Forecasting COVID-19 daily contraction in Sierra Leone with Implications for Policy Formulation

Abstract

When the World Health Organisation (WHO) announced COVID-19 to be a global pandemic great chaos was brought to the world economy, with institutions and economies forced to close down in a bid to save lives. While it was a well-known fact that there was no immediate cure for the disease, institutions had no option but to utilise non-traditional means of containing the alarming spread of the virus. All around the world, institutions like the health sector and central banks utilised various forecast models as a way of projecting the scale of the spread of the virus and its overall impact on economic well-being. The findings from the ARIMA (4,1,4) model indicate that there is a likelihood of the virus spreading at a rate of 13 contracted cases per day up to 28th February 2021 and beyond. The key take home from this study shows that institutions, particularly the health sector, must stay alert to ensure measures are continually set in place to curb the likelihood of the virus spreading above and beyond the projected estimate. At the same time, institutional support from the central bank, with its stimulus packages, should continue in a bid to diffuse or allay worries of a possible collapse in the economy.

Keywords

• COVID-19
• ARIMA Model
• Forecast
• Sierra Leone

References

1. Ahmar, A. S., & del Val, E. B. (2020). SutteARIMA: Short-term forecasting method, a case: Covid-19 and stock market in Spain. Science of The Total Environment, 138883. https://dx.doi.org/10.1016/j.scitotenv.2020.138883.
2. Andam, K.S., Edeh, H.O., Victor, P.K. & James. I.T. (2020). Estimating the Economic Costs of COVID-19 in Nigeria. Strategy Support Program, Working Paper 63. Nigeria.
3. Asteriou, D., & Hall, S.G. (2011). ARIMA models and the Box-Jenkins methodology. Applied Econometrics, 2(2):265–286.
4. Azad, S., & Poonia, N. (2020). Short-term forecasts of COVID-19 spread across Indian states until 1 May 2020. https://dx.doi.org/10.20944/preprints202004.0491.v1.
5. Fernandes, N. (2020). Economic Effects of Coronavirus Outbreak (COVID-19) on the World Economy. IESE Business School Working Paper No. WP-1240-E. http://dx.doi.org/10.2139/ssrn.3557504.
6. Barua, S. (2020). Understanding Coronanomics: The Economic Implications of the Coronavirus (COVID-19) Pandemic. https://dx.doi.org/10.2139/ssrn.3566477.
7. Benvenuto, D., Giovanetti, M., Vassallo, L., Angeletti, S., & Ciccozzi, M. (2020). Application of the ARIMA model on the COVID-2019 epidemic dataset. https://dx.doi.org/10.1016/j.dib.2020.105340.
8. Ceylan, Z. (2020). Estimation of COVID-19 prevalence in Italy, Spain, and France. Science of The Total Environment. https://dx.doi.org/10.1016/j.scitotenv.2020.138817.

