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1.
International Journal of Computing Science and Mathematics ; 15(4):408-420, 2022.
Article in English | Web of Science | ID: covidwho-2070788

ABSTRACT

A pandemic like COVID-19 being a highly infectious disease has severely affected mankind and business activities. Seeing the critical situation, the honourable Prime Minister of India has called for a lockdown in the entire country in order to suppress the spread of this pandemic. While there are many debates about the spread of disease and lockdown in the entire country, we wish to mathematically understand the diffusion of this pandemic in the context of four highly infected states of India. Moreover, through this paper, we wish to examine the impact of these lockdown periods in order to understand the spread of COVID-19.

2.
Psychosomatic Medicine ; 84(5):A7, 2022.
Article in English | EMBASE | ID: covidwho-2002987

ABSTRACT

SARS-CoV-2 is highly infectious and has ability to mutate into newer, more contagious, and lethal strains. Moreover, presence of comorbidities and low immunity increases the COVID-19 susceptibility and severity. Thus, COVID-19 is challenging to treat and eradicate globally. This increase stress and anxiety among the patients, worsening their condition. Even health care workers (HCWs) are distressed and anxious while managing the COVID-19. Mental stress and depression increases risk of COVID-19. Yogic breathing techniques may be beneficial in improving immunity and reducing stress and anxiety. The present study investigated the effectiveness of short and controlled Yoga-based breathing protocols in COVID-positive, COVID-recovered and HCWs. Study subjects were recruited from Postgraduate Institute of Medical Education and Research, Chandigarh, India from 13th October, 2020 to 7th January 2021. Each group was randomly divided into intervention or yoga group and non-intervention or control group. COVID-positive practiced a 5-min routine and COVID-recovered and HCW practiced 5-min and 18-min routines for 15 days. Pre-post estimation of neuropsychological parameters and heart rate variability and baseline, 7th and 15th day estimation of biochemical parameters, 6-minute walk and 1-minute sit-stand tests were conducted. Based on Ayurveda, Prakriti-type was assessed. WBC count was elevated in COVID-positive intervention (p<0.001) and control groups (p=0.003). WBC count (p=0.002) and D-dimer (p=0.002) was decreased in COVID-recovered intervention. A non-significant reduction in perceived stress and tension was noted in COVID-positive intervention. Tension was reduced and quality of life improved in HCW intervention (p>0.05). The Kapha Prakriti (48.9 %) was dominant among COVID-19 infected (positive and recovered) subjects. Distance covered in 6-min increased after intervention in COVID-positive (p=0.01) and HCW (p=0.002). The covered distance was more after intervention in all groups than control sub-group. COVID-positive intervention group shows reduced heart rate (p>0.05) and high-frequency power (p=0.01). The interventions were capable of improving exercise capacity in patients and HCW and reduced cardiovascular risk in COVID-19. The studied breathing protocol can be integrated for the management of COVID-19 and is beneficial to HCWs.

3.
Asian Journal of Chemistry ; 34(8):1893-1920, 2022.
Article in English | Scopus | ID: covidwho-1964681

ABSTRACT

Thiazoles are notable five-membered heterocyclic rings and their moieties can be found in several biologically active compounds of natural origin, as well as synthetic molecules that possess a wide range of pharmacological activities. Inflammation is the common cause that is associated with different disorders and diseases such as psoriasis, arthritis, infections, asthma, cancer, etc. In this article, the synthesis pattern of these novel molecules are discussed and their anti-inflammatory activities against cyclooxygenase-1 (COX-1), cyclooxygenase-2 (COX-2) and lipoxygenase (LOX) were reviewed and documented. The potent 26 thiazole analogs were validated with molecular docking against main protease (6LU7) and spike binding domain ACE2 receptor (6M0J) to defeat from the COVID-19 infections. Among this, THI9a showed excellent binding energy and affinity against deadly SAR CoV-2. The reviewed and theoretical study information strongly suggested that thiazole derivatives can be used for the development of futuristic target drugs against death-causing diseases like SAR-CoV-2. © 2022 Chemical Publishing Co.. All rights reserved.

