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Modeling and Analysis of COVID-19 Based on a Deterministic Compartmental Model and Bayesian Inference

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Mathematical Modeling and Intelligent Control for Combating Pandemics

Part of the book series: Springer Optimization and Its Applications ((SOIA,volume 203))

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References

  1. WHO Coronavirus (COVID-19) Dashboard. https://covid19.who.int

  2. Zhou, F., Hu, T.J., Zhang, X.Y., Lai, K., Chen, J.H., Zhou, X.H.: The association of intensity and duration of non-pharmacological interventions and implementation of vaccination with COVID-19 infection, death, and excess mortality: Natural experiment in 22 European countries. J. Infect. Public Health 15(5), 499–507 (2022)

    Article  Google Scholar 

  3. Randolph, H.E., Barreiro, L.B.: Herd immunity: understanding COVID-19. Immunity 52(5), 737–741 (2020)

    Article  Google Scholar 

  4. Cumbler, E., Wittig, M., Jacobson, N., McClain, H., Treat, A., Radin, J., Harry, E.: Contingency planning for health care worker masks in case of medical supply chain failure: lessons learned in novel mask manufacturing from COVID-19 pandemic. Amer. J. Infect. Control 49(10), 1215–1220 (2021)

    Article  Google Scholar 

  5. Ayoobi, N., Sharifrazi, D., Alizadehsani, R., Shoeibi, A., Gorriz, J.M., Moosaei, H., Mosavi, A.: Time series forecasting of new cases and new deaths rate for COVID-19 using deep learning methods. Results Phys. 27, 104495 (2021)

    Article  Google Scholar 

  6. Weizman, O., Duceau, B., Trimaille, A., Pommier, T., Cellier, J., Geneste, L., Bonnet, G.: Machine learning-based scoring system to predict in-hospital outcomes in patients hospitalized with COVID-19. Arch. Cardiovasc. Dis. 115(12), 617–626 (2022)

    Article  Google Scholar 

  7. Althobaity, Y., Wu, J., Tildesley, M.J.: Non-pharmaceutical interventions and their relevance in the covid-19 vaccine rollout in Saudi Arabia and Arab Gulf countries. Infect. Dis. Modell. 7(3), 545–560 (2022)

    Google Scholar 

  8. Hethcote, H.W.: The mathematics of infectious diseases. SIAM Rev. 42(4), 599–653 (2000)

    Article  MathSciNet  MATH  Google Scholar 

  9. Kucharski, A.J., Russell, T.W., Diamond, C., Liu, Y., Edmunds, J., Funk, S., Flasche, S.: Early dynamics of transmission and control of COVID-19: a mathematical modelling study. Lancet Infect. Dis. 20(5), 553–558 (2020)

    Article  Google Scholar 

  10. Peng, L., Yang, W., Zhang, D., Zhuge, C., Hong, L. Epidemic analysis of COVID-19 in China by dynamical modeling (2020). Preprint arXiv:2002.06563

    Google Scholar 

  11. Iboi, E.A., Ngonghala, C.N., Gumel, A.B.: Will an imperfect vaccine curtail the COVID-19 pandemic in the US? Infect. Dis. Modell. 5, 510–524 (2020)

    Google Scholar 

  12. Shen, M., Zu, J., Fairley, C.K., Pagán, J.A., An, L., Du, Z., Zhang, L.: Projected COVID-19 epidemic in the United States in the context of the effectiveness of a potential vaccine and implications for social distancing and face mask use. Vaccine 39(16), 2295–2302 (2021)

    Article  Google Scholar 

  13. Delamater, P.L., Street, E.J., Leslie, T.F., Yang, Y.T., Jacobsen, K.H.: Complexity of the basic reproduction number (R0). Emerg. Infect. Dis. 25(1), 1 (2019)

    Article  Google Scholar 

  14. Hamra, G., MacLehose, R., Richardson, D.: Markov chain Monte Carlo: an introduction for epidemiologists. Int. J. Epidemiol. 42(2), 627–634 (2013)

    Article  Google Scholar 

  15. Betancourt, M.: A conceptual introduction to Hamiltonian Monte Carlo (2017). Preprint arXiv:1701.02434

    Google Scholar 

  16. Acuña-Zegarra, M.A., Díaz-Infante, S., Baca-Carrasco, D., Olmos-Liceaga, D.: COVID-19 optimal vaccination policies: a modeling study on efficacy, natural and vaccine-induced immunity responses. Math. Biosci. 337, 108614 (2021)

    Article  MathSciNet  MATH  Google Scholar 

  17. Iyaniwura, S.A., Falcão, R.C., Ringa, N., Adu, P.A., Spencer, M., Taylor, M., Otterstatter, M.: Mathematical modeling of COVID-19 in British Columbia: an age-structured model with time-dependent contact rates. Epidemics 39, 100559 (2022)

