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1.
Annals of Library and Information Studies ; 69(3):208-220, 2022.
Article in English | Scopus | ID: covidwho-2081660

ABSTRACT

The current study curates a list of authentic and open-access sources of alphanumeric COVID-19 pandemic data. We have gathered 74 datasets from 42 sources, including sources from 18 countries. The datasets are searched through the Kaggle and GitHub repositories besides Google, providing a representation of varieties of pandemic-related datasets. The datasets are categorized according to their sources-primary and secondary, and according to their geographical distribution. While analyzing the dataset, we came across some classes in which the datasets can be categorized. We present the categorization in the form of taxonomy and highlight the present COVID-19 data collection and use challenges. The study will help researchers and data curators in the identification and classification of pandemic data. © 2022, National Institute of Science Communication and Policy Research. All rights reserved.

2.
2021 International Conference on Biomedical Ontologies, ICBO 2021 ; 3073:122-127, 2021.
Article in English | Scopus | ID: covidwho-1695755

ABSTRACT

Ontologies have emerged to become critical to support data and knowledge representation, standardization, integration, and analysis. The SARS-CoV-2 pandemic led to the rapid proliferation of COVID-19 data, as well as the development of many COVID-19 ontologies. In the interest of supporting data interoperability, we initiated a community-based effort to harmonize COVID-19 ontologies. Our effort involves the collaborative discussion among developers of seven COVID-19 related ontologies, and the merging of four ontologies. This effort demonstrates the feasibility of harmonizing these ontologies in an interoperable framework to support integrative representation and analysis of COVID-19 related data and knowledge. © 2021 Copyright for this paper by its authors.

3.
3rd Iberoamerican Knowledge Graph and Semantic Web Conference and the 2nd Indo-American Knowledge Graphs and Semantic Web Conference, KGSWC 2021 ; 1459 CCIS:153-168, 2021.
Article in English | Scopus | ID: covidwho-1592744

ABSTRACT

The CODO ontology was designed to capture data about the Covid-19 pandemic. The goal of the ontology was to collect epidemiological data about the pandemic so that medical professionals could perform contact tracing and answer questions about infection paths based on information about relations between patients, geography, time, etc. We took information from various spreadsheets and integrated it into one consistent knowledge graph that could be queried with SPARQL and visualized with the Gruff tool in AllegroGraph. The ontology is published on Bioportal and has been used by two projects to date. This paper describes the process used to design the initial ontology and to develop transformations to incorporate data from the Indian government about the pandemic. We went from an ontology to a large knowledge graph with approximately 5M triples in a few months. Our experience demonstrates some common principles that apply to the process of scaling up from an ontology model to a knowledge graph with real-world data. © 2021, Springer Nature Switzerland AG.

4.
Ind Psychiatry J ; 30(Suppl 1): S25-S28, 2021 Oct.
Article in English | MEDLINE | ID: covidwho-1497490

ABSTRACT

BACKGROUND: The coronavirus-19 (COVID-19) pandemic has affected millions of people across the world since early 2020. Besides the large number of case fatalities, this virus has produced significant health-related sequelae involving multiple systems of the body. As with previous coronavirus infections, this was also found to be associated with various neuropsychiatric symptoms. Psychosis has been uncommon, and the few reported cases across the world have forwarded association with either raised inflammatory markers or the consequences of social isolation. MATERIALS AND METHODS: This is a retrospective descriptive study of 12 patients, who were admitted with COVID-19 infection and psychosis, between March 2020 and December 2020. Cases of head injury, any neurological or metabolic illnesses, and substance use disorders were excluded. RESULTS: Cases with psychosis formed only 0.19% of all cases of COVID-19 admissions. All of them were young male and employed. Most of them had abrupt onset of psychosis with confusion, delusions, hallucinations, agitation, and sleep disturbances. Investigations including inflammatory markers (C-reactive protein) and computerized tomography scans were largely normal. Medications used were mainly benzodiazepines and antipsychotics. Most of the cases resolved within the second week, and follow-up after a month did not elicit any residual symptoms in majority. Diagnosis was acute and transient psychotic disorder (about 75%), bipolar affective disorder (2 cases), and schizophrenia (one). CONCLUSIONS: The major findings included nonreactive inflammatory markers, quick resolution of symptoms, requirement of low doses of antipsychotic drugs, and no long-term sequelae.

