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An ARIMA model to forecast the spread and the final size of COVID-2019 epidemic in Italy

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  • Gaetano Perone

Abstract

Coronavirus disease (COVID-2019) is a severe ongoing novel pandemic that is spreading quickly across the world. Italy, that is widely considered one of the main epicenters of the pandemic, has registered the highest COVID-2019 death rates and death toll in the world, to the present day. In this article I estimate an autoregressive integrated moving average (ARIMA) model to forecast the epidemic trend over the period after April 4, 2020, by using the Italian epidemiological data at national and regional level. The data refer to the number of daily confirmed cases officially registered by the Italian Ministry of Health (www.salute.gov.it) for the period February 20 to April 4, 2020. The main advantage of this model is that it is easy to manage and fit. Moreover, it may give a first understanding of the basic trends, by suggesting the hypothetic epidemic's inflection point and final size.

Suggested Citation

  • Gaetano Perone, 2020. "An ARIMA model to forecast the spread and the final size of COVID-2019 epidemic in Italy," Health, Econometrics and Data Group (HEDG) Working Papers 20/07, HEDG, c/o Department of Economics, University of York.
    • Handle: RePEc:yor:hectdg:20/07
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    References listed on IDEAS

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    1. Fanelli, Duccio & Piazza, Francesco, 2020. "Analysis and forecast of COVID-19 spreading in China, Italy and France," Chaos, Solitons & Fractals, Elsevier, vol. 134(C).
    2. Elliott, Graham & Rothenberg, Thomas J & Stock, James H, 1996. "Efficient Tests for an Autoregressive Unit Root," Econometrica, Econometric Society, vol. 64(4), pages 813-836, July.
    3. Engle, Robert F, 1982. "Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation," Econometrica, Econometric Society, vol. 50(4), pages 987-1007, July.
    4. Dickey, David A & Fuller, Wayne A, 1981. "Likelihood Ratio Statistics for Autoregressive Time Series with a Unit Root," Econometrica, Econometric Society, vol. 49(4), pages 1057-1072, June.
    5. Xingyu Zhang & Tao Zhang & Alistair A Young & Xiaosong Li, 2014. "Applications and Comparisons of Four Time Series Models in Epidemiological Surveillance Data," PLOS ONE, Public Library of Science, vol. 9(2), pages 1-16, February.
    6. Ya-wen Wang & Zhong-zhou Shen & Yu Jiang, 2018. "Comparison of ARIMA and GM(1,1) models for prediction of hepatitis B in China," PLOS ONE, Public Library of Science, vol. 13(9), pages 1-11, September.
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    Cited by:

    1. Gaetano Perone, 2022. "Using the SARIMA Model to Forecast the Fourth Global Wave of Cumulative Deaths from COVID-19: Evidence from 12 Hard-Hit Big Countries," Econometrics, MDPI, vol. 10(2), pages 1-23, April.
    2. Gaetano Perone, 2022. "Comparison of ARIMA, ETS, NNAR, TBATS and hybrid models to forecast the second wave of COVID-19 hospitalizations in Italy," The European Journal of Health Economics, Springer;Deutsche Gesellschaft für Gesundheitsökonomie (DGGÖ), vol. 23(6), pages 917-940, August.
    3. Francesco Busato & Bruno Chiarini & Gianluigi Cisco & Maria Ferrara & Elisabetta Marzano, 2020. "Lockdown Policies: A Macrodynamic Perspective for Covid-19," CESifo Working Paper Series 8465, CESifo.
    4. Cia Vei Tan & Sarbhan Singh & Chee Herng Lai & Ahmed Syahmi Syafiq Md Zamri & Sarat Chandra Dass & Tahir Bin Aris & Hishamshah Mohd Ibrahim & Balvinder Singh Gill, 2022. "Forecasting COVID-19 Case Trends Using SARIMA Models during the Third Wave of COVID-19 in Malaysia," IJERPH, MDPI, vol. 19(3), pages 1-12, January.
    5. Emerson Abraham Jackson, 2021. "Forecasting COVID-19 Daily Contraction in Sierra Leone with Implications for Policy Formulation," Journal of Economic Policy Researches, Istanbul University, Faculty of Economics, vol. 8(1), pages 29-43, January.
    6. Abdallah S. A. Yaseen, 2022. "Impact of social determinants on COVID-19 infections: a comprehensive study from Saudi Arabia governorates," Palgrave Communications, Palgrave Macmillan, vol. 9(1), pages 1-9, December.
    7. Yichen Yang & Shifeng Fang & Hua Wu & Jiaqiang Du & Haomiao Tu & Wei He, 2021. "Spatiotemporal Trends and Driving Factors of Urban Livability in the Yangtze River Delta Agglomeration," Sustainability, MDPI, vol. 13(23), pages 1-19, November.

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    More about this item

    Keywords

    COVID-2019; infection disease; pandemic; time series; ARIMA model; forecasting models;
    All these keywords.

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • I18 - Health, Education, and Welfare - - Health - - - Government Policy; Regulation; Public Health

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