IDEAS home Printed from https://ideas.repec.org/a/eee/chsofr/v140y2020ics0960077920305476.html
   My bibliography  Save this article

Modeling and forecasting the spread and death rate of coronavirus (COVID-19) in the world using time series models

Author

Listed:
  • Maleki, Mohsen
  • Mahmoudi, Mohammad Reza
  • Heydari, Mohammad Hossein
  • Pho, Kim-Hung

Abstract

Coronaviruses are a huge family of viruses that affect neurological, gastrointestinal, hepatic and respiratory systems. The numbers of confirmed cases are increased daily in different countries, especially in Unites State America, Spain, Italy, Germany, China, Iran, South Korea and others. The spread of the COVID-19 has many dangers and needs strict special plans and policies. Therefore, to consider the plans and policies, the predicting and forecasting the future confirmed cases are critical. The time series models are useful to model data that are gathered and indexed by time. Symmetry of error's distribution is an essential condition in classical time series. But there exist cases in the real practical world that assumption of symmetric distribution of the error terms is not satisfactory. In our methodology, the distribution of the error has been considered to be two-piece scale mixtures of normal (TP–SMN). The proposed time series models works well than ordinary Gaussian and symmetry models (especially for COVID-19 datasets), and were fitted initially to the historical COVID-19 datasets. Then, the time series that has the best fit to each of the dataset is selected. Finally, the selected models are applied to predict the number of confirmed cases and the death rate of COVID-19 in the world.

