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Pandemics have the potential to cause immense disruption of our everyday activities and has impact on the communities and societies mainly through the restrictions applied to the business activities, services, manufacturing, but also education, transportation etc. Therefore, it is important to create suitable prediction models to establish convenient methods for the planning of the operations and processes to cope with the difficulty. In this paper, the prediction model for the spread of the viral disease in term of the estimated maximal weekly confirmed cases and weekly deaths using the Weibull distribution as a theoretical model for statistical data processing is presented. The theoretical prediction model was applied and confirmed on the data available for the whole world and compared to the situation in Europe and Slovakia for the pandemic waves and can be used for the more precise prediction of the pandemic situation and to enhance planning of the activities and processes regarding to the restrictions applied during the worsening pandemic situation.
Słowa kluczowe
Wydawca
Czasopismo
Rocznik
Tom
Strony
179--186
Opis fizyczny
Bibliogr. 45 poz., rys., tab.
Twórcy
autor
- Training Centre Lest 962 63 Pliesovce, Slovakia
autor
- Training Centre Lest 962 63 Pliesovce, Slovakia
autor
- Technical University of Kosice Faculty of Aeronautics Rampova 7, 040 01 Kosice, Slovakia
autor
- Technical University of Kosice Faculty of Aeronautics Rampova 7, 040 01 Kosice, Slovakia
Bibliografia
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- [4] A.K. Gupta, V. Singh, P. Mathur and C.M. Travieso-Gonzalez. “Prediction of COVID-19 pandemic measuring criteria using support vector machine, prophet and linear regression models in Indian scenario”. Journal of Interdisciplinary Mathematics, vol. 24(1), pp. 89-108, 2021. doi: 10.1080/09720502.2020.1833458
- [5] P. Guha. “Spatiotemporal Analysis of COVID-19 Pandemic and Predictive Models based on Artificial Intelligence for different States of India”. Journal of The Institution of Engineers (India): Series B, vol. 102(6), pp. 1265-1274, 2021. doi: 10.1007/s40031-021-00617-2
- [6] A.I. Shahin and S. Almotairi. “A deep learning BiLSTM encoding-decoding model for COVID-19 pandemic spread forecasting”. Fractal and Fractional, vol. 5(4),175, 2021. doi: 10.3390/fractalfract5040175
- [7] M. Humayun and A. Alsayat. “Prediction Model for Coronavirus Pandemic Using Deep Learning”. Computer Systems Science and Engineering, vol. 40(3), pp. 947-961, 2021. doi: 10.32604/CSSE.2022.019288
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- [9] A. Safari, R. Hosseini and M. Mazinani. “A novel deep interval type-2 fuzzy LSTM (DIT2FLSTM) model applied to COVID-19 pandemic time-series prediction”. Journal of Biomedical Informatics, vol. 123,103920, 2021. doi: 10.1016/j.jbi.2021.103920 [10] B. Cheng and Y.-M. Wang. “A logistic model and predictions for the spread of the COVID-19 pandemic”. Chaos, vol. 30(12),123135, 2020. doi: 10.1063/5.0028236
- [11] S.L. Smith, J. Shiffman, Y.R. Shawar and Z.C. Shroff. “The rise and fall of global health issues: an arenas model applied to the COVID-19 pandemic shock”. Globalization and Health, vol. 17(1),33, 2021. doi: 10.1186/s12992-021- 00691-7
- [12] R. Wang, C. Ji, Z. Jiang, Z., Y. Wu, L. Yin and Y. Li. “A ShortTerm Prediction Model at the Early Stage of the COVID-19 Pandemic Based on Multisource Urban Data”. IEEE Transactions on Computational Social Systems, vol. 8(4),9371309, pp. 1021-1028, 2021. doi: 10.1109/TCSS.2021.3060952
- [13] S. Cabaro, V., D’Esposito, T. Di Matola, T.S. Sale, M. Cennamo, D. Terracciano, V. Parisi, F. Oriente, G. Portella, F. Beguinot, L. Atripaldi, M. Sansone, and P. Formisano. “Cytokine signature and COVID-19 prediction models in the two waves of pandemics”. Scientific Reports, vol. 11(1),20793, 2021. doi: 10.1038/s41598-021-00190-0
- [14] E. Berbenni and S. Colombo. “The impact of pandemics: revising the Spanish Flu in Italy in light of models’ predictions, and some lessons for the COVID-19 pandemic”. Journal of Industrial and Business Economics, vol. 48(2), pp. 219-243, 2021. doi: 10.1007/s40812-021-00182-1
- [15] A.M.B. de Oliveira, J.M. Binner, A. Mandal, L. Kelly and G.J. Power. “Using GAM functions and Markov-Switching models in an evaluation framework to assess countries’ performance in controlling the COVID-19 pandemic”. BMC Public Health, vol. 21(1),2173, 2021. doi: 10.1186/s12889-021- 11891-6.
