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Modeling of COVID-19 Cases in Pakistan Using Lifetime Probability Distributions

Author

Listed:
  • Muhammad Ahsan-ul-Haq

    (University of the Punjab)

  • Mukhtar Ahmed

    (Minhaj University Lahore)

  • Javeria Zafar

    (University of the Punjab)

  • Pedro Luiz Ramos

    (University of São Paulo)

Abstract

The Coronavirus Disease (COVID-19) is a respiratory disease that caused a large number of deaths all over the world since its outbreak. The World Health Organization (WHO) has declared the outbreak a global pandemic. The understanding of the random process related to the behavior infection of COVID-19 is an important health and economic problem. In the proposed study, we analyze the frequency of daily confirmed cases of COVID-19 using different two-parameter lifetime probability distributions. We consider the data from the period of March 11, 2020, to July 25, 2020, of Pakistan. We consider nine lifetime probability distributions for the analysis purpose and the selection of best fit was carried out using log-likelihood, AIC, BIC, RMSE, and R2 goodness-of-fit measures. Results indicate that Weibull distribution provides generally the best-fit probability distribution.

Suggested Citation

  • 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.
    • Handle: RePEc:spr:aodasc:v:9:y:2022:i:1:d:10.1007_s40745-021-00338-9
    DOI: 10.1007/s40745-021-00338-9
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    References listed on IDEAS

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    7. Pedro L Ramos & Diego C Nascimento & Paulo H Ferreira & Karina T Weber & Taiza E G Santos & Francisco Louzada, 2019. "Modeling traumatic brain injury lifetime data: Improved estimators for the Generalized Gamma distribution under small samples," PLOS ONE, Public Library of Science, vol. 14(8), pages 1-22, August.
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    Cited by:

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    2. Vali Borimnejad & Sahar Dehyouri, 2022. "Content Analysis of the Economic Problems of Covid-19 Disease on Businesses: A Case Study of Tehran Province, Iran," Annals of Data Science, Springer, vol. 9(5), pages 1069-1083, October.

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