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1.
Machine Learning for Healthcare Systems: Foundations and Applications ; : 109-129, 2023.
Article in English | Scopus | ID: covidwho-20241481

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.

2.
Millennial Asia ; 14(1):54-84, 2023.
Article in English | Scopus | ID: covidwho-2243369

ABSTRACT

In India, the coronavirus (COVID-19) pandemic-induced country-wide regulatory lockdown and consequential supply-chain disruptions and market instability have all posed serious challenges before the regulators and policymakers. Amid the pandemic, the stock market showed return volatilities primarily due to the unexpected investors' behaviour. One of the behavioural biases is herding, which has the power to wreck the market equilibrium and shatter the market efficiency. Given that the pandemic has generated unprecedented spirals of uncertainties across the globe, thereby creating interruptions in the pattern of stock market investment decisions, this study examined the herding behaviour of 54 stocks of banking and financial services sectors listed in the national stock exchange. In the quantile regression framework, the study provides evidence of the presence of herding for public sector banking and financial services under the bull market conditions during the pandemic in the 90th quantile of the return distribution. This finding has implications for the mispricing of financial assets in these sectors. So, the study suggests removing information asymmetry among the market participants and devising policy initiatives for ensuring market stability. © 2023 Association of Asia Scholars.

3.
Folia Geographica ; 64(1):69-89, 2022.
Article in English | Web of Science | ID: covidwho-1980873

ABSTRACT

In this review article, we intend to initiate a discussion on the possibilities of implementing the 15-minute city concept (FMC) in Slovak cities. Our research motivation is the relatively high potential of the idea to contribute to solving current problems of sustainable urban development to strengthen cities' resilience. It is not only about coping with the impacts of the COVID 19 pandemic but also about the need for synergy of mitigation and adaptation measures in the context of climate change and the transition to a low- to the zero-carbon development paradigm. Last but not least, it can be pointed out that the 15-minute city model will also contribute to reducing inequalities between different parts of cities, which is one of the consequences of poorly regulated suburbanization processes. The paper is structured in several parts. In the introductory section, we look for common features of the concept and its theoretical framework within various traditions and paradigms of geographical thought. We then analyse its basic functions and dimensions that are considered when planning this concept in the current conditions of urban life. We also address specific applications in world metropolises while pointing out that the concept is not rigid and can still be adapted to local natural, historical, socio-economic conditions and intraurban structures. In the last section, we present the first examples of implementing the 15-minute city ideas in Slovak cities.

4.
Journal of Environmental Management & Tourism ; 13(4):1089-1099, 2022.
Article in English | ProQuest Central | ID: covidwho-1934682

ABSTRACT

In the BRICS region, international tourism is considered a significant contributor to employment, forex earnings, and gross domestic product. In this context, this study examined the impact of tourism on the growth of BRICS economies by employing PMG based ARDL panel data analysis technique over an augmented neo-classical growth model during a period from 1995 to 2019. The results support a positive impact of international tourism on the growth of BRICS nations when their levels of human development are controlled in the long run. So, this study adds another feather to the extant empirical evidence of the tourism-led growth hypothesis in the BRICS region. Therefore, the policies of tourism sector development/expansion can supplement in achieving an elevated real economic growth in BRICS economies.

5.
International Journal of Global Environmental Issues ; 21(1):59-81, 2022.
Article in English | ProQuest Central | ID: covidwho-1855050

ABSTRACT

This study examined the stock markets' responses to the unprecedented outbreak of the COVID-19 pandemic in SAARC countries. The results support these countries' surge in stock market return volatilities amid the rapid spread of the COVID-19 infection caused by investors' pessimistic sentiments. The intensive media coverage of information related to the pandemic has weakened investors' sentiments and caused sudden market plunges in the SAARC region. During the pandemic, the performances of the stock markets in SAARC countries are found to be influenced by the number of COVID-19 confirmed and death cases, and movements in the fear index. The implication is that the stock markets of the SAARC region do not qualify to be semi-strong information efficient. This implication is important for investors.

6.
Journal of Research in Medical and Dental Science ; 10(1):162-168, 2022.
Article in English | Web of Science | ID: covidwho-1798306

ABSTRACT

Background: The Coronavirus disease 2019 (Covid-19) is caused by the virus SARS COV-2 and is declared as a global pandemic by WHO. It is known that in SARS COV-2 virus infection causes haematological changes and often present the potential to optimize the monitoring of infectious process or to indicate the suspicion of their severity. The present study was conducted to study the routine haematological parameters including NLR (Neutrophil lymphocyte Ratio) in Covid-19 patients and to assess their utility in identifying severity of the disease. Methods: This retrospective study was conducted on 257 Covid-19 RT PCR positive cases. The cases were divided into mild, moderate, and severe category as per MoHFW. Haematological parameters were measured by Fully Automated Hematology cell counter using blood sample collected in EDTA vacutainers. MS Excel was used for data analysis and ANOVA tests were applied to test statistical significance. P<0.05 was considered statistically significant. Results: Degree of severity of Covid-19 cases could be correlated with older age group. Most important haematological parameters noted in adults are Eosinopenia in 84%, Monocytopenia in 64%, NLR >3 in 18.32 %, and Leucopoenia (17.5%). Severe category showed higher proportion of NLR >3 in 40.6%, Neutrophilia in 31.2%, Leucocytosis in 28.2% and lymphocytopenia in 12.5%. Conclusions: Haematological parameters in Covid-19 positive cases could help to predict a patient risk and outcome in the Indian scenario that will provide guidance to subsequent clinical practice.

