.66 % , respectively. [ABSTRACT FROM AUTHOR]

COVID-19 has emerged as the greatest threat in recent times, causing extensive mortality and morbidity in the entire world. India is among the highly affected countries suffering severe disruptions due this pandemic. To overcome the adverse effects of COVID-19, vaccination has been identified as the most effective preventive measure globally. However, a growing amount of hesitancy has been observed among the general public regarding the efficacy and possible side-effects of vaccination. Such hesitancy may proved to be the greatest hindrance towards combating this deadly pandemic. This paper introduces a multimodal deep learning method for Indian Twitter user classification, leveraging both content-based and network-based features. To explore the fundamental features of different modalities, improvisations of transformer models, BERT and GraphBERT are utilized to encode the textual and network structure information. The proposed approach thus integrates multiple data representations, utilizing the advances in both transformer based deep learning as well as multimodal learning. Experimental results demonstrates the efficacy of proposed approach over state of the art approaches. Aggregated feature representations from multiple modalities embed additional information that improves the classification results. The findings of the proposed model has been further utilized to perform a study on the dynamics of COVID-19 vaccine hesitancy in India. [ABSTRACT FROM AUTHOR]

This paper discusses the effects of COVID-19-induced pandemic on tea plantation workers during lockdown phases in India. The tea industry, being a labour-intensive, employs around 1.2 million permanent workers who usually reside within the plantations along with their families, making the largest employer in the formal private sector. Drawing from secondary data and narratives from in-depth telephonic interviews with various key informants during and post-lockdown, this study shows that plantation workers faced livelihood crisis due to subsequent lockdowns during both the waves of COVID-19 health crisis. Poor health infrastructure in the tea estates, weak trade union and existing wage determination methods are responsible for livelihood crisis for plantation workers during pandemic. Except state-assisted social assistance benefits in terms of ration, unlike permanent workers of other sectors and industries, regular plantation workers did not even receive compensated wages from the employer during lockdown, following 'no-wage for no-work' clause. Ineffective labour market institutions and rigid managementality failed to protect tea plantation workers during the crisis. [ABSTRACT FROM AUTHOR]

This paper attempts to estimate long-run forecasting of Indian GDP for the post-COVID period using the factor error correction model (FECM). The model builds on a dynamic factor model that directly and indirectly captures many dimensions affecting the cycles of GDP. The availability of big data enables the extraction of a few common factors from large dimensions, which essentially produces better precision in forecasting estimates. We first extract leading factors and then add proxy policy variables to establish their long-run relationship with the GDP which produces insignificant in-sample bias. The estimated long-run relationship has been employed to predict GDP for 2022–35. We found three major dynamic factors that capture 80% of variations from 56 quarterly variables of the Indian economy. These three factors with their four lags and four exogenous policy instruments have been included in the FECM model for forecasting estimation. We find that the real GDP is expected to grow at 4–8% annually, depending upon the actual realisation of external shocks and policies. The expected rise in temperature and oil prices seems to be dampening the growth. Whereas, the institutional reforms for making more effective public investment and the currency digitalisation that reduces cash requirements could play an expansionary role. If the oil price and the temperature remain at the current level, the growth rate can go closer to approximately 8%. [ABSTRACT FROM AUTHOR]

Coronavirus disease so called as COVID-19 is an infectious disease and its spread takes place due to human interaction by their pathogen materials during coughing and sneezing. COVID-19 is basically a respiratory disease as evidence proved that a large number of infected people died due to short breathing. Most widely and uncontrollably spreading unknown viral genome infecting people worldwide was announced to be 2019–2020 nCoV by WHO on January 30, 2020. Based on the seriousness of its spread and unavailability of vaccination or any form of treatment, it was an immediate health-emergency of concern of international-level. The paper analyses effects of this virus in countries, such as India and United States on day-to-day basis because of their greater variability. In this study, various performance measures, such as root mean square error (RMSE), mean absolute error (MAE), coefficient of determination (R 2) , mean absolute standard error (MASE) and mean absolute percentage error (MAPE) which characterize models' performances. R 2 value has been achieved to be closest to 1, i.e., 0.999 from Wavelet Neuronal Network Fuzzified Inferences' Layered Multivariate Adaptive Regression Spline (WNNFIL–MARS) for both the countries' data. It is important to capture the essence of this pandemic affecting millions of the population daily ever since its spread began from January, 2020. [ABSTRACT FROM AUTHOR]

