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1.
Finance India ; 36(3):1129-1146, 2022.
Article in English | Scopus | ID: covidwho-2073798

ABSTRACT

Bitcoin becoming more popular among investors as it provides high returns but due to unregulated property, it is highly volatile in nature. Amidst pandemic spread across world, anyone who hold bitcoin would have keenly watched market with alarming fluctuations recently. Investors are looking for assets that are not impacted by slowdown triggered by lockdown. The study aims to analyze volatility dynamics of Bitcoin from FY2015 to FY2020 by performing general GARCH analysis for modelling by extracting Daily price data from coinmarketcap.com. The study incorporates Augmented Dickey Fuller test for checking stationarity of the series, ARCH LM test for heteroskedasticity and Ljung Box test for determining the mean equation and estimating the variance equation with GARCH (1,1) model in EVIEWS. The results approve that GARCH model is better model works better in period of the high volatility. © Indian Institute of Finance.

2.
European psychiatry : the journal of the Association of European Psychiatrists ; 64(Suppl 1):S665-S665, 2021.
Article in English | EuropePMC | ID: covidwho-2046216

ABSTRACT

Introduction In India, Coronavirus pandemic started in the month of march 2020 and is growing day by day. In view of India being one of the most populous countries, it is hard to follow social distancing and abide by the lockdown rules. Therefore, as of December 2020, total number of covid-19 cases has crossed the 10 million. But the recovery rate in India is high, so the fear due to Covid-19 has decreased in intensity. Objectives To assess level of perceived stress in isolated covid-19 patients To assess level of hopelessness in isolated covid-19 patients Methods 30 Patients of diagnosed Covid-19 positive,who were isolated in covid care setting in Uttar Pradesh(India),above 18yrs of age,of both sexes and willing to participate in the study were included, their socio-demographic data collected. Beck’s hopelessness scale and Perceived stress scale were administered. Infection severity upto moderate was selected and ICU patients were excluded. Results were analysed using SPSS software. Results It was observed that level of hopelessness increased with increasing age and increasing severity of covid-19.Level of perceived stress also increased with increasing age and increasing covid severity. There was no relation seen between hopelessness level and perceived stress level and no difference was seen in the levels of hopelessness and perceived stress between the two sexes. Conclusions Levels of hopelessness and stress increased with increasing age and increasing severity of covid-19.No relation seen between hopelessness level and perceived stress level and no difference was seen in the levels of hopelessness and perceived stress between the two sexes. Disclosure No significant relationships.

