International Journal of

ADVANCED AND APPLIED SCIENCES

EISSN: 2313-3724, Print ISSN: 2313-626X

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 Volume 9, Issue 3 (March 2022), Pages: 71-81

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 Original Research Paper

 Title: Regression modeling and correlation analysis spread of COVID-19 data for Pakistan

 Author(s): Dure Jabeen 1, *, Ingila Rahim 1, Rumaisa Iftikhar 1, M. Rafiullah 2, M. Rashid Kamal Ansari 1

 Affiliation(s):

 1Department of Electronics and Mathematics, Sir Syed University of Engineering and Technology, Karachi, Pakistan
 2Department of Mathematics, COMSATS University Islamabad, Lahore, Pakistan

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 * Corresponding Author. 

  Corresponding author's ORCID profile: https://orcid.org/0000-0002-6743-2911

 Digital Object Identifier: 

 https://doi.org/10.21833/ijaas.2022.03.009

 Abstract:

This study presents a mathematical analysis of the coronavirus spread in Pakistan by analyzing the (COVID-19) situation in six provinces, including Gilgit Baltistan, Azad Jammu Kashmir and federal capital (seven zones) individually. The influence of each province and the Federal Capital territory is then observed over the other territories. By subdividing the associated data into confirmed cases, death cases, and recovery cases, the dependence of the (COVID-19) situation from one province to the other provinces is investigated. Since the worsening circumstance in the neighboring countries were considered as a catalyst to initiate the outburst in Pakistan, it seemed necessary to have an understanding of the situation in neighboring countries, particularly, Iran, India, and Bangladesh. Exploratory data analysis is utilized to understand the behavior of confirmed cases, death cases, and recovery cases data of (COVID-19) in Pakistan. Also, an understanding of the pandemic spread during different waves of (COVID-19) is obtained. Depending on the individual situation in each of the provinces, it is expected to have a different ARIMA model in each case. Hunt for the most suitable ARIMA models is an essential part of this study. The time-series data forecasts by processing the most suitable ARIMA models to observe the influence of one territory over the other. Moreover, forecasting for the month of August 2021 is performed and a possible correlation with actual data is determined. Linear, multiple regression, and exponential models have been applied and the best-fitted model is acquired. The information obtained from such analysis can be employed to vary possible parameters and variables in the system to achieve optimal performance. 

 © 2022 The Authors. Published by IASE.

 This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

 Keywords: COVID-19, ARIMA, Correlation, Forecast, Confirmed cases, Death cases, Recovery cases

 Article History: Received 25 September 2021, Received in revised form 3 January 2022, Accepted 3 January 2022

 Acknowledgment 

No Acknowledgment.

 Compliance with ethical standards

 Conflict of interest: The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

 Citation:

 Jabeen D, Rahim I, and Iftikhar R et al. (2022). Regression modeling and correlation analysis spread of COVID-19 data for Pakistan. International Journal of Advanced and Applied Sciences, 9(3): 71-81

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 Figures

 Fig. 1 Fig. 2 Fig. 3 Fig. 4 Fig. 5 Fig. 6 Fig. 7 Fig. 8 Fig. 9 Fig. 10 Fig. 11 

 Tables

 Table 1 Table 2 Table 3 Table 4 Table 5 Table 6 Table 7 Table 8 Table 9  

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 References (23)