7.
1st International Conference on Advanced Network Technologies and Intelligent Computing, ANTIC 2021 ; 1534 CCIS:453-472, 2022.
Article in English | Scopus | ID: covidwho-1750539

ABSTRACT

Due to the exponential rise of COVID-19 worldwide, it is important that the artificial intelligence community address to analyze CXR images for early classification of COVID-19 patients. Unfortunately, it is very difficult to collect data in such epidemic situations, which is essential for better training of deep convolutional neural networks. To address the limited dataset challenge, the author makes use of a deep transfer learning approach. The presence of limited number of COVID-19 samples may lead to biased learning due to class imbalance. To resolve class imbalance, we propose a new class weighted loss function that reduces biasness and improves COVID-19 sensitivity. Classification and preprocessing are two concrete components of this study. For classification, we compare five pre-trained deep neural networks architectures i.e. DenseNet169, InceptionResNetV2, MobileNet, Vgg19 and NASNetMobile as a baseline to achieve transfer learning. This study is conducted using two fused datasets where samples are collected from four heterogeneous data resources. Based on number of classes we make four different classification scenarios to compare five baseline architectures in two stages. These scenarios are COVID-19 vs non- COVID-19, COVID-19 vs Pneumonia vs Normal, COVID-19 pneumonia vs Viral vs Bacterial pneumonia vs Normal and COVID19 vs Normal vs Virus + Bacterial pneumonia. The primary goal of this study is to improve COVID-19 sensitivity. Experimental outcomes show that DenseNet169 achieves the highest accuracy and sensitivity for COVID-19 detection with score of 95.04% and 100% for 4-class classification and 99.17% and 100% for 3 class-classification. © 2022, Springer Nature Switzerland AG.

8.
Global Journal of Emerging Market Economies ; : 09749101211070960, 2022.
Article in English | Sage | ID: covidwho-1649725

ABSTRACT

This article examined the impact of the unanticipated outbreak of global public health crisis, COVID-19 pandemic, on the equity market performances and on the degree of integration of these markets in BRICS bloc. The empirical analyses lend support to the weakened equity market integration in the BRICS economies amid the pandemic, and the key driving forces include the rate of inflation, the real rate of interest, real exchange rate and composite leading indicator in the long-run, and trade performance and composite leading indicator in the short-run. The implications on the one hand, indicate increased opportunities for international portfolio diversification, and on the other hand, suggest for controlling the macroeconomic uncertainties of inflation, interest rate and exchange rate fluctuations during global health crisis to promote stable economic conditions for ensuring equity market integration in the long-run.

10.
Millennial Asia ; : 09763996211032356, 2021.
Article in English | Sage | ID: covidwho-1325273

ABSTRACT

In India, the coronavirus (COVID-19) pandemic-induced country-wide regulatory lockdown and consequential supply-chain disruptions and market instability have all posed serious challenges before the regulators and policymakers. Amid the pandemic, the stock market showed return volatilities primarily due to the unexpected investors? behaviour. One of the behavioural biases is herding, which has the power to wreck the market equilibrium and shatter the market efficiency. Given that the pandemic has generated unprecedented spirals of uncertainties across the globe, thereby creating interruptions in the pattern of stock market investment decisions, this study examined the herding behaviour of 54 stocks of banking and financial services sectors listed in the national stock exchange. In the quantile regression framework, the study provides evidence of the presence of herding for public sector banking and financial services under the bull market conditions during the pandemic in the 90th quantile of the return distribution. This finding has implications for the mispricing of financial assets in these sectors. So, the study suggests removing information asymmetry among the market participants and devising policy initiatives for ensuring market stability.

11.
Transnational Corporations Review ; : 17, 2021.
Article in English | Web of Science | ID: covidwho-1254244

ABSTRACT

Since 2020, the world has been passing through a difficult time due to the outbreak of COVID-19 Pandemic. This novel public health emergency has created both demand- and supply-side shocks affecting both real and financial sectors of economies globally. One of the noteworthy immediate consequences of it was sudden nosedive of stock markets across countries in the globe. In this pre-text, this study examined the stock market behaviour in 15 selected Asian markets amid the pandemic. The results infer about the surge in market return volatilities amid the rapid spread of the coronavirus which was primarily triggered through the impaired investors' sentiments due to the announcement effects. During this period, the stock market performances in selected Asian countries have been observed to be influenced by the reporting of the number of COVID-19 confirmed cases and death cases, stock index returns, market volatility, oil prices, inflation rate, and interest rates.

