The Data Science Met with the COVID-19: Revealing the Most Critical Measures Taken for the COVID-19 Pandemic

Abdullah Talha Kabakuş

doi:10.35377/saucis.03.03.771501

EN

The Data Science Met with the COVID-19: Revealing the Most Critical Measures Taken for the COVID-19 Pandemic

Abstract

The whole world has been fighting against the novel coronavirus 2019 (COVID-19) for months. Despite the advances in medical sciences, more than 235,000 people have died so far. And, despite all the measures taken for it, more than 3 million people have become sick of the COVID-19. The measures taken for the COVID-19 vary through countries. So, revealing the most critical measures is necessary for a better fight against both the COVID-19 and possible similar pandemics in the future. To this end, an analysis of the worldwide measures, which were taken so far, for the COVID-19 pandemic was proposed within this paper. Since it is still early days, for the best of our knowledge, there does not exist a single dataset contains all the features utilized within this study. Therefore, a novel global dataset containing the data regarding the COVID-19 for 52 countries around the world was constructed by combining various datasets. Then, the feature importance techniques were employed to reveal the importance of the utilized features which means revealing the most important measures taken for the COVID-19 pandemic for our case. Within the analysis, four features were utilized, namely, the population density, the walking mobility, the driving mobility, and the number of lockdown days. According to the experimental result, the population density was found as the most important feature which means the most critical measure in terms of increasing the spread of the COVID-19 pandemic. The order of the importance of the other features was found as the walking mobility, the driving mobility, and the number of lockdown days, respectively.

Keywords

Thanks

We would like to thank Professor of Pediatrics Nimet Kabakus for his insightful comments.

References

N. Zhu et al., “A novel coronavirus from patients with pneumonia in China, 2019,” N. Engl. J. Med., vol. 382, pp. 727–733, 2020, doi: 10.1056/NEJMoa2001017.
J. F. W. Chan et al., “A familial cluster of pneumonia associated with the 2019 novel coronavirus indicating person-to-person transmission: a study of a family cluster,” Lancet, vol. 395, pp. 514–523, 2020, doi: 10.1016/S0140-6736(20)30154-9.
“COVID-19 CORONAVIRUS PANDEMIC,” Worldometer, 2020. https://www.worldometers.info/coronavirus/ (accessed May 05, 2020).
A. Wilder-Smith and D. O. Freedman, “Isolation, quarantine, social distancing and community containment: Pivotal role for old-style public health measures in the novel coronavirus (2019-nCoV) outbreak,” J. Travel Med., vol. 27, no. 2, pp. 1–4, 2020, doi: 10.1093/jtm/taaa020.
X. Jiang et al., “Towards an Artificial Intelligence Framework for Data-Driven Prediction of Coronavirus Clinical Severity,” Comput. Mater. Contin., vol. 63, no. 1, pp. 537–551, 2020, doi: 10.32604/cmc.2020.010691.
A. Strzelecki and M. Rizun, “Infodemiological Study Using Google Trends on Coronavirus Epidemic in Wuhan, China,” Int. J. Online Biomed. Eng., vol. 16, no. 4, pp. 139–146, 2020, doi: 10.3991/ijoe.v16i04.13531.
“Google Trends,” Google, 2020. https://trends.google.com/trends (accessed Aug. 02, 2020).
M. Fang et al., “CT radiomics can help screen the Coronavirus disease 2019 (COVID-19): a preliminary study,” Sci. China Inf. Sci., vol. 63, no. 7, pp. 1–8, 2020, doi: 10.1007/s11432-020-2849-3.