    Article  Google Scholar 

  18. Acuña-Zegarra, M.A., Santana-Cibrian, M., Velasco-Hernandez, J.X.: Modeling behavioral change and COVID-19 containment in Mexico: a trade-off between lockdown and compliance. Math. Biosci. 325, 108370 (2020)

    Article  MathSciNet  MATH  Google Scholar 

  19. Andrade, J., Duggan, J.: An evaluation of Hamiltonian Monte Carlo performance to calibrate age-structured compartmental SEIR models to incidence data. Epidemics 33, 100415 (2020)

    Article  Google Scholar 

  20. CSSEGISandData: COVID-19 Data Repository by the Center for Systems Science and Engineering (CSSE) at Johns Hopkins University (2023). https://github.com/CSSEGISandData/COVID-19

  21. Li, Q., Guan, X., Wu, P., Wang, X., Zhou, L., Tong, Y., et al., Feng, Z.: Early transmission dynamics in Wuhan, China, of novel coronavirus–infected pneumonia. New England J. Med. 382, 1199–1207 (2020)

    Google Scholar 

  22. Mizumoto, K., Kagaya, K., Zarebski, A., Chowell, G.: Estimating the asymptomatic proportion of coronavirus disease 2019 (COVID-19) cases on board the Diamond Princess cruise ship, Yokohama, Japan, 2020. Eurosurveillance 25(10), 2000180 (2020)

    Article  Google Scholar 

  23. Tang, B., Wang, X., Li, Q., Bragazzi, N.L., Tang, S., Xiao, Y., Wu, J.: Estimation of the transmission risk of the 2019-nCoV and its implication for public health interventions. J. Clin. Med. 9(2), 462 (2020)

    Article  Google Scholar 

  24. Morocco Population (2023) - Worldometer. https://www.worldometers.info/world-population/morocco-population/

  25. Zou, L., Ruan, F., Huang, M., Liang, L., Huang, H., Hong, Z., Wu, J.: SARS-CoV-2 viral load in upper respiratory specimens of infected patients. New England J. Med. 382(12), 1177–1179 (2020)

    Article  Google Scholar 

  26. Lye, A., Cicirello, A., Patelli, E.: Sampling methods for solving Bayesian model updating problems: a tutorial. Mechan. Syst. Signal Process. 159, 107760 (2021)

    Article  Google Scholar 

  27. Hoffman, M.D., Gelman, A.: The No-U-Turn sampler: adaptively setting path lengths in Hamiltonian Monte Carlo. J. Mach. Learn. Res. 15(1), 1593–1623 (2014)

    MathSciNet  MATH  Google Scholar 

  28. Stan Development Team: Stan Modeling Language User’s Guide and Reference Manual, Version 2.17. 0 (2017)

    Google Scholar 

  29. Kim, J.H., Marks, F., Clemens, J.D.: Looking beyond COVID-19 vaccine phase 3 trials. Nature Med. 27(2), 205–211 (2021)

    Article  Google Scholar 

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Acknowledgements

This research was supported by the National Center for Scientific and Technical Research (CNRST) of Morocco and Sidi Mohamed Ben Abdellah University (USMBA), grant number: Cov/2020/54.

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Authors and Affiliations

  1. LIMAS, Faculty of Sciences, Sidi Mohamed Ben Abdellah University, Fez, Morocco

    Touria Jdid, Mohammed Benbrahim & Mohammed Nabil Kabbaj

  2. LPAIS, Faculty of Sciences, Sidi Mohamed Ben Abdellah University, Fez, Morocco

    Mohamed Naji

Authors
  1. Touria Jdid
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  2. Mohammed Benbrahim
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  3. Mohammed Nabil Kabbaj
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  4. Mohamed Naji
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Corresponding author

Correspondence to Touria Jdid .

Editor information

Editors and Affiliations

  1. Department of Medical Research, China Medical University Hospital, Taichung, Taiwan

    Zakia Hammouch

  2. University of Hassan II Casablanca, Casablanca, Morocco

    Mohamed Lahby

  3. Department of Mathematics and Computer Science, Cankaya University, Etimesgut, Türkiye

    Dumitru Baleanu

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Jdid, T., Benbrahim, M., Kabbaj, M.N., Naji, M. (2023). Modeling and Analysis of COVID-19 Based on a Deterministic Compartmental Model and Bayesian Inference. In: Hammouch, Z., Lahby, M., Baleanu, D. (eds) Mathematical Modeling and Intelligent Control for Combating Pandemics. Springer Optimization and Its Applications, vol 203. Springer, Cham. https://doi.org/10.1007/978-3-031-33183-1_4

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