  1. Anderson RM, Heesterbeek H, Klinkenberg D, and Hollingsworth TD (2020). How will country-based mitigation measures influence the course of the COVID-19 epidemic? The Lancet, 395(10228): 931-934. https://doi.org/10.1016/S0140-6736(20)30567-5   [Google Scholar]
  2. Fargana A, Arifutzzaman A, and Rakhimov AA (2021). Spreading analysis of COVID-19 epidemic in Bangladesh by dynamical mathematical modelling. European Journal of Medical and Educational Technologies, 14(3): em2109. https://doi.org/10.30935/ejmets/10959   [Google Scholar]
  3. Ghosh S (2020). Predictive model with analysis of the initial spread of COVID-19 in India. International Journal of Medical Informatics, 143: 104262. https://doi.org/10.1016/j.ijmedinf.2020.104262   [Google Scholar] PMid:32911257 PMCid:PMC7445130
  4. Gunnell D, Appleby L, and Arensman E et al. (2020). Suicide risk and prevention during the COVID19 pandemic. The Lancet Psychiatry, 7(6): 468-471. https://doi.org/10.1016/S2215-0366(20)30171-1   [Google Scholar]
  5. Guo J, Chen H, and Chen S (2020). Improved kernel recursive least squares algorithm based online prediction for nonstationary time series. IEEE Signal Processing Letters, 27: 1365-1369. https://doi.org/10.1109/LSP.2020.3011892   [Google Scholar]
  6. Hamzah FB, Lau C, Nazri H, Ligot DV, Lee G, Tan CL, and Chung MH (2020). CoronaTracker: Worldwide COVID-19 outbreak data analysis and prediction. Bulletin of World Health Organisation. https://doi.org/10.2471/BLT.20.255695   [Google Scholar]
  7. Imai N, Cori A, Dorigatti I, Baguelin M, Donnelly CA, Riley S, Ferguson NM (2020). Report 3: Transmissibility of 2019-nCoV. Imperial College London COVID-19 Response Team, London, UK.   [Google Scholar]
  8. Jung SM, Akhmetzhanov AR, Hayashi K, Linton NM, Yang Y, Yuan B, and Nishiura H (2020). Real-time estimation of the risk of death from novel coronavirus (COVID-19) infection: Inference using exported cases. Journal of Clinical Medicine, 9(2): 523. https://doi.org/10.3390/jcm9020523   [Google Scholar] PMid:32075152 PMCid:PMC7074479
  9. Kane MJ, Price N, Scotch M, and Rabinowitz P (2014). Comparison of ARIMA and random forest time series models for prediction of avian influenza H5N1 outbreaks. BMC Bioinformatics, 15(1): 1-9. https://doi.org/10.1186/1471-2105-15-276   [Google Scholar] PMid:25123979 PMCid:PMC4152592
  10. Kawohl W and Nordt C (2020). COVID-19, unemployment, and suicide. The Lancet Psychiatry, 7(5): 389-390. https://doi.org/10.1016/S2215-0366(20)30141-3   [Google Scholar]
  11. Khajanchi S, Das DK, and Kar TK (2018). Dynamics of tuberculosis transmission with exogenous reinfections and endogenous reactivation. Physica A: Statistical Mechanics and its Applications, 497: 52-71. https://doi.org/10.1016/j.physa.2018.01.014   [Google Scholar]
  12. Li XL (2020). Convolutional PCA for multiple time series. IEEE Signal Processing Letters, 27: 1450-1454. https://doi.org/10.1109/LSP.2020.3016185   [Google Scholar]
  13. Read JM, Bridgen JRE, Cummings DAT, Ho A, and Jewell CP (2021). Novel coronavirus 2019-nCoV (COVID-19): Early estimation of epidemiological parameters and epidemic size estimates. Philosophical Transactions of the Royal Society B: Biological Sciences, 376: 20200265. https://doi.org/10.1098/rstb.2020.0265   [Google Scholar] PMid:34053269 PMCid:PMC8165596
  14. Sharma RR, Kumar M, Maheshwari S, and Ray KP (2020). EVDHM-ARIMA-based time series forecasting model and its application for COVID-19 cases. IEEE Transactions on Instrumentation and Measurement, 70: 1-10. https://doi.org/10.1109/TIM.2020.3041833   [Google Scholar]
  15. Shastri S, Singh K, Kumar S, Kour P, and Mansotra V (2020). Time series forecasting of COVID-19 using deep learning models: India-USA comparative case study. Chaos, Solitons and Fractals, 140: 110227. https://doi.org/10.1016/j.chaos.2020.110227   [Google Scholar] PMid:32843824 PMCid:PMC7440083
  16. Talkhi N, Fatemi NA, Ataei Z, and Nooghabi MJ (2021). Modeling and forecasting number of confirmed and death caused COVID-19 in IRAN: A comparison of time series forecasting methods. Biomedical Signal Processing and Control, 66: 102494. https://doi.org/10.1016/j.bspc.2021.102494   [Google Scholar] PMid:33594301 PMCid:PMC7874981
  17. Tomar A and Gupta N (2020). Prediction for the spread of COVID-19 in India and effectiveness of preventive measures. Science of the Total Environment, 728: 138762. https://doi.org/10.1016/j.scitotenv.2020.138762   [Google Scholar] PMid:32334157 PMCid:PMC7169890
  18. Wang YW, Shen ZZ, and Jiang Y (2018). Comparison of ARIMA and GM (1, 1) models for prediction of hepatitis B in China. PloS One, 13(9): e0201987. https://doi.org/10.1371/journal.pone.0201987   [Google Scholar] PMid:30180159 PMCid:PMC6122800
  19. WHO (2020). Coronavirus disease 2019 (COVID-19): Situation report, 73. World Health Organization, Geneva, Switzerland.   [Google Scholar]
  20. WHO (2021). Coronavirus disease (COVID-19). World Health Organization, Geneva, Switzerland.   [Google Scholar]
  21. Zareie B, Roshani A, Mansournia MA, Rasouli MA, and Moradi G (2020). A model for COVID-19 prediction in Iran based on China parameters. Archives of Iranian Medicine, 23(4): 244-248. https://doi.org/10.34172/aim.2020.05   [Google Scholar] PMid:32271597
  22. Zhan C, Tse CK, Lai Z, Hao T, and Su J (2020). Prediction of COVID-19 spreading profiles in South Korea, Italy and Iran by data-driven coding. PloS One, 15(7): e0234763. https://doi.org/10.1371/journal.pone.0234763   [Google Scholar] PMid:32628673 PMCid:PMC7337285
  23. Zhong L, Mu L, Li J, Wang J, Yin Z, and Liu D (2020). Early prediction of the 2019 novel coronavirus outbreak in the mainland China based on simple mathematical model. IEEE Access, 8: 51761-51769. https://doi.org/10.1109/ACCESS.2020.2979599   [Google Scholar] PMid:32391240 PMCid:PMC7176026