4.
Acs Es&T Water ; : 9, 2022.
Article in English | Web of Science | ID: covidwho-1927045

ABSTRACT

Monitoring wastewater for SARS-CoV-2 from populations smaller than those served by wastewater treatment plants may help identify small spatial areas (subsewersheds) where COVID-19 infections are present. We sampled wastewater from three nested locations with different sized populations within the same sewer network at a university campus and quantified SARS-CoV-2 RNA using reverse transcriptase droplet digital polymerase chain reaction (PCR). SARS-CoV-2 RNA concentrations and/or concentrations normalized by PMMoV were positively associated with laboratory-confirmed COVID-19 cases for both the sewershed level and the subsewershed level. We also used an antigen-based assay to detect the nucleocapsid (N) antigen from SARS-CoV-2 in wastewater samples at the sewershed level. The N antigen was regularly detected at the sewershed level, but the results were not associated with either laboratory-confirmed COVID-19 cases or SARS-CoV-2 RNA concentrations. The results of this study indicate that wastewater monitoring based on quantification of SARS-CoV-2 RNA using PCR-based methods is associated with COVID-19 cases at multiple geographic scales within the subsewershed level and can serve to aid the public health response.

5.
Value in health : the journal of the International Society for Pharmacoeconomics and Outcomes Research ; 25(7):S453-S453, 2022.
Article in English | EuropePMC | ID: covidwho-1905195
6.
2021 Sustainable Leadership and Academic Excellence International Conference, SLAE 2021 ; 2021-January, 2021.
Article in English | Scopus | ID: covidwho-1901498

ABSTRACT

This article emanates from the pilot phase of a qualitative study of the impact on academic fatigue and retention for The Determined Ones (TDO) students, studying at the Higher Colleges of Technology Campuses, UAE. The purpose of the study was to identify effective strategies for online learning that will be enhanced for the TDO students, thereby reduce academic fatigue and increase retention. The COVID-19 pandemic has had a remarkable influence on approaches to the day to day activities around the world, an influence which had led to a 'new normal'. In the spring of 2020, with the abrupt and essential transition from on-campus learning to distance learning, students and educators had limited time to prepare for such a massive shift in teaching and learning. Not many could have been prepared for such a shift, and new approach in teaching and learning. The impact has been felt more by students with disabilities, because their normal routines have been abandoned, leading to anxiety and stress resulting from the unknown. The move to online learning was a reactive than a proactive approach because no one apparently saw the COVID-19 pandemic and its subsequent impact on lifestyles coming. By summer 2020, as a result of online learning, key issues relating to academic fatigue and retention in students were widely identified through surveys and other data. This study emanated from these concerns to provide the opportunity to address the issues from a reactive approach into a proactive one, including the use of methods that will enhance student retention. Although digital technologies are a regular part of learning in the 21st Century, it cannot be denied that the sudden change to online learning platforms has affected both students and educators. Institutions went digital, relying on video conferencing programs like Zoom and Microsoft Teams for individuals to carry on working in isolation from their homes. Therefore, as the majority of interactions moved to this virtual realm, with the most widely used software being Zoom, it has come to be commonly referred to as 'zoom academic fatigue' as stated by [1]. Consequently, combating this new form of exhaustion has directly impacted on students' learning, especially for students with disabilities in higher education institutions. In the United Arab Emirates (UAE), The Determined Ones (TDOs) is the official appellation given to people with disabilities. This study will therefore be referring to students with disabilities at The Higher Colleges of Technology (HCT), in Abu Dhabi, the higher education institution where this study is taking place. Reports from surveys conducted at HCT in the summer of 2020 when the Covid 19 pandemic was raging revealed that the transition to an online learning platform left students feeling tired, anxious and stressed out as they waited for the next video call lesson. Their normal routines no longer applied. The effects of this academic fatigue within online classrooms requires attention and solutions to combat it. © 2021 IEEE.

