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Analyzing the trend in COVID-19 data: The structural break approach

Year 2022, Volume: 14 Issue: 3, 72 - 96, 02.03.2023
https://doi.org/10.33818/ier.889467

Abstract

In this paper, we have considered three important variables concerning COVID-19 viz., (i) the number of daily new cases, (ii) the number of daily total cases, and (iii) the number of daily deaths, and proposed a modelling procedure, so that the nature of trend in these series could be studied appropriately and then used for identifying the current phase of the pandemic including the phase of containment, if happening /happened, in any country. The proposed modelling procedure gives due consideration to structural breaks in the series. The data from four countries, Brazil, India, Italy and the UK, have been used to study the efficacy of the proposed model. Regarding the phase of infection in these countries, we have found, using data till 19 May 2020, that both Brazil and India are in the increasing phase with infections rising up and further up, but Italy and the UK are in decreasing/containing phase suggesting that these two countries are expected to be free of this pandemic in due course of time provided their respective trend continues. The forecast performance of this model has also established its superiority, as compared to two other standard trend models viz., polynomial and exponential trend models.

Thanks

We are grateful to Professor Mohitosh Kejriwal for providing us with the GAUSS code of the Kejriwal and Perron (2010) test.

References

  • [1] Coronavirus disease 2019 (COVID-19): situation report, 72. World Health Organization 2020.
  • [2] Wu JT, Leung K, Leung GM. Nowcasting and forecasting the potential domestic and international spread of the 2019-nCoV outbreak originating in Wuhan, China: a modelling study. The Lancet 2020;395:689-97.
  • [3] Calafiore GC, Novara C, Possieri C. A modified sir model for the covid-19 contagion in italy. arXiv preprint arXiv:200314391 2020.
  • [4] Kucharski AJ, Russell TW, Diamond C, Liu Y, Edmunds J, Funk S, et al. Early dynamics of transmission and control of COVID-19: a mathematical modelling study. The lancet infectious diseases 2020.
  • [5] Simha A, Prasad RV, Narayana S. A simple stochastic sir model for covid 19 infection dynamics for karnataka: Learning from europe. arXiv preprint arXiv:200311920 2020.
  • [6] Anastassopoulou C, Russo L, Tsakris A, Siettos C. Data-based analysis, modelling and forecasting of the novel coronavirus (2019-Ncov) outbreak. medRxiv 2020.
  • [7] Nesteruk I. Statistics-based predictions of coronavirus epidemic spreading in mainland China. 2020.
  • [8] Nabi KN. Forecasting COVID-19 Pandemic: A Data-Driven Analysis. Chaos, Solitons & Fractals 2020:110046.
  • [9] Fanelli D, Piazza F. Analysis and forecast of COVID-19 spreading in China, Italy and France. Chaos, Solitons & Fractals 2020;134:109761.
  • [10] Mandal M, Jana S, Nandi SK, Khatua A, Adak S, Kar T. A model based study on the dynamics of COVID-19: Prediction and control. Chaos, Solitons & Fractals 2020:109889.
  • [11] Zhang X, Ma R, Wang L. Predicting turning point, duration and attack rate of COVID-19 outbreaks in major Western countries. Chaos, Solitons & Fractals 2020:109829.
  • [12] Tomar A, Gupta N. Prediction for the spread of COVID-19 in India and effectiveness of preventive measures. Science of The Total Environment 2020:138762.
  • [13] Yonar H, Yonar A, Tekindal MA, Tekindal M. 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. EJMO 2020;4:160-5.
  • [14] Rafiq D, Suhail SA, Bazaz MA. Evaluation and prediction of COVID-19 in India: a case study of worst hit states. Chaos, Solitons & Fractals 2020:110014.
  • [15] Chintalapudi N, Battineni G, Amenta F. COVID-19 disease outbreak forecasting of registered and recovered cases after sixty day lockdown in Italy: A data driven model approach. Journal of Microbiology, Immunology and Infection 2020.
  • [16] Ribeiro MHDM, da Silva RG, Mariani VC, dos Santos Coelho L. Short-term forecasting COVID-19 cumulative confirmed cases: Perspectives for Brazil. Chaos, Solitons & Fractals 2020:109853.
  • [17] Singhal A, Singh P, Lall B, Joshi SD. Modeling and prediction of COVID-19 pandemic using Gaussian mixture model. Chaos, Solitons & Fractals 2020:110023.
  • [18] Chakraborty T, Ghosh I. Real-time forecasts and risk assessment of novel coronavirus (COVID-19) cases: A data-driven analysis. Chaos, Solitons & Fractals 2020:109850.
  • [19] Dickey DA, Fuller WA. Distribution of the estimators for autoregressive time series with a unit root. Journal of the American statistical association 1979;74:427-31.
  • [20] Perron P. The great crash, the oil price shock, and the unit root hypothesis. Econometrica: journal of the Econometric Society 1989:1361-401.
  • [21] Zivot E, Andrews DWK. Further evidence on the great crash, the oil-price shock, and the unit-root hypothesis. Journal of business & economic statistics 2002;20:25-44.
  • [22] Vogelsang TJ, Perron P. Additional tests for a unit root allowing for a break in the trend function at an unknown time. International Economic Review 1998:1073-100.
  • [23] Kim D, Perron P. Unit root tests allowing for a break in the trend function at an unknown time under both the null and alternative hypotheses. Journal of econometrics 2009;148:1-13.
  • [24] Carrion-i-Silvestre JL, Kim D, Perron P. GLS-based unit root tests with multiple structural breaks under both the null and the alternative hypotheses. Econometric theory 2009:1754-92.
  • [25] Perron P, Yabu T. Testing for shifts in trend with an integrated or stationary noise component. Journal of Business & Economic Statistics 2009;27:369-96.
  • [26] Kejriwal M, Perron P. A sequential procedure to determine the number of breaks in trend with an integrated or stationary noise component. Journal of Time Series Analysis 2010;31:305-28.
  • [27] Bai J, Perron P. Computation and analysis of multiple structural change models. Journal of applied econometrics 2003;18:1-22.
  • [28] Stock JH. A class of tests for integration and cointegration. Cointegration, Causality and Forecasting A Festschrift in Honour of Clive WJ Granger 1999:137-67.
  • [29] Ng S, Perron P. Lag length selection and the construction of unit root tests with good size and power. Econometrica 2001;69:1519-54.
  • [30] Ljung GM, Box GE. On a measure of lack of fit in time series models. Biometrika 1978;65:297-303.
  • [31] Bai J, Perron P. Estimating and testing linear models with multiple structural changes. Econometrica 1998:47-78.
Year 2022, Volume: 14 Issue: 3, 72 - 96, 02.03.2023
https://doi.org/10.33818/ier.889467