3.
Pediatrics ; 149, 2022.
Article in English | EMBASE | ID: covidwho-2003274

ABSTRACT

Background: Pre-COVID literature suggests that viral infections account for about 90% of cases of fever in infants ≤56 days of age. Given the reduction in non-SARS-CoV-2 viral infections observed during the COVID-19 pandemic, we sought to determine if SBI accounted for a higher than usual proportion of fever cases in this age group during this period. Methods: We performed a multi-center, retrospective chart review of infants age ≤56 days presenting with fever to emergency departments (EDs) of six community hospitals affiliated with the same academic children's hospital. We compared the incidence of SBIs, viral meningitis, and viral bronchiolitis during March 2020 - February 2021 (pandemic year) with the same calendar months in the two preceding years (pre-pandemic years). Results: From March 2018 to February 2021, 543 febrile infants presented to the EDs, 95 during the pandemic year (Mar 2020-Feb 2021) compared to 231 and 217 in the pre-pandemic years (Mar 2018- Feb 2019 and Mar 2019-Feb 2020, respectively). The incidence of SBI was 28.4% (27/95) during the pandemic year compared to 11.6% (27/231) and 6.9% (15/217) in the pre-pandemic years (p<0.001);bacteremia 13.7% (13/95) during the pandemic year compared to 2.2% (5/231) and 1.4% (3/217) in the pre-pandemic years;and UTI 19% (18/95) during the pandemic compared to 11.3% (26/231) and 6.5% (14/217) in the pre-pandemic years (TABLE 1). Five patients were diagnosed with bacterial meningitis over the three-year period, four of them during the pandemic year, a rate of 4.2% (4/95). Rate of positivity for viral CSF PCR during the pandemic year was 6.4% (3/47) compared to 20.8% (25/120) and 20.4% (23/113) in the pre-pandemic years (Mar 2018-Feb 2019 and Mar 2019-Feb 2020 respectively;p=0.070). 2.1% (2/95) febrile young infants were admitted with a co-morbid diagnosis of bronchiolitis during the pandemic year compared to 4.3% (10/231) and 6.0% (13/217) in the pre-pandemic years (Mar 2018-Feb 2019 and Mar 2019-Feb 2020 respectively;p=0.310). The risk ratio for SBI (FIGURE 1) for pre-pandemic year 1 (referent;Mar 2018-Feb 2019) compared to the pandemic year (Mar 2020-Feb 2021) was 2.43 (95% CI 1.51-3.92;Bonferroni adjusted p=0.001);and the risk ratio for pre-pandemic year 2 (referent;Mar 2019-Feb 2020) compared to the pandemic year was 4.11 (95%CI 2.29-7.37;Bonferroni adjusted p<0.001). Conclusion: The COVID-19 pandemic led to an increase in the proportion of SBIs among febrile infants ≤56 days of age. This is likely a result of reduction in non-SARS-CoV-2 viral infections. Greater vigilance is thus warranted in the evaluation of febrile infants during the COVID-19 pandemic. (Table Presented).

5.
Journal of Bronchology & Interventional Pulmonology ; 02:02, 2022.
Article in English | MEDLINE | ID: covidwho-1973314
6.
Asian Journal of Pharmaceutical and Clinical Research ; 15(6):116-118, 2022.
Article in English | EMBASE | ID: covidwho-1918276

ABSTRACT

Objective: The study's aim was to determine the neutrophil-to-lymphocyte ratio (NLR) is most helpful predictor factor for COVID-19-related serious illness. Methods: A total of 51 patients with COVID-19 infection with laboratory-confirmed reports were enrolled in this study: Age, neutrophil-to-lymphocyte (LYMLYM) ratio (NLR), an examination, and comparison. Data analysis, compilation, and report writing were completed based on the acquired data. Using SPSS.ver-23, standard statistical procedures were used to analyze the mean and standard deviation, as well as the Pearson correlation. If p<0.05, it is deemed significant. Results: The mean hemoglobin level was 12.44±3.55%, the mean platelet count was 1.95±0.65 cumm, the mean white blood cell count was 17400 ±6455.22 cumm, and the mean NLR was 5.72±1.24. When we looked at people who had hypertension, diabetes mellitus, and high cholesterol, we found that the NLR value was significantly higher in people with these diseases (p=0.05). Conclusion: We found that NLR is an excellent way to predict COVID-19-infected patients who are likely to get a lot of other illnesses and have a lot of problems early on.

7.
Indian Journal of Environmental Protection ; 42(5):573-580, 2022.
Article in English | Scopus | ID: covidwho-1904551

ABSTRACT

Governments across the world are making considerable efforts in confronting COVID-19, from nationwide lockdowns to hygiene measures and maintaining social distancing. But at the same time, role of aerosols or/and the high concentrations of fine particulate matter or/and AQI levels in infection transmission and increasing the prevalence, morbidity and mortality of pandemic has been largely unexplored specifically in India where pollution attains peak in October and November every year. In the present study, we collected data regarding air quality index and COVID-19 determinants of four Indian cities : Bangalore, Delhi, Mumbai and Shillong from 1 October 2020 to 16 November 2020. We performed an analysis of variance on the regression model to estimate and quantify the strength of relationship between COVID-19 determinants and air pollution index (AQI). Results show that AQI has a significant impact on both response variables, that is COVID-19 cases as well as mortality (p < 0.05 at 95% confidence level) in Delhi, Mumbai and Bangalore (p < 0.05) but in Shillong no impact of AQI on COVID-19 cases and AQI (p = 0.343), as well as deaths (p = 0.664), was observed. We conclude that it is both conceivable and reasonable to suspect the role of increased AQI levels in aggravating COVID-19 morbidity and mortality. Thus, we recommend that critical meteorological conditions, like haze/smog caused by factors, like stubble burning or firing crackers should be predicted and monitored more systematically as they may lead to deterioration of respiratory problems. As the whole world is striving to fight against the deadly pandemic, it is extremely imperative to focus not only on human health as a part of response but also on global planetary health. Short term measures that can minimize supplementary risks, like adverse weather situations including pollution, poor air quality should be considered more meticulously and judiciously so that new flares of COVID-19 morbidity and mortality can be restricted. © 2022 - Kalpana Corporation.