E. Dong, H. Du, and L. Gardner, “An interactive web-based dashboard to track COVID-19 in real time,” Lancet Infect. Dis., 2020, doi: 10.1016/S1473-3099(20)30120-1.
“Novel Coronavirus (COVID-19) Cases, provided by JHU CSSE,” Johns Hopkins University, 2020. https://github.com/CSSEGISandData/COVID-19 (accessed May 05, 2020).
“pandas: Python Data Analysis Library,” 2020. https://pandas.pydata.org (accessed May 05, 2020).
P. Chandro, “porimol/countryinfo: A Python module for returning data about countries, ISO info and states/provinces within them,” 2019. https://github.com/porimol/countryinfo (accessed May 05, 2020).
“COVID‑19 - Mobility Trends Reports - Apple,” Apple, 2020. https://www.apple.com/covid19/mobility (accessed May 05, 2020).
“Countries by Population Density 2020 - StatisticsTimes.com,” StatisticsTimes.com, 2020. http://statisticstimes.com/demographics/countries-by-population-density.php (accessed May 05, 2020).
J. Papastylianou, “COVID-19 Lockdown dates by country | Kaggle,” 2020. https://www.kaggle.com/jcyzag/covid19-lockdown-dates-by-country (accessed May 05, 2020).
F. Pedregosa et al., “Scikit-learn: Machine Learning in Python,” J. Mach. Learn. Res., vol. 12, pp. 2825–2830, 2011.
H. U. Zacharias, M. Altenbuchinger, and W. Gronwald, “Statistical Analysis of NMR Metabolic Fingerprints: Established Methods and Recent Advances,” Metabolites, vol. 8, no. 3, pp. 1–10, 2018, doi: 10.3390/metabo8030047.
S. C. Nayak, B. B. Misra, and H. S. Behera, “Impact of Data Normalization on Stock Index Forecasting,” Int. J. Comput. Inf. Syst. Ind. Manag. Appl., vol. 6, pp. 257–269, 2014.
A. Zien, N. Krämer, S. Sonnenburg, and G. Rätsch, “The Feature Importance Ranking Measure,” in The European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD 2009), 2009, pp. 694–709, doi: 10.1007/978-3-642-04174-7_45.
K. B. Prakash, S. S. Imambi, M. Ismail, T. Pavan Kumar, and Y. V. R. Naga Pawan, “Analysis, Prediction and Evaluation of COVID-19 Datasets using Machine Learning Algorithms,” Int. J. Emerg. Trends Eng. Res., vol. 8, no. 5, pp. 2199–2204, 2020, doi: 10.30534/ijeter/2020/117852020.
A. K. M. B. Haque, T. H. Pranto, A. A. Noman, and A. Mahmood, “Insight about Detection, Prediction and Weather Impact of Coronavirus (COVID-19) using Neural Network,” Int. J. Artif. Intell. Appl., vol. 11, no. 4, pp. 67–81, 2020, doi: 10.5121/ijaia.2020.11406.
S. Patil, A. Patil, and V. M. Phalle, “Life Prediction of Bearing by Using Adaboost Regressor,” in Proceedings of the TRIBOINDIA-2018 An International Conference on Tribology, 2018, pp. 1–8, doi: 10.2139/ssrn.3398399.
S. Patil, S. Desai, A. Patil, V. M. Phalle, V. Handikherkar, and F. S. Kazi, “Remaining Useful Life (RUL) Prediction of Rolling Element Bearing Using Random Forest and Gradient Boosting Technique,” in Proceedings of the ASME International Mechanical Engineering Congress and Exposition, Proceedings (IMECE 2018), 2018, pp. 1–7, doi: 10.1115/IMECE2018-87623.
V. C. C. Cheng et al., “Escalating infection control response to the rapidly evolving epidemiology of the Coronavirus disease 2019 (COVID-19) due to SARS-CoV-2 in Hong Kong,” Infect. Control Hosp. Epidemiol., pp. 1–6, 2020, doi: 10.1017/ice.2020.58.
“Rational use of personal protective equipment for coronavirus disease 2019 (COVID-19),” World Health Organization (WHO). pp. 1–7, 2020.
M. Lazzerini and G. Putoto, “COVID-19 in Italy: momentous decisions and many uncertainties,” Lancet Glob. Heal., vol. 8, no. 5, pp. e641–e642, 2020, doi: 10.1016/S2214-109X(20)30110-8.

Details

Primary Language

English

Subjects

Artificial Intelligence

Journal Section

Research Article

Authors

Abdullah Talha Kabakuş ^*
0000-0003-2181-4292
Türkiye

Publication Date

December 30, 2020

Submission Date

July 19, 2020

Acceptance Date

October 20, 2020

Published in Issue

Year 2020 Volume: 3 Number: 3

DOI

https://doi.org/10.35377/saucis.03.03.771501

IZ

https://izlik.org/JA47LN72MU

Cite

RIS / Bibtex

APA

Kabakuş, A. T. (2020). The Data Science Met with the COVID-19: Revealing the Most Critical Measures Taken for the COVID-19 Pandemic. Sakarya University Journal of Computer and Information Sciences, 3(3), 201-209. https://doi.org/10.35377/saucis.03.03.771501

AMA

1.Kabakuş AT. The Data Science Met with the COVID-19: Revealing the Most Critical Measures Taken for the COVID-19 Pandemic. SAUCIS. 2020;3(3):201-209. doi:10.35377/saucis.03.03.771501

Chicago

Kabakuş, Abdullah Talha. 2020. “The Data Science Met With the COVID-19: Revealing the Most Critical Measures Taken for the COVID-19 Pandemic”. Sakarya University Journal of Computer and Information Sciences 3 (3): 201-9. https://doi.org/10.35377/saucis.03.03.771501.

EndNote

Kabakuş AT (December 1, 2020) The Data Science Met with the COVID-19: Revealing the Most Critical Measures Taken for the COVID-19 Pandemic. Sakarya University Journal of Computer and Information Sciences 3 3 201–209.

IEEE

[1]A. T. Kabakuş, “The Data Science Met with the COVID-19: Revealing the Most Critical Measures Taken for the COVID-19 Pandemic”, SAUCIS, vol. 3, no. 3, pp. 201–209, Dec. 2020, doi: 10.35377/saucis.03.03.771501.

ISNAD

Kabakuş, Abdullah Talha. “The Data Science Met With the COVID-19: Revealing the Most Critical Measures Taken for the COVID-19 Pandemic”. Sakarya University Journal of Computer and Information Sciences 3/3 (December 1, 2020): 201-209. https://doi.org/10.35377/saucis.03.03.771501.

JAMA

1.Kabakuş AT. The Data Science Met with the COVID-19: Revealing the Most Critical Measures Taken for the COVID-19 Pandemic. SAUCIS. 2020;3:201–209.

MLA

Kabakuş, Abdullah Talha. “The Data Science Met With the COVID-19: Revealing the Most Critical Measures Taken for the COVID-19 Pandemic”. Sakarya University Journal of Computer and Information Sciences, vol. 3, no. 3, Dec. 2020, pp. 201-9, doi:10.35377/saucis.03.03.771501.

Vancouver

1.Abdullah Talha Kabakuş. The Data Science Met with the COVID-19: Revealing the Most Critical Measures Taken for the COVID-19 Pandemic. SAUCIS. 2020 Dec. 1;3(3):201-9. doi:10.35377/saucis.03.03.771501