5.
The Journal of the Association of Physicians of India ; 69(7):11-12, 2021.
Article in English | Scopus | ID: covidwho-1431386

ABSTRACT

BACKGROUND: Since its first identification in December 2019, in WUHAN (CHINA), SARS-COV-2, causative agent of Corona virus pandemic, has affected millions of people worldwide, causing thousands of death. There is much speculation about the interplay between ACEI/ARB and Corona virus infection, as for internalization into host cell SARS-COV-2 binds through S spike protein to ACE-2, aided TMPRSS2. METHODS: A record based observational study has been conducted (data obtained from the clinics of fourteen physicians) in two worst affected districts of West Bengal, to find out the association of ACEI/ARB on patients, suffering from Corona virus infection. The study-protocol has already been approved by Clinical Research Ethics Committee of Calcutta School of Tropical Medicine. (IEC Ref. No: CREC-STM/2020-AS-37) Results: Increasing age, male sex and presence of co-morbidities (viz. Diabetes, COPD) are significantly associated with the occurrence of moderate and severe disease. Drugs (viz. ACEI/ARB), though are associated with less severe disease, have not achieved statistical significance, in the present study. CONCLUSION: Drugs, like ACEI/ARB, should be continued in patients suffering from COVID-19 infection, (if they are already on these drugs). © Journal of the Association of Physicians of India 2011.

6.
Journal of Association of Physicians of India ; 69(7):28-33, 2021.
Article in English | Scopus | ID: covidwho-1361002

ABSTRACT

Background: Since its first identification in December 2019, in WUHAN (CHINA), SARS-COV-2, causative agent of Corona virus pandemic, has affected millions of people worldwide, causing thousands of death. There is much speculation about the interplay between ACEI/ARB and Corona virus infection, as for internalization into host cell SARS-COV-2 binds through S spike protein to ACE-2, aided TMPRSS2. Methods: A record based observational study has been conducted (data obtained from the clinics of fourteen physicians) in two worst affected districts of West Bengal, to find out the association of ACEI/ARB on patients, suffering from Corona virus infection. The study-protocol has already been approved by Clinical Research Ethics Committee of Calcutta School of Tropical Medicine. (IEC Ref. No: CREC-STM/2020-AS-37) Results: Increasing age, male sex and presence of co-morbidities (viz. Diabetes, COPD) are significantly associated with the occurrence of moderate and severe disease. Drugs (viz. ACEI/ARB), though are associated with less severe disease, have not achieved statistical significance, in the present study. Conclusion: Drugs, like ACEI/ARB, should be continued in patients suffering from COVID-19 infection, (if they are already on these drugs). © 2021 Journal of Association of Physicians of India. All rights reserved.

7.
International Journal of Computational Biology and Drug Design ; 14(2):130-137, 2021.
Article in English | EMBASE | ID: covidwho-1288695

ABSTRACT

The rapid spreading of the coronavirus in India and its behaviour for the near future has been studied and analysed as accurately as possible using the SEIR model as a fundamental tool. The official covid-19 data of infected and death cases in India upto 10th October, 2020 have been considered as raw data. The value of various parameters of the model is optimised by feeding the raw data in the simulation model. The various parameters are defined as infection rate, basic reproduction number, death rate, recovery time, exposure time, and other parameters to optimise the best fit model. The total population of India is considered 1.36 billion people. The simulation results that the number of recovered people will be 2.8 × 108 and number of deaths will be 4.2 × 106 after 800 days for the total population of India. In an ideal scenario, at the end of the pandemic total death count is expected to be of the order of 106 which is a big challenge.

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