Forecasting COVID-19 Daily Contraction in Sierra Leone with Implications for Policy Formulation

Jackson Emerson

doi:https://dx.doi.org/10.26650/JEPR795665

Araştırma Makalesi

DOI :10.26650/JEPR795665 IUP :10.26650/JEPR795665 Tam Metin (PDF)

Forecasting COVID-19 Daily Contraction in Sierra Leone with Implications for Policy Formulation

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.

Anahtar Kelimeler: COVID-19, ARIMA Model, Forecast, Sierra Leone

JEL Classification : C22 , C53 , C82 , I12 , I18

Referanslar

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. google scholar
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. google scholar
Asteriou, D., & Hall, S.G. (2011). ARIMA models and the Box-Jenkins methodology. Applied Econometrics, 2(2):265–286. google scholar
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. google scholar
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. google scholar
Barua, S. (2020). Understanding Coronanomics: The Economic Implications of the Coronavirus (COVID-19) Pandemic. https://dx.doi.org/10.2139/ssrn.3566477. google scholar
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. google scholar
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. google scholar
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. google scholar
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. google scholar
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. google scholar
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. google scholar
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). google scholar
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. google scholar
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. google scholar
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. google scholar
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. google scholar
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. google scholar
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. google scholar
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. google scholar
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. google scholar
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. google scholar
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-epidemicprediction-of-covid-19-and-its-significance-5755.pdf. google scholar
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. google scholar
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. google scholar
Mills, Terence C. (1990). Time Series Techniques for Economists. United Kingdom: Cambridge University Press. google scholar
NewsNigeria. (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-leonegovernor-we-have-restored-international-confidence-on-the-economy-amidst-covid/. google scholar
Ozili, P.K. (2020). COVID-19 Pandemic and Economic Crisis: The Nigerian Experience and Structural Causes. https://dx.doi.org/10.2139/ssrn.3567419. google scholar
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. google scholar
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. google scholar
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. google scholar
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. google scholar
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. google scholar
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. google scholar
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. google scholar

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DIŞA AKTAR

APA

Jackson, E.A. (2021). Forecasting COVID-19 Daily Contraction in Sierra Leone with Implications for Policy Formulation. İktisat Politikası Araştırmaları Dergisi, 8(1), 29-43. https://doi.org/10.26650/JEPR795665

AMA

Jackson E A. Forecasting COVID-19 Daily Contraction in Sierra Leone with Implications for Policy Formulation. İktisat Politikası Araştırmaları Dergisi. 2021;8(1):29-43. https://doi.org/10.26650/JEPR795665

ABNT

Jackson, E.A. Forecasting COVID-19 Daily Contraction in Sierra Leone with Implications for Policy Formulation. İktisat Politikası Araştırmaları Dergisi, [Publisher Location], v. 8, n. 1, p. 29-43, 2021.

Chicago: Author-Date Style

Jackson, Emerson Abraham,. 2021. “Forecasting COVID-19 Daily Contraction in Sierra Leone with Implications for Policy Formulation.” İktisat Politikası Araştırmaları Dergisi 8, no. 1: 29-43. https://doi.org/10.26650/JEPR795665

Chicago: Humanities Style

Jackson, Emerson Abraham,. “Forecasting COVID-19 Daily Contraction in Sierra Leone with Implications for Policy Formulation.” İktisat Politikası Araştırmaları Dergisi 8, no. 1 (May. 2024): 29-43. https://doi.org/10.26650/JEPR795665

Harvard: Australian Style

Jackson, EA 2021, 'Forecasting COVID-19 Daily Contraction in Sierra Leone with Implications for Policy Formulation', İktisat Politikası Araştırmaları Dergisi, vol. 8, no. 1, pp. 29-43, viewed 5 May. 2024, https://doi.org/10.26650/JEPR795665

Harvard: Author-Date Style

Jackson, E.A. (2021) ‘Forecasting COVID-19 Daily Contraction in Sierra Leone with Implications for Policy Formulation’, İktisat Politikası Araştırmaları Dergisi, 8(1), pp. 29-43. https://doi.org/10.26650/JEPR795665 (5 May. 2024).

MLA

Jackson, Emerson Abraham,. “Forecasting COVID-19 Daily Contraction in Sierra Leone with Implications for Policy Formulation.” İktisat Politikası Araştırmaları Dergisi, vol. 8, no. 1, 2021, pp. 29-43. [Database Container], https://doi.org/10.26650/JEPR795665

Vancouver

Jackson EA. Forecasting COVID-19 Daily Contraction in Sierra Leone with Implications for Policy Formulation. İktisat Politikası Araştırmaları Dergisi [Internet]. 5 May. 2024 [cited 5 May. 2024];8(1):29-43. Available from: https://doi.org/10.26650/JEPR795665 doi: 10.26650/JEPR795665

ISNAD

Jackson, EmersonAbraham. “Forecasting COVID-19 Daily Contraction in Sierra Leone with Implications for Policy Formulation”. İktisat Politikası Araştırmaları Dergisi 8/1 (May. 2024): 29-43. https://doi.org/10.26650/JEPR795665

Cilt 8, Sayı 12021, S. 29-43

ZAMAN ÇİZELGESİ

Gönderim	16.09.2020
Kabul	08.12.2020
Çevrimiçi Yayınlanma	29.01.2021

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This license lets others remix, tweak, and build upon your work non-commercially, and although their new works must also acknowledge you and be non-commercial, they don’t have to license their derivative works on the same terms.

İktisat Politikası Araştırmaları Dergisi

Araştırma Makalesi

Forecasting COVID-19 Daily Contraction in Sierra Leone with Implications for Policy Formulation

PDF Görünüm

Referanslar

Atıflar

DIŞA AKTAR

APA

AMA

ABNT

Chicago: Author-Date Style

Chicago: Humanities Style

Harvard: Australian Style

Harvard: Author-Date Style

MLA

Vancouver

ISNAD

ZAMAN ÇİZELGESİ

LİSANS

PAYLAŞ