The COVID-19 epidemic has brought attention to the vulnerability of new illnesses, and immunization remains a viable option for resuming normal life. This paper examines the influence of COVID-19 vaccination on the death rate and the performance of stock market in India. For this study, COVID-19 vaccination and death rate data is gathered from the Ministry of Health and Family Welfare (MoHFW) portal, and the data for the stock index is taken from the Bombay Stock Exchange (BSE), India. In order to achieve a precise representation of feature significance and distribution, EDA (Exploratory Data Analysis) is utilized in this study. The impact of COVID-19 immunization on the mortality rate and stock market index is investigated using both statistical analysis and Machine Learning Regression-based models. The models are remarkably accurate in reproducing actual result. The empirical study suggests that vaccination has a strong positive impact on the stock market and reducing the death rate. Furthermore, the policies recommended by government and monetary authorities coupled with COVID-19 vaccine supported the stock market recovery in pandemic. [ABSTRACT FROM AUTHOR]

India is severely affected by the COVID-19 pandemic and is facing an unprecedented public health emergency. While the country's immediate measures focus on combating the coronavirus spread, it is important to investigate the impacts of the current crisis on India's renewable energy transition and air quality. India's economic slowdown is mainly compounded by the collapse of global oil prices and the erosion of global energy demand. A clean energy transition is a key step in enabling the integration of energy and climate. Millions in India are affected owing to fossil fuel pollution and the increasing climate heating that has led to inconceivable health impacts. This paper attempts to study the impact of COVID-19 on India's climate and renewable energy transitions through machine learning algorithms. India is observing a massive collapse in energy demand during the lockdown as its coal generation is suffering the worst part of the ongoing pandemic. During this current COVID-19 crisis, the renewable energy sector benefits from its competitive cost and the Indian government's must-run status to run generators based on renewable energy sources. In contrast to fossil fuel-based power plants, renewable energy sources are not exposed to the same supply chain disruptions in this current pandemic situation. India has the definite potential to surprise the global community and contribute to cost-effective decarbonization. Moreover, the country has a good chance of building more flexibility into the renewable energy sector to avoid an unstable future. [ABSTRACT FROM AUTHOR]

This paper analyses the impact of the various waves of COVID-19 on wages and employment. The government of India announced a strict nationwide lockdown on March 25, 2020, to contain the COVID-19 pandemic. People lost their jobs or faced wage cuts during the lockdown. Globally, women experienced a greater loss in employment and paid working hours than men. To understand the short-term and long-term impact of COVID-19 in Ahmedabad city, we conducted four rounds of surveys, online during March–April 2020 and March–April 2021, and offline in September 2021 and September–October 2022. We found that women and low-skilled workers were more likely to have received a wage cut during the lockdown period. During the pandemic year 2021, less than a tenth of respondents reported changing their jobs, while in the post-pandemic year, 2022, more than a quarter of the respondents reported change in economic activity. The logistic model showed that this churning of jobs post-pandemic was true for low skill and high skill occupations. Further, in the post-pandemic period, there was a shift from wage employment to self-employment, among both low- and high-skilled categories of occupations. Given the varying impact of COVID by gender and occupation, policies must be designed to address vulnerabilities and encourage businesses. [ABSTRACT FROM AUTHOR]

India is one of the worst hit regions by the second wave of COVID-19 pandemic and 'Black fungus' epidemic. This paper revisits the Bombay Plague epidemic of India and presents six fractional-order models (FOMs) of the epidemic based on observational data. The models reveal chaotic dispersion and interactive coupling between multiple species of rodents. Suitable controllers based on fuzzy logic concept are designed to stabilise chaos to an infection-free equilibrium as well as to synchronise a chaotic trajectory with a regular non-chaotic one so that the unpredictability dies out. An FOM of COVID-19 is also proposed that displays chaotic propagation similar to the plague models. The index of memory and heredity that characterise FOMs are found to be crucial parameters in understanding the progression of the epidemics, capture the behaviour of transmission more accurately and reveal enriched complex dynamics of periodic to chaotic evolution, which otherwise remain unobserved in the integral models. The theoretical analyses successfully validated by numerical simulations signify that the results of the past Plague epidemic can be a pathway to identify infected regions with the closest scenarios for the present second wave of Covid-19, forecast the course of the outbreak, and adopt necessary control measures to eliminate chaotic transmission of the pandemic. [ABSTRACT FROM AUTHOR]

