Forecasting the Spreading of COVID-19 across Nine Countries from Europe, Asia, and the American Continents Using the ARIMA Models

Since mid-November 2019, when the first SARS-CoV-2-infected patient was officially reported, the new coronavirus has affected over 10 million people from which half a million died during this short period. There is an urgent need to monitor, predict, and restrict COVID-19 in a more efficient manner. This is why Auto-Regressive Integrated Moving Average (ARIMA) models have been developed and used to predict the epidemiological trend of COVID-19 in Ukraine, Romania, the Republic of Moldova, Serbia, Bulgaria, Hungary, USA, Brazil, and India, these last three countries being otherwise the most affected presently. To increase accuracy, the daily prevalence data of COVID-19 from 10 March 2020 to 10 July 2020 were collected from the official website of the Romanian Government GOV.RO, World Health Organization (WHO), and European Centre for Disease Prevention and Control (ECDC) websites. Several ARIMA models were formulated with different ARIMA parameters. ARIMA (1, 1, 0), ARIMA (3, 2, 2), ARIMA (3, 2, 2), ARIMA (3, 1, 1), ARIMA (1, 0, 3), ARIMA (1, 2, 0), ARIMA (1, 1, 0), ARIMA (0, 2, 1), and ARIMA (0, 2, 0) models were chosen as the best models, depending on their lowest Mean Absolute Percentage Error (MAPE) values for Ukraine, Romania, the Republic of Moldova, Serbia, Bulgaria, Hungary, USA, Brazil, and India (4.70244, 1.40016, 2.76751, 2.16733, 2.98154, 2.11239, 3.21569, 4.10596, 2.78051). This study demonstrates that ARIMA models are suitable for making predictions during the current crisis and offers an idea of the epidemiological stage of these regions.

[1]  M. Thomson,et al.  Potential of environmental models to predict meningitis epidemics in Africa , 2006, Tropical medicine & international health : TM & IH.

[2]  Tao Zhang,et al.  Comparative Study of Four Time Series Methods in Forecasting Typhoid Fever Incidence in China , 2013, PloS one.

[3]  E. Nsoesie,et al.  A Simulation Optimization Approach to Epidemic Forecasting , 2013, PloS one.

[4]  H. Ren,et al.  The development of a combined mathematical model to forecast the incidence of hepatitis E in Shanghai, China , 2013, BMC Infectious Diseases.

[5]  Gabriela Lindemann von Trzebiatowski,et al.  Time-series analysis in the medical domain: A study of Tacrolimus administration and influence on kidney graft function , 2014, Comput. Biol. Medicine.

[6]  G. T. Wilson Time Series Analysis: Forecasting and Control,5th Edition, by George E. P. Box, Gwilym M. Jenkins, Gregory C. Reinsel and Greta M. Ljung, 2015. Published by John Wiley and Sons Inc., Hoboken, New Jersey, pp. 712. ISBN: 978-1-118-67502-1 , 2016 .

[7]  Sermin Elevli,et al.  Drinking water quality control: control charts for turbidity and pH , 2016 .

[8]  Kai Wang,et al.  Time Prediction Models for Echinococcosis Based on Gray System Theory and Epidemic Dynamics , 2017, International journal of environmental research and public health.

[9]  Shaun S. Wulff,et al.  Time Series Analysis: Forecasting and Control, 5th edition , 2017 .

[10]  L. Sattenspiel,et al.  Defining epidemics in computer simulation models: How do definitions influence conclusions? , 2017, Epidemics.

[11]  Hongbing Tao,et al.  Epidemiology and ARIMA model of positive-rate of influenza viruses among children in Wuhan, China: A nine-year retrospective study. , 2018, International journal of infectious diseases : IJID : official publication of the International Society for Infectious Diseases.

[12]  Ya-wen Wang,et al.  Comparison of ARIMA and GM(1,1) models for prediction of hepatitis B in China , 2018, PloS one.

[13]  Z. Ul Haq,et al.  Forecasting the seasonality and trend of pulmonary tuberculosis in Jiangsu Province of China using advanced statistical time-series analyses , 2019, Infection and drug resistance.

[14]  Farzad Firouzi Jahantigh,et al.  Reducing demand uncertainty in the platelet supply chain through artificial neural networks and ARIMA models , 2019, Comput. Biol. Medicine.

[15]  L. Cao,et al.  Relationship of meteorological factors and human brucellosis in Hebei province, China. , 2019, The Science of the total environment.

[16]  Chuanrong Zhang,et al.  A comparative time series analysis and modeling of aerosols in the contiguous United States and China. , 2019, The Science of the total environment.