9.
1st International Conference on Technologies for Smart Green Connected Society 2021, ICTSGS 2021 ; 107:11039-11050, 2022.
Article in English | Scopus | ID: covidwho-1874841

ABSTRACT

The number of elderly persons, who generally have chronic illnesses and have seen a substantial spread of the novel coronavirus in recent years, has grown in recent years (COVID-19). The majority of older persons suffering from various chronic diseases have died as a result of infection with this virus. Thousands of people have died as a result of this. Coronavirus has also caused several problems in hospitals, and people are not being treated as a result of the large number of patients who require medical care. Medical and paramedic personnel have also been infected, and there is a risk that the virus will spread to the patient and his attendants via medical and paramedical workers. You may avoid this problem by staying at home, where you can monitor your whereabouts and warn in an emergency. The Internet of Things was critical in real-time monitoring of COVID-19-infected patients. Many cloud-based IoT applications are available;nevertheless, high latency, bandwidth, and energy consumption are significant concerns with cloud computing. Fog computing attempts to address cloud computing issues such as bandwidth, network latency, and energy consumption. Similarly, one of the primary fog computing applications is data handling generated by healthcare IoT devices. Healthcare IoT devices generate a large amount of data, which must be managed effectively with low latency, no failures, low energy consumption, low cost, and high accuracy. © The Electrochemical Society

10.
1st International Conference on Technologies for Smart Green Connected Society 2021, ICTSGS 2021 ; 107:4327-4335, 2022.
Article in English | Scopus | ID: covidwho-1874779

ABSTRACT

This research paper is based on a fieldwork project, undertaken by the group of volunteer students in collaboration with Indian Development for Human Care (IDHC society), India. Slums are a global phenomenon that may be witnessed in nearly any city on the planet. Children from low-income families who live in slums are deprived of numerous essential facilities that non-slum children enjoy in general, therefore they require special attention. We as a team started the research by understanding the work and establishing good relationships with the staff and students to comprehend the way of their working in a better way. We were introduced to the youngsters with whom we will be working for their better future. We started by teaching them basic English, mathematics, basic habits, personal hygiene;we decided to plant trees as a good environment is necessary for our well-being, in this research work we spread awareness in our neighborhood regarding planting trees and keeping the surroundings clean. Also, spread awareness regarding Covid-19 to underprivileged children. © The Electrochemical Society

11.
7th International Conference on Wireless Communications, Signal Processing and Networking, WiSPNET 2022 ; : 130-134, 2022.
Article in English | Scopus | ID: covidwho-1874360

ABSTRACT

It is extremely difficult to monitor and manage infected patients during the COVID-19 pandemic. This IoT wearable monitoring gadget is developed to measure the indicators of COVID-19. Patients' GPS data is used to notify medical authorities of their infection status. A wearable sensor is affixed to the body and connected to an edge node in the IoT cloud where the data is processed and analyzed in order to monitor health. A temperature sensor, GPS, SpO2 sensor, IR sensor, and accelerometer make up the system. The Arduino UNO processor is used in this gadget. The patient's body temperature is obtained using the temperature sensor. The location of the infected patient is tracked using a GPS sensor. Human movement is detected using an accelerometer. The SpO2 sensor measures the blood oxygen saturation level. The heart rate is detected using a pulse sensor. Information about preventive measures, warnings, and actions is stored in a cloud database. COVID-19 symptom readings are measured using this approach for monitoring and analysis. © 2022 IEEE.

12.
Nature Sustainability ; : 2, 2022.
Article in English | Web of Science | ID: covidwho-1852506

ABSTRACT

COVID-19 lockdowns stalled protected area management in many countries. New research examines how fire and on-site protected area management are interlinked, demonstrating the novel use of satellite data and statistical modelling.

