ABSTRACT
According to Chinese health officials, almost 250 million people in China may have caught Covid-19 in the first 20 days of December. Due to the Covid-19 pandemic and its global spread, there is a significant impact on our health system and economy, causing many deaths and slowing down worldwide economic progress. The recent pandemic continues to challenge the health systems worldwide, including a life that realizes a massive increase in various medical resource demands and leads to a critical shortage of medical equipment. Therefore, physical and virtual analysis of day-to-day death, recovery cases, and new cases by accurately providing the training data are needed to predict threats before they are outspread. Machine learning algorithms in a real-life situation help the existing cases and predict the future instances of Covid-19. Providing accurate training data to the learning algorithm and mapping between the input and output class labels minimizes the prediction error. Polynomials are usually used in statistical analysis. Furthermore, using this statistical information, the prediction of upcoming cases is more straightforward using those same algorithms. These prediction models combine many features to predict the risk of infection being developed. With the help of prediction models, many areas can be strengthened beforehand to cut down risks and maintain the health of the citizens. Many predictions before the second wave of Covid-19 were realized to be accurate, and if we had worked on it, we would have decreased the fatality rate in India. In particular, nine standard forecasting models, such as linear regression (LR), polynomial regression (PR), support vector machine (SVM), Holt's linear, Holt-Winters, autoregressive (AR), moving average (MA), seasonal autoregressive integrated moving average (SARIMA), and autoregressive combined moving average (ARIMA), are used to forecast the alarming factors of Covid-19. The models make three predictions: the number of new cases, deaths, and recoveries over the next 10 days. To identify the principal features of the dataset, we first grouped different types of cases as per the date and plotted the distribution of active and closed cases. We calculated various valuable stats like mortality and recovery rates, growth factor, and doubling rate. Our results show that the ARIMA model gives the best possible outcomes on the dataset we used with the most minor root mean squared error of 23.24, followed by the SARIMA model, which offers somewhat close results to the AR model. It provides a root mean square error (RMSE) of 25.37. Holt's linear model does not have any considerable difference with a root mean square error of 27.36. Holt's linear model has a value very close to the moving average (MA) model, which results in the root mean square of 27.43. This research, like others, is also not free from any shortcomings. We used the 2019 datasets, which missed some features due to which models like Facebook Prophet did not predict results up to the mark;so we excluded those results in our outcomes. Also, the python package for the Prophet is a little non-functional to work on massive Covid-19 datasets appropriately. The period is better, where there is a need for more robust features in the datasets to support our framework. © 2023 River Publishers.
ABSTRACT
Coronavirus disease 2019 (COVID-19), a pandemic that is triggered by a novel coronavirus, named severe acute respiratory syndrome coronavirus 2 (SARSCoV-2) or 2019-nCoV, causes primarily respiratory discomfort along with other mild symptoms/no symptoms, leading to severe illness and death, if proper care is not taken. At present, COVID-19 is the resilient reason for a large number of human casualties worldwide as well as a cause of crucial economic loss posturing global threat. There is a necessity of intensive research to elucidate the pathogenic mechanisms of COVID-19, which would assist in understating the susceptibility towards the infection as well as prompt development of effective prevention and treatment strategies. Over the years, clinical studies have indicated the risk of various pathogenic infections prejudiced due to preexisting chronic diseases as well as ABO blood group types to a larger extent. In line of this, current COVID-19 infection-associated clinical studies intensely endorse the relationship of blood group type of individual and risk of COVID-19 infection. In this chapter, various clinical studies from January 2020 to June 2020 have been summarized to highlight the eminence of ABO blood group and COVID-19 infection susceptibility in human population. These reports evidently support the fact that individuals with A histo blood group were found to be more vulnerable to COVID-19 infection whereas individuals with blood group O were less likely to get infected with virus. To get deeper insight in this fact, many more studies are desirable in order to further explicate the promising protective role of the blood group O and it will be supportive for designing and planning several additional countermeasures against COVID-19 infection. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2021.