12.
Annals of the Romanian Society for Cell Biology ; 25(1):6002-6013, 2021.
Article in English | Scopus | ID: covidwho-1130211

ABSTRACT

Background: COVD 19 has ended up becoming one of the biggest challenges that the world has had to face, universally. To tackle the potential challenges that COVID-19 may bring forward to any healthcare institution, hospitals are to structure their operations strategically, operationally and monetarily so as to somehow survive and arise on top, especially in these trying times. Aims & Objectives: This systemic review aims to address the changes that Hospitals need to imbibe for smooth working under the stressful times. Methods: A systemic review was conducted in the field of COVID-19 and changes that this pandemic has brought in Indian Hospitals. The target was specifically on OPD and Emergency Department. 70 articles were referred and only twenty-four were thoroughly studied to focus on OPD and Emergency Department of Hospital. Result: Hospitals in India were caught off guard in the wake of confusion that erupted due to the emergence of this global pandemic, however, as time went by, hospitals started embracing and adopting the process of reengineering. These changes ranged from variation in lounge areas, OPD, manpower and so on. Almost every aspect of the hospital has been altered by new operational changes, rules and regulations put forward by Government of India. Conclusion: With proactive patient administration being the definitive goal, The Indian Hospitals have to re-engineer themselves to sustain all challenges like this Pandemic. Hospitals should reengineer themselves operationally, strategically with considering overall approach of patient safety and satisfaction. © 2021, Universitatea de Vest Vasile Goldis din Arad. All rights reserved.

13.
Millennial Asia ; 2020.
Article in English | Scopus | ID: covidwho-920980

ABSTRACT

On the onset of the year 2020, the unprecedented outbreak of novel coronavirus, initially as a human health epidemic and later as a global pandemic, has wobbled the economies of affected countries across the globe. The consequential unexpected occurrences of supply- and demand-side shocks forced the economies to trim down their growth prospects. The interplay of these shocks has generated spirals of downturns in all major economic sectors, including the financial sector in affected countries. Specifically, the stock markets immediately nosedived, following the outbreak of the global spread of coronavirus disease 2019 (COVID-19). Thus, we examine the behaviour of the selected Asian stock markets amid the huge uncertainties of the corona pandemic and find the occurrences of volatility clustering in these markets. Such volatility clustering primarily occurred, owing to the pessimistic and panic sentiments of investors, and the increase in the number of COVID-19 confirmed cases, changes in oil prices, and exchange rates were found to be significant in channelizing the fears and uncertainties of coronavirus pandemic to cause unexpected nosedives in Asian stock markets. © 2020 Association of Asia Scholars.

14.
Sci Total Environ ; 754: 142363, 2021 Feb 01.
Article in English | MEDLINE | ID: covidwho-765609

ABSTRACT

We are currently facing the COVID-19 pandemic which is the consequence of severe acute respiratory syndrome coronavirus (SARS-CoV-2). Since no specific vaccines or drugs have been developed till date for the treatment of SARS-CoV-2 infection, early diagnosis is essential to further combat this pandemic. In this context, the reliable, rapid, and low-cost technique for SARS-CoV-2 diagnosis is the foremost priority. At present reverse transcription polymerase chain reaction (RT-PCR) is the reference technique presently being used for the detection of SARS-CoV-2 infection. However, in a number of cases, false results have been noticed in COVID-19 diagnosis. To develop advanced techniques, researchers are continuously working and in the series of constant efforts, nanomaterials-enabled biosensing approaches can be a hope to offer novel techniques that may perhaps meet the current demand of fast and early diagnosis of COVID-19 cases. This paper provides an overview of the COVID-19 pandemic and nanomaterials-enabled biosensing approaches that have been recently reported for the diagnosis of SARS-CoV-2. Though limited studies on the development of nanomaterials enabled biosensing techniques for the diagnosis of SARS-CoV-2 have been reported, this review summarizes nanomaterials mediated improved biosensing strategies and the possible mechanisms that may be responsible for the diagnosis of the COVID-19 disease. It is reviewed that nanomaterials e.g. gold nanostructures, lanthanide-doped polysterene nanoparticles (NPs), graphene and iron oxide NPs can be potentially used to develop advanced techniques offered by colorimetric, amperometric, impedimetric, fluorescence, and optomagnetic based biosensing of SARS-CoV-2. Finally, critical issues that are likely to accelerate the development of nanomaterials-enabled biosensing for SARS-CoV-2 infection have been discussed in detail. This review may serve as a guide for the development of advanced techniques for nanomaterials enabled biosensing to fulfill the present demand of low-cost, rapid and early diagnosis of COVID-19 infection.


Subject(s)
Biosensing Techniques , COVID-19 , Nanostructures , Humans , Pandemics
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