Data Science for COVID-19, Volume 2: Societal and Medical Perspectives presents the most current and leading-edge research into the applications of a variety of data science techniques for the detection, mitigation, treatment and elimination of the COVID-19 virus. At this point, Cognitive Data Science is the most powerful tool for researchers to fight COVID-19. Thanks to instant data-analysis and predictive techniques, including Artificial Intelligence, Machine Learning, Deep Learning, Data Mining, and computational modeling for processing large amounts of data, recognizing patterns, modeling new techniques, and improving both research and treatment outcomes is now possible.
1. Title of Book - p. i 2. Cover image - p. i 3. Title page - p. ii 4. Table of Contents - p. iii 5. Copyright - p. iii 6. Contributors - p. xxi 7. Foreword - p. xxxi 8. Preface - p. xxxv 9. 1. Essentials of the COVID-19 coronavirus - p. 1 10. 2. Docking study of transmembrane serine protease type 2 inhibitors for the treatment of COVID-19 - p. 27 11. 3. Gut-lung cross talk in COVID-19 pathology and fatality rate - p. 41 12. 4. Data sharing and privacy issues arising with COVID-19 data and applications - p. 61 13. 5. COVID-19 outlook in the United States of America: a data-driven thematic approach - p. 77 14. 6. Artificial intelligence and COVID-19: fighting pandemics - p. 95 15. 7. Data science: a survey on the statistical analysis of the latest outbreak of the 2019 pandemic novel coronavirus disease (COVID-19) using ANOVA - p. 113 16. 8. Application of big data in COVID-19 epidemic - p. 141 17. 9. Artificial intelligence-based solutions for COVID-19 - p. 167 18. 10. Telemedicine applications for pandemic diseases, with a focus on COVID-19 - p. 191 19. 11. Impact of COVID-19 and lockdown policies on farming, food security, and agribusiness in West Africa - p. 209 20. 12. Study and impact analysis of COVID-19 pandemic clinical data on infection spreading - p. 225 21. 13. Toward analyzing the impact of healthcare treatments in industry 4.0 environment—a self-care case study during COVID-19 outbreak - p. 243 22. 14. Big data processing and analysis on the impact of COVID-19 on public transport delay - p. 257 23. 15. The role of societal research and centers in analyzing society in pandemic times - p. 279 24. 16. Modeling and predicting the spread of COVID-19: a continental analysis - p. 299 25. 17. Applications of Building Information Modeling for COVID-19 spread assessment due to the organization of building artifacts - p. 319 26. 18. COVID-19 diagnosis—myths and protocols - p. 335 27. 19. “Quarantined within a quarantine”: COVID-19 and GIS Dynamic Scenario Modeling in Tasmania, Australia - p. 355 28. 20. Essentials of COVID-19 and treatment approaches - p. 397 29. 21. Coronavirus epidemic and its social/mental dimensions: the Turkey case - p. 423 30. 22. Coronavirus: a scientometric study of worldwide research publications - p. 433 31. 23. The effects of COVID-19 pandemic on Western Balkan financial markets - p. 449 32. 24. Prioritization of health emergency research and disaster preparedness: a systematic assessment of the coronavirus disease 2019 pandemic - p. 465 33. 25. A review on epidemiology, genomic characteristics, spread, and treatments of COVID-19 - p. 487 34. 26. Control of antibiotic resistance and superinfections as a strategy to manage COVID-19 deaths - p. 507 35. 27. Assessment of global research trends in the application of data science and deep and machine learning to the COVID-19 pandemic - p. 531 36. 28. Identification of lead inhibitors of TMPRSS2 isoform 1 of SARS-CoV-2 target using neural network, random forest, and molecular docking - p. 547 37. 29. The linkage between the epidemic of COVID-19 and oil prices: case of Saudi Arabia, January 22 to April 17 - p. 577 38. 30. Role of big geospatial data in the COVID-19 crisis - p. 589 39. 31. COVID-19: will it be a game changer in higher education in India? - p. 611 40. 32. Are the northern and southern regions equally affected by the COVID-19 pandemic? An empirical evidence from Nigeria - p. 631 41. 33. COVID-19 lethality reduction using artificial intelligence solutions derived from telecommunications systems - p. 647 42. 34. The significance of daily incidence and mortality cases due to COVID-19 in some African countries - p. 667 43. 35. Data interpretation leading to image processing: a hybrid perspective to a global pandemic, COVID-19 - p. 681 44. 36. COVID-19: in the direction of monitoring the pandemic in India - p. 705 45. 37. Potential antiviral therapies for coronavirus disease 2019 (COVID-19) - p. 729 46. Index - p. 749
Data Science for COVID-19, Volume 2: Societal and Medical Perspectives presents the most current and leading-edge research into the applications of a variety of data science techniques for the detection, mitigation, treatment and elimination of the COVID-19 virus. At this point, Cognitive Data Science is the most powerful tool for researchers to fight COVID-19. Thanks to instant data-analysis and predictive techniques, including Artificial Intelligence, Machine Learning, Deep Learning, Data Mining, and computational modeling for processing large amounts of data, recognizing patterns, modeling new techniques, and improving both research and treatment outcomes is now possible.