9. Chintalapudi, N., Battineni, G., & Amenta, F. (2020). 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, 53(3), 396-403. https://dx.doi.org/10.1016/j.jmii.2020.04.004.
10. Dehesh, T., Mardani-Fard, H. A., & Dehesh, P. (2020). Forecasting of covid-19 confirmed cases in different countries with ARIMA models. https://dx.doi.org/10.1101/2020.03.13.20035345.
11. Ding, G., Li, X., Shen, Y., & Fan, J. (2020). Brief Analysis of the ARIMA model on the COVID-19 in Italy. https://dx.doi.org/10.1101/2020.04.08. 20058636
12. Ekinci, C., Akyildirim, E., & Corbet, S. (2019). Analysing the dynamic influence of us macroeconomic news releases on Turkish stock markets. Finance Res. Lett., 31, 155-164. https://dx.doi.org/10.1016/j.frl.2019.04.021.
13. Heigermoser, M., & Glauben, T. (2020). COVID-19, the oil price slump and food security in low-income countries, IAMO Policy Brief, No. 37e. Institute of Agricultural Development in Transition Economies (IAMO), Halle (Saale).
14. Iacus, S.M., Natale, F., Santamaria, C., Spyratos, S., & Vespe, M. (2020). Estimating and projecting air passenger traffic during the COVID-19 coronavirus outbreak and its socio-economic impact. Safety Science, 129, 1-11. https://dx.doi.org/10.1016/j.ssci.2020.104791.
15. Ilie, O., Cojocariu, R., Ciobica, A., Timofte, S., Mavroudis, I., & Doroftei, B. (2020). Forecasting the Spreading of COVID-19 across Nine Countries from Europe, Asia, and the American Continents Using the ARIMA Models. Microorganisms, 8(8), 1-18. https://dx.doi.org/10.3390/microorganisms8081158.
16. IMF. (2020). IMF Executive Board Approves Immediate Debt Relief for 25 Countries. Retrieved from https://www.imf.org/eng/News/Articles/2020/04/13/pr20151-imf-executive-board-approves-immediate-debt-relief-for-25-countries.
17. Jackson, E.A. (2020). Emerging innovative thoughts on globalization amidst the contagion of COVID-19. In: Leal Filho W., Azul A., Brandli L., Ozuyar, P.G. (ed.) Industry, Innovation and Infrastructure. Encyclopedia of the UN Sustainable Development Goals. Springer, Cham. https://dx.doi.org/10.1007/978-3-319-71059-4_131-1.
18. Jackson, E.A. (2020b). Understanding SLL/US$ exchange rate dynamics in Sierra Leone using Box-Jenkins ARIMA approach. Theoretical and Practical Research in the Economic Fields, 11(1), 5-20. https://dx.doi.org/10.14505/tpref.v11.1(21).01.
19. Jackson, E.A. (2018). Comparison between Static and Dynamic Forecast in Autoregressive Integrated Moving Average for Seasonally Adjusted Headline Consumer Price Index. https://dx.doi.org/10.2139/ssrn.3162606.
20. Jackson, E.A. & Jabbie, M. (2020). Twin Deficits hypothesis as an indication of government failure in Sierra Leone: An empirical investigation (2007 to 2018). Journal of Economic Policy Researches, 7(1), 43-68. https://dx.doi.org/10.26650/JEPR658440.
21. Kufel, T. (2020). ARIMA-based forecasting of the dynamics of confirmed Covid-19 cases for selected European countries. Equilibrium. Quarterly Journal of Economics and Economic Policy, 15(2), 181–204. https://dx.doi.org/10.24136/eq.2020.009.
22. Kumar, P., Kalita, H., Patairiya, S., Sharma, Y. D., Nanda, C., Rani, M., Rahmani, J., & Bhagavathula, A. S. (2020). Forecasting the dynamics of COVID-19 pandemic in top 15 countries in April 2020: ARIMA model with machine learning approach. https://dx.doi.org/10.1101/2020.03.30.20046227.
23. Li Y, Wang B, Peng R, Zhou C, Zhan Y, & Liu Z. (2020). Mathematical Modeling and Epidemic Prediction of COVID-19 and Its Significance to Epidemic Prevention and Control Measures. Ann Infect Dis Epidemiol. 5(1): 1052. Retrieved from http://www.remedypublications.com/open-access/mathematical-modeling-and-epidemic-prediction-of-covid-19-and-its-significance-5755.pdf.
24. Markwell, A., Mitchell, R., Wright, A.L. & Brown, A.F. (2020). Clinical and ethical challenges for emergency departments during communicable disease outbreaks: Can lessons from Ebola Virus Disease be applied to the COVID‐19 pandemic? Emergency Medicine Australasia, 32, 520-524. https://dx.doi.org/10.1111/1742-6723.13514.
25. McKibbin, W.J., & Fernando, R, (2020). The Global Macroeconomic Impacts of COVID-19: Seven Scenarios. CAMA Working Paper No. 19/2020. https://dx.doi.org/10.2139/ssrn.3547729.
26. Mills, Terence C. (1990). Time Series Techniques for Economists. United Kingdom: Cambridge University Press. News
27. Nigeria. (2020). Bank of Sierra Leone Governor: We have restored International Confidence on the economy amidst COVID. Retrieved from https://www.thenewsnigeria.com.ng/2020/09/19/bank-of-sierra-leone-governor-we-have-restored-international-confidence-on-the-economy-amidst-covid/.
28. Ozili, P.K. (2020). COVID-19 Pandemic and Economic Crisis: The Nigerian Experience and Structural Causes. https://dx.doi.org/10.2139/ssrn.3567419.
29. Patwardhan, C. (2020). SARS-COV-2 pandemic: understanding the impact of lockdown in the most affected states of India. Retrieved from https://arxiv.org/pdf/2004.13632.pdf.
30. Perone, G. (2020). An ARIMA model to forecast the spread and the final size of COVID-2019 epidemic in Italy. HEDG - Health Econometrics and Data Group Working Paper Series, University of York. https://dx.doi.org/10.2139/ssrn.3564865.
31. Ribeiro, M. H. D. M., da Silva, R. G., Mariani, V. C., & dos Santos Coelho, L. (2020). Short-term forecasting COVID-19 cumulative confirmed cases: perspectives for Brazil. Chaos, Solitons & Fractals, 109853. https://dx.doi.org/10.1016/ j.chaos.2020.109853.
32. Sharif, A., Aloui, C., & Yarovaya, L. (2020). COVID-19 Pandemic, Oil Prices, Stock Market, Geopolitical Risk and Policy Uncertainty Nexus in the US Economy: Fresh Evidence from the Wavelet-Based Approach. https://dx.doi.org/10.2139/ssrn.3574699.
33. Singh, R.K., Rani, M., Bhagavathula, A.S., Sah, R., Rodriguez-Morales, A.J., Kalita, H., Nanda, C., Sharma, S., Sharma, Y.D., Rabaan, A.A., Rajmani, J., and Kumar, P. (2020). Prediction of thee COVID-19 Pandemic for the Top 15 Autoregressive Integrated Moving Average (ARIMA) Model. JMIR Public Health Surveill, 6(2), e19115. https://dx.doi.org/10.2196/19115.
34. Tandon, H., Ranjan, P., Chakraborty, T., & Suhag, V. (2020). Coronavirus (COVID-19): ARIMA based time-series analysis to forecast near future. Retrieved from https://arxiv.org/ftp/arxiv/papers/2004/2004.07859.pdf.
35. Yonar, H, Yonar, A, Tekindal, M. A, & Tekindal. M. (2020). 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. Eurasian Journal of Medicine and Oncology, 4(2), 160-165. https://doi.org/10.14744/ejmo.2020.28273