Covid-19 and Economics Forecasting on Advanced and Emerging Countries
DOI:
https://doi.org/10.18381/eq.v18i1.7222Abstract
Objective: To estimate the size and the dynamics of the coronavirus (covid-19) pandemic in Advanced, Emerging, and Developing Economies, and to determine its implications for economic growth. Methodology: A susceptible Infected Recovered (sir) model is implemented, we calculate the size of the pandemic through numerical integration and phase diagrams for covid-19 trajectory; finally, we use ensemble models (random forest) to forecast economic growth. Results: We confirm that there are differences in pandemic spread and size among countries; likewise, the trajectories show a long-term spiral cycle. Economic recovery is expected to be slow and gradual for most of the economies. Limitations: All countries differ in covid-19 test applications, which could lead to inaccurate total confirmed cases and an imprecise estimate of the pandemic’s spread and size. In addition, there is a lack of leading indicators in some countries, generating a higher mse of some machine learning models. Originality: To implement economic-epidemiological models to analyze the evolution and virus’ spreading throughout time. Conclusions: It is found the pandemic’s final size to be between 74-77%. Likewise, it is demonstrated that covid-19 is endemic, with a constant prevalence of 9 years on average. The spread of the pandemic has caused countries to self-induce in an unprecedented recession with a slow recovery.Downloads
References
Acemoglu, D., Chernozhukov, V., Werning, I., & Whinston, M. (2020). Optimal targeted lockdowns in a multi-group sir Model. National Bureau of Economic Research: nber Working Paper 27102. Retrieved from https://www.nber.org/papers/w27102.pdf
Atkeson, A. (2020). What will be the economic impact of covid-19 in the us? Rough estimates of disease scenarios. National Bureau of Economic Research: nber Working Paper 26867. Retrieved from https://www.nber.org/papers/w26867.pdf
Breiman, L. (2001). Random Forests. Machine Learning, 5.32.
Eichenbaum, M., Rebelo, S., & Trabandt, M. (2020). The macroeconomics of epidemics. National Bureau of Economic Research: nber Working Paper 26882. Retrieved from https://www.nber.org/papers/w26882.pdf
Gros, C., Valenti, R., Schneider, L., Valenti, K., & Gros, D. (2020). Containment efficiency and control strategies for the Corona pandemic costs. Cornell University. Retrieved from https://arxiv.org/abs/2004.00493v2
Hastie, T., Tibshirani, R., & Friedman, J. (2009). The elements of statistical learning: Data mining, inference,
and prediction. Stanford: Springer.
International Monetary Fund. (August 2nd, 2020). Retrieved from Real GDP growth: https://www.imf.org/external/datamapper/NGDP_RPCH@WEO/OEMDC/ADVEC/WEOWORLD
Kermack, W. & McKendrick, A. (1927). A contribution to the mathematical theory of epidemics. Proceedings of the Royal Society, 115 (772), 700-721. DOI:10.1098/rspa.1927.0118
Lars, J. & Werner, R. (2006). The macroeconomic effects of a pandemic in Europe - A model-based assessment. Brussels: European Comission.
Ma, X., Zhou, Y., & Cao, H. (2013). Global stability of the endemic equilibrium of a discrete sir epidemic model. Advances in Difference Equations, 42, 1-19. DOI:10.1186/1687-1847-2013-42
Organisation for Economic Cooperation and Development-oecd. (2020). Gross domestic product (gdp) (indicator). Retrieved from https://data.oecd.org/gdp/gross-domestic-product-gdp.htm
Paarlberg, P., Seitzinger, H., & Lee, J. (2007). Economic impacts of regionalization of a highly pathogenic avian influenza outbreak in the United States. Journal of Agricultural and Applied Economics, 39 (2), 325-333.
Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., ... & Duchesnay,E. (2011). (2011). Scikit-learn: Machine learning in Python. Journal of Machine Learning Research, 12, 2825-830.
Rodrigues, H. (2016). Application of SIR epidemiological model: New trends. International Journal of Applied Mathematics and Informatics, 10, 92-99.
Roser, M., Ritchie, H., Ortiz-Ospina, E., & Hasell, J. (2020). Coronavirus Pandemic (covid-19). Retrieved August 28, 2020, from https://ourworldindata.org/coronavirus
Sargent, T. & Stachurski, J. (7 August 2020). Quantitative economics with Python. Retrieved from
https://python.quantecon.org/_downloads/pdf/quantitative_economics_with_python.pdf
Segal, M. R. (2004). Machine Learning Benchmarks and Random Forest Regression. UCSF: Center for Bioinformatics and Molecular Biostatistics, 1-14.
Smith, R., Keogh-Brown, M., Barnett, T., & Tait, J. (2009). The economy-wide impact of pandemic influenza on the UK: A computable general equilibrium modelling experiment. The BMJ, 339, 1-7. DOI:10.1136/bmj.b4571
Stock, J. (2020). Data gaps and the policy response to the novel Coronavirus. Harvard Environmental Economics Program: Discussion Paper Series. Retrieved from https://heep.hks.harvard.edu/files/heep/files/dp_82_stock_data_gaps_policy_response_novel_coronavirus.pdf
Toda, A. (2020). Susceptible-Infected-Recovered (sir) Dynamics of covid-19 and Economic Impact. Cornell University. Retrieved from https://arxiv.org/pdf/2003.11221.pdf
Zhang, Y. & Haghani, A. (2015). A gradient boosting method to improve travel time prediction. Transportation Research Part C, 308-324.
Published
How to Cite
Issue
Section
License
El contenido publicado en EconoQuantum se encuentra bajo una Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
