A Comparative NLP-Based Study on the Current Trends and Future Directions in COVID-19 Research
COVID-19 is a global health crisis that has altered human life and still promises to create ripples of death and destruction in its wake. The sea of scientific literature published over a short time-span to understand and mitigate this global phenomenon necessitates concerted efforts to organize our...
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| Format: | Article |
| Language: | English |
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IEEE
2021-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/9437220/ |
| _version_ | 1819129759300321280 |
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| author | Priyankar Bose Satyaki Roy Preetam Ghosh |
| author_facet | Priyankar Bose Satyaki Roy Preetam Ghosh |
| author_sort | Priyankar Bose |
| collection | DOAJ |
| description | COVID-19 is a global health crisis that has altered human life and still promises to create ripples of death and destruction in its wake. The sea of scientific literature published over a short time-span to understand and mitigate this global phenomenon necessitates concerted efforts to organize our findings and focus on the unexplored facets of the disease. In this work, we applied natural language processing (NLP) based approaches on scientific literature published on COVID-19 to infer significant keywords that have contributed to our social, economic, demographic, psychological, epidemiological, clinical, and medical understanding of this pandemic. We identify key terms appearing in COVID literature that vary in representation when compared to other virus-borne diseases such as MERS, Ebola, and Influenza. We also identify countries, topics, and research articles that demonstrate that the scientific community is still reacting to the short-term threats such as transmissibility, health risks, treatment plans, and public policies, underpinning the need for collective international efforts towards long-term immunization and drug-related challenges. Furthermore, our study highlights several long-term research directions that are urgently needed for COVID-19 such as: global collaboration to create international open-access data repositories, policymaking to curb future outbreaks, psychological repercussions of COVID-19, vaccine development for SARS-CoV-2 variants and their long-term efficacy studies, and mental health issues in both children and elderly. |
| first_indexed | 2024-12-22T08:48:49Z |
| format | Article |
| id | doaj.art-f9cbe2466e594bf3b0b9731f2359a116 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-22T08:48:49Z |
| publishDate | 2021-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-f9cbe2466e594bf3b0b9731f2359a1162022-12-21T18:32:02ZengIEEEIEEE Access2169-35362021-01-019783417835510.1109/ACCESS.2021.30821089437220A Comparative NLP-Based Study on the Current Trends and Future Directions in COVID-19 ResearchPriyankar Bose0https://orcid.org/0000-0002-7943-1422Satyaki Roy1https://orcid.org/0000-0001-6767-266XPreetam Ghosh2https://orcid.org/0000-0003-3880-5886Department of Computer Science, Virginia Commonwealth University, Richmond, VA, USADepartment of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USADepartment of Computer Science, Virginia Commonwealth University, Richmond, VA, USACOVID-19 is a global health crisis that has altered human life and still promises to create ripples of death and destruction in its wake. The sea of scientific literature published over a short time-span to understand and mitigate this global phenomenon necessitates concerted efforts to organize our findings and focus on the unexplored facets of the disease. In this work, we applied natural language processing (NLP) based approaches on scientific literature published on COVID-19 to infer significant keywords that have contributed to our social, economic, demographic, psychological, epidemiological, clinical, and medical understanding of this pandemic. We identify key terms appearing in COVID literature that vary in representation when compared to other virus-borne diseases such as MERS, Ebola, and Influenza. We also identify countries, topics, and research articles that demonstrate that the scientific community is still reacting to the short-term threats such as transmissibility, health risks, treatment plans, and public policies, underpinning the need for collective international efforts towards long-term immunization and drug-related challenges. Furthermore, our study highlights several long-term research directions that are urgently needed for COVID-19 such as: global collaboration to create international open-access data repositories, policymaking to curb future outbreaks, psychological repercussions of COVID-19, vaccine development for SARS-CoV-2 variants and their long-term efficacy studies, and mental health issues in both children and elderly.https://ieeexplore.ieee.org/document/9437220/COVID-19natural language processingcoefficient of variationmean squared error |
| spellingShingle | Priyankar Bose Satyaki Roy Preetam Ghosh A Comparative NLP-Based Study on the Current Trends and Future Directions in COVID-19 Research IEEE Access COVID-19 natural language processing coefficient of variation mean squared error |
| title | A Comparative NLP-Based Study on the Current Trends and Future Directions in COVID-19 Research |
| title_full | A Comparative NLP-Based Study on the Current Trends and Future Directions in COVID-19 Research |
| title_fullStr | A Comparative NLP-Based Study on the Current Trends and Future Directions in COVID-19 Research |
| title_full_unstemmed | A Comparative NLP-Based Study on the Current Trends and Future Directions in COVID-19 Research |
| title_short | A Comparative NLP-Based Study on the Current Trends and Future Directions in COVID-19 Research |
| title_sort | comparative nlp based study on the current trends and future directions in covid 19 research |
| topic | COVID-19 natural language processing coefficient of variation mean squared error |
| url | https://ieeexplore.ieee.org/document/9437220/ |
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