8.
Journal of Pacific Rim Psychology ; 16:18, 2022.
Article in English | Web of Science | ID: covidwho-1886894

ABSTRACT

While most studies have been reporting the psychological issues being faced by the public due to the global spread of coronavirus and sudden restrictions and changes accompanying it, the present study attempted to explore dynamic human experiences during the COVID-19 pandemic and resultant lockdown, so as to understand the psycho-social factors that acted as adaptive resources or as buffers to maintain a stable mental state amidst this crisis. In-depth telephonic interviews with 30 participants were conducted to explore their experiences in dealing with the COVID-19 pandemic and the lockdown. Thematic analysis performed to identify the positive and protective factors that helped people adapt in a healthy way revealed that although the initial response of the participants to the pandemic was "optimistic bias" followed by downplaying the seriousness of the issue, later they demonstrated increased realization and acceptance to the seriousness of the situation. Upon realizing the situation, their positive psychological resources acted as a buffer against the ill effects of the pandemic, and they used both cognitive and behavioral coping. The study clearly demonstrates that crisis in life is not just a source of stress, anxiety, and uncertainty but also an opportunity to test one's psychological resources to learn and grow.

9.
1st International Conference on Technologies for Smart Green Connected Society 2021, ICTSGS 2021 ; 107:16557-16571, 2022.
Article in English | Scopus | ID: covidwho-1874871

ABSTRACT

In 2015, a resolution known as Agenda 2030 was passed by United Nations General Assembly in which seventeen goals for Sustainable Development were laid down for global dignity, peace and prosperity. The post- pandemic era became full of uncertainties in pursuing those Sustainable Development Goals (SDGs) and its implementation became a challenge especially for the developing economies like India. The country is facing a tremendous gap in arranging for resources to meet the climatic changes and attaining the SDGs. India requires 170 billion dollars per year from 2015-2030 to fulfill the Sustainable Development Goals as per the estimation done by National Determined Contribution, a body setup after Paris agreement 2015 to monitor the efforts of the country towards reducing national emissions and adapting to climate change. There is a huge concern amongst the various agencies on exploring the ways to fill this financing gap especially after the economic slowdown seen in the post pandemic era. This research paper analyses the challenges imposed by the COVID 19 pandemic on financing for SDGs and also explores the options to mitigate them. The articles and research papers related to SDG financing are reviewed by the researchers to arrive at the above mentioned statements. This paper is an attempt to draw the attention of worldwide authorities towards this grim situation as sustainable finance is far from reality in India and requires immediate up scaling. © The Electrochemical Society

10.
16th IEEE International Conference on Automatic Face and Gesture Recognition (FG) ; 2021.
Article in English | Web of Science | ID: covidwho-1853423