ABSTRACT
The hard lesson learnt from the COVID-19 pandemic is lack of understanding the risk and preparedness for response which resulted in millions of losses of lives and unprecedented cascading effects. This chapter analyzes how frontier technologies are supporting the key stakeholders to manage the COVID-19 crises—protect lives, livelihoods, and enhance the quality of risk governance in Asia and the Pacific. This chapter addresses five key lessons emerging from the COVID-19 response: (1) making risk assessment more dynamics, (2) empowering at risk communities, (3) managing a global risk with local action, (4) managing uncertainties, and (5) bridging the gaps in knowledge and understanding in systemic risks. This chapter also outlines three key enablers—frontier technologies, data science, and national innovation systems that help to prepare for the future crises. The nature and scale of risk has changed. In our increasingly complex inter-connected world, managing risk forms the key to preparing for the future. Smart preparedness is the way forward. Artificial Intelligence (AI), Big Data, Machine Learning, 5G technologies, drones, automated vehicles, robotics, etc., were used to track, monitor, warn, and support logistics as well as its rapid diagnostic and telemedicine. A wide-range of risk analytics such as impact forecasting and risk informed early warning, indexing and creating risk matrix to target at risk communities which have been developed and put to use in response to the COVID-19 pandemic and its intersection with extreme climate events. Digital solutions can help enhanced preparedness to protect at risk communities but also strengthen their resilience. © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer 2022.
ABSTRACT
The Corona Virus Disease of 2019 is characterized by a serious epidemic (COVID-19). The acute respiratory syndrome is caused by the coronavirus, which is followed by an inflammatory response in the host. Systemic inflammatory response syndrome (SIRS) is a condition in which the body causes acute breathing problems, multiple organ impairment disorder, and even in the early stages of multiple organ failure extreme COVID-19. Increased development of anti-inflammatory cytokines in the late stages of serious disease causes the immune system's reaction to becoming controlled, resulting in immune fatigue. Pandemics have wreaked havoc on humanity's strata, wiped out whole nations, and strengthening immunity is long overdue. A strong immune system is needed to fight a viral infection. Multivitamin-rich diets improve pathogen immunity by triggering immune responses in several immune cells, as an example. Various immune-stimulating herbs, plants, and spices like chicory, Tinospora cordifolia, Withania somnifera, myrrh, ginger, etc., must be included to counteract the pathogens. © 2022 by the authors.
ABSTRACT
Immunity plays a fundamental role in the maintenance and protection of the human body from infectious and pathogenic microorganisms. It requires regular intake of nutrients for proper functioning of the immune system. Due to an unbalanced lifestyle and consumption of ready-to-eat foods, immunity is being affected negatively. Inflammation and immunity are influenced by diet and nutrition. Simple sugars, trans fats, refined carbs, and processed meat, among other meals, may induce inflammation while simultaneously counteracting the anti-inflammatory benefits of omega-3 fatty acids. As a result, unhealthy food intake may enhance systemic inflammation in individuals, boosting the generation of IL-6. Dietary nutrition is a well-known aspect of immune system maintenance, with the significance of micronutrients prominently featured in a variety of scientific literary works. Currently, global population is susceptible viral infection such as COVID-19. This viral strain is directly attacking the immunity of the individual and bringing it at risk. When a patient's immune system isn't operating correctly, COVID-19 is thought to raise the harshness of the infection or make it more vulnerable to contagious diseases. This review paper will help in understanding the immune responses mechanism along with diet balance and maintaining the sufficiency of vitamins and minerals to fight against COVID-19 infection.Copyright © 2023 The Author(s)
ABSTRACT
The coronavirus disease (COVID-19) was first detected in Wuhan, China, in the month of December 2019. Further, in March 2020, the COVID-19 epidemic was described by the World Health Organisation (WHO) as a global pandemic. COVID-19 quickly spread around the world in the following months, affecting about 2.5 million individuals by April 2020. World markets, including the pharmaceutical industry, were devastated by this pandemic. Although no specific solution for this emerging infectious disease is currently available, the pharmaceutical industry is helping policymakers meet unmet COVID-19 desires, ranging from research and advancement initiatives on possible prevention methods to the management of the supply chain of drugs in times of crisis. Changes in demand, commodity shortages, contact adjustments, etc., are hindering developments in the mechanism of technology, research and development and are putting an impact on the health market of COVID-19. Other implications of COVID-19 on the physical condition and pharmaceutical market may include acceptance delays, heading to self-sufficiency in the delivery chain, etc. In addition, the pharmaceutical markets are battling to sustain natural consumer flows, as the latest pandemic has had an effect on access to essential drugs at reasonable rates, which is the key priori-ty of all pharmaceutical systems.Copyright © 2022 Bentham Science Publishers.
