Modeling and analysis of recovery time for the COVID-19 patients: a Bayesian approach

References

• Abbas, K., Abbasi, N. Y., Ali, A., Khan, S. A., Manzoor, S., Khalil, A., … Altaf, M. (2019). Bayesian analysis of three-parameter Frechet distribution with medical applications. Computational and Mathematical Methods in Medicine, 2019, 9089856. doi:10.1155/2019/9089856
   PubMed Web of Science ®Google Scholar
• Adnan, H. (2021). Bayes estimators of Weibull-Lindley Rayleigh distribution parameters using Lindley’s approximation. Turkish Journal of Computer and Mathematics Education (TURCOMAT), 12(12), 508–517.
   Google Scholar
• Afify, A. Z., Yousof, H. M., Cordeiro, G. M., Ortega, E. M., & Nofal, Z. M. (2016). The Weibull Fréchet distribution and its applications. Journal of Applied Statistics, 43(14), 2608–2626. doi:10.1080/02664763.2016.1142945
   Web of Science ®Google Scholar
• Almongy, H. M., Almetwally, E. M., Aljohani, H. M., Alghamdi, A. S., & Hafez, E. (2021). A new extended Rayleigh distribution with applications of COVID-19 data. Results in Physics, 23, 104012. doi:10.1016/j.rinp.2021.104012
   PubMed Web of Science ®Google Scholar
• AlSayegh, M. A. K., & Iqbal, A. (2021). The impact of the precautionary measures taken in the kingdom of Bahrain to contain the outbreak of COVID-19. Arab Journal of Basic and Applied Sciences, 28(1), 195–203. doi:10.1080/25765299.2021.1903143
   Google Scholar
• Alzaatreh, A., Lee, C., & Famoye, F. (2013). A new method for generating families of continuous distributions. METRON, 71(1), 63–79. doi:10.1007/s40300-013-0007-y
   Google Scholar
• Bakar, M. A., Mohamed, N., Pratama, D. A., Yusran, M. F. A., Aleng, N. A., Yanuar, Z., & Niken, L. (2021). Modelling lock-down strictness for COVID-19 pandemic in ASEAN countries by using hybrid ARIMA-SVR and hybrid SEIR-ANN. Arab Journal of Basic and Applied Sciences, 28(1), 204–224. doi:10.1080/25765299.2021.1902606
   Google Scholar
• Bherwani, H., Anjum, S., Kumar, S., Gautam, S., Gupta, A., Kumbhare, H., … Kumar, R. (2021). Understanding COVID-19 transmission through Bayesian probabilistic modeling and GIS-based Voronoi approach: A policy perspective. Environment, Development and Sustainability, 23(4), 5846–5864. doi:10.1007/s10668-020-00849-0
   PubMed Web of Science ®Google Scholar
• Car, Z., Baressi Šegota, S., Anđelić, N., Lorencin, I., & Mrzljak, V. (2020). Modeling the spread of COVID-19 infection using a multilayer perceptron. Computational and Mathematical Methods in Medicine, 2020, 5714714. doi:10.1155/2020/5714714
   PubMed Web of Science ®Google Scholar
• De Oliveira, A. C. S., Morita, L. H. M., Da Silva, E. B., Zardo, L. A. R., Fontes, C. J. F., & Granzotto, D. C. T. (2020). Bayesian modeling of COVID-19 cases with a correction to account for under-reported cases. Infectious Disease Modelling, 5, 699–713. doi:10.1016/j.idm.2020.09.005
   PubMedGoogle Scholar
• Din, A., Li, Y., Khan, T., & Zaman, G. (2020). Mathematical analysis of spread and control of the novel corona virus (COVID-19) in China. Chaos, Solitons, and Fractals, 141, 110286. doi:10.1016/j.chaos.2020.110286
   PubMed Web of Science ®Google Scholar
• Fréchet, M. (1927). Sur la loi de probabilité de l‘écart maximum. Annales Societatis Mathematicae Polonae, 6, 93–116. doi:10.11429/sugaku1947.46.39
   Google Scholar
• Gencer, G., & Gencer, K. (2019). Comparison of maximum likelihood and Bayes estimators under symmetric and asymmetric loss functions by means of Tierney Kadane’s approximation for Weibull distribution. Turkish Journal of Science and Technology, 14(2), 69–78.