ABSTRACT

In the COVID pandemic situation, crowd counting became one of the tools to monitor if the social-distancing norms are being followed or not. However, in designing crowd counting algorithm, there are several challenges such as background noise, camera-to-objects distance, occlusion, and variations due to illumination, scale, and viewpoint. In this research, we propose a novel pipeline for density estimation in crowd counting. The proposed pipeline makes use of an encoder-decoder-based architecture in which we explore the family of EfficientNets for the encoder architecture. For the decoder, we propose a deeper attention network to assist the model in a better distinction between foreground and background pixels. We empirically show that for a crowd counting dataset, the use of average pooling operation for any backbone architecture of encoder gives a significant improvement in performance. In terms of Mean Absolute Error, the proposed pipeline outperforms existing state-of-the-art techniques by a large margin on large-scale and small-scale counting datasets, UCF-QNRF and UCF_CC_50 dataset. We also achieve state-of-the-art results on the ShanghaiTech and Mall datasets. We additionally propose a crowd counting dataset captured using drones. We perform benchmark experiments on this dataset with existing and the proposed methods.The proposed dataset can be found at http://www.iab-rubric.org/resources/CrowdUAV.html.

11.
2022 International Conference for Advancement in Technology, ICONAT 2022 ; 2022.
Article in English | Scopus | ID: covidwho-1788722

ABSTRACT

With the increase in the cases of COVID-19, the necessity of improving testing and treatment is increasing rapidly. Many techniques are currently being used by the medical fraternity for detection of COVID-19 in a patient such as RT-PCR, Chest CT Scan Images, Chest X-Ray scans, etc. Among these techniques, a Chest CT scan has proven to be highly accurate for screening of the novel coronavirus. But a trained professional like a radiologist is needed to analyze the CT scan and determine whether the patient is positive or not. Due to the sudden spike in the number of infections, there is a shortage of such professionals. A machine learning based system can be highly effective in assisting the doctors if it can accurately predict COVID-19 from a chest CT scan. However, the number of chest CT scan images available are very less in order to build an accurate machine learning based predictive model. We present a generative model for data augmentation of COVID-19 positive and negative Chest CT images. We use Conditional DCGAN for generating nearly 1502 COVID-19 positive and 1510 negative images thus extending a publicly available dataset. We also build predictive models using pre-trained models like VGG and ResNet to detect COVID-19, achieving an accuracy upto 87.7%. We also apply the technique of knowledge distillation to build a lightweight and computationally cheap predictive model that has an accuracy of 86.2% and is nearly 11 times smaller than the best model available on the dataset. © 2022 IEEE.

12.
Proceedings of the Vldb Endowment ; 14(12):2655-2658, 2021.
Article in English | Web of Science | ID: covidwho-1744575

ABSTRACT

Kernel density visualization (KDV) is a commonly used visualization tool for many spatial analysis tasks, including disease outbreak detection, crime hotspot detection, and traffic accident hotspot detection. Although the most popular geographical information systems, e.g., QGIS, and ArcGIS, can also support this operation, these solutions are not scalable to generate a single KDV for datasets with million-scale data points, let alone to support exploratory operations (e.g., zoom in, zoom out, and panning operations) with KDV in near real-time (< 5 sec). In this demonstration, we develop a near real-time visualization system, called KDV-Explorer, that is built on top of our prior study on the efficient kernel density computation. Participants will be invited to conduct some kernel density analysis on three large-scale datasets (up to 1.3 million data points), including the traffic accident dataset, crime dataset and COVID-19 dataset. We will also compare the performance of our solution and the solutions in QGIS and ArcGIS.

13.
16th IEEE International Conference on Automatic Face and Gesture Recognition, FG 2021 ; 2021.
Article in English | Scopus | ID: covidwho-1713988

ABSTRACT

In the COVID pandemic situation, crowd counting became one of the tools to monitor if the social-distancing norms are being followed or not. However, in designing crowd counting algorithm, there are several challenges such as background noise, camera-to-objects distance, occlusion, and variations due to illumination, scale, and viewpoint. In this research, we propose a novel pipeline for density estimation in crowd counting. The proposed pipeline makes use of an encoder-decoder-based architecture in which we explore the family of EfficientN ets for the encoder architecture. For the decoder, we propose a deeper attention network to assist the model in a better distinction between foreground and background pixels. We empirically show that for a crowd counting dataset, the use of average pooling operation for any backbone architecture of encoder gives a significant improvement in performance. In terms of Mean Absolute Error, the proposed pipeline outperforms existing state-of-the-art techniques by a large margin on large-scale and small-scale counting datasets, UCF-QNRF and UCF _CC_50 dataset. We also achieve state-of-the-art results on the ShanghaiTech and Mall datasets. We additionally propose a crowd counting dataset captured using drones. We perform benchmark experiments on this dataset with existing and the proposed methods. The proposed dataset can be found at http://www.iab-rubric.org/resources/CrowdUAV.html. © 2021 IEEE.

