In this study, the ANN approach was applied to
analyze COVID-19 new cases in Moldova. The employed data covers the period 1
January 2020 – 25 March 2021 and the out-of-sample period ranges over the
period 26 March – 31 July 2021. The residuals and forecast evaluation criteria
(Error, MSE and MAE) of the applied model indicate that the model is quite
stable. The results of the study indicate that daily COVID-19 cases in Moldova
are likely to remain high over the out-of-sample period. Amongst other
suggested policy directions, there is need for the government of Moldova to
ensure adherence to safety guidelines while continuing to create awareness
about the COVID-19 pandemic.
Country : Zimbabwe
1 Dr. Smartson. P. NYONI2 Mr. Thabani NYONI3 Mr. Tatenda. A. CHIHOHO
ZICHIRe Project, University of Zimbabwe, Harare, Zimbabwe
Maradze,
T. C., Nyoni, S. P., & Nyoni, T (2021). Modeling and Forecasting COVID-19
mortalities in the United States of America using artificial neural networks
(ANN). International Journal of innovations in Engineering and Technology
(IRJIET), 5 (3):533-539
Moldova
country assessment report (2020). TRANSITION REPORT 2020-21 THE STATE STRIKES
BACK, pp 1-3.
Naizhuo
Zhao., Katia Charland., Mabel Carabali., Elaine O., Nsoesie., Mathieu
MaheuGiroux., Erin Rees., Mengru Yuan., Cesar Garcia Balaguera., Gloria
Jaramillo Ramirez., & Kate Zinszer (2020). Machine learning and dengue
forecasting: Comparing random forests and artificial neural networks for
predicting dengue burden at national and sub-national scales in Colombia. PLOS
Neglected Tropical Diseases | https://doi.org/10.1371/journal.pntd.0008056
Nyoni,
S. P., & Nyoni, T (2021). Forecasting ART coverage in Egypt using
artificial neural networks. International Journal of Innovations in Engineering
and Technology (IRJIET), 5 (3): 161-165.
World
Bank (2020). Moldova: Assessing the impact of COVID-19 and the drought on jobs,
firms, and households, pp 1-3.
