IDEAS home Printed from https://ideas.repec.org/a/eee/chsofr/v138y2020ics0960077920303258.html
   My bibliography  Save this article

Statistical analysis of forecasting COVID-19 for upcoming month in Pakistan

Author

Listed:
  • Yousaf, Muhammad
  • Zahir, Samiha
  • Riaz, Muhammad
  • Hussain, Sardar Muhammad
  • Shah, Kamal

Abstract

In this paper, we have conducted analysis based on data obtained from National Institute of Health (NIH) - Islamabad and produced a forecast of COVID-19 confirmed cases as well as the number of deaths and recoveries in Pakistan using the Auto-Regressive Integrated Moving Average Model (ARIMA). The fitted forecasting models revealed high exponential growth in the number of confirmed cases, deaths and recoveries in Pakistan. Based on our model prediction the number of confirmed cases will be increased by 2.7 times, 95% prediction interval for the number of cases at the end of May 2020 = (5681 to 33079). There could be up to 500 deaths, 95% prediction interval = (168 to 885) and there could be eightfold increase in the number of recoveries, 95% prediction interval = (2391 to 16126). The forecasting results of COVID-19 are alarming for May in Pakistan. The health officials and government should adopt new strategies to control the pandemic from further spread until a proper treatment or vaccine is developed.