Abstract

References

  • [1] Coronavirus disease 2019 (COVID-19): situation report, 72. World Health Organization 2020.
  • [2] Wu JT, Leung K, Leung GM. Nowcasting and forecasting the potential domestic and international spread of the 2019-nCoV outbreak originating in Wuhan, China: a modelling study. The Lancet 2020;395:689-97.
  • [3] Calafiore GC, Novara C, Possieri C. A modified sir model for the covid-19 contagion in italy. arXiv preprint arXiv:200314391 2020.
  • [4] Kucharski AJ, Russell TW, Diamond C, Liu Y, Edmunds J, Funk S, et al. Early dynamics of transmission and control of COVID-19: a mathematical modelling study. The lancet infectious diseases 2020.
  • [5] Simha A, Prasad RV, Narayana S. A simple stochastic sir model for covid 19 infection dynamics for karnataka: Learning from europe. arXiv preprint arXiv:200311920 2020.
  • [6] Anastassopoulou C, Russo L, Tsakris A, Siettos C. Data-based analysis, modelling and forecasting of the novel coronavirus (2019-Ncov) outbreak. medRxiv 2020.
  • [7] Nesteruk I. Statistics-based predictions of coronavirus epidemic spreading in mainland China. 2020.
  • [8] Nabi KN. Forecasting COVID-19 Pandemic: A Data-Driven Analysis. Chaos, Solitons & Fractals 2020:110046.
  • [9] Fanelli D, Piazza F. Analysis and forecast of COVID-19 spreading in China, Italy and France. Chaos, Solitons & Fractals 2020;134:109761.
  • [10] Mandal M, Jana S, Nandi SK, Khatua A, Adak S, Kar T. A model based study on the dynamics of COVID-19: Prediction and control. Chaos, Solitons & Fractals 2020:109889.
  • [11] Zhang X, Ma R, Wang L. Predicting turning point, duration and attack rate of COVID-19 outbreaks in major Western countries. Chaos, Solitons & Fractals 2020:109829.
  • [12] Tomar A, Gupta N. Prediction for the spread of COVID-19 in India and effectiveness of preventive measures. Science of The Total Environment 2020:138762.
  • [13] Yonar H, Yonar A, Tekindal MA, Tekindal M. 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. EJMO 2020;4:160-5.
  • [14] Rafiq D, Suhail SA, Bazaz MA. Evaluation and prediction of COVID-19 in India: a case study of worst hit states. Chaos, Solitons & Fractals 2020:110014.
  • [15] Chintalapudi N, Battineni G, Amenta F. COVID-19 disease outbreak forecasting of registered and recovered cases after sixty day lockdown in Italy: A data driven model approach. Journal of Microbiology, Immunology and Infection 2020.
  • [16] Ribeiro MHDM, da Silva RG, Mariani VC, dos Santos Coelho L. Short-term forecasting COVID-19 cumulative confirmed cases: Perspectives for Brazil. Chaos, Solitons & Fractals 2020:109853.
  • [17] Singhal A, Singh P, Lall B, Joshi SD. Modeling and prediction of COVID-19 pandemic using Gaussian mixture model. Chaos, Solitons & Fractals 2020:110023.
  • [18] Chakraborty T, Ghosh I. Real-time forecasts and risk assessment of novel coronavirus (COVID-19) cases: A data-driven analysis. Chaos, Solitons & Fractals 2020:109850.
  • [19] Dickey DA, Fuller WA. Distribution of the estimators for autoregressive time series with a unit root. Journal of the American statistical association 1979;74:427-31.
  • [20] Perron P. The great crash, the oil price shock, and the unit root hypothesis. Econometrica: journal of the Econometric Society 1989:1361-401.
  • [21] Zivot E, Andrews DWK. Further evidence on the great crash, the oil-price shock, and the unit-root hypothesis. Journal of business & economic statistics 2002;20:25-44.
  • [22] Vogelsang TJ, Perron P. Additional tests for a unit root allowing for a break in the trend function at an unknown time. International Economic Review 1998:1073-100.
  • [23] Kim D, Perron P. Unit root tests allowing for a break in the trend function at an unknown time under both the null and alternative hypotheses. Journal of econometrics 2009;148:1-13.
  • [24] Carrion-i-Silvestre JL, Kim D, Perron P. GLS-based unit root tests with multiple structural breaks under both the null and the alternative hypotheses. Econometric theory 2009:1754-92.
  • [25] Perron P, Yabu T. Testing for shifts in trend with an integrated or stationary noise component. Journal of Business & Economic Statistics 2009;27:369-96.
  • [26] Kejriwal M, Perron P. A sequential procedure to determine the number of breaks in trend with an integrated or stationary noise component. Journal of Time Series Analysis 2010;31:305-28.
  • [27] Bai J, Perron P. Computation and analysis of multiple structural change models. Journal of applied econometrics 2003;18:1-22.
  • [28] Stock JH. A class of tests for integration and cointegration. Cointegration, Causality and Forecasting A Festschrift in Honour of Clive WJ Granger 1999:137-67.
  • [29] Ng S, Perron P. Lag length selection and the construction of unit root tests with good size and power. Econometrica 2001;69:1519-54.
  • [30] Ljung GM, Box GE. On a measure of lack of fit in time series models. Biometrika 1978;65:297-303.
  • [31] Bai J, Perron P. Estimating and testing linear models with multiple structural changes. Econometrica 1998:47-78.
There are 31 citations in total.