The impact of COVID-19 has revamped all aspects of human life including education sector, and it has completely changed the educational environment across the globe. Due to the pandemic, the methodical functions of educational institutions have stopped, and new phases have started like online class, online evaluation, and indoor activities. Students are the vital players in education sector, and their opinions play an indispensable role while formulating the policies by the government. In the pandemic, students' perspective on education environment finds new dimension. Therefore, this paper has made an honest attempt to know the coastal Karnataka students' stance on college educational environment. In this study, six major components of education environment are considered, namely (1) online class, (2) teaching and learning, (3) evaluation, (4) college administration, (5) extracurricular activities, and (6) teachers. The study is descriptive in nature, and data was collected from 347 college students of Coastal Karnataka. To support the main objective, a hypothesis has been developed with the help of review of literature and is tested by using ANOVA and independent t-test. The path analysis is used to analyze the casual relationships among components of college educational environment, overall perception, and student performance. The result of the study found that the college students have positive perception towards online class (M=3.14), teaching and learning (M=3.704), evaluation (M=3.38), college and administration (M=3.83), extracurricular activities (M=3.87), and teachers (M=3.63). The result of the hypothesis testing revealed that there is no significant difference in the student perception towards various components of college education system. Path analysis results show that there is relation and effect between components of college education, overall perception, and students' performance. The study concludes that students agree with policies and actions taken by colleges to carry out classes during COVID-19 pandemic irrespective of demographic and educational difference. [ABSTRACT FROM AUTHOR]

The COVID-19 lockdown has led all the citizens (mobile subscribers) of India to stay at home and rather work from home. The people have started consuming more channel utilization (in mobile communication) through a continuous long duration conversations and more internet data through more streaming content as well as logging on to work from home. It was also reflected in how data demand from residential areas rose as compared to commercial areas. Consequently the bandwidth and channel saturation has evolved out to be a severe problem thereby affecting the work performance of all online offices and multi-national companies. This research paper proposes the simulation based experimental study of DITMC technique for mitigating this effect with a special concern in North Western Rajasthan part of India. The simulation results show that significant enhancement of 60.52% in channel utilization and bandwidth optimization is possible with negligible overhead of 0.23%. This technique also enables the telecom operators to ponder research in this field that will promisingly lead to manage augmented number of mobile subscribers (independent of any lockdown period) in limited bandwidth thereby using the spectrum efficiently. [ABSTRACT FROM AUTHOR]

The exponential spread of Covid-19 is not only a serious concern for public health but has also severely affected the global economy. India is not an exception. The banking sector must plan innovatively in a wide range of scenarios focusing upon Covid-19 specific requirements. It becomes essential to examine the impact of Covid-19 on the performance of the Indian banking sector and take focused initiatives at both the tactical and the strategic levels. This paper offers the Covid-19 Impact on Banking Ontology (Covid19-IBO) that provides semantic information about the impact of Covid-19 on the banking sector of India. The developed ontology has been verified and validated and has been made available on the Linked Open Data cloud. It can be utilized to annotate the related data to provide meaningful insights. The Covid-19 ontologies already available have some overlapping information that causes redundancy. Unified integration of these ontologies is required to operate upon them unambiguously. It becomes reasonable to develop a matching approach to link all these ontologies semantically. We, therefore, also provide a schema matching approach with reasonable results to map the Covid-19 ontologies. [ABSTRACT FROM AUTHOR]

Novel Coronavirus (COVID-19) has become one of the deadliest pandemics that has affected almost all the nations in the world. Lockdown and systematic re-opening of shopping malls, offices, etc. is still one of the major weapons against this virus. However, the government and medical agencies take long time to reopen the places due to risks involved in this deadly virus. The delay to reopen places has resulted in sharp decline in the growth of economy. In this paper a current context aware framework is proposed which uses multiple inputs for a specific region to decide whether to open it or not. The proposed framework used series of deep neural network models to generate recommendations specific to a particular region. Most of the inputs are real-time and readily available with the government. The main aim is to develop framework which can be used in any kind of pandemic even in small region to easily contain it. However, it has been tested using opensource data available for COVID-19. Data was crawled from web for 22 districts of Haryana state of India. Experimental result proved the efficiency of proposed framework. [ABSTRACT FROM AUTHOR]