   Google Scholar
• Gencer, G., & Gencer, K. (2020). Estimations for the odd Weibull distribution under progressive type-II right censored samples. Sakarya Üniversitesi Fen Bilimleri Enstitüsü Dergisi, 24(1), 29–36.
   Google Scholar
• Haj Ismail, A., Dawi, E., Jwaid, T., Mahmoud, S. T., & AbdelKader, A. (2021). Simulation of the evolution of the Covid-19 pandemic in the United Arab Emirates using the sir epidemical model. Arab Journal of Basic and Applied Sciences, 28(1), 128–134. doi:10.1080/25765299.2021.1890900
   Google Scholar
• Jordan, R. E., Adab, P., & Cheng, K. (2020). Covid-19: Risk factors for severe disease and death. British Medical Journal, 368, m1198. doi:10.1136/bmj.m1198
   Google Scholar
• Kampf, G., Todt, D., Pfaender, S., & Steinmann, E. (2020). Persistence of coronaviruses on inanimate surfaces and their inactivation with biocidal agents. The Journal of Hospital Infection, 104(3), 246–251. doi:10.1016/j.jhin.2020.01.022
   PubMed Web of Science ®Google Scholar
• Korkmaz, M. Ç., Yousof, H. M., & Ali, M. M. (2017). Some theoretical and computational aspects of the odd Lindley Fréchet distribution. İstatistikçiler Dergisi: İstatistik ve Aktüerya, 10(2), 129–140.
   Google Scholar
• Krishna, E., Jose, K., Alice, T., & Ristić, M. M. (2013). The Marshall-Olkin Fréchet distribution. Communications in Statistics - Theory and Methods, 42(22), 4091–4107. doi:10.1080/03610926.2011.648785
   Web of Science ®Google Scholar
• Kutal, D. H., & Qian, L. (2018). A non-mixture cure model for right-censored data with Fréchet distribution. Stats, 1(1), 176–188. 10.3390/stats1010013.
   Google Scholar
• Lavanya, A., & Alexander, T. L. (2016). Estimation of parameters using Lindley’s method. International Journal of Advanced Research, 4(12), 1767–1778. doi:10.21474/IJAR01/2579
   Google Scholar
• Levy, T. J., Richardson, S., Coppa, K., Barnaby, D. P., McGinn, T., … Becker, L. B, the Northwell COVID-19 Research Consortium. (2020). Estimating survival of hospitalized COVID-19 patients from admission information. medRxiv, 1–29.
   Google Scholar
• Lindley, D. V. (1980). Approximate Bayesian methods. Trabajos de Estadistica Y de Investigacion Operativa, 31(1), 223–245. doi:10.1007/BF02888353
   Google Scholar
• Lutchmansingh, D. D., Knauert, M. P., Antin-Ozerkis, D. E., Chupp, G., Cohn, L., Dela Cruz, C. S., … Possick, J. D. (2021). A clinic blueprint for post-COVID-19 RECOVERY: Learning from the past, looking to the future. Chest, 159(3), 949–958. doi:10.1016/j.chest.2020.10.067
   PubMed Web of Science ®Google Scholar
• Manevski, D., Ružić Gorenjec, N., Kejžar, N., & Blagus, R. (2020). Modeling COVID-19 pandemic using Bayesian analysis with application to Slovene data. Mathematical Biosciences, 329, 108466. doi:10.1016/j.mbs.2020.108466
   PubMed Web of Science ®Google Scholar
• Nemati, M., Ansary, J., & Nemati, N. (2020). Machine-learning approaches in COVID-19 survival analysis and discharge-time likelihood prediction using clinical data. Patterns (New York, N.Y.), 1(5), 100074. doi:10.1016/j.patter.2020.100074
   PubMedGoogle Scholar
• Nesteruk, I. (2020). Statistics-based predictions of coronavirus epidemic spreading in mainland China. doi:10.1101/2020.02.12.20021931
   Google Scholar
• Niazkar, M., Eryılmaz Türkkan, G., Niazkar, H. R., & Türkkan, Y. A. (2020). Assessment of three mathematical prediction models for forecasting the COVID-19 outbreak in Iran and Turkey. Computational and Mathematical Methods in Medicine, 2020, 7056285. doi:10.1155/2020/7056285
   PubMed Web of Science ®Google Scholar