Details

Primary Language English
Subjects Economics
Journal Section Articles
Authors

Nityananda Sarkar

Kushal Banik Chowdhury

Publication Date March 2, 2023
Submission Date March 2, 2021
Published in Issue Year 2022 Volume: 14 Issue: 3

Cite

APA Sarkar, N., & Banik Chowdhury, K. (2023). Analyzing the trend in COVID-19 data: The structural break approach. International Econometric Review, 14(3), 72-96. https://doi.org/10.33818/ier.889467
AMA Sarkar N, Banik Chowdhury K. Analyzing the trend in COVID-19 data: The structural break approach. IER. March 2023;14(3):72-96. doi:10.33818/ier.889467
Chicago Sarkar, Nityananda, and Kushal Banik Chowdhury. “Analyzing the Trend in COVID-19 Data: The Structural Break Approach”. International Econometric Review 14, no. 3 (March 2023): 72-96. https://doi.org/10.33818/ier.889467.
EndNote Sarkar N, Banik Chowdhury K (March 1, 2023) Analyzing the trend in COVID-19 data: The structural break approach. International Econometric Review 14 3 72–96.
IEEE N. Sarkar and K. Banik Chowdhury, “Analyzing the trend in COVID-19 data: The structural break approach”, IER, vol. 14, no. 3, pp. 72–96, 2023, doi: 10.33818/ier.889467.
ISNAD Sarkar, Nityananda - Banik Chowdhury, Kushal. “Analyzing the Trend in COVID-19 Data: The Structural Break Approach”. International Econometric Review 14/3 (March 2023), 72-96. https://doi.org/10.33818/ier.889467.
JAMA Sarkar N, Banik Chowdhury K. Analyzing the trend in COVID-19 data: The structural break approach. IER. 2023;14:72–96.
MLA Sarkar, Nityananda and Kushal Banik Chowdhury. “Analyzing the Trend in COVID-19 Data: The Structural Break Approach”. International Econometric Review, vol. 14, no. 3, 2023, pp. 72-96, doi:10.33818/ier.889467.
Vancouver Sarkar N, Banik Chowdhury K. Analyzing the trend in COVID-19 data: The structural break approach. IER. 2023;14(3):72-96.