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23-10-2020 | Original Article

ARIMA models for predicting the end of COVID-19 pandemic and the risk of second rebound

Authors: Zohair Malki, El-Sayed Atlam, Ashraf Ewis, Guesh Dagnew, Ahmad Reda Alzighaibi, Ghada ELmarhomy, Mostafa A. Elhosseini, Aboul Ella Hassanien, Ibrahim Gad

Published in: Neural Computing and Applications | Issue 7/2021

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Abstract

Globally, many research works are going on to study the infectious nature of COVID-19 and every day we learn something new about it through the flooding of the huge data that are accumulating hourly rather than daily which instantly opens hot research avenues for artificial intelligence researchers. However, the public’s concern by now is to find answers for two questions; (1) When this COVID-19 pandemic will be over? and (2) After coming to its end, will COVID-19 return again in what is known as a second rebound of the pandemic? In this work, we developed a predictive model that can estimate the expected period that the virus can be stopped and the risk of the second rebound of COVID-19 pandemic. Therefore, we have considered the SARIMA model to predict the spread of the virus on several selected countries and used it for predicting the COVID-19 pandemic life cycle and its end. The study can be applied to predict the same for other countries as the nature of the virus is the same everywhere. The proposed model investigates the statistical estimation of the slowdown period of the pandemic which is extracted based on the concept of normal distribution. The advantages of this study are that it can help governments to act and make sound decisions and plan for future so that the anxiety of the people can be minimized and prepare the mentality of people for the next phases of the pandemic. Based on the experimental results and simulation, the most striking finding is that the proposed algorithm shows the expected COVID-19 infections for the top countries of the highest number of confirmed cases will be manifested between Dec-2020 and Apr-2021. Moreover, our study forecasts that there may be a second rebound of the pandemic in a year time if the currently taken precautions are eased completely. We have to consider the uncertain nature of the current COVID-19 pandemic and the growing inter-connected and complex world, that are ultimately demanding flexibility, robustness and resilience to cope with the unexpected future events and scenarios.

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Lai CC, Shih TP, Ko WC, Tang HJ, Hsueh PR (2020) Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and coronavirus disease-2019 (COVID-19): the epidemic and the challenges. Int J Antimicrob Agents 55(3):105924. https://doi.org/10.1016/j.ijantimicag.2020.105924CrossRef

WHO (2020) Coronavirus. https://www.who.int/health-topics/coronavirus. Accessed 13 April 2020

WHO (2020) Rolling updates on coronavirus disease (COVID-19). https://www.who.int/emergencies/diseases/novel-coronavirus-2019/events-as-they-happen. Accessed 15 April 2020

WHO (2020) Coronavirus disease 2019 (COVID-19) situation report-97. 2020. https://www.who.int/docs/default-source/coronaviruse/situation-reports/20200426-sitrep-97-covid-19.pdf?sfvrsn=d1c3e800_6. Accessed 24 April 2020

Qiu H, Wu J, Hong L, Luo Y, Song Q, Chen D (2020) Clinical and epidemiological features of 36 children with coronavirus disease 2019 (COVID-19) in zhejiang, china: an observational cohort study. Lancet Infect Dis. https://doi.org/10.1016/s1473-3099(20)30198-5CrossRef

Wu J, Liu J, Zhao X, Liu C, Wang W, Wang D, Xu W, Zhang C, Yu J, Jiang B, Cao H, Li L (2020) Clinical characteristics of imported cases of coronavirus disease 2019 (COVID-19) in jiangsu province: a multicenter descriptive study. Clin Infect Dis. https://doi.org/10.1093/cid/ciaa199CrossRef

WHO (2020) Coronavirus. https://www.who.int/health-topics/coronavirus. Accessed 30 April 2020

Worldometer (2020) COVID-19 CORONAVIRUS PANDEMIC. https://www.worldometers.info/coronavirus/. Accessed 9 May 2020

Yang P, Liu P, Li D, Zhao D (2020) Corona virus disease 2019, a growing threat to children? J Infect. https://doi.org/10.1016/j.jinf.2020.02.024CrossRef

10.

Cao W, Fang Z, Hou G, Han M, Xu X, Dong J, Zheng J (2020) The psychological impact of the COVID-19 epidemic on college students in china. Psychiatry Res 287:112934. https://doi.org/10.1016/j.psychres.2020.112934CrossRef

11.

Ho CS, Chee CY, Ho RC (2020) Mental health strategies to combat the psychological impact of covid-19 beyond paranoia and panic. Ann Acad Med Singapore 49(1):1–3CrossRef

12.