ABSTRACT
Background: Recently emerged COVID-19 pandemic has caused a large number of deaths with lacs of confirmed cases worldwide posturing a grim situation and severe threat to public health. There is an imperative necessity of analyzing emerging clinical and laboratory data of COVID-19 pa-tients, which may contribute to elucidate the pathogenic mechanism and development of effective prevention and treatment countermeasures. Method(s): Under this article, the emerging role of High-Density Lipoprotein (HDL) was analyzed by collecting recently published articles related to this field having clinical data of COVID-19 patients. Result(s): Based on the recently published reports of laboratory-confirmed COVID-19 infected hospitalized patients it was consistently observed that levels of HDL were low at the time of admission to hospi-tal and remained relatively low during the disease course i.e., treatment, recovery, and discharge stage. It was also reported critically that levels of HDL in the patients, those did not survive, decreased continu-ously until death. Conclusion(s): These clinical reports of patients have risen the concern about probable infection and worsen the clinical outcome of a healthy person having a compromised level of HDL for COVID-19 infection. Eventually, these findings stated that there is a strong association of low HDL levels with a higher risk of COVID-19 infection and further severity of the illness. Proper attention is needed to understand the significance of altered quantity and quality of HDL in COVID-19 patients compared to healthy controls, so that appropriate therapies could be given at the right time to combat severity and mortality due to this infection.Copyright © 2021 Bentham Science Publishers.
ABSTRACT
In the wake of the COVID-19 pandemic and the subsequent nationwide lockdown, India faces significant policy challenges, both humanitarian as well as economic. Vast numbers of its population of 1.3 billion people are self-employed informal sector workers and daily wage earners who lack access to social security. Many of these workers are facing job and income losses, and food shortages, and require direct support in terms of cash and food. It is also becoming increasingly apparent that significant mental health concerns have arisen in the face of the pandemic and the lockdown, both due to the economic uncertainty as well as the social distancing measures, which have impacted community connectedness. In this paper, we report the short-term impacts of the COVID-19 pandemic on employment and mental health outcomes as well as dietary habits among a subset of India's economically vulnerable population in crowded urban settings. The data comes from over 96 households with respondents aged 18-45 residing across Lucknow and various parts of Uttar Pradesh. This crisis could act as a turning point where more inclusive, gender sensitive policies can be formulated while steps are taken to redress the economy. © 2023 DMICS.
ABSTRACT
Pollution is one of the leading risk factors for the deterioration of the environment, mankind's poor health, and endangerment of the plant kingdom. The exploration of water pollution levels through a new remote sensing model "Water Pollution Index” makes this study unique, which is derived from the weighted overlay technique using land surface temperature, Chlorophyll Index, NCAI, and backscattering values from Sentinel 1, Sentinel 2, and Landsat 8 data sets. This chapter is concerned with the qualitative study of water pollution of the Yamuna river stretch, Delhi. To substantiate the results, sources are taken from different published papers and ground surveys. The objective is to define the pollution level and its contributing factors, algae blooming, sewage debris, coronavirus disease 2019 (COVID-19) shutdown impact, and rain in different seasons for two consecutive years, 2019 and 2020. A noticeable difference is found in the annual result indicating less pollution in 2020 especially in premonsoon data compared to 2019. © 2023 Elsevier Inc. All rights reserved.
ABSTRACT
Elucidation of molecular markers related to the mounted immune response is crucial for understanding the disease pathogenesis. In this article, we present the mass-spectrometry-based metabolomic and proteomic data of blood plasma of COVID-19 patients collected at two-time points, which showed a transition from non-severe to severe conditions during these time points. Metabolites were extracted and subjected to mass spectrometric analysis using the Q-Exactive mass spectrometer. For proteomic analysis, depleted plasma samples were tryptic digested and subjected to mass spectrometry analysis. The expression of a few significant proteins was also validated by employing the targeted proteomic approach of multiple reaction monitoring (MRM). Integrative pathway analysis was performed with the significant proteins to obtain biological insights into disease severity. For discussion and more information on the dataset creation, please refer to the related full-length article (Suvarna et al., 2021). © 2022 The Author(s)
ABSTRACT
The Covid-19 pandemic has caused severe economic depression and has disrupted the supply chains of various industries. The automobile industry which contributes significantly to the Indian economy was gravely hit due to the lockdowns, semiconductor shortage and the uncertainty associated with the pandemic. This research paper analyses the effect of Covid-19 on the automobile sales in India using the time series modelling approach. The data recorded by SIAM from 2012 to 2019 was used to develop the Autoregressive Integrated Moving Average (ARIMA) model following the Box-Jenkins methodology. ARIMA model (2, 1, 3) was chosen as it had the lowest AIC and BIC criteria. This model was used to forecast the sales from 2020 to 2021 to give a picture of the expected automobile sales had the pandemic not occurred. The forecasted data from the model developed has then been compared with actual automobile sales data during the pandemic to gauge the level of impact Covid-19 had on the Indian automobile industry. The paper also explores the associated challenges that the automobile industry had to face due to the pandemic. © 2022 The authors and IOS Press.