Suggested Citation

  • Yousaf, Muhammad & Zahir, Samiha & Riaz, Muhammad & Hussain, Sardar Muhammad & Shah, Kamal, 2020. "Statistical analysis of forecasting COVID-19 for upcoming month in Pakistan," Chaos, Solitons & Fractals, Elsevier, vol. 138(C).
    • Handle: RePEc:eee:chsofr:v:138:y:2020:i:c:s0960077920303258
    DOI: 10.1016/j.chaos.2020.109926
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0960077920303258
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.chaos.2020.109926?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Fotios Petropoulos & Spyros Makridakis, 2020. "Forecasting the novel coronavirus COVID-19," PLOS ONE, Public Library of Science, vol. 15(3), pages 1-8, March.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Gaetano Perone, 2022. "Using the SARIMA Model to Forecast the Fourth Global Wave of Cumulative Deaths from COVID-19: Evidence from 12 Hard-Hit Big Countries," Econometrics, MDPI, vol. 10(2), pages 1-23, April.
    2. Khan, Firdos & Saeed, Alia & Ali, Shaukat, 2020. "Modelling and forecasting of new cases, deaths and recover cases of COVID-19 by using Vector Autoregressive model in Pakistan," Chaos, Solitons & Fractals, Elsevier, vol. 140(C).
    3. Muhammad Ahsan-ul-Haq & Mukhtar Ahmed & Javeria Zafar & Pedro Luiz Ramos, 2022. "Modeling of COVID-19 Cases in Pakistan Using Lifetime Probability Distributions," Annals of Data Science, Springer, vol. 9(1), pages 141-152, February.
    4. Abdullah, & Ahmad, Saeed & Owyed, Saud & Abdel-Aty, Abdel-Haleem & Mahmoud, Emad E. & Shah, Kamal & Alrabaiah, Hussam, 2021. "Mathematical analysis of COVID-19 via new mathematical model," Chaos, Solitons & Fractals, Elsevier, vol. 143(C).
    5. Rohitash Chandra & Yixuan He, 2021. "Bayesian neural networks for stock price forecasting before and during COVID-19 pandemic," PLOS ONE, Public Library of Science, vol. 16(7), pages 1-32, July.
    6. Zeroual, Abdelhafid & Harrou, Fouzi & Dairi, Abdelkader & Sun, Ying, 2020. "Deep learning methods for forecasting COVID-19 time-Series data: A Comparative study," Chaos, Solitons & Fractals, Elsevier, vol. 140(C).
    7. Kalantari, Mahdi, 2021. "Forecasting COVID-19 pandemic using optimal singular spectrum analysis," Chaos, Solitons & Fractals, Elsevier, vol. 142(C).
    8. Wang, Peipei & Zheng, Xinqi & Li, Jiayang & Zhu, Bangren, 2020. "Prediction of epidemic trends in COVID-19 with logistic model and machine learning technics," Chaos, Solitons & Fractals, Elsevier, vol. 139(C).
    9. Muhammad Saeed & Ijaz Ahmad & Muhammad Ahmad Usman, 2021. "Do the stocks' returns and volatility matter under the COVID-19 pandemic? A Case Study of Pakistan Stock Exchange," iRASD Journal of Economics, International Research Alliance for Sustainable Development (iRASD), vol. 3(1), pages 13-26, june.
    10. Feroze, Navid, 2020. "Forecasting the patterns of COVID-19 and causal impacts of lockdown in top five affected countries using Bayesian Structural Time Series Models," Chaos, Solitons & Fractals, Elsevier, vol. 140(C).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Masum, Mohammad & Masud, M.A. & Adnan, Muhaiminul Islam & Shahriar, Hossain & Kim, Sangil, 2022. "Comparative study of a mathematical epidemic model, statistical modeling, and deep learning for COVID-19 forecasting and management," Socio-Economic Planning Sciences, Elsevier, vol. 80(C).
    2. Chakraborty, Tanujit & Ghosh, Indrajit, 2020. "Real-time forecasts and risk assessment of novel coronavirus (COVID-19) cases: A data-driven analysis," Chaos, Solitons & Fractals, Elsevier, vol. 135(C).
    3. Emrah Gecili & Assem Ziady & Rhonda D Szczesniak, 2021. "Forecasting COVID-19 confirmed cases, deaths and recoveries: Revisiting established time series modeling through novel applications for the USA and Italy," PLOS ONE, Public Library of Science, vol. 16(1), pages 1-11, January.
    4. Camacho, Carmen & Vasilakis, Chrysovalantis, 2023. "Transmissible Diseases, Vaccination and Inequality," IZA Discussion Papers 16504, Institute of Labor Economics (IZA).
    5. Paolo Berta & Paolo Paruolo & Stefano Verzillo & Pietro Giorgio Lovaglio, 2020. "A bivariate prediction approach for adapting the health care system response to the spread of COVID-19," PLOS ONE, Public Library of Science, vol. 15(10), pages 1-14, October.
    6. Semenoglou, Artemios-Anargyros & Spiliotis, Evangelos & Makridakis, Spyros & Assimakopoulos, Vassilios, 2021. "Investigating the accuracy of cross-learning time series forecasting methods," International Journal of Forecasting, Elsevier, vol. 37(3), pages 1072-1084.
    7. Dalton Garcia Borges de Souza & Erivelton Antonio dos Santos & Francisco Tarcísio Alves Júnior & Mariá Cristina Vasconcelos Nascimento, 2021. "On Comparing Cross-Validated Forecasting Models with a Novel Fuzzy-TOPSIS Metric: A COVID-19 Case Study," Sustainability, MDPI, vol. 13(24), pages 1-25, December.
    8. Konstantinos Demertzis & Dimitrios Tsiotas & Lykourgos Magafas, 2020. "Modeling and Forecasting the COVID-19 Temporal Spread in Greece: An Exploratory Approach Based on Complex Network Defined Splines," IJERPH, MDPI, vol. 17(13), pages 1-17, June.
    9. Khalid A. Kheirallah & Belal Alsinglawi & Abdallah Alzoubi & Motasem N. Saidan & Omar Mubin & Mohammed S. Alorjani & Fawaz Mzayek, 2020. "The Effect of Strict State Measures on the Epidemiologic Curve of COVID-19 Infection in the Context of a Developing Country: A Simulation from Jordan," IJERPH, MDPI, vol. 17(18), pages 1-11, September.
    10. Michał Wieczorek & Jakub Siłka & Dawid Połap & Marcin Woźniak & Robertas Damaševičius, 2020. "Real-time neural network based predictor for cov19 virus spread," PLOS ONE, Public Library of Science, vol. 15(12), pages 1-18, December.
    11. Jordan J Bird & Chloe M Barnes & Cristiano Premebida & Anikó Ekárt & Diego R Faria, 2020. "Country-level pandemic risk and preparedness classification based on COVID-19 data: A machine learning approach," PLOS ONE, Public Library of Science, vol. 15(10), pages 1-20, October.
    12. Petropoulos, Fotios & Makridakis, Spyros & Stylianou, Neophytos, 2022. "COVID-19: Forecasting confirmed cases and deaths with a simple time series model," International Journal of Forecasting, Elsevier, vol. 38(2), pages 439-452.
    13. Aman Ullah & Tao Wang & Weixin Yao, 2022. "Nonlinear modal regression for dependent data with application for predicting COVID‐19," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 185(3), pages 1424-1453, July.
    14. Giacomo De Nicola & Marc Schneble & Göran Kauermann & Ursula Berger, 2022. "Regional now- and forecasting for data reported with delay: toward surveillance of COVID-19 infections," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 106(3), pages 407-426, September.
    15. Kavadi, Durga Prasad & Patan, Rizwan & Ramachandran, Manikandan & Gandomi, Amir H., 2020. "Partial derivative Nonlinear Global Pandemic Machine Learning prediction of COVID 19," Chaos, Solitons & Fractals, Elsevier, vol. 139(C).
    16. Aman Khakharia & Vruddhi Shah & Sankalp Jain & Jash Shah & Amanshu Tiwari & Prathamesh Daphal & Mahesh Warang & Ninad Mehendale, 2021. "Outbreak Prediction of COVID-19 for Dense and Populated Countries Using Machine Learning," Annals of Data Science, Springer, vol. 8(1), pages 1-19, March.
    17. Timo Dimitriadis & Tobias Fissler & Johanna Ziegel, 2020. "The Efficiency Gap," Papers 2010.14146, arXiv.org, revised Sep 2022.
    18. Kalantari, Mahdi, 2021. "Forecasting COVID-19 pandemic using optimal singular spectrum analysis," Chaos, Solitons & Fractals, Elsevier, vol. 142(C).
    19. Wieczorek, Michał & Siłka, Jakub & Woźniak, Marcin, 2020. "Neural network powered COVID-19 spread forecasting model," Chaos, Solitons & Fractals, Elsevier, vol. 140(C).
    20. Petropoulos, Fotios & Apiletti, Daniele & Assimakopoulos, Vassilios & Babai, Mohamed Zied & Barrow, Devon K. & Ben Taieb, Souhaib & Bergmeir, Christoph & Bessa, Ricardo J. & Bijak, Jakub & Boylan, Joh, 2022. "Forecasting: theory and practice," International Journal of Forecasting, Elsevier, vol. 38(3), pages 705-871.
      • Fotios Petropoulos & Daniele Apiletti & Vassilios Assimakopoulos & Mohamed Zied Babai & Devon K. Barrow & Souhaib Ben Taieb & Christoph Bergmeir & Ricardo J. Bessa & Jakub Bijak & John E. Boylan & Jet, 2020. "Forecasting: theory and practice," Papers 2012.03854, arXiv.org, revised Jan 2022.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:chsofr:v:138:y:2020:i:c:s0960077920303258. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Thayer, Thomas R. (email available below). General contact details of provider: https://www.journals.elsevier.com/chaos-solitons-and-fractals .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.