1. Title of Book - p. i 2. Cover image - p. i 3. Title page - p. ii 4. Table of Contents - p. iii 5. Copyright - p. iii 6. Contributors - p. xxi 7. Foreword - p. xxxi 8. Preface - p. xxxv 9. 1. Essentials of the COVID-19 coronavirus - p. 1 10. 2. Docking study of transmembrane serine protease type 2 inhibitors for the treatment of COVID-19 - p. 27 11. 3. Gut-lung cross talk in COVID-19 pathology and fatality rate - p. 41 12. 4. Data sharing and privacy issues arising with COVID-19 data and applications - p. 61 13. 5. COVID-19 outlook in the United States of America: a data-driven thematic approach - p. 77 14. 6. Artificial intelligence and COVID-19: fighting pandemics - p. 95 15. 7. Data science: a survey on the statistical analysis of the latest outbreak of the 2019 pandemic novel coronavirus disease (COVID-19) using ANOVA - p. 113 16. 8. Application of big data in COVID-19 epidemic - p. 141 17. 9. Artificial intelligence-based solutions for COVID-19 - p. 167 18. 10. Telemedicine applications for pandemic diseases, with a focus on COVID-19 - p. 191 19. 11. Impact of COVID-19 and lockdown policies on farming, food security, and agribusiness in West Africa - p. 209 20. 12. Study and impact analysis of COVID-19 pandemic clinical data on infection spreading - p. 225 21. 13. Toward analyzing the impact of healthcare treatments in industry 4.0 environment—a self-care case study during COVID-19 outbreak - p. 243 22. 14. Big data processing and analysis on the impact of COVID-19 on public transport delay - p. 257 23. 15. The role of societal research and centers in analyzing society in pandemic times - p. 279 24. 16. Modeling and predicting the spread of COVID-19: a continental analysis - p. 299 25. 17. Applications of Building Information Modeling for COVID-19 spread assessment due to the organization of building artifacts - p. 319 26. 18. COVID-19 diagnosis—myths and protocols - p. 335 27. 19. “Quarantined within a quarantine”: COVID-19 and GIS Dynamic Scenario Modeling in Tasmania, Australia - p. 355 28. 20. Essentials of COVID-19 and treatment approaches - p. 397 29. 21. Coronavirus epidemic and its social/mental dimensions: the Turkey case - p. 423 30. 22. Coronavirus: a scientometric study of worldwide research publications - p. 433 31. 23. The effects of COVID-19 pandemic on Western Balkan financial markets - p. 449 32. 24. Prioritization of health emergency research and disaster preparedness: a systematic assessment of the coronavirus disease 2019 pandemic - p. 465 33. 25. A review on epidemiology, genomic characteristics, spread, and treatments of COVID-19 - p. 487 34. 26. Control of antibiotic resistance and superinfections as a strategy to manage COVID-19 deaths - p. 507 35. 27. Assessment of global research trends in the application of data science and deep and machine learning to the COVID-19 pandemic - p. 531 36. 28. Identification of lead inhibitors of TMPRSS2 isoform 1 of SARS-CoV-2 target using neural network, random forest, and molecular docking - p. 547 37. 29. The linkage between the epidemic of COVID-19 and oil prices: case of Saudi Arabia, January 22 to April 17 - p. 577 38. 30. Role of big geospatial data in the COVID-19 crisis - p. 589 39. 31. COVID-19: will it be a game changer in higher education in India? - p. 611 40. 32. Are the northern and southern regions equally affected by the COVID-19 pandemic? An empirical evidence from Nigeria - p. 631 41. 33. COVID-19 lethality reduction using artificial intelligence solutions derived from telecommunications systems - p. 647 42. 34. The significance of daily incidence and mortality cases due to COVID-19 in some African countries - p. 667 43. 35. Data interpretation leading to image processing: a hybrid perspective to a global pandemic, COVID-19 - p. 681 44. 36. COVID-19: in the direction of monitoring the pandemic in India - p. 705 45. 37. Potential antiviral therapies for coronavirus disease 2019 (COVID-19) - p. 729 46. Index - p. 749