13.
PubMed; 2021.
Preprint in English | PubMed | ID: ppcovidwho-333877

ABSTRACT

BACKGROUND: COVID-19 vaccines play a vital role in combating the COVID-19 pandemic. Social media provides a rich data source to study public perception of COVID-19 vaccines. OBJECTIVE: In this study, we aimed to examine public perception and discussion of COVID-19 vaccines on Twitter in the US, as well as geographic and demographic characteristics of Twitter users who discussed about COVID-19 vaccines. METHODS: Through Twitter streaming Application Programming Interface (API), COVID-19-related tweets were collected from March 5 th , 2020 to January 25 th , 2021 using relevant keywords (such as "corona", "covid19", and "covid"). Based on geolocation information provided in tweets and vaccine-related keywords (such as "vaccine" and "vaccination"), we identified COVID-19 vaccine-related tweets from the US. Topic modeling and sentiment analysis were performed to examine public perception and discussion of COVID-19 vaccines. Demographic inference using computer vision algorithm (DeepFace) was performed to infer the demographic characteristics (age, gender and race/ethnicity) of Twitter users who tweeted about COVID-19 vaccines. RESULTS: Our longitudinal analysis showed that the discussion of COVID-19 vaccines on Twitter in the US reached a peak at the end of 2020. Average sentiment score for COVID-19 vaccine-related tweets remained relatively stable during our study period except for two big peaks, the positive peak corresponds to the optimism about the development of COVID-19 vaccines and the negative peak corresponds to worrying about the availability of COVID-19 vaccines. COVID-19 vaccine-related tweets from east coast states showed relatively high sentiment score. Twitter users from east, west and southern states of the US, as well as male users and users in age group 30-49 years, were more likely to discuss about COVID-19 vaccines on Twitter. CONCLUSIONS: Public discussion and perception of COVID-19 vaccines on Twitter were influenced by the vaccine development and the pandemic, which varied depending on the geographics and demographics of Twitter users.

14.
PubMed; 2021.
Preprint in English | PubMed | ID: ppcovidwho-333837

ABSTRACT

IMPORTANCE: SARS-CoV-2. OBJECTIVE: To determine the characteristics, changes over time, outcomes, and severity risk factors of SARS-CoV-2 affected children within the National COVID Cohort Collaborative (N3C). DESIGN: Prospective cohort study of patient encounters with end dates before May 27th, 2021. SETTING: 45 N3C institutions. PARTICIPANTS: Children <19-years-old at initial SARS-CoV-2 testing. MAIN OUTCOMES AND MEASURES: Case incidence and severity over time, demographic and comorbidity severity risk factors, vital sign and laboratory trajectories, clinical outcomes, and acute COVID-19 vs MIS-C contrasts for children infected with SARS-CoV-2. RESULTS: 728,047 children in the N3C were tested for SARS-CoV-2;of these, 91,865 (12.6%) were positive. Among the 5,213 (6%) hospitalized children, 685 (13%) met criteria for severe disease: mechanical ventilation (7%), vasopressor/inotropic support (7%), ECMO (0.6%), or death/discharge to hospice (1.1%). Male gender, African American race, older age, and several pediatric complex chronic condition (PCCC) subcategories were associated with higher clinical severity (p <= 0.05). Vital signs (all p<=0.002) and many laboratory tests from the first day of hospitalization were predictive of peak disease severity. Children with severe (vs moderate) disease were more likely to receive antimicrobials (71% vs 32%, p<0.001) and immunomodulatory medications (53% vs 16%, p<0.001). Compared to those with acute COVID-19, children with MIS-C were more likely to be male, Black/African American, 1-to-12-years-old, and less likely to have asthma, diabetes, or a PCCC (p < 0.04). MIS-C cases demonstrated a more inflammatory laboratory profile and more severe clinical phenotype with higher rates of invasive ventilation (12% vs 6%) and need for vasoactive-inotropic support (31% vs 6%) compared to acute COVID-19 cases, respectively (p<0.03). CONCLUSIONS: In the largest U.S. SARS-CoV-2-positive pediatric cohort to date, we observed differences in demographics, pre-existing comorbidities, and initial vital sign and laboratory test values between severity subgroups. Taken together, these results suggest that early identification of children likely to progress to severe disease could be achieved using readily available data elements from the day of admission. Further work is needed to translate this knowledge into improved outcomes.

