Modeling Covid-19 Infection Cases and Vaccine in 5 Countries Highly Vaccinations

Year 2021, Volume: 13 Issue: 2, 403 - 417, 31.12.2021

Mostafa Abotaleb Tatiana Makarovskikh Harun Yonar , Pradeep Mishra , Amr Badr , Kadir Karakaya , Aynur Yonar

https://doi.org/10.47000/tjmcs.905508

Cited By: 2

Abstract

COVID-19 has become the most important and crucial agenda in the world in the last year. COVID-19 has taken many lives around the world and millions of people have been infected. To get rid of this depression caused by COVID-19, many countries have started big campaigns for vaccine production. In this study, data on infection cases and vaccinations conducted in England, Germany, Israel, Russia, and the USA were analyzed from January 3, 2020, to March 3, 2021. We used univariate time series models, where the results are very accurate, rather than epmdicolgical models. In this article we used BATS, TBATS, Holt’s linear trend, and ARIMA models to recognize the pattern of spread of covid 19 infection cases. The best models are specified for all countries that have the least error according to MAPE. Findings obtained in this study have been reported extensively in England, Germany, Israel, Russia, and the USA with tables and figures. Using the results and forecasts obtained in this study, England, Germany, Israel, Russia, and the USA can take COVID-19 measures for the future.

Keywords

ARIMA, BATS, COVID-19, Holt’s Linear, TBATS

References

• [1] Abotaleb, M.S.A., Predicting COVID-19 cases using some statistical models: An application to the cases reported in China Italy and USA, Academic Journal of Applied Mathematical Sciences, 6(4)(2020), 32–40. doi.org/10.32861/ajams.64.32.40.
• [2] Abotaleb, M.S.A., Makarovskikh, T., The Research of Mathematical Models for Forecasting Covid-19 Cases, In: Strekalovsky A., Kochetov Y., Gruzdeva T., Orlov A. (eds) Mathematical Optimization Theory and Operations Research: Recent Trends. MOTOR 2021. Communications in Computer and Information Science, vol 1476. Springer, Cham., 2021. https://doi.org/10.1007/978-3-030-86433-0_21.
• [3] Abotaleb, M.S.A., Makarovskikh, T.A., Development of algorithms for choosing the best time series models and neural networks to predict COVID-19 cases, Bulletin of the South Ural State University. Ser. Computer Technologies, Automatic Control, Radio Electronics, 21(3)(2021), 26-–35. DOI:10.14529/ctcr210303.
• [4] Abotalebi, M., Makarovskikh, T., System for forecasting COVID-19 cases using time-series and neural networks models, Engineering Proceedings, 5(1)(46)(2021). https://doi.org/10.3390/engproc2021005046.
• [5] Bubar, K.M., Reinholt, K., Kissler, S.M., Lipsitch, M., Cobey, S. et al., . Model-informed COVID-19 vaccine prioritization strategies by age and serostatus, Science, 371(6532)(2021), 916–921.
• [6] Chatfield, C.A., The Analysis of Time Series: An Introduction, R. CRC Press, 2019.
• [7] De Livera, A.M., Hyndman, R.J., Snyder, R.D., Forecasting time series with complex seasonal patterns using exponential smoothing, Journal of the American Statistical Association, 106(496)(2011), 1513–1527.
• [8] Frain, J., Lecture Notes on Univariate Time Series Analysis and Box Jenkins Forecasting, Economic. Research and Publications, 1992.
• [9] Holt, C.E., Forecasting Seasonals and Trends by Exponentially Weighted Averages (ONR Memorandum No. 52), Carnegie Institute of Technology, Pittsburgh USA, 1957.
• [10] Sardar, I., Karakaya, K., Makarovskikh, T., Abotaleb, M., Hussain, S.A. etal., Machine learning-based Covid-19 forecasting: Impact on Pakistan stock exchange, International Journal of Agricultural and Statistical Sciences, (2021). DocID:https://connectjournals.com/03899.2021.17.53.
• [11] Kirchgassner, G.J., Introduction to Modern Time Series Analysis, Springer Science & Business Media, 2012.
• [12] Makarovskikh, T.A., Abotaleb, M.S.A., Automatic selection of ARIMA model parameters to forecast COVID-19 infection and death cases, Bulletin of the South Ural State University. Series: Computational Mathematics and Software Engineering,10(2)(2021), 20-–37.(in Russian) DOI:10.14529/cmse210202.
• [13] Makarovskikh, T., Abotaleb, M., Comparison Between Two Systems for Forecasting Covid-19 Infected Cases. In: Byrski A., Czach´orski T., Gelenbe E., Grochla K., Murayama Y. (eds). Computer Science Protecting Human Society Against Epidemics. ANTICOVID 2021. IFIP Advances in Information and Communication Technology, vol 616. Springer, Cham., 2021. https://doi.org/10.1007/978-3-030-86582-5_10.
• [14] Mishra, P., Fatih, C., Rawat, D., Sahu, S., Pandey, S.S.et al., Trajectory of COVID-19 data in India: Investigation and project using artificial neural network, Fuzzy Time Series and ARIMA Models, Annual Research & Review in Biology, 35(9)(2020), 46–54.
• [15] Mishra, P., Al Khatib, M.G., Sardar, I. et al., Modeling and forecasting of sugarcane production in India, Sugar Tech., 23(2021), 1317—1324 . https://doi.org/10.1007/s12355-021-01004-3.
• [16] Mishra, P., Matuka, A., Abotaleb, M.S.A., Weerasinghe, W.P.M.C.N., Karakaya, K. et al., Modeling and forecasting of milk production in the SAARC countries and China, Modeling Earth Systems and Environment, (2021), 1–13.
• [17] Mohammed, J., Khatib, A.M., Mishra, P., Adjei, P., Singh, P.K. et al., Modeling and forecasting of Covid-19 from the context of Ghana, African Review of Economics and Finance, (2020), 1–18.
• [18] Rostami-Tabar, B., Rendon-Sanchez, J.F., Forecasting COVID-19 daily cases using phone call data, Applied Soft Computing, 100(2021), 106932.
• [19] Seale, H., Heywood, A.E., Leask, J., Sheel, M., Durrheim, D. N. et al., Examining Australian public perceptions and behaviors towards a future COVID-19 vaccine, BMC Infectious Diseases, 21(1)(2021), 1–9.
• [20] Tekindal, M.A., Yonar, H., Yonar, A., Tekindal, M., Çevrimli, M.B. et al., Analyzing COVID-19 outbreak for Turkey and eight country with curve estimation models, Box-Jenkins (ARIMA), Brown linear exponential smoothing method, autoregressive distributed lag (ARDL) and SEIR Models, Eurasian J Vet Sci, Covid-19 Special Issue, 142–155.
• [21] Yonar, H., Yonar, A., Tekindal, M. A., Tekindal, M., Modeling and forecasting for the number of cases of the COVID-19 pandemic with the curve estimation models, the Box-Jenkins and exponential smoothing methods, EJMO, 4(2)(2020), 160–165.
• [22] Young, W.L., The Box-Jenkins approach to time series analysis and forecasting: principles and applications, RAIRO-Operations Research-Recherche Op´erationnelle, 11(1977), 129–143.