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COVID-19: A master stroke of Nature

Artificial Intelligence and Analytics, Healthcare and Life Science, Virtusa Corporation, New York, NY, USA

Special Issues: Coronavirus disease 2019: Modeling, Control and Prediction

This article presents the status of countries affected by COVID-19 (as of mid-May 2020) and their preparedness to combat the after-effects of the pandemic. The report also provides an analysis of how human behavior may have triggered such a global pandemic and why humans need to consider living sustainably to make our future world livable for all. COVID-19 originated in the city of Wuhan, China in December 2019. As of mid-May, it has spread to 213 countries and territories worldwide. The World Health Organization has declared COVID-19 a global pandemic, with a death toll of over 300,000 to date. The U.S. is currently the most impacted country. Collaborative efforts of scientists and politicians across the world will be needed to better plan and utilize global health resources to combat this global pandemic. Machine learning-based prediction models could also help by identifying potential COVID-19-prone areas and individuals. The cause of the emergence of COVID-19 is still a matter of research; however, one consistent theme is humanity’s unsustainable behavior. By sustainably interacting with nature, humans may have avoided this pandemic. If unsustainable human practices are not controlled through education, awareness, behavioral change, as well as sustainable policy creation and enforcement, there could be several such pandemics in our future.
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1. Griggs DJ, Staffordsmith M, Gaffney O, et al. (2013) Policy: Sustainable development goals for people and planet. Nature 495: 305–307.    

2. Peeri NC, Shrestha N, Rahman MS, et al. (2020) The SARS, MERS and novel coronavirus (COVID-19) epidemics, the newest and biggest global health threats: what lessons have we learned? Int J Epidemiol.

3. Soper GA (1919) The lessons of the pandemic. Science 49: 501–506.    

4. WHO (2020) Coronavirus disease 2 19 COVID-19 : situation report, 51. Available from: https://www.who.int/docs/default-source/coronaviruse/situation-reports/20200311-sitrep-51-covid-19.pdf?sfvrsn=1ba62e57_10.

5. WEF (2020) History of Pandemics. Available from: https://www.weforum.org/agenda/2020/03/a-visual-history-of-pandemics/.

6. Guo Y, Cao Q, Hong Z, et al. (2020) The origin, transmission and clinical therapies on coronavirus disease 2019 (COVID-19) outbreak–an update on the status. Mil Med Res 7: 1–10.

7. Lu R, Zhao X, Li J, et al. (2020) Genomic characterisation and epidemiology of 2019 novel coronavirus: implications for virus origins and receptor binding. Lancet 395: 565–574.    

8. WHO (2020) Coronavirus disease 2 19 COVID-19 : situation report, 114. Available from: https://www.who.int/docs/default-source/coronaviruse/situation-reports/20200513-covid-19-sitrep-114.pdf?sfvrsn=17ebbbe_4.

9. Worldmeters (2020) Coronavirus Cases. Available from: https://www.worldometers.info/coronavirus/.

10. NTI and JHCHS (2019) Global Health Security Index. Available from: https://www.ghsindex.org/wp-content/uploads/2019/10/2019-Global-Health-Security-Index.pdf.

11. Nam CW (2019) World economic outlook for 2019 and 2020. Available from: https://www.cesifo.org/en/publikationen/2019/article-journal/world-economic-outlook-2019-and-2020.

12. Kaplan J, Frias L, McFall-Johnsen M (2020) A third of the global population is on coronavirus lockdown-here's our constantly updated list of countries and restrictions. Available from: https://www.businessinsider.com/countries-on-lockdown-coronavirus-italy-2020-3.

13. Liu J, Mooney HA, Hull V, et al. (2015) Systems integration for global sustainability. Science 347.

14. Ives CD, Abson DJ, Von Wehrden H, et al. (2018) Reconnecting with nature for sustainability. Sustain Sci 13: 1389–1397.    

15. Soga M, Gaston KJ (2016) Extinction of experience: the loss of human-nature interactions. Front Ecol Environ 14: 94–101.    

16. Taylor LH, Latham SM, Woolhouse ME (2001) Risk factors for human disease emergence. Philos Trans R Soc B 356: 983–989.    

17. Andersen KG, Rambaut A, Lipkin WI, et al. (2020) The proximal origin of SARS-CoV-2. Nat Med, 1–3.

18. Suwannarong K, Schuler S (2016) Bat consumption in Thailand. Inf Ecol Epidemiol 6: 29941–29941.

19. Daly N (2020) Chinese citizens push to abolish wildlife trade as coronavirus persists. Available from: https://www.nationalgeographic.com/animals/2020/01/china-bans-wildlife-trade-after-coronavirus-outbreak/#close.