15.
Circulation ; 144(SUPPL 1), 2021.
Article in English | EMBASE | ID: covidwho-1635486

ABSTRACT

Background: Historically, pts have been admitted for overnight observation following atrial fibrillation (AF) ablation. The COVID-19 pandemic ushered the need to consider same day discharge (SDD). It remains unclear how to identify pts who can safely undergo SDD. Objective: To evaluate acute (within 4 hrs) and subacute (within 24 hrs) safety of SDD post AF ablation;we also sought to identify predictors of safe discharge. Methods: All pts undergoing AF ablation at our center following the end of the COVID imposed lockdown were enrolled. In each pt, ICE guided single transseptal puncture using the VersaCross® (Baylis) system was performed. Following ablation, protamine was not administered;all femoral venous access sites were closed with Vascade™ (Cardiva Medical) closure devices. Pts ambulated after 2 hrs of bedrest. Pts who had SDD were compared to those who stayed for overnight observation. Results: The cohort included 226 pts (65 ± 10 yrs, 157 [69%] male, 118 [52%] PAF, CHA2DS2 -VASc 2.4 ± 1.7). Cryo PVI was performed in 193 (85%) pts;34 (15%) pts had a redo procedure. SDD was attempted in 126 pts and successfully accomplished in 115 (91%) pts at 251 + 72 minutes from procedure end. The most common reason for failed SDD attempt was access site oozing necessitating additional bedrest. No SDD pt had a major complication. Overnight observation was performed in 100 (44%) pts, most commonly due to physician/pt preference. Compared to pts who had SDD, these pts were older and more likely to have heart failure and history of TIA/stroke. Oozing within the first 4 hrs was observed at a similar rate to SDD pts. A minor complication was seen in 1 pt each in SDD and overnight stay group between 4 and 24 hrs of ablation (Figure). Conclusions: Our study shows that when attempted, SDD after AF ablation can be accomplished in >90% of pts. Venous access site oozing was the greatest hinderance to pts going home. However, if pts had no issue 4 hrs after AF ablation, they had an uneventful course over the next 24 hrs. (Figure Presented).

17.
National Journal of Physiology, Pharmacy and Pharmacology ; 11(9):964-970, 2021.
Article in English | EMBASE | ID: covidwho-1554393

ABSTRACT

Background: The second largest populous country India underwent a complete lockdown to curb the rampant spread and to prepare the health sector of our country to face the coronavirus disease (COVID)-19 pandemic. The curtailed access to family, friends, and other social support systems caused loneliness and mental issues such as anxiety and depression. Aim and Objectives: The present study was taken to find the impact of social lockdown on the psychological and mental health of the participants and to find out the differences between different sex, age groups, and occupations. Materials and Methods: An online cross-sectional study was administered to 1047 participants in the age group of 16–70 years through Google forms to assess the psychological and mental status of the residents of Delhi-NCR, India. The psychological status was judged by IES(R), while mental health status was checked by Depression, Anxiety and Stress Scale-21 (DASS-21). Results: The mean age of the participants was 48.29 ± 26.16 years. About 75.4% of participants had a moderate psychological impact of the lockdown as calculated by the IES(R) scale. DASS-21 scale revealed that the participants suffered from mild to moderate depression, anxiety and stress. Females had higher IES(R) but males scored higher DASS-21 scores. Healthcare workers along with the auxiliary staff suffered maximal mental agony. Conclusion: In the post-COVID-19 days, all efforts should be directed to minimize the toxic emotional and psychological impact of social lockdown by having tele psychological counseling programs with special emphasis to older age groups and medical fraternity.