Lai CC, Wang CY, Wang YH, Hsueh SC, Ko WC, Hsueh PR (2020) Global epidemiology of coronavirus disease 2019 (COVID-19): disease incidence, daily cumulative index, mortality, and their association with country healthcare resources and economic status. Int J Antimicrob Agents 55(4):105946. https://doi.org/10.1016/j.ijantimicag.2020.105946CrossRef

13.

Luo J (2020) Data-driven innovation lab, when will COVID-19 end? Data-driven prediction. http://ddi.sutd.edu.sg

14.

Dandekar R, Barbastathis G (2020) Quantifying the effect of quarantine control in covid-19 infectious spread using machine learning. medRxiv. https://doi.org/10.1101/2020.04.03.20052084CrossRef

15.

Murray CJ (2020) Forecasting the impact of the first wave of the COVID-19 pandemic on hospital demand and deaths for the USA and European economic area countries. medRxiv. https://doi.org/10.1101/2020.04.21.20074732CrossRef

16.

Organization WH (2020) Rational use of personal protective equipment for coronavirus disease (covid-19): interim guidance, 27 february 2020. Technical report. World Health Organization

17.

Bayyurt L, Bayyurt B (2020) Forecasting of COVID-19 cases and deaths using ARIMA models. medrxiv. https://doi.org/10.1101/2020.04.17.20069237CrossRef

18.

Tandon H, Ranjan P, Chakraborty T, Suhag V (2020) Coronavirus (covid-19): arima based time-series analysis to forecast near future. 2004.07859

19.

Organization WH (2020) Report of the WHO-China joint mission on coronavirus disease 2019 (COVID-19). https://www.who.int/docs/default-source/coronaviruse/who-china-joint-mission-on-covid-19-final-report.pdf. Accessed 28 Feb 2020

20.

Anne R (2020) ARIMA modelling of predicting COVID-19 infections https://doi.org/10.1101/2020.04.18.20070631

21.

Brockwell PJ, Davis RA (2016) Introduction to time series and forecasting. Springer, New YorkCrossRef

22.

Box GE, Jenkins GM, Reinsel GC, Ljung GM (2015) Time series analysis: forecasting and control. Wiley, HobokenMATH

23.

Paolella MS (2018) ARMA model identification. In: Linear models and time-series analysis. Wiley, Hoboken, p 405–442. https://doi.org/10.1002/9781119432036.ch9

24.

Sarıca B, Eğrioğlu E, Aşıkgil B (2016) A new hybrid method for time series forecasting: AR–ANFIS. Neural Comput Appl 29(3):749–760. https://doi.org/10.1007/s00521-016-2475-5CrossRef

25.

Diop ML, Kengne W (2021) Piecewise autoregression for general integer-valued time series. J Stat Plan Inference 211:271–286. https://doi.org/10.1016/j.jspi.2020.07.003MathSciNetCrossRefMATH

26.

(2014) The moving average models MA(1) and MA(2). In: Basic data analysis for time series with R. Wiley, Hoboken, p 51–57. https://doi.org/10.1002/9781118593233.ch6

27.

Al-Douri Y, Hamodi H, Lundberg J (2018) Time series forecasting using a two-level multi-objective genetic algorithm: a case study of maintenance cost data for tunnel fans. Algorithms 11(8):123. https://doi.org/10.3390/a11080123MathSciNetCrossRefMATH

28.

Chintalapudi N, Battineni G, Amenta F (2020) COVID-19 virus outbreak forecasting of registered and recovered cases after sixty day lockdown in Italy: a data driven model approach. J Microbiol Immunolo Infect. https://doi.org/10.1016/j.jmii.2020.04.004CrossRef

29.

Ryabko D (2019) Asymptotic nonparametric statistical analysis of stationary time series. Springer, New York. https://doi.org/10.1007/978-3-030-12564-6CrossRef

30.

Liang YH (2008) Combining seasonal time series ARIMA method and neural networks with genetic algorithms for predicting the production value of the mechanical industry in taiwan. Neural Comput Appl 18(7):833–841. https://doi.org/10.1007/s00521-008-0216-0CrossRef

31.

Soares F, Silveira T, Freitas H (2020) Hybrid approach based on SARIMA and artificial neural networks for knowledge discovery applied to crime rates prediction. In: Proceedings of the 22nd international conference on enterprise information systems. SCITEPRESS - Science and Technology Publications. https://doi.org/10.5220/0009412704070415

32.

Eze N, Asogwa O, Obetta A, Ojide K, Okonkwo C (2020) A time series analysis of federal budgetary allocations to education sector in Nigeria (1970–2018). Am J Appl Math Stat 8(1):1–8

33.

Rebala G, Ravi A, Churiwala S (2019) An introduction to machine learning. Springer, New YorkCrossRef

34.