20. Jeffry (2020) Bat meat still popular in parts of Indonesia, despite coronavirus fears. Available from: https://www.reuters.com/article/us-china-health-indonesia-bats/bat-meat-still-popular-in-parts-of-indonesia-despite-coronavirus-fears-idUSKBN20511R.

21. McDougall J (2020) The paleo diet is uncivilized (and unhealthy and untrue). Available from: https://www.healmindbody.com/paleo-diet/.

22. Henry AG, Ungar PS, Passey BH, et al. (2012) The diet of Australopithecus sediba. Nature 487: 90–93.    

23. Mercader J (2009) Mozambican grass seed consumption during the Middle Stone Age. Science 326: 1680–1683.    

24. Henry AG, Brooks AS, Piperno DR (2011) Microfossils in calculus demonstrate consumption of plants and cooked foods in Neanderthal diets (Shanidar III, Iraq; Spy I and II, Belgium). P Natl Acad Sci USA 108: 486–491.    

25. Revedin A, Aranguren B, Becattini R, et al. (2010) Thirty thousand-year-old evidence of plant food processing. P Natl Acad Sci USA 107: 18815–18819.    

26. Diaz J, Taylor DB (2020) Celebrities, Athletes and Politicians Who Have Tested Positive for Coronavirus. Available from: https://www.nytimes.com/article/coronavirus-celebrities-actors-politicians.html.

27. Graham F, Castelvecchi D, Mallapaty S (2020) Daily briefing: Cruise ship coronavirus outbreak gave scientists 'an ideal experiment'. Available from: https://www.nature.com/articles/d41586-020-00932-6.

28. Singh SK, Taylor RW, Rahman MM, et al. (2018) Developing robust arsenic awareness prediction models using machine learning algorithms. J Environ Manag, 125–137.

29. Singh SK, Taylor RW, Su H (2017) Developing sustainable models of arsenic mitigation technologies in the Middle-Ganga Plain in India. Curr Sci 113.

30. Pham BT, Van Phong T, Nguyenthoi T, et al. (2020) Ensemble modeling of landslide susceptibility using random subspace learner and different decision tree classifiers. Geocarto Int, 1–23.

31. Nhu VH, Shirzadi A, Shahabi H, et al. (2020) Shallow Landslide Susceptibility Mapping: A Comparison between Logistic Model Tree, Logistic Regression, Naïve Bayes Tree, Artificial Neural Network, and Support Vector Machine Algorithms. Int J Environ Res Public Health 17: 2749.    

32. Singh SK, Taylor RW (2019) Assessing the role of risk perception in ensuring sustainable arsenic mitigation. Groundwater Sustain Dev 9: 100241.    

33. Randhawa GS, Soltysiak MP, Roz HE, et al. (2020) Machine learning using intrinsic genomic signatures for rapid classification of novel pathogens: COVID-19 case study. Plos One 15: e0232391.    

34. Li L, Qin L, Xu Z, et al. (2020) Artificial Intelligence Distinguishes COVID-19 from Community Acquired Pneumonia on Chest CT. Radiology, 200905–200905.

35. Sun YXH, Koh V, Marimuthu K, et al. (2020) Epidemiological and clinical predictors of COVID-19. Clin Inf Dis.

36. Wynants L, Calster BV, Collins GS, et al. (2020) Prediction models for diagnosis and prognosis of covid-19 infection: systematic review and critical appraisal. Bmj, 369.

37. Singh SK, Vedwan N (2015) Mapping Composite Vulnerability to Groundwater Arsenic Contamination: An Analytical Framework and a Case Study in India. Nat Hazards 75: 1883–1908.    

38. Al-Shamsi HO, Alhazzani W, Alhuraiji A, et al. (2020) A practical approach to the management of cancer patients during the novel coronavirus disease 2019 (COVID-19) pandemic: an international collaborative group. Oncologist, e936–e945.

39. Collaborative C (2020) Global guidance for surgical care during the COVID-19 pandemic. Br J Surg.

40. Holmes EA, O'Connor RC, Perry VH, et al. (2020) Multidisciplinary research priorities for the COVID-19 pandemic: a call for action for mental health science. Lancet Psychiatry 7: 547–560.    

41. Singh SK (2020) Global decision support dashboard of COVID-19. AIMS Med Sci 7: 40–42.    

© 2020 the Author(s), licensee AIMS Press. This is an open access article distributed under the terms of the Creative Commons Attribution Licese (http://creativecommons.org/licenses/by/4.0)

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