• Oguntunde, P. E., Khaleel, M. A., Ahmed, M. T., & Okagbue, H. I. (2019). The Gompertz Fréchet distribution: Properties and applications. Cogent Mathematics & Statistics, 6(1), 1568662. doi:10.1080/25742558.2019.1568662
   Google Scholar
• Rayleigh, L. (1880). XII. On the resultant of a large number of vibrations of the same pitch and of arbitrary phase. The London, Edinburgh, and Dublin Philosophical Magazine and Journal of Science, 10(60), 73–78. doi:10.1080/14786448008626893
   Google Scholar
• Rothe, C., Schunk, M., Sothmann, P., Bretzel, G., Froeschl, G., Wallrauch, C., … Hoelscher, M. (2020). Transmission of 2019-nCoV infection from an asymptomatic contact in Germany. The New England Journal of Medicine, 382(10), 970–971. doi:10.1056/NEJMc2001468
   PubMed Web of Science ®Google Scholar
• Sana, M., & Faizan, M. (2019). Bayesian estimation for Nadarajah-Haghighi distribution based on upper record values. Pakistan Journal of Statistics and Operation Research, 15(1), 217–230. doi:10.18187/pjsor.v15i1.2569
   Web of Science ®Google Scholar
• Shafiq, A., Lone, S., Sindhu, T. N., El Khatib, Y., Al-Mdallal, Q. M., & Muhammad, T. (2021). A new modified Kies Fréchet distribution: Applications of mortality rate of Covid-19. Results in Physics, 28, 104638. doi:10.1016/j.rinp.2021.104638
   PubMed Web of Science ®Google Scholar
• Sharma, V. K., Singh, S. K., & Singh, U. (2017). Classical and Bayesian methods of estimation for power Lindley distribution with application to waiting time data. Communications for Statistical Applications and Methods, 24(3), 193–209. doi:10.5351/CSAM.2017.24.3.193
   Web of Science ®Google Scholar
• Tang, Y., & Wang, S. (2020). Mathematic modeling of COVID-19 in the United States. Emerging Microbes & Infections, 9(1), 827–829. doi:10.1080/22221751.2020.1760146
   PubMed Web of Science ®Google Scholar
• Tanis, C., & Saracoglu, B. (2019). Comparisons of six different estimation methods for log-Kumaraswamy distribution. Thermal Science, 23(Suppl. 6), 1839–1847. doi:10.2298/TSCI190411344T
   Google Scholar
• Tierney, L., & Kadane, J. B. (1986). Accurate approximations for posterior moments and marginal densities. Journal of the American Statistical Association, 81(393), 82–86. doi:10.1080/01621459.1986.10478240
   Web of Science ®Google Scholar
• Vodˇa, V. G. (1972). On the inverse Rayleigh distributed random variable. Reports of Statistical Application Research JUSE, 19(4), 13–21.
   Google Scholar
• WHO. (2020a). Coronavirus disease 2019 (COVID-19), Situation Report – 51. WHO.
   Google Scholar
• WHO. (2020b). Coronavirus disease 2019 (COVID-19), Situation Report – 53. WHO
   Google Scholar
• Yousof, H. M., Altun, E., & Hamedani, G. (2018). A new extension of Frechet distribution with regression models, residual analysis and characterizations. Journal of Data Science, 16(4), 743–770. doi:10.6339/JDS.201810_16(4).00005
   Google Scholar
• Zhu, N., Zhang, D., Wang, W., Li, X., Yang, B., Song, J., … Tan, W. (2020). A novel coronavirus from patients with pneumonia in China, 2019. New England Journal of Medicine, 382(8), 727–733. doi:10.1056/NEJMoa2001017
   PubMed Web of Science ®Google Scholar
• Zou, L., Ruan, F., Huang, M., Liang, L., Huang, H., Hong, Z., … Wu, J. (2020). SARS-CoV-2 viral load in upper respiratory specimens of infected patients. The New England Journal of Medicine, 382(12), 1177–1179. doi:10.1056/NEJMc2001737
   PubMed Web of Science ®Google Scholar
• Zuo, M., Khosa, S. K., Ahmad, Z., & Almaspoor, Z. (2020). Comparison of COVID-19 pandemic dynamics in Asian countries with statistical modeling. Computational and Mathematical Methods in Medicine, 2020, 4296806. doi:10.1155/2020/4296806
   PubMed Web of Science ®Google Scholar