Chakrabarti A, Ghosh JK (2011) AIC, BIC and recent advances in model selection. Philosophy of statistics. Elsevier, Amsterdam, pp 583–605. https://doi.org/10.1016/b978-0-444-51862-0.50018-6CrossRef

35.

Chen P, Niu A, Liu D, Jiang W, Ma B (2018) Time series forecasting of temperatures using SARIMA: an example from Nanjing. IOP Conf Ser Mater Sci Eng 394:052024. https://doi.org/10.1088/1757-899x/394/5/052024CrossRef

36.

Davis RA (2013) Of borders and bodies: the second wave begins. The Spanish flu. Palgrave Macmillan, London, pp 47–68. https://doi.org/10.1057/9781137339218_3CrossRef

37.

Molgaard CA (2019) Military vital statistics the spanish flu and the first world war. Significance 16(4):32–37. https://doi.org/10.1111/j.1740-9713.2019.01301.xCrossRef

38.

Taubenberger JK, Morens DM (2006) 1918 Influenza: the mother of all pandemics. Emerg Infect Dis 12(1):15–22. https://doi.org/10.3201/eid1209.05-0979CrossRef

39.

Guarner J (2020) Three emerging coronaviruses in two decades. Am J Clin Pathol 153(4):420–421. https://doi.org/10.1093/ajcp/aqaa029CrossRef

40.

Quan C, Shi W, Yang Y, Yang Y, Liu X, Xu W, Li H, Li J, Wang Q, Tong Z, Wong G, Zhang C, Ma S, Ma Z, Fu G, Zhang Z, Huang Y, Song H, Yang L, Liu WJ, Liu Y, Liu W, Gao GF, Bi Y (2018) New threats from h7n9 influenza virus: spread and evolution of high- and low-pathogenicity variants with high genomic diversity in wave five. J Virol 92(11):e00301–18. https://doi.org/10.1128/jvi.00301-18CrossRef

41.

Contini C, Nuzzo MD, Barp N, Bonazza A, Giorgio RD, Tognon M, Rubino S (2020) The novel zoonotic COVID-19 pandemic: an expected global health concern. J Infect Dev Ctries 14(03):254–264. https://doi.org/10.3855/jidc.12671CrossRef

42.

Yan Y, Shin WI, Pang YX, Meng Y, Lai J, You C, Zhao H, Lester E, Wu T, Pang CH (2020a) The first 75 days of novel coronavirus (SARS-CoV-2) outbreak: recent advances, prevention, and treatment. Int J Environ Res Public Health 17(7):2323. https://doi.org/10.3390/ijerph17072323CrossRef

43.

Yan Y, Chang L, Wang L (2020b) Laboratory testing of SARS-CoV, MERS-CoV, and SARS-CoV-2 (2019-nCoV): current status, challenges, and countermeasures. Rev Med Virol. https://doi.org/10.1002/rmv.2106CrossRef

44.

Cohen J (2020) Accuracy of estimate Of 100,000 To 240,000 Covid-19 deaths hinges on key assumptions. https://www.forbes.com/sites/joshuacohen/2020/04/02/accuracy-of-estimate-of-100000-to-240000-covid-19-deaths-hinges-on-key-assumptions/#41150b03144e. Accessed 2 April 2020

45.

Donovan J (2020) Social-media companies must flatten the curve of misinformation. Nature. https://doi.org/10.1038/d41586-020-01107-zCrossRef

46.

Malki Z, Atlam ES, Hassanien AE, Dagnew G, Elhosseini MA, Gad I (2020) Association between weather data and COVID-19 pandemic predicting mortality rate: machine learning approaches. Chaos Solitons Fractals 138:110137. https://doi.org/10.1016/j.chaos.2020.110137MathSciNetCrossRef

47.

Direkoglu C, Sah M (2020) Worldwide and regional forecasting of coronavirus (covid-19) spread using a deep learning model. https://doi.org/10.1101/2020.05.23.20111039

48.

Tian Y, Luthra I, Zhang X (2020) Forecasting COVID-19 cases using machine learning models. https://doi.org/10.1101/2020.07.02.20145474

Title: ARIMA models for predicting the end of COVID-19 pandemic and the risk of second rebound
Authors: Zohair Malki
El-Sayed Atlam
Ashraf Ewis
Guesh Dagnew
Ahmad Reda Alzighaibi
Ghada ELmarhomy
Mostafa A. Elhosseini
Aboul Ella Hassanien
Ibrahim Gad
Publication date: 23-10-2020
Publisher: Springer London
Published in: Neural Computing and Applications / Issue 7/2021
Print ISSN: 0941-0643
Electronic ISSN: 1433-3058
DOI: https://doi.org/10.1007/s00521-020-05434-0

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