Forecasting of COVID-19 infections in E7 countries and proposing some policies based on the Stringency Index

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İhsan Erdem Kayral
Sencer Buzrul


Forecasting, mathematical modeling, SARS-CoV-2, COVID-19, Stringency Index


COVID-19 infection data of Emerging 7 (E7) countries, namely Brazil, China, India, Indonesia, Mexico, Russia, and Turkey were described by an empirical model or a special case of this empirical model. Near-future forecasts were also performed. Moreover, the causalities between the Stringency Index’s indicators and total cases in E7 countries in COVID-19 period were examined. Countries were grouped as “stationary,” “transition,” and “exponential” based on the data and model fits. The proposed models produced good fits to the COVID-19 data of E7 countries and it was possible to predict the number of cases in the near future. Some policies to control total cases in E7 countries were also proposed in the final phase of this study based on the findings and forecasting in these countries.

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1. Cakir Z, Savas HB. A mathematical modelling for the COVID-19 pandemic in Iran. Ortadogu Med J 2020;12(2):206–210. ortadogutipdergisi.715612
2. Mondal MRH, Bharati S, Podder P. Data analytics for novel coronavirus disease. Inform Med Unlock 2020;20:100374. imu.2020.100374
3. Yan B, Zhang X, Wu L, et al. Why do countries respond differently to COVID-19? A comparative study of Sweden, China, France, and Japan, American Review of Public Administration 2020;50(6–7), pp. 762–769 0275074020942445
4. Hale T, Angrist N, Kira B, et al. Variation in government responses to COVID-19, BSG-WP-2020/032 2020; Version 6, pp. 1–23.
5. Fanelli D, Piazza F. Analysis and forecasting of COVID-19 spreading in China, Italy and France. Chaos Solit Fract 2020;134:109761. https://doi. org/10.1016/j.chaos.2020.109761
6. Corradini MG, Peleg M. Estimating non-isothermal bacterial growth in foods from isothermal experimental data. J Appl Microbiol 2005;99:187–200.
7. Granger C. Investigating causal relations by econometric models and cross-spectral Methods, Econometrica 1969;37(3):424–38. 2307/1912791
8. Lopez L, Weber S. Testing for Granger causality in panel data, University of Neuchatel Institute of Economic Research IRENE Working paper 2017;17-03:1–12. 1536867X1701700412
9. Lau L, Ng C, Cheah S, et al. Panel data analysis (stationarity, cointegration, and causality). In: Environmental Kuznets Curve (EKC) a manual. Academic Press, London, 2019. https://doi. org/10.1016/C2018-0-00657-X