In instances where the "youth bulge" in developing nations is aggravating the unemployment crisis and contributing to a detrimental cycle of events, inclusive growth (IG) has emerged as a cutting-edge development model. The academic interest in inclusive growth and unemployment-related research is plentiful, but the exclusive search into youth employment for inclusive growth is sparse. Based on a conceptual framework that offers an overview of the topic, this paper examines studies on youth unemployment concerning inclusive growth. It specifically explores the concept of inclusive growth, the financial and socio-psychological aspects of youth unemployment, and ways of tackling unemployment, such as youth entrepreneurship and skill development in pursuit of inclusive growth. By addressing these challenges, the assessment reveals ways to achieve SDGs such as SDG 8 (decent work and economic growth) and SDG 10 (reduced disparities). The relevant literature was extracted using qualitative content analysis and recursive content abstraction approaches. The study adds to the multidisciplinary literature on youth studies, entrepreneurship, and development economics. This study summarises the current level of knowledge on these topics, identifies research gaps, and offers interesting future research directions that will benefit academics and practitioners. [ABSTRACT FROM AUTHOR]

The Novel Coronavirus which emerged in India on January/30/2020 has become a catastrophe to the country on the basis of health and economy. Due to rapid variations in the transmission of COVID-19, an accurate prediction to determine the long term effects is infeasible. This paper has introduced a nonlinear mathematical model to interpret the transmission dynamics of COVID-19 infection along with providing vaccination in the precedence. To minimize the level of infection and treatment burden, the optimal control strategies are carried out by using the Pontryagin's Maximum Principle. The data validation has been done by correlating the estimated number of infectives with the real data of India for the month of March/2021. Corresponding to the model, the basic reproduction number R 0 is introduced to understand the transmission dynamics of COVID-19. To justify the significance of parameters we determined the sensitivity analysis of R 0 using the parameters value. In the numerical simulations, we concluded that reducing R 0 below unity is not sufficient enough to eradicate the COVID-19 disease and thus, it is required to increase the vaccination rate and its efficacy by motivating individuals to take precautionary measures. [ABSTRACT FROM AUTHOR]

Mining is an essential industry that will provide key materials needed for India infrastructure development. Mining is one of the core sectors and growth driver of Indian economy. Minerals and ores provide basic raw materials to many important industries like power generation (thermal), iron and steel, cement, petroleum and natural gas, petrochemicals, fertilizers, precious and semi-precious metals/stones for jewellery, electrical and electronics equipment, glass, ceramics, etc. The mining sector in India was poised for robust growth in financial year 2020–2021, on the back of rising demand from end-use sectors and fresh investments announced by the mining companies. However, the spread of COVID-19, right at the beginning of the financial year, has led to disruptions across industries. There is a significant scope for new mining capacities in iron ore, bauxite, coal and other minerals and considerable opportunities for future discoveries of sub-surface deposits. Infrastructure projects continue to provide lucrative business opportunities for steel, zinc and aluminium producers. Iron and steel make up a core component for the real estate sector. Demand for these metals is set to continue given strong growth expectations for the residential and commercial building industry. India's mining sector expected to show positive growth in 2021 compared to 2020. As different arms of the government work with stakeholders to bring the economy on the growth path, it is important for the policymakers of mining to continue making such changes to the policies, which can ramp up the contribution of the sector to GDP. In the present paper, an overview of impact of lockdown on the mining industry in India is discussed with special reference to production scenario of minerals, employment, export-import scenario of ores and minerals and other developments during the period. [ABSTRACT FROM AUTHOR]

