Global Forecasting Confirmed and Fatal Cases of COVID-19 Outbreak Using Autoregressive Integrated Moving Average Model

The world health organization (WHO) formally proclaimed the novel coronavirus, called COVID-19, a worldwide pandemic on March 11 2020. In December 2019, COVID-19 was first identified in Wuhan city, China, and now coronavirus has spread across various nations infecting more than 198 countries. As the cities around China started getting contaminated, the number of cases increased exponentially. As of March 18 2020, the number of confirmed cases worldwide was more than 250,000, and Asia alone had more than 81,000 cases. The proposed model uses time series analysis to forecast the outbreak of COVID-19 around the world in the upcoming days by using an autoregressive integrated moving average (ARIMA). We analyze data from February 1 2020 to April 1 2020. The result shows that 120,000 confirmed fatal cases are forecasted using ARIMA by April 1 2020. Moreover, we have also evaluated the total confirmed cases, the total fatal cases, autocorrelation function, and white noise time-series for both confirmed cases and fatalities in the COVID-19 outbreak.

[1]  W. Ramalho,et al.  Expected impact of COVID-19 outbreak in a major metropolitan area in Brazil , 2020, medRxiv.

[2]  N. Biller-Andorno,et al.  Challenging Operations: An Ethical Framework to Assist Humanitarian Aid Workers in their Decision-making Processes , 2014, PLoS currents.

[3]  Extended SIR prediction of the epidemics trend of COVID-19 in Italy and compared with Hunan, China , 2020, medRxiv.

[4]  Qianglin Zeng,et al.  Time series analysis of temporal trends in the pertussis incidence in Mainland China from 2005 to 2016 , 2016, Scientific Reports.

[5]  Weizhong Yang,et al.  Forecasting incidence of hemorrhagic fever with renal syndrome in China using ARIMA model , 2011, BMC infectious diseases.

[6]  Jyh-Shing Roger Jang,et al.  ANFIS: adaptive-network-based fuzzy inference system , 1993, IEEE Trans. Syst. Man Cybern..

[7]  Giovanni Petri,et al.  On the predictability of infectious disease outbreaks , 2017, Nature Communications.

[8]  J. Shaman,et al.  Forecasting seasonal outbreaks of influenza , 2012, Proceedings of the National Academy of Sciences.

[9]  R. Duval,et al.  Human Coronaviruses: Insights into Environmental Resistance and Its Influence on the Development of New Antiseptic Strategies , 2012, Viruses.

[10]  S. Dowell,et al.  Severe Acute Respiratory Syndrome Coronavirus on Hospital Surfaces , 2004, Clinical infectious diseases : an official publication of the Infectious Diseases Society of America.

[11]  J. Epstein,et al.  Isolation and characterization of a bat SARS-like coronavirus that uses the ACE2 receptor , 2013, Nature.

[12]  Prof. Dr. Günter Kampf,et al.  Antiseptic Stewardship , 2018, Springer International Publishing.

[13]  S. Lo,et al.  A familial cluster of pneumonia associated with the 2019 novel coronavirus indicating person-to-person transmission: a study of a family cluster , 2020, The Lancet.

[14]  Dervis Karaboga,et al.  Adaptive network based fuzzy inference system (ANFIS) training approaches: a comprehensive survey , 2018, Artificial Intelligence Review.

[15]  D. J. Weber,et al.  Transmission of SARS and MERS coronaviruses and influenza virus in healthcare settings: the possible role of dry surface contamination☆ , 2015, Journal of Hospital Infection.

[16]  S Cauchemez,et al.  Transmission scenarios for Middle East Respiratory Syndrome Coronavirus (MERS-CoV) and how to tell them apart. , 2013, Euro surveillance : bulletin Europeen sur les maladies transmissibles = European communicable disease bulletin.

[17]  Jing Shan,et al.  2019 Novel coronavirus: where we are and what we know , 2020, Infection.

[18]  G Chowell,et al.  Real-time forecasting of epidemic trajectories using computational dynamic ensembles. , 2019, Epidemics.

[19]  C. Tian,et al.  Time-series modelling and forecasting of hand, foot and mouth disease cases in China from 2008 to 2018 , 2019, Epidemiology and Infection.

[20]  Lin-Fa Wang,et al.  Review of Bats and SARS , 2006, Emerging infectious diseases.

[21]  Wang Jianwei,et al.  Extended SIR Prediction of the Epidemics Trend of COVID-19 in Italy and Compared With Hunan, China , 2020, Frontiers in Medicine.

[22]  2019 Novel coronavirus: where we are and what we know , 2020, Infection.

[23]  G. Kampf Antiseptic Stewardship: Biocide Resistance and Clinical Implications , 2018 .

[24]  W. Feng,et al.  Trends in and correlations between antibiotic consumption and resistance of Staphylococcus aureus at a tertiary hospital in China before and after introduction of an antimicrobial stewardship programme , 2018, Epidemiology and Infection.

[25]  Eduardo Massad,et al.  Forecasting versus projection models in epidemiology: The case of the SARS epidemics , 2005, Medical Hypotheses.

[26]  D. Hamer Faculty Opinions recommendation of Genomic characterisation and epidemiology of 2019 novel coronavirus: implications for virus origins and receptor binding. , 2020, Faculty Opinions – Post-Publication Peer Review of the Biomedical Literature.

[27]  Le Hoang Son,et al.  Analysis of Outbreak and Global Impacts of the COVID-19 , 2020, Healthcare.

[28]  W. Liang,et al.  Clinical characteristics of 2019 novel coronavirus infection in China , 2020, medRxiv.

[29]  Sasikiran Kandula,et al.  Inference and Forecast of the Current West African Ebola Outbreak in Guinea, Sierra Leone and Liberia , 2014, PLoS currents.

[30]  Qianyun Liu,et al.  Emerging coronaviruses: Genome structure, replication, and pathogenesis , 2020, Journal of medical virology.

[31]  E. Holmes,et al.  Genomic characterisation and epidemiology of 2019 novel coronavirus: implications for virus origins and receptor binding , 2020, The Lancet.

[32]  Mevlut Ture,et al.  Comparison of four different time series methods to forecast hepatitis A virus infection , 2006, Expert Syst. Appl..

[33]  Alon Herschhorn,et al.  Lattice engineering enables definition of molecular features allowing for potent small-molecule inhibition of HIV-1 entry , 2019, Nature Communications.

[34]  A. Gandomi,et al.  Time Series Analysis and Forecast of the COVID-19 Pandemic in India using Genetic Programming , 2020, Chaos, Solitons & Fractals.