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Uncovering the Hidden Patterns of the COVID-19 Global Pandemic: An in-Depth Data Analytics Approach

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Abstract

COVID-19 is a highly infectious respiratory illness caused by the novel coronavirus. It was first identified in Wuhan, China in December 2019 and has since spread globally, infecting and killing a vast number of people, leading to a worldwide pandemic. The pandemic has left the world in disarray. We wished to apply data analytics and regression models to understand and study the data – OwiD (Our World in Data) real time covid dataset - to analyse and draw trends and factors that led to the widespread of the virus. Doing so, allows us to identify key factors and trends that played a vital role in the rapid spread of the virus. We can thus determine the underlying hidden patterns of key factors. This will help provide a better understanding and determine the potential reasons COVID-19 took the world by storm with its fast-paced spread.

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The data is available in the given link: https://ourworldindata.org/coronavirus

References

  1. Wang W, Tang J, Wei F. Updated understanding of the outbreak of 2019 novel coronavirus (2019-nCoV) in Wuhan, China. J Med Virol. 2020;92(4):441–7.

    Article  Google Scholar 

  2. Borio C. The Covid-19 economic crisis: dangerously unique. Bus Econ. 2020;55(4):181–90.

    Article  Google Scholar 

  3. Xu C, Yu Y, Chen Y, Lu Z. Forecast analysis of the epidemic trend of COVID-19 in the USA by a generalised fractional-order SEIR model. Nonlinear Dyn. 2020;101(3):1621–34.

    Article  Google Scholar 

  4. Su X, Yan X, Tsai CL. Linear regression. Wiley Interdisciplinary Reviews: Comput Stat. 2012;4(3):275–94.

    Article  Google Scholar 

  5. Mandayam AU, Rakshith AC, Siddesha S, Niranjan SK. (2020, November). Prediction of Covid-19 pandemic based on Regression. In 2020 fifth international conference on research in computational intelligence and communication networks (ICRCICN) (pp. 1–5). IEEE.

  6. Liang LL, Tseng CH, Ho HJ, Wu CY. Covid-19 mortality is negatively associated with test number and government effectiveness. Sci Rep. 2020;10(1):1–7.

    Article  Google Scholar 

  7. Aiken LS, West SG, Pitts SC. 2003. Multiple linear regression. Handbook of psychology, pp.481–507.

  8. K D.P., Patan R, Ramachandran M, G A.H. Partial derivative nonlinear global pandemic machine learning prediction of covid 19. Chaos Solitons Fractals. 2020;139:110056.

  9. Rath S, Tripathy A, Tripathy AR. Prediction of new active cases of coronavirus disease (COVID-19) pandemic using multiple linear regression model. Diabetes Metabolic Syndrome: Clin Res Reviews. 2020;14(5):1467–74.

    Article  Google Scholar 

  10. Awad M, Khanna R. 2015. Support vector regression. In Efficient learning machines (pp. 67–80). Apress, Berkeley, CA.

  11. Uchenna N, Oreoluwa A, Rotimi O, Oluwatobi B, James A. Forecasting infectious Disease Outbreak using support Vector Regression (SVR) Case Study: Measles (Rubeola). PhD, Babcock University, Federal Polytechnic Ilaro; 2020.

  12. Parbat D, Chakraborty M. A python based support vector regression model for prediction of COVID19 cases in India. Volume 138. Chaos, Solitons & Fractals; 2020. p. 109942.

  13. Zhang GP. Time series forecasting using a hybrid ARIMA and neural network model. Volume 50. Neurocomputing; 2003. pp. 159–75.

  14. Sato RC. Disease management with ARIMA model in time series. Einstein (Sao Paulo). 2013;11:128–31.

    Article  Google Scholar 

  15. Zhao D, Zhang R, Zhang H, He S. Prediction of global omicron pandemic using ARIMA, MLR, and Prophet models. Sci Rep. 2022;12(1):1–13.

    Google Scholar 

Download references

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Correspondence to M. Vergin Raja Sarobin.

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Sarobin, M.V.R., Rathore, J., Mishra, R. et al. Uncovering the Hidden Patterns of the COVID-19 Global Pandemic: An in-Depth Data Analytics Approach. SN COMPUT. SCI. 5, 981 (2024). https://doi.org/10.1007/s42979-024-03317-y

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