[17]  Yan Zhao,et al.  Clinical Characteristics of 138 Hospitalized Patients With 2019 Novel Coronavirus-Infected Pneumonia in Wuhan, China. , 2020, JAMA.

[18]  T. Chakraborty,et al.  Real-time forecasts and risk assessment of novel coronavirus (COVID-19) cases: A data-driven analysis , 2020, Chaos, Solitons & Fractals.

[19]  Sotiris Kotsiantis,et al.  COVID-19: A Comparison of Time Series Methods to Forecast Percentage of Active Cases per Population , 2020, Applied Sciences.

[20]  Zeynep Ceylan Estimation of COVID-19 prevalence in Italy, Spain, and France , 2020, Science of The Total Environment.

[21]  Ting Yu,et al.  Epidemiological and clinical characteristics of 99 cases of 2019 novel coronavirus pneumonia in Wuhan, China: a descriptive study , 2020, The Lancet.

[22]  A. Rodríguez-Morales,et al.  Prediction of the COVID-19 Pandemic for the Top 15 Affected Countries: Advanced Autoregressive Integrated Moving Average (ARIMA) Model , 2020, JMIR Public Health and Surveillance.

[23]  B. Grenfell,et al.  Disease and healthcare burden of COVID-19 in the United States , 2020, Nature Medicine.

[24]  H. Rothan,et al.  The epidemiology and pathogenesis of coronavirus disease (COVID-19) outbreak , 2020, Journal of Autoimmunity.

[25]  Mustafa Agah Tekindal,et al.  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 , 2020 .

[26]  Y. Hu,et al.  Clinical features of patients infected with 2019 novel coronavirus in Wuhan, China , 2020, The Lancet.

[27]  Lanjuan Li,et al.  Epidemiological, clinical and virological characteristics of 74 cases of coronavirus-infected disease 2019 (COVID-19) with gastrointestinal symptoms , 2020, Gut.

[28]  Adnan Sözen,et al.  Comparative analysis and forecasting of COVID-19 cases in various European countries with ARIMA, NARNN and LSTM approaches , 2020, Chaos, Solitons & Fractals.

[29]  Zexiao Lin,et al.  Diarrhoea may be underestimated: a missing link in 2019 novel coronavirus , 2020, Gut.

[30]  S. Zhang,et al.  Clinical findings in a group of patients infected with the 2019 novel coronavirus (SARS-Cov-2) outside of Wuhan, China: retrospective case series , 2020, BMJ.

[31]  F. Amenta,et al.  COVID-19 virus outbreak forecasting of registered and recovered cases after sixty day lockdown in Italy: A data driven model approach , 2020, Journal of Microbiology, Immunology and Infection.

[32]  J. Xiang,et al.  Clinical course and risk factors for mortality of adult inpatients with COVID-19 in Wuhan, China: a retrospective cohort study , 2020, The Lancet.

[33]  Y. Song,et al.  SARS-CoV-2 induced diarrhoea as onset symptom in patient with COVID-19 , 2020, Gut.

[34]  X. Rodó,et al.  The end of social confinement and COVID-19 re-emergence risk , 2020, Nature Human Behaviour.

[35]  Huji Xu,et al.  Digestive system is a potential route of COVID-19: an analysis of single-cell coexpression pattern of key proteins in viral entry process , 2020, Gut.

[36]  K. Bhaskaran,et al.  OpenSAFELY: factors associated with COVID-19 death in 17 million patients , 2020, Nature.

[37]  K. Bhaskaran,et al.  Factors associated with COVID-19-related death using OpenSAFELY , 2020, Nature.

[38]  B. Young,et al.  COVID-19 in gastroenterology: a clinical perspective , 2020, Gut.

[39]  J. Ji,et al.  Real-time estimation and prediction of mortality caused by COVID-19 with patient information based algorithm , 2020, Science of The Total Environment.

[40]  H. Shan,et al.  Gastrointestinal symptoms of 95 cases with SARS-CoV-2 infection , 2020, Gut.

[41]  Marta Giovanetti,et al.  Application of the ARIMA model on the COVID-2019 epidemic dataset , 2020, Data in Brief.

[42]  K. Yuen,et al.  Clinical Characteristics of Coronavirus Disease 2019 in China , 2020, The New England journal of medicine.

[43]  J. Demongeot,et al.  Temperature Decreases Spread Parameters of the New Covid-19 Case Dynamics , 2020, Biology.

[44]  A. Ahmar,et al.  SutteARIMA: Short-term forecasting method, a case: Covid-19 and stock market in Spain , 2020, Science of The Total Environment.

[45]  G. Gao,et al.  A Novel Coronavirus from Patients with Pneumonia in China, 2019 , 2020, The New England journal of medicine.