- [16] A.K. Dhaiban and B.K. Jabbar. “An optimal control model of COVID-19 pandemic: a comparative study of five countries”. OPSEARCH, vol. 58(4), pp. 790-809, 2021. doi: 10.1007/s12597-020-00491-4
- [17] C. Donadee and K.E. Rudd. “Mortality prediction models: Another barrier to racial equity in a pandemic”. American Journal of Respiratory and Critical Care Medicine, vol. 204(2), pp. 120-121, 2021. doi: 10.1164/rccm.202103- 0809ED
- [18] M. Saban, V. Myers, O. Luxenburg and R. Wilf-Miron. “Tipping the scales: a theoretical model to describe the differential effects of the COVID-19 pandemic on mortality”. International Journal for Equity in Health, vol. 20(1),140, 2021. doi: 10.1186/s12939-021-01470-x
- [19] J. Jankhonkhan and W. Sawangtong. “Model predictive control of COVID-19 pandemic with social isolation and vaccination policies in Thailand”. Axioms, vol. 10(4),274, 2021. doi: 10.3390/axioms10040274
- [20] T. Akamatsu, T. Nagae, M., Osawa, K. Satsukawa, T. Sakai and D. Mizutani. „Model-based analysis on social acceptability and feasibility of a focused protection strategy against the COVID-19 pandemic”. Scientific Reports, vol. 11(1),2003, 2021. doi: 10.1038/s41598-021-81630-9
- [21] X. Tang, Z. Li, X. Hu, Z. Xu and L. Peng. “Self-correcting error-based prediction model for the COVID-19 pandemic and analysis of economic impacts”. Sustainable Cities and Society, vol. 74,103219, 2021. doi: 10.1016/j.scs.2021.103219
- [22] K.C. Kiptum. “Logistic model for adherence to ministry of health protocols and guidelines by public transport vehicles in Kenya during COVID-19 pandemic”. Engineering and Applied Science Research, vol. 49(1), pp. 88-95, 2022. doi: 10.14456/easr.2022.10
- [23] F. Jiao, L. Huang, R. Song and H. Huang. “An improved stllstm model for daily bus passenger flow prediction during the COVID-19 pandemic”. Sensors, vol. 21(17),5950, 2021. doi: 10.3390/s21175950
- [24] H.-S. Lee, E.A. Degtereva and A.M. Zobov. “The impact of the COVID-19 pandemic on cross-border mergers and acquisitions’ determinants: New empirical evidence from quasi-poisson and negative binomial regression models”. Economies, vol. 9(4),184, 2021. doi: 10.3390/economies9040184
- [25] A.K. Konyalıoğlu, T. Beldek, and T. Özcan. “An Optimized Nonlinear Grey Bernoulli Model for Forecasting the Electricity Consumption During COVID-19 Pandemic: A Case for Turkey”. Lecture Notes in Networks and Systems, vol. 307, pp. 649-656, 2022. doi: 10.1007/978-3-030-85626- 7_76
- [26] A. Maštalský and E. Dolný. “Behavioral models of isolated individuals and entities”. Acta Avionica, vol. 24 (2), pp. 25- 30, 2021. doi: 10.35116/aa.2021.0013
- [27] W. Weibull. “A Statistical Distribution Function of Wide Applicability”. Journal of Applied Mechanics, pp. 293-297, 1951.
- [28] T. Thanh Thach and R. Briš. “An additive Chen-Weibull distribution and its applications in reliability modeling”. Quality and Reliability Engineering International, vol. 37(1), pp. 352-373. 2021. doi: 10.1002/qre.2740
- [29] C.W. Zhang “Weibull parameter estimation and reliability analysis with zero-failure data from high-quality products”. Reliability Engineering and System Safety, vol. 207, 107321, 2021. doi: 10.1016/j.ress.2020.107321
- [30] B. Silahli, K.D. Dingec, A. Cifter, and N. Aydin. “Portfolio value-at-risk with two-sided Weibull distribution: Evidence from cryptocurrency markets”. Finance Research Letters, vol. 38, 101425, 2021. doi: 10.1016/j.frl.2019.101425.