The outbreak of novel and recent coronavirus disease 2019, caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus, has made an emergency throughout the world. In India, the outspread of the pandemic was observed on 3 March 2020, and after that exponential growth in the cases was observed in the country. Owing to the widespread transmission, high population density, high testing capacity and ineffective treatment, a continuous rise in cases was observed due to the pandemic in India. In this paper, we have discussed the trend and spread of COVID-19 spread in India with time, history of initial confirmed cases, the impact of phased manner lockdown, age- and gender-wise trend of cases and comparison of cases with the other most affected countries. The study uses exploratory data analysis to describe the current situation of COVID-19 cases in India till 16 August 2020, with the help of data from the Ministry of Health and Family Welfare, Government of India (GOI) and the World Health Organization (WHO). As of August 16, the total number of confirmed cases in India crossed 2.5 million marks with over 50,000 causalities. With more patients recovering and being discharged from hospitals and home isolation (in case of mild and moderate cases), the total recoveries have crossed the 1.8 million mark with a recovery rate of more than 70% and case fatality rate of 1.94% which is maintained below the global average and is on a continuous positive slide. The study also enlightens the preventive and stringent measures taken by India to combat the COVID-19 situation along with the future prospects. The GOI is following its proactive and preemptive approach for management, prevention and containment of COVID-19 in collaboration with the WHO. [ABSTRACT FROM AUTHOR]

The paper has a dual focus. One, is to use India as a case study to reflect upon the political, economic and socio-cultural structures of the country and how they shape the particular vulnerabilities in the society which have been aggravated in the COVID19 crisis-scape. Stories about Indian democracy, Casteism and Untouchability, Hindutva and Islamophobia emerging from the pandemic scenario have been extended to show global parallels. Stories of cultural resourcefulness and examples of people's power in the Indian context have also been represented to provide a formative assessment of how India is faring in the pandemic crisis. Two, it gathers emergent questions about human and social life which are being re-emphasized and created by the pandemic for Psychology to focus on. It makes use of Indian psychological concepts to re-envision self-other relationships in Post Coronial world. It makes a plea for strengthening the critical-cultural approach in 'Post-Coronial' Psychology. [ABSTRACT FROM AUTHOR]

Traditionally, the idea of being a victim is associated with a crime, accident, trickery or being duped. With the advent of globalisation and rapid growth in the information technology sector, the world has opened itself to numerous vulnerabilities. These vulnerabilities range from individual-centric privacy issues to collective interests in the form of a nation's political and economic interests. While we have victims who can identify themselves as victims, there are also victims who can barely identify themselves as victims, and there are those who do not realise that they have become victims. Misinformation, disinformation, fake news and other methods of spreading questionable content can be regarded as a new and increasingly widespread type of collective victimisation. This paper, drawing on recent examples from India, examines and analyses the rationale and modus operandi—both methods and types—that lead us to regard questionable content as a new form of collective victimisation. [ABSTRACT FROM AUTHOR]

While the wide-ranging impact of COVID-19 on incomes and livelihoods of people around the world will take some time to become known and understood, it is already clear that those who are in manual, mostly low-wage, occupations are among the worst-hit workers. This paper uses data from a sample survey of migrant workers to come up with useful parameters for estimating the potential losses from possible retrenchment of migrant workers due to the pandemic. The paper employs a simple estimation model using parameters derived from data collected from a KNOMAD-ILO survey of low-skilled migrant workers in the India–Saudi Arabia migration corridor, conducted during 2016–2017. An important finding is that the aggregate losses that low-skilled Indian workers in Saudi Arabia are likely to incur due to COVID-19-related retrenchment may be as high as 21% of their expected earnings. Adding recruitment costs can push up their losses to 36% of expected or potential earnings, while the aggregate remittances to their families could drop by USD 2 billion. [ABSTRACT FROM AUTHOR]

Tracking and analyzing the labour market dynamics at regular, frequent intervals is critical. However, this was not possible for India, a large emerging economy with a significant population undergoing demographic transition, due to a paucity of data. We use the new dataset Centre for Monitoring Indian Economy (CMIE)—Consumer Pyramids Household Survey (CPHS) and use a panel to create Labour Flow Charts and Transition Matrices for India from January 2019 to December 2021. To the best of our knowledge, this is the first time these were created for India. We then use that to look at the impact of Covid-19 on the Indian labour market. We not only look at transitions between employment, unemployment and out of labour force, but also across types of employment—full-time and part-time. The rich data also allows us to consider heterogeneity in the labour market and look at the differential impact of the pandemic across different education groups and gender. From the labour flow charts and transition probabilities, we find that while all groups have been impacted, the magnitude of the impact is different across groups. The recovery is also uneven, and the extent depends on education levels. Further, we do an event study analysis to examine the likelihood of getting a full-time job across different educational and gender groups. Men, on average, enjoy a higher likelihood of getting a full-time job than women. The likelihood coefficients also go up with increasing educational qualifications. Looking at skill heterogeneity, while the likelihood of getting a full-time job either goes down for most groups during the pandemic or the change is minuscule, strikingly it goes up for those with no education, for both men and women. The likelihood coefficients remain elevated for men even after the restrictions are removed, and that for women reverts to the level seen before the pandemic. Finally, this paper provides a way to continuously monitor the dynamics of the labour market as data is released in the regular intervals in the future, which would be of great value for researchers and policymakers alike. [ABSTRACT FROM AUTHOR]