15.
5th International Conference on IoT in Social, Mobile, Analytics and Cloud (I-SMAC) ; : 286-289, 2021.
Article in English | Web of Science | ID: covidwho-1779080

ABSTRACT

Coronavirus disease (Covid-19) is a serious health problem for the world. Most of the countries are affected by this infectious disease. Many countries have started vaccination against Covid-19. The number of confirmed cases every day changes rapidly. Public health planners want to know these numbers in advance to arrange health facilities accordingly. Many machine learning models have been developed for the prediction of the number of Covid-infected people. The accuracy of these models depends upon the training data. Data collected during the period when there is no vaccination and data collected during the vaccination period have different properties. The models trained on different datasets perform differently. In this paper, we study the effect of the data collected during the vaccination period. The study will be helpful in generating more accurate prediction models for the vaccination period.

16.
Journal of Clinical and Experimental Hepatology ; 12:S62-S63, 2022.
Article in English | PMC | ID: covidwho-1768280
17.
12th International Conference on Computing Communication and Networking Technologies, ICCCNT 2021 ; 2021.
Article in English | Scopus | ID: covidwho-1752368

ABSTRACT

Medical data transmission and sharing, especially during this COVID-19 pandemic period, on the open channel have become more important for remote diagnosis and treatment purpose. However, the alteration and unauthorized distribution of image data has become easier, and thus the big issue of copy-protection and ownership conflicts has attracted more attention for healthcare research community. Further, large amount of confidential and personal medical records is often stored on cloud environments. However, outsourcing medical data possibly brings the great security and privacy issue, since the confidential records are shared to the third party. In this paper, a robust X-Ray image watermarking is proposed by using Non-Subsampled Contourlet Transform (NSCT) and Multiresolution Singular Value Decomposition (MSVD). For watermark embedding, the maximum entropy component of X-Ray carrier image is firstly decomposed using NSCT. Then, low and high frequency details of carrier and mark image is obtained using MSVD. Further, conceal the watermark detail through modifying the detail of carrier image via the suitable factor. Finally, Shamir's (k, n) secret sharing algorithm is employed to obtain secure marked carrier image. Objective evaluations on 200 X-Ray images of COVID-19 patients demonstrate that the proposed algorithm has not only an excellent invisibility but a strong robustness against the various attacks. The results also show that our algorithm outperforms the related image watermarking algorithms, since it is also suitable for applications in the multi-cloud. © 2021 IEEE.

18.
5th IEEE International Conference on Computational Systems and Information Technology for Sustainable Solutions, CSITSS 2021 ; 2021.
Article in English | Scopus | ID: covidwho-1741145

ABSTRACT

Machine Learning is a key branch of Artificial Intelligence that concentrates on the development of computational algorithms by creating models. It has caught major attention in the technological domain due to its various applications in speech recognition, recommendation engines, computer vision, automated stock trading etc. The model's performance is dependent on the dataset provided and its accuracy can easily be enhanced by expanding the training dataset. Post Covid-19, it has been observed that phishing websites are appallingly on the rise, especially the phishing attacks. These attacks are caused by cybercriminals using PDF's, Microsoft office documents and other attachments via emails. This paper focusses on discussion and comparison of different machine learning algorithms that are capable of detecting phishing emails and websites. The experiments have shown that that MultinomialNB attains the highest efficiency of 98.06% for phishing email detection and Decision Tree Classifier offers the maximum efficiency of 95.41% for phishing website detection. © 2021 IEEE.

