Forecasting Covid-19 New Cases in Bosnia And Herzegovina

Abstract

Bosnia and Herzegovina, just like any other affected country in the globe, was not able to escape the deadly COVID-19 pandemic. The disease has caused a lot of suffering in the country, especially in terms of loss of life and economic damage. In this piece of work, the ANN technique was applied to analyze confirmed COVID-19 cases in Bosnia and Herzegovina. This study is based on daily new cases of COVID-19 in Bosnia and Herzegovina for the period 1 January 2020 – 25 March 2021. The out-of-sample forecast covers the period 26 March 2021 – 31 July 2021. The residuals and forecast evaluation criteria (Error, MSE and MAE) of the applied model tell us that the model is stable and indeed suitable for forecasting purposes. The results of the study indicate that daily COVID-19 cases in Bosnia and Herzegovina are likely to drop to zero around late May 2021 onwards. Control and preventive measures should be observed in the country despite the projections.

Country : Zimbabwe

1 Dr. Smartson. P. NYONI2 Mr. Thabani NYONI3 Mr. Tatenda. A. CHIHOHO

  1. ZICHIRe Project, University of Zimbabwe, Harare, Zimbabwe
  2. SAGIT Innovation Center, Harare, Zimbabwe
  3. Independent Health Economist, Harare, Zimbabwe

IRJIET, Volume 5, Issue 6, June 2021 pp. 680-685

doi.org/10.47001/IRJIET/2021.506119

References

  1. Ahmad, A., et al. (2020). The Number of Confirmed Cases of COVID-19 by Using Machine Learning: Methods and Challenges, Archives of Computational Methods in Engineering, pp: 1 – 9.
  2. Ferguson, N., et al. (2020). Impact of Non-pharmaceutical Interventions (NPIs) to Reduce COVID-19 Mortality and Healthcare Demand, Imperial College, London.
  3. Papastefanopoulos, V., Linardatos, P., & Kotsiantis, S. (2020). COVID-19: A Comparison of Time Series Methods to Forecast Percentage of Active Cases Per Population, Applied Sciences, 10 (3880): 1 – 15.
  4. Ramchandani, A., Fan, C., & Mostafavi, A. (2020). DeepCOVIDNet: An Interpretable Deep Learning Model for Predictive Surveillance of COVID-19 Using Heterogeneous Features and Interactions, IEEE Access, 8: 1 – 16.
  5. Sanders, J. M., et al. (2020). Pharmacologic Treatments for Coronavirus Disease 2019 – A Review, JAMA, 323 (18): 1824 – 1836.
  6.   Sun, L., et al. (2020). Adaptive Feature Selection Guided Deep Forest for COVID-19 Classification With Chest CT, IEEE Journal of Biomedical and Health Informatics, 24 (10): 2798 – 2805.
  7. WHO (2020). Novel Coronavirus in China, WHO, Geneva.
  8. Zu, Z. Y., et al. (2020). Coronavirus Disease 2019 (COVID-19): A Perspective From China, Radiology, 296 (2): 15 – 25.