18.
24th International Conference on Discovery Science, DS 2021 ; 12986 LNAI:422-432, 2021.
Article in English | Scopus | ID: covidwho-1499373

ABSTRACT

The detection and removal of misinformation from social media during high impact events, e.g., COVID-19 pandemic, is a sensitive application since the agency in charge of this process must ensure that no unwarranted actions are taken. This suggests that any automated system used for this process must display both high prediction accuracy as well as high explainability. Although Deep Learning methods have shown remarkable prediction accuracy, accessing the contextual information that Deep Learning-based representations carry is a significant challenge. In this paper, we propose a data-driven solution that is based on a popular latent variable model called Independent Component Analysis (ICA), where a slight loss in accuracy with respect to a BERT model is compensated by interpretable contextual representations. Our proposed solution provides direct interpretability without affecting the computational complexity of the model and without designing a separate system. We carry this study on a novel labeled COVID-19 Twitter dataset that is based on socio-linguistic criteria and show that our model’s explanations highly correlate with humans’ reasoning. © 2021, Springer Nature Switzerland AG.

19.
2021 International Conference on Intelligent Technologies, CONIT 2021 ; 2021.
Article in English | Scopus | ID: covidwho-1416192

ABSTRACT

SARS-CoV-2, also known as COVID-19 or Coronavirus, is a viral contagious disease that is infected by a novel coronavirus, and has been rapidly spreading across the globe. It is very important to test and isolate people to reduce spread, and from here comes the need to do this quickly and efficiently. According to some studies, Chest-CT outperforms RT-PCR lab testing, which is the current standard, when diagnosing COVID-19 patients. Due to this, computer vision researchers have developed various deep learning systems that can predict COVID-19 using a Chest-CT scan correctly to a certain degree. The accuracy of these systems is limited since deep learning neural networks such as CNNs (Convolutional Neural Networks) need a significantly large quantity of data for training in order to produce good quality results. Since the disease is relatively recent and more focus has been on CXR (Chest XRay) images, the available chest CT Scan image dataset is much less. We propose a method, by utilizing GANs, to generate synthetic chest CT images of both positive and negative COVID-19 patients. Using a pre-built predictive model, we concluded that around 40% of the generated images are correctly predicted as COVID-19 positive. The dataset thus generated can be used to train a CNN-based classifier which can help determine COVID-19 in a patient with greater accuracy. © 2021 IEEE.

20.
Journal of Clinical and Diagnostic Research ; 15(8):OR01*-OR04, 2021.
Article in English | EMBASE | ID: covidwho-1362739

ABSTRACT

Corona Virus Disease 2019 (COVID-19) infection can have myriad presentations ranging from non specific constitutional symptoms to respiratory failure and extrapulmonary manifestations. As COVID-19 is viewed predominantly as an illness of the respiratory tract, extrapulmonary manifestations are often overlooked. The case series is of seven COVID-19 diagnosed patients who presented with diarrhoea, without respiratory symptoms. Clinico-demographic characteristics, hospital course and outcome of these patients are described here. Median age of the patients was 42 years. There were four males and three females. Three patients had diabetes mellitus and one had hypertension, one had hypothyroidism and one had non-Hodgkin’s lymphoma along with tuberculosis. Five patients had fever while all had diarrhoea as the predominant presenting complaint. Median duration of symptoms was four days before admission. Laboratory abnormalities included anaemia (n=5;57.1%), lymphopenia (n=3;42.9%) and elevated inflammatory markers i.e., ferritin and C reactive protein (n=2;28.6%). Most patients did not require any specific treatment other than supportive care. All patients were successfully discharged after a median hospital stay of 10 days. Isolated diarrhoea without respiratory symptoms can be presenting complaint of COVID-19 and should be considered by clinicians in current pandemic scenario.

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