Background: The global COVID-19 pandemic has brought forth multifaceted healthcare challenges, including the emergence of COVID-19-associated mucormycosis (CAM). This study investigates demographics, clinical presentation, endoscopic grading, radiological correlations, treatment modalities, and outcomes of CAM in Central India. Methods: A retrospective analysis of 312 CAM patients (April-June 2021). Data included demographics, clinical profiles, endoscopic and radiological findings, laboratory results, and treatments. A novel endoscopic grading system was utilized to assess the severity of mucormycosis lesions. Statistical analysis, including chi-squared tests, was performed. Results: Among 312 patients, 65% were male, 82% had comorbidities, and 84% presented with facial swelling. Endoscopic grading correlated with radiological manifestations (p < 0.05) and intraoperative findings. Surgical debridement and antifungal therapy were common treatments, yielding varied clinical outcomes. Mortality was 13% in advanced grades. Conclusion: This study underscores the role of endoscopic grading in risk stratification and prognosis. Findings contribute to CAM management knowledge, underscoring tailored interventions' significance. [ABSTRACT FROM AUTHOR]

While many studies have documented adverse impact of multiple chronic conditions or multimorbidity on COVID-19 outcomes in patients, there is scarcity of report on how physicians managed these patients. We investigated the experiences and challenges of clinicians in managing patients with multimorbidity throughout the COVID-19 pandemic in Odisha state, India. To understand the factors influencing illness management and the adaptive responses of physicians alongside the evolving pandemic, we followed a longitudinal qualitative study design. Twenty-three physicians comprising general practitioners, specialists, and intensivists, were telephonically interviewed in-depth. Saldana's longitudinal qualitative data analysis method was employed for data analysis. COVID-19 pandemic initially diverted the attention of health systems, resulting in reduced care. With time, the physicians overcame fear, anxiety, and feelings of vulnerability to COVID-19 and started prioritising patients with multimorbidity for treatment and vaccination. All physicians recommended teleconsultation and digital health records to benefit chronic illness care during future public health crises. The findings underscore the transformative potential of physician resilience and adaptation during the COVID-19 pandemic, emphasizing the importance of prioritizing patients with multimorbidity, incorporating teleconsultation, and implementing digital health records in healthcare systems to enhance chronic illness care and preparedness for future public health crises. [ABSTRACT FROM AUTHOR]

Infectious diseases are highly contagious due to rapid transmission and very challenging to diagnose in the early stage. Artificial Intelligence and Machine Learning now become a strategic weapon in assisting infectious disease prevention, rapid-response in diagnosis, surveillance, and management. In this paper, a bifold COVID_SCREENET architecture is introduced for providing COVID-19 screening solutions using Chest Radiography (CR) images. Transfer learning using nine pre-trained ImageNet models to extract the features of Normal, Pneumonia, and COVID-19 images is adapted in the first fold and classified using baseline Convolutional Neural Network (CNN). A Modified Stacked Ensemble Learning (MSEL) is proposed in the second fold by stacking the top five pre-trained models, and then the predictions resulted. Experimentation is carried out in two folds: In first fold, open-source samples are considered and in second fold 2216 real-time samples collected from Tamilnadu Government Hospitals, India, and the screening results for COVID data is 100% accurate in both the cases. The proposed approach is also validated and blind reviewed with the help of two radiologists at Thanjavur Medical College & Hospitals by collecting 2216 chest X-ray images between the month of April and May. Based on the reports, the measures are calculated for COVID_SCREENET and it showed 100% accuracy in performing multi-class classification. [ABSTRACT FROM AUTHOR]