ABSTRACT
"COVID-19 and Omics Technologies” is a comprehensive, integrative assessment of recent information and knowledge collected on SARS-CoV-2 and COVID-19 during the pandemic based on omics technologies. It demonstrates how omics technologies could better investigate the infectious disease and propose solutions to the current concerns. The value of multi-omics technologies in understanding disease etiology and host response, discovering infection biomarkers and illness prediction, identifying vaccine candidates, discovering therapeutic targets, and tracing pathogen evolution is discussed in this book. These factors combine to make it a valuable resource to enhance understanding of both "Omics technology” and "COVID-19" as a disease. The book covers the most recent understanding of COVID-19 and the applications of cutting-edge studies, making it accessible to a large multidisciplinary readership. The book explains how high-throughput technologies and systems biology might assist to solve the pandemic's challenges and deconstruct and appreciate the substantial contributions that omics technologies have made in predicting the path of this unforeseeable pandemic. Features: In-depth summary of clinical presentation, epidemiological impact, and long-term sequelae of COVID-19 pandemic. A systematic overview of omics-based approaches to the study of COVID-19 biology. Recent research results and some pointers to future advancements in methodologies used. Detailed examples from recent studies on COVID-19 encompassing different omics methodologies. A detailed description of methodologies and notes on the applications of state-of-the-art technologies. This book is intended for scientists who need to understand the biology of COVID-19 from the perspective of omics investigations, as well as researchers who want to employ omics-based technologies in disease biology. © 2022 selection and editorial matter, Sanjeeva Srivastava;individual chapters, the contributors.
ABSTRACT
The pandemic compelled more exposure to online media in different forms like online education, interactions, gaming, and collaboration, which aggravated the cyberbullying issue. Cyberbullying can now occur in several different mediums due to the renewed lifestyle challenges spawned by the pandemic. Hence, it is imperative to assess the antecedents of cyberbullying behaviour (CBB). General Strain Theory (GST) is taken as a grounded theory to understand the underlying mechanisms of strain and anger and their impact on deviant outcomes like CBB. The current study adds to the GST literature by investigating the association between stress and anger, leading to cyberbullying behaviour. The study also examines the extent to which parenting factors (monitoring, communication, and trust) moderate adolescents' involvement in cyberbullying. An online survey was used to collect data from 221 high school Indian students for this purpose. As per the results, there is a direct relationship between strain, anger, and cyberbullying. The study confirms an indirect relationship between strain and cyberbullying through anger. The findings suggest that parental influences are important in moderating the relationship between strain and anger in adolescent cyberbullying behaviour. The study recommends strategies for parents, educators, and healthcare providers when dealing with cyberbullying behaviour.