19.
3rd International Conference on Data and Information Sciences, ICDIS 2021 ; 318:341-350, 2022.
Article in English | Scopus | ID: covidwho-1718601

ABSTRACT

Objective: The objective of this paper is to analyze approved areas of medical research related to COVID-19 from the United Arab Emirates (UAE) and World Health Organization (WHO) in order to identify key topics and themes for these two entities. The paper attempts to understand the key focus areas of the government and private agencies for further medical research in response to COVID-19. Research Design and Methods: In view of availability of large volumes of documents and advancements in computing systems, text mining has emerged as a significant tool to analyze large volumes of unstructured data. For this paper, we have applied latent semantic analysis (LSA) and singular value decomposition for text clustering. Findings: The findings of terms analysis results show various focus areas of medical research communities for UAE and WHO. Nutrition is a key theme of research in UAE whereas alternative medicines or infection study emerged as key focus areas for WHO. Further analysis of topic modeling indicates that topics like pneumonia and prevention approach has been a focus of approved research for WHO. Contribution/Value Added: The study contributes to text mining literature by providing a framework for analyzing research or policy documents at country or organization level. This can help to understand the key themes in COVID-19 response by various countries and organizations and identify the focus areas for them. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

20.
PubMed; 2021.
Preprint in English | PubMed | ID: ppcovidwho-329400

ABSTRACT

Background: The majority of U.S. reports of COVID-19 clinical characteristics, disease course, and treatments are from single health systems or focused on one domain. Here we report the creation of the National COVID Cohort Collaborative (N3C), a centralized, harmonized, high-granularity electronic health record repository that is the largest, most representative U.S. cohort of COVID-19 cases and controls to date. This multi-center dataset supports robust evidence-based development of predictive and diagnostic tools and informs critical care and policy. Methods and Findings: In a retrospective cohort study of 1,926,526 patients from 34 medical centers nationwide, we stratified patients using a World Health Organization COVID-19 severity scale and demographics;we then evaluated differences between groups over time using multivariable logistic regression. We established vital signs and laboratory values among COVID-19 patients with different severities, providing the foundation for predictive analytics. The cohort included 174,568 adults with severe acute respiratory syndrome associated with SARS-CoV-2 (PCR >99% or antigen <1%) as well as 1,133,848 adult patients that served as lab-negative controls. Among 32,472 hospitalized patients, mortality was 11.6% overall and decreased from 16.4% in March/April 2020 to 8.6% in September/October 2020 (p = 0.002 monthly trend). In a multivariable logistic regression model, age, male sex, liver disease, dementia, African-American and Asian race, and obesity were independently associated with higher clinical severity. To demonstrate the utility of the N3C cohort for analytics, we used machine learning (ML) to predict clinical severity and risk factors over time. Using 64 inputs available on the first hospital day, we predicted a severe clinical course (death, discharge to hospice, invasive ventilation, or extracorporeal membrane oxygenation) using random forest and XGBoost models (AUROC 0.86 and 0.87 respectively) that were stable over time. The most powerful predictors in these models are patient age and widely available vital sign and laboratory values. The established expected trajectories for many vital signs and laboratory values among patients with different clinical severities validates observations from smaller studies, and provides comprehensive insight into COVID-19 characterization in U.S. patients. Conclusions: This is the first description of an ongoing longitudinal observational study of patients seen in diverse clinical settings and geographical regions and is the largest COVID-19 cohort in the United States. Such data are the foundation for ML models that can be the basis for generalizable clinical decision support tools. The N3C Data Enclave is unique in providing transparent, reproducible, easily shared, versioned, and fully auditable data and analytic provenance for national-scale patient-level EHR data. The N3C is built for intensive ML analyses by academic, industry, and citizen scientists internationally. Many observational correlations can inform trial designs and care guidelines for this new disease.

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