- [31] R. Alshenawy, A. Al-Alwan, E.M. Almetwally, A.Z. Afify and H.M. Almongy. “Progressive type-ii censoring schemes of extended odd Weibull exponential distribution with applications in medicine and engineering”. Mathematics, Vol. 8(10), 1679, pp. 1-19, 2020. doi: 10.3390/math8101679
- [32] S.M.M. Rahman, J. Kim and B. Laratte. “Disruption in Circularity? Impact analysis of COVID-19 on ship recycling using Weibull tonnage estimation and scenario analysis method”. Resources, Conservation and Recycling, vol. 164, 105139, 2021. doi: 10.1016/j.resconrec.2020.105139
- [33] A. Abebaw Gessesse and R. Mishra. “Genetic AlgorithmBased Fuzzy Programming Method for Multi-objective Stochastic Transportation Problem Involving Three-Parameter Weibull Distribution”. Advances in Intelligent Systems and Computing,, vol. 1170, pp. 155-167. 2021. doi: 10.1007/978-981-15-5411-7_11
- [34] K. Draganová, K. Semrád, M. Blišťanová, T. Musil and R. Jurč. “Influence of disinfectants on airport conveyor belts”. Sustainability (Switzerland), vol. 13(19),10842, 2021. doi: 10.3390/su131910842
- [35] P. Niu, Z. Wang, S. Liu and K. Jia. “Demand Forecast of Restoring Air Material of Helicopter Based on NHPP and Weibull Model”. Journal of Physics: Conference Series, vol. 1676(1), 012089, 2020. doi: 10.1088/1742- 6596/1676/1/012089
- [36] P. Strzelecki. “Determination of fatigue life for low probability of failure for different stress levels using 3-parameter Weibull distribution”. International Journal of Fatigue, vol. 145, 2021. doi: 10.1016/j.ijfatigue.2020.106080
- [37] K. Semrád, J. Čerňan and K. Draganová. “Rolling Contact Fatigue Life Evaluation Using Weibull Distribution”. Mechanics, Materials Science & Engineering Journal. vol. 2(3), p. 28-33, 2016. doi: 10.13140/RG.2.1.3338.9849
- [38] Y. Wang, Z. Chen, Y. Zhang, X. Li and Z. Li. “Remaining useful life prediction of rolling bearings based on the threeparameter Weibull distribution proportional hazards model”. Insight: Non-Destructive Testing and Condition Monitoring, vol. 62(12), pp. 710-718, 2021. doi: 10.1784/INSI.2020.62.12.710
- [39] W.-S. Lei, Z. Yu, P. Zhang and G. Qian. “Standardized Weibull statistics of ceramic strength”. Ceramics International, vol. 47(4), pp. 4972-4993, 2021. doi: 10.1016/j.ceramint.2020.10.073
- [40] K. Semrád, K. Draganová, P. Košcák, and J. Cernan. “Statistical prediction models of impact damage of airport conveyor belts”. Transportation Research Procedia, vol. 51, pp. 11-19, 2020. doi: 10.1016/j.trpro.2020.11.003
- [41] B. Belhadj, L. Abdelkader and A. Chateauneuf. “Weibull probabilistic model of moisture concentration build up in a fiber graphite/epoxy polymer composite under varying hydrothermal conditions”. Periodica Polytechnica Mechanical Engineering, vol. 65(1), pp. 27-38, 2021. doi: 10.3311/PPme.13653
- [42] S. Guo, X. Wang, Y. Liu, X. Zhu and Y. Zhai, “A comparison study of three types of parameter estimation methods on weibull model”. Advances in Intelligent Systems and Computing, vol. 1244 AISC, pp. 706-711, 2021. doi: 10.1007/978-3-030-53980-1_103.
- [43] M. Sumair, T. Aized, S.A.R. Gardezi, S.U.U. Rehman and S.M.S. Rehman. “A novel method developed to estimate Weibull parameters”. Energy Reports, vol. 6, pp. 1715- 1733, 2020. doi: 10.1016/j.egyr.2020.06.017
- [44] H. Saboori, G. Barmalzan and S.M. Ayat. “Generalized Modified Inverse Weibull Distribution: Its Properties and Applications”. Sankhya B, vol. 82(2), pp. 247-269, 2020. doi: 10.1007/s13571-018-0182-1
- [45] L. Hongxiang, F.P. Shan and S. Baofeng. “A comparative study of modified Weibull distributions in proportional hazards models”. AIP Conference Proceedings, vol. 2266, 090011, 2020. doi: 10.1063/5.0018428.
Uwagi
Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu „Społeczna odpowiedzialność nauki” - moduł: Popularyzacja nauki i promocja sportu (2022-2023).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-5a884047-4d56-44b1-a0cf-9ab865f74ca2