Comparison and forecasting of some nonlinear models for time series numbers of Baghdad Covid_19 infections
DOI:
https://doi.org/10.62933/302mze77Keywords:
Time series, logistic model, Chapman-Richard model, Von Bertalanf model, AIC,BICAbstract
In this research paper, three nonlinear models of time series models will be addressed, represented in the Logistic model, Chapman Richard model and Von Bertalanff model, which is used to predict the number of people infected with the Covid_19 virus for Baghdad Governorate for the period from (1/10/2021) to (31/12/2021) and compare these models using the criteria (AIC, BIC, H-Q), and that One of the most important conclusions reached was that the Logistic model is the best model to represent the time series of the number of people infected with the Covid_19 virus through statistical criteria (AIC, BIC, H-Q), and there is a great convergence between the actual and estimated numbers of people infected with the Covid_19 virus.
References
Al-Nassir, A.-M. H., Abed, M. Q. ., & Ibraheem, W. S. (2014). Estimating the Spectral Power Density Function of Non-Gaussian Second Order Autoregressive Model. Journal of Asian Scientific Research, 4(9), 513–521.
Al-Saffar, R. S., Rasheed, A. A. R., & Sadik, N. J. (2021). Nonlinear modeling of the factors affecting the disease Covid-19 by adopting the method of sampling by reference. International Journal of Agricultural and Statistical Sciences, 2301-2307.
Abbas Gulmurad Beg Murad, Sozan Saber Haider Ali, (2023) ANALYSIS OF PROBABILITY DISTRIBUTION FOR CORONA VIRUS MORTALITY IN IRAQ BEFORE AND AFTER THE DISCOVERY OF THE EPIDEMIOLOGICAL VACCINES. Journal of Northeastern University, 26(1), 514-533.
Aljabri, M., Aljameel, S. S., Khan, I. U., Aslam, N., Charouf, S. M. B., & Alzahrani, N. (2022). Machine learning model for sentiment analysis of COVID-19 tweets. Int J Adv Sci Eng Inf Technol, 12(3), 1206-14.
Amaral, M. T. R., Conceição, K. S., de ANDRADE, M. G., & Padovani, C. R. (2020). Generalized growth curve model for covid-19 in brazilian states. Brazilian Journal of Biometrics, 38(2), 125-146.
Bozdogan, H.(2000), Akaike's Information Criterion and Recent Developments in Information Complexity., Journal of Mathematical Psychology, Vo. 44, Issue 1, P.p 62-91
Clark, A. J., Lake, L. W., & Patzek, T. W. (2011, October). Production forecasting with logistic growth models. In SPE Annual Technical Conference and Exhibition? (pp. SPE-144790). SPE.
Ibraheem, W. S. (2011). Adaptive Filtering Method using of Non-Gaussian Moving Average model from First order (simulation study). Journal of Al-Rafidain University College For Sciences (Print ISSN: 1681-6870, Online ISSN: 2790-2293), (2), 79-93.
Ibrahim, W. S. (2020). To determine the most important factors affecting pancreatic cancer in Iraq using the logistic regression model. Periodicals of Engineering and Natural Sciences (PEN), 8(1), 277-283.
Ibrahim, W. S., & Hajar, F. T. (2019). Use the logistic regression model to find the most important factors affecting lung cancer in Iraq in 2017. Journal of Administration and Economics, (121).
Ibrahim, W. S., Majeed, G. H., & Anber, J. A. (2022). Determine the most important factors affecting breast cancer in Iraq. Periodicals of Engineering and Natural Sciences (PEN), 10(2), 260-266.
Islam, L., Rahman, M., Ahmad, N., Sharmin, T., & Sorna, J. F. (2021). A Crowdsourced Contact Tracing Model to Detect COVID-19 Patients using Smartphones. arXiv preprint arXiv:2112.01244.,, Vol. 11 (2021).No.5,P:1818-1824, DOI:10.18517 /ijaseit.11.5.14644
Katsanevakis, S., & Maravelias, C. D. (2008). Modelling fish growth: multi‐model inference as a better alternative to a priori using von Bertalanffy equation. Fish and fisheries, 9(2), 178-187.
Jbarah, A., & AL-Saffar, R. S. (2021). DETERMINE THE MOST IMPORTANT FACTORS AFFECTING THE CORONA VIRUS TO BUILD A MODEL USING THE MAIN COMPOUNDS. International Journal of Agricultural & Statistical Sciences, 17.
Ma, J., (2020),Estimating epidemic exponential growth rate and basic reproduction number, Infectious Disease Modelling, (5), 129-141.
Mahdi, N. N., Abed, H. T., & Sadik, N. J. (2020). The discriminant analysis in the evaluation of cancers diseases in Iraq. Int J Adv Sci Eng Inf Technol, 10(5), 2170-2176.
Martínez, F., Martínez, F., & Jacinto, E. (2020). Performance evaluation of the NASNet convolutional network in the automatic identification of COVID-19. International Journal on Advanced Science, Engineering and Information Technology, 10(2), 662.
Mohammad, A. J. (2021). Using the penalized splines regression method to estimate the logistic model and using a practical application. journal of the college of basic education, 27(113).
Mohammed, A. J., & Ibrahim, W. S. (2020). Study of the probability of change in share prices based on the value of the Babylon Hotel using logistic regression. Journal of Administration and Economics, (123).
Na'am, J., Pranata, F. S., Hidayat, R., Adif, A. M., & Ellyzarti, E. (2021). Automated Identification Model of Ground-Glass Opacity in CT-Scan Image by COVID-19. International Journal on Advanced Science, Engineering and Information Technology, 11(2), 595-602.
Rasheed, A. A. R., Abd, H. T., & Sadik, N. J. (2022). Forecasting the Number of (Infected, Recovered, Death) For COVID-19 in Iraq by Using Neural Network Models. Mathematical Statistician and Engineering Applications, 71(4), 4984-4996.
Ratkowsky, D. A. (1986). Statistical properties of alternative parameterizations of the von Bertalanffy growth curve. Canadian Journal of Fisheries and Aquatic Sciences, 43(4), 742-747.
Reddy, T., Shkedy, Z., Janse van Rensburg, C., Mwambi, H., Debba, P., Zuma, K., & Manda, S. (2021). Short-term real-time prediction of total number of reported COVID-19 cases and deaths in South Africa: a data driven approach. BMC medical research methodology, 21, 1-11.
Trappey, C. V., & Wu, H. Y. (2008). An evaluation of the time-varying extended logistic, simple logistic, and Gompertz models for forecasting short product lifecycles, Advanced Engineering informatics, 22(4). 421-430.
Wang, X. S., Wu, J., & Yang, Y. (2012). Richards model revisited: Validation by and application to infection dynamics. Journal of theoretical biology, 313, 12-19.
Downloads
Published
Issue
Section
License
Copyright (c) 2024 Jinan Abdullah Anber, Mohammed Qadoury Abed , Wadhah Sabri Ibrahim (Author)
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
Licensed under a CC-BY license: https://creativecommons.org/licenses/by-nc-sa/4.0/
