[1] Al-Ani G.B., Statistical modeling of the novel COVID-19 epidemic in Iraq, Epidemiol. Methods.,
10(s1) (2021), 20200025.
[2] Al-Osh M.A. and Alzaid A.A., First-order integer-valued autoregressive (INAR(1)) process, J. TimeSer. Anal., 8 (1987), 261–275.
[3] Altun E., El-Morshedy M. and Eliwa M.S., A study on discrete Bilal distribution with properties andapplications on integer-valued autoregressive process, Revstat Stat. J., (2020).
[4] Altun E. and Mamode Khan N.A., Modelling with the novel INAR(1)-PTE process, Methodol. Comput.Appl. Probab., (2021) https://doi.org/10.1007/s11009-021-09878-2.
[5] Alzaid A.A. and Al-Osh M.A., First-order integer-valued autoregressive (INAR(1)) process: distributionaland regression properties, Stat. Neerl., 42 (1988), 53–61.
[6] Alzaid A.A. and Al-Osh M.A., Some autoregressive moving average processes with generalized Poissonmarginal distributions, Ann. Inst. Stat. Math., 45 (1993), 223–232.
[7] Bermúdez L. and Dimitris A.K., Multivariate INAR(1) regression models based on the Sarmanov
distribution, Mathematics., 9(5) (2021), 505.
[8] Bourguignon M., Rodrigues J. and Santos-Neto M., Extended Poisson INAR(1) processes with
equidispersion, underdispersion and overdispersion, J. Appl. Stat., 46(1) (2019), 101–118.
[9] Castellares F., Ferrari S.L.P. and Lemonte A.J., On the Bell distribution and its associated regressionmodel for count data, Appl. Math. Model., 56 (2018), 172–185.
[10] Chakaraborty S. and Chakravarty D., Discrete gamma distribution: properties and parameter estimation,Commun. Stat. - Theory Methods., 41 (2012), 3301–3324.
[11] Chattopadhyay S., Maiti R., Das S. and Biswas A., Change-point analysis through INAR processwith application to some COVID-19 data, Stat. Neerl., 76(1) (2022), 4–34.
[12] Eliwa M.S., Altun E., El-Dawoody M. and El-Morshedy M., A new three-parameter discrete distributionwith associated INAR(1) process and applications, IEEE Access, 8 (2020), 91150–91162.
[13] Gumel A.B., Iboi E.A., Ngonghala C.N. and Elbasha E.H., A primer on using mathematics to understandCOVID-19 dynamics: Modeling, analysis and simulations, Infect. Dis. Model., 6 (2021),
148–168.
[14] HagmarkP.E., On construction and simulation of count data models, Math. Comput. Simul., 77(1)(2008), 72–80.[15] Hegazy M., Abd EL-Kader R., AL-Dayian G. and EL-Helbawy A.A.-A., Discrete inverted Kumaraswamydistribution: Properties and estimation, Pak. J. Stat. Oper. Res., 18(1) (2022), 297–328.
[16] Irshad M.R. و Chesneau C., Dćruz V. and Maya R., Discrete pseudo Lindley distribution: properties,
estimation and application on INAR(1) process, Math. Comput. Appl., 26(4) 2021, 76.
[17] Maleki M., Mahmoudi M.R., Wraith D. and Pho K.H., Time series modelling to forecast the confirmedand recovered cases of COVID-19, Travel. Med. Infect. Dis., 37 (2020), 101742.
[18] Miletić Ilić A.V., Ristić M.M., Nastić A.S. and Bakouch H.S., An INAR(1) model based on a mixed
dependent and independent counting series, J. Statist. Comput. Simul., 88(2) (2018), 290–304.
[19] Muse A.H., Tolba A.H., Fayad E., Abu Ali O.A., Nagy M. and Yusuf M., Modelling the COVID-
19 mortality rate with a new versatile modification of the log-Logistic distribution, Comput. Intell.
Neurosci., 2021 (2021), 8640794.
[20] Pascual L., Romo J. and Ruiz E., Bootstrap predictive inference for ARIMA processes, J. Time Ser.Anal., 25(4) (2004), 449–65.
[21] Pourreza H., Baloui Jamkhaneh E. and Deiri E., A family of Gamma-generated distributions: Statisticalproperties an applications, Stat. Methods. Med. Res., 30(8) (2021), 1850–1873.
[22] Roy D., The discrete normal distribution, Commun. Stat. - Theory Methods., 32(10) (2003), 1871–1883.
[23] Roy D., Discrete Rayleigh distribution, IEEE Trans. Reliab., 53 (2004), 255–260.
[24] Shamma N., Mohammadpour M. and Shirozhan M., A time series model based on dependent zero
inflated counting series, Comput. Stat., 35 (2020), 1737–1757.
[25] Shirozhan M. and Mohammadpour M., A new class of INAR(1) model for count time series, J. Stat.Comput .Simul., 88(7) (2018), 1348–1368.
[26] Tovissodé C.F., Honfo S.H., Doumaté J.T. and Glélé Kakaï R., On the discretization of continuousprobability distributions using a probabilistic rounding mechanism, Mathematics., 9 (2021) 555.[27] Triacca M. and Triacca U., Forecasting the number of confirmed new cases of COVID-19 in Italy forthe period from 19 May to 2 June 2020, Infect. Dis. Model., 6 (2021), 362–369.