In this paper, a Negative Binomial (NB) Integer-valued Autoregressive
model of order 1, INAR (1), is used to model and forecast the cumulative number
of confirmed COVID-19 infected cases in Kenya independently for the three waves
starting from 14th March 2020 to 1st February 2021. The
first wave was experienced from 14th March 2020 to 15th September 2020, the second wave from around 15th September 2020 to 1st February 2021 and the third wave was experienced from 1st February
2021 to 3rd June 2021. 5, 10, and 15-day-ahead forecasts are obtained for these three
waves and the performance of the NB-INAR (1) model analysed.
References
[1]
Al-Osh, M.A. and Alzaid, A.A. (1987) First-Order Integer-Valued Autoregressive (INAR (1)) Process. Journal of Time Series Analysis, 8, 261-275. https://doi.org/10.1111/j.1467-9892.1987.tb00438.x
[2]
Al-Qaness, M.A.A., Ewees, A.A., Fan, H. and Abd El Aziz, M. (2020) First-Order Integer-Valued Autoregressive (INAR (1)) Process. Journal of Clinical Medicine, 9, 674. https://doi.org/10.3390/jcm9030674
[3]
Argawu, A.S. (2020) Modeling and Forecasting of COVID-19 New Cases in the Top 10 Infected African Countries Using Regression and Time Series Models. Cold Spring Harbor Laboratory Press, medRxiv. https://doi.org/10.1101/2020.09.23.20200113
[4]
Ceylan, Z. (2020) Estimation of COVID-19 Prevalence in Italy, Spain, and France. Science of the Total Environment, 729, Article ID: 138817. https://doi.org/10.1016/j.scitotenv.2020.138817
[5]
Massey Jr, F.J. (2020) The Kolmogorov-Smirnov Test for Goodness of Fit. Journal of the American Statistical Association, 46, 68-78. https://doi.org/10.1080/01621459.1951.10500769
[6]
McKenzie, E. (1984) Some Simple Models for Discrete Variate Time Series. Water Resources & Bulletin, 21, 645-650. https://doi.org/10.1111/j.1752-1688.1985.tb05379.x
[7]
Sahai, A.K., Rath, N., Sood, V. and Singh, M.P. (2020) ARIMA Modelling & Forecasting of COVID-19 in Top Five Affected Countries. Diabetes & Metabolic Syndrome: Clinical Research & Reviews, 14, 1419-1427. https://doi.org/10.1016/j.dsx.2020.07.042
[8]
Şahin, U. and Şahin, T. (2020) Forecasting the Cumulative Number of Confirmed Cases of COVID-19 in Italy, UK and USA Using Fractional Nonlinear Grey Bernoulli Model. Chaos, Solitons & Fractals, 138, Article ID: 109948. https://doi.org/10.1016/j.chaos.2020.109948
[9]
Takele, R. (2020) Stochastic Modelling for Predicting COVID-19 Prevalence in East Africa Countries. Infectious Disease Modelling, 5, 598-607. https://doi.org/10.1016/j.idm.2020.08.005
[10]
Triacca, M. and Triacca, U. (2021) Forecasting the Number of Confirmed New Cases of COVID-19 in Italy for the Period from 19 May to 2 June 2020. Infectious Disease Modelling, 6, 362-369. https://doi.org/10.1016/j.idm.2021.01.003
[11]
Zeroual, A., Harrou, F., Dairi, A. and Sun, Y. (2020) Deep Learning Methods for Forecasting COVID-19 Time-Series Data: A Comparative Study. Chaos, Solitons & Fractals, 140, Article ID: 110121. https://doi.org/10.1016/j.chaos.2020.110121