ABSTRACT
Introduction: Telemedicine acted as one of the biggest medium in treating Coronavirus Disease-2019 (COVID-19) patients during the second wave of the still ongoing pandemic. Although the symptoms were taken care of and treated through teleconsultation, the loneliness and social support system of these patients went largely unrecognised. The morbidity pattern, effect of self-isolation and quarantine, uncertainties in social support were major contributors to loneliness among patients suffering from COVID-19. Aim: To estimate the proportion of loneliness and level of social support experienced by COVID-19 patients seeking advice from a telemedicine centre of Kolkata and to find out their socio-clinical profile and the associated relationship. Materials and Methods: An observational study with cross-sectional design was conducted on 403 COVID-19 patients who had taken advice from the telemedicine centre of Institute of Post Graduate Medical Education and Research (IPGME and R), Kolkata for a period of 12 weeks (May-July 2021). Loneliness was assessed by the 11-item De Jong Gierveld Loneliness scale, whereas social support was assessed using 12-item Multidimensional Scale of Perceived Social Support scale through telephonic interview. Data were tabulated in the Microsoft Office Excel 2019 (Microsoft Corp, Redmond, WA, USA) and the analysis was performed using Statistical Package for the Social Sciences (IBM, New York City, USA) version 25.0. Results: Out of 403, more than half of the study population, 194 (48.2%) belonged to 18-35 years of age. Of the total, 235 (58.3%) were males, 319 (79.2%) were currently married and 300 (74.4%) were Hindus. About 142 (35.2%) respondents had experienced severe loneliness, while 297 (73.7%) had experienced high social support. There was a significant negative correlation found between loneliness and social support (r=-0.495, p-value <0.01). It was found that being male, belonging to nuclear family, education upto higher secondary level, being addicted, loneliness due to physical distancing, and those who had socialised frequently had higher odds of loneliness, whereas unemployed, unskilled, semiskilled and skilled occupation, having one chronic disease had lower odds of social support. Conclusion: About 338 (84%) patients had experienced loneliness which was strikingly high. This shows a deeper aspect into the actual picture of how COVID-19 impacts mental health of those who are affected. Future interventions are needed to address loneliness and develop social support system along with addressing healthcare needs of COVID-19 patients.
ABSTRACT
The new technological developments in the oil and gas industry have always pushed the operators to work into new frontiers such as heavy oil, HTHP, deep waters, and unconventional reservoirs. The oversupply accounted by shale extraction has led to shifts in markets and reduced oil prices, followed by slow demand growth due to the COVID-19 pandemic and an increase in acceptance of alternate energy resources, mandating the companies to adopt digital solutions to facilitate cost reduction and improving the operational efficiency in terms of increased production and reduced time. Digital oil field (DOF) is capable of transforming the future of the oil industry by creating opportunities to connect diverse operations, breaking silos, and achieving real value for the investments. The faster availability of cheaper data infrastructure is bringing Artificial Intelligence-driven technology and the latest data automation to the DOF. This chapter focuses on the DOF, its components, along the challenges encountered in widely accepting the technology. The chapter also addresses the emerging technologies which are being merged with DOF for better decision-making and optimization. © 2023 selection and editorial matter, Anirbid Sircar, Gautami Tripathi, Namrata Bist, Kashish Ara Shakil and Mithileysh Sathiyanarayanan;individual chapters, the contributors.
ABSTRACT
The novel coronavirus disease 2019 (COVID-19) caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) began in Wuhan, China, in December 2019 and quickly spread across the worldwide. It becomes a global pandemic and risk to the healthcare system of almost every nation around the world. In this study thirty natural compounds of 19 Indian herbal plants were used to analyze their binding with eight proteins associated with CO VID-19. Based on the molecular docking as well as ADMET analysis, isovitexin, glycyrrhizin, sitosterol, and piperine were identified as potential herbal medicine candidates. On comparing the binding affinity with Ivermectin, we have found that the inhibition potentials of the Trigonella foenum-graecum (fenugreek), Glycyrrhiza glabra (licorice), Tinospora cordifolia (giloy) and Piper nigrum (black pepper) are very promising with no side-effects.
ABSTRACT
The COVID-19 emerged as a pandemic and affected many nations. World Health Organization [WHO] declared it as a worldwide pandemic alert on March 11, 2020, people had to stay indoors with lockdowns imposed, turning all daily activities to a halt. The lockdown was lifted in phase by manner from June 2020 as the cases were in control. A rise in pandemic was observed in many countries again in 2021, it was termed as the second wave of COVID-19. In India daily cases reached the mark of 4 lakhs in April 2021. This resulted in increased demand for oxygen supplies and other medical equipment’s to tackle the situation. With this, social media or the microblogging platforms like Twitter became a popular means of expressing emotions, making request for help and a daily information channel. The present study analyses the Twitter data extracted using Twitter API. It analyses and classifies people's sentiments related to the supply of oxygen during the second wave of the pandemic in India. The paper analyses the sentiment of the tweets for Indian users from June 20th, 2021, to June 26th, 2021, using Natural language processing (NLP) and Machine Learning (ML) techniques. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.