Suggested Citation

  • Maleki, Mohsen & Mahmoudi, Mohammad Reza & Heydari, Mohammad Hossein & Pho, Kim-Hung, 2020. "Modeling and forecasting the spread and death rate of coronavirus (COVID-19) in the world using time series models," Chaos, Solitons & Fractals, Elsevier, vol. 140(C).
    • Handle: RePEc:eee:chsofr:v:140:y:2020:i:c:s0960077920305476
    DOI: 10.1016/j.chaos.2020.110151
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0960077920305476
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.chaos.2020.110151?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Mohsen Maleki & Mohammad Reza Mahmoudi, 2017. "Two-Piece location-scale distributions based on scale mixtures of normal family," Communications in Statistics - Theory and Methods, Taylor & Francis Journals, vol. 46(24), pages 12356-12369, December.
    2. Mohammad Reza Mahmoudi & Mohsen Maleki, 2017. "A new method to detect periodically correlated structure," Computational Statistics, Springer, vol. 32(4), pages 1569-1581, December.
    3. A. Hajrajabi & M. Maleki, 2019. "Nonlinear semiparametric autoregressive model with finite mixtures of scale mixtures of skew normal innovations," Journal of Applied Statistics, Taylor & Francis Journals, vol. 46(11), pages 2010-2029, August.
    4. Jimmy Boon Som Ong & Mark I-Cheng Chen & Alex R Cook & Huey Chyi Lee & Vernon J Lee & Raymond Tzer Pin Lin & Paul Ananth Tambyah & Lee Gan Goh, 2010. "Real-Time Epidemic Monitoring and Forecasting of H1N1-2009 Using Influenza-Like Illness from General Practice and Family Doctor Clinics in Singapore," PLOS ONE, Public Library of Science, vol. 5(4), pages 1-11, April.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Ayman Batisha, 2023. "A lighthouse to future opportunities for sustainable water provided by intelligent water hackathons in the Arabsphere," Palgrave Communications, Palgrave Macmillan, vol. 10(1), pages 1-13, December.
    2. Hwang, Eunju, 2022. "Prediction intervals of the COVID-19 cases by HAR models with growth rates and vaccination rates in top eight affected countries: Bootstrap improvement," Chaos, Solitons & Fractals, Elsevier, vol. 155(C).
    3. Eunju Hwang, 2023. "Improvement on Forecasting of Propagation of the COVID-19 Pandemic through Combining Oscillations in ARIMA Models," Forecasting, MDPI, vol. 6(1), pages 1-18, December.
    4. Schaum, A. & Bernal-Jaquez, R. & Alarcon Ramos, L., 2022. "Data-assimilation and state estimation for contact-based spreading processes using the ensemble kalman filter: Application to COVID-19," Chaos, Solitons & Fractals, Elsevier, vol. 157(C).
    5. Christian Acal & Manuel Escabias & Ana M. Aguilera & Mariano J. Valderrama, 2021. "COVID-19 Data Imputation by Multiple Function-on-Function Principal Component Regression," Mathematics, MDPI, vol. 9(11), pages 1-23, May.
    6. Isra Al-Turaiki & Fahad Almutlaq & Hend Alrasheed & Norah Alballa, 2021. "Empirical Evaluation of Alternative Time-Series Models for COVID-19 Forecasting in Saudi Arabia," IJERPH, MDPI, vol. 18(16), pages 1-19, August.
    7. Seonwoo Kang & Jungsuk Kang, 2022. "Age Differences in Psychological Antecedents and Behavioral Consequences of Stigmatization Associated with COVID-19 among Koreans," IJERPH, MDPI, vol. 19(14), pages 1-15, July.
    8. Antonio Barrera & Patricia Román-Román & Juan José Serrano-Pérez & Francisco Torres-Ruiz, 2021. "Two Multi-Sigmoidal Diffusion Models for the Study of the Evolution of the COVID-19 Pandemic," Mathematics, MDPI, vol. 9(19), pages 1-29, September.
    9. Mike K. P. So & Lupe S. H. Chan & Amanda M. Y. Chu, 2021. "Financial Network Connectedness and Systemic Risk During the COVID-19 Pandemic," Asia-Pacific Financial Markets, Springer;Japanese Association of Financial Economics and Engineering, vol. 28(4), pages 649-665, December.
    10. Antoni Wiliński & Łukasz Kupracz & Aneta Senejko & Grzegorz Chrząstek, 2022. "COVID-19: average time from infection to death in Poland, USA, India and Germany," Quality & Quantity: International Journal of Methodology, Springer, vol. 56(6), pages 4729-4746, December.
    11. Jane K L Teh & David A Bradley & Jack Bee Chook & Kee Huong Lai & Woo Teck Ang & Kok Lay Teo & Suat-Cheng Peh, 2021. "Multivariate visualization of the global COVID-19 pandemic: A comparison of 161 countries," PLOS ONE, Public Library of Science, vol. 16(5), pages 1-11, May.
    12. Víctor Blanco & Ricardo Gázquez & Marina Leal, 2023. "Mathematical optimization models for reallocating and sharing health equipment in pandemic situations," TOP: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 31(2), pages 355-390, July.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Atefeh Zarei & Zahra Khodadadi & Mohsen Maleki & Karim Zare, 2023. "Robust mixture regression modeling based on two-piece scale mixtures of normal distributions," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 17(1), pages 181-210, March.
    2. Akram Hoseinzadeh & Mohsen Maleki & Zahra Khodadadi, 2021. "Heteroscedastic nonlinear regression models using asymmetric and heavy tailed two-piece distributions," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 105(3), pages 451-467, September.
    3. Mahmoudi, Mohammad Reza & Heydari, Mohammad Hossein & Roohi, Reza, 2019. "A new method to compare the spectral densities of two independent periodically correlated time series," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 160(C), pages 103-110.
    4. Mohsen Maleki & Darren Wraith & Reinaldo B. Arellano-Valle, 2019. "A flexible class of parametric distributions for Bayesian linear mixed models," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 28(2), pages 543-564, June.
    5. Xiao-Guang Yue & Xue-Feng Shao & Rita Yi Man Li & M. James C. Crabbe & Lili Mi & Siyan Hu & Julien S Baker & Liting Liu & Kechen Dong, 2020. "Risk Prediction and Assessment: Duration, Infections, and Death Toll of the COVID-19 and Its Impact on China’s Economy," JRFM, MDPI, vol. 13(4), pages 1-26, April.
    6. Abdol Rassoul Zarei & Mohammad Reza Mahmoudi, 2020. "Ability Assessment of the Stationary and Cyclostationary Time Series Models to Predict Drought Indices," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 34(15), pages 5009-5029, December.
    7. Taesik Lee & Hayong Shin, 2016. "Combining syndromic surveillance and ILI data using particle filter for epidemic state estimation," Flexible Services and Manufacturing Journal, Springer, vol. 28(1), pages 233-253, June.
    8. Mohammad Reza Mahmoudi & Abdol Rassoul Zarei, 2022. "Using Periodic Copula to Assess the Relationship Between Two Meteorological Cyclostationary Time Series Datasets," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 36(11), pages 4363-4388, September.
    9. Ayaz Hyder & David L Buckeridge & Brian Leung, 2013. "Predictive Validation of an Influenza Spread Model," PLOS ONE, Public Library of Science, vol. 8(6), pages 1-20, June.
    10. Mahmoudi, Mohammad Reza & Baleanu, Dumitru & Mansor, Zulkefli & Tuan, Bui Anh & Pho, Kim-Hung, 2020. "Fuzzy clustering method to compare the spread rate of Covid-19 in the high risks countries," Chaos, Solitons & Fractals, Elsevier, vol. 140(C).
    11. Mohammad Reza Mahmoudi, 2023. "Cyclic clustering approach to impute missing values for cyclostationary hydrological time series," Quality & Quantity: International Journal of Methodology, Springer, vol. 57(3), pages 2619-2639, June.
    12. Jean-Paul Chretien & Dylan George & Jeffrey Shaman & Rohit A Chitale & F Ellis McKenzie, 2014. "Influenza Forecasting in Human Populations: A Scoping Review," PLOS ONE, Public Library of Science, vol. 9(4), pages 1-8, April.
    13. Soumya Das & Marc G. Genton & Yasser M. Alshehri & Georgiy L. Stenchikov, 2021. "A cyclostationary model for temporal forecasting and simulation of solar global horizontal irradiance," Environmetrics, John Wiley & Sons, Ltd., vol. 32(8), December.
    14. Logan C Brooks & David C Farrow & Sangwon Hyun & Ryan J Tibshirani & Roni Rosenfeld, 2015. "Flexible Modeling of Epidemics with an Empirical Bayes Framework," PLOS Computational Biology, Public Library of Science, vol. 11(8), pages 1-18, August.
    15. Javier E. Contreras-Reyes & Mohsen Maleki & Daniel Devia Cortés, 2019. "Skew-Reflected-Gompertz Information Quantifiers with Application to Sea Surface Temperature Records," Mathematics, MDPI, vol. 7(5), pages 1-14, May.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:chsofr:v:140:y:2020:i:c:s0960077920305476. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Thayer, Thomas R. (email available below). General contact details of provider: https://www.journals.elsevier.com/chaos-solitons-and-fractals .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.