Since the beginning of 2020, the world has
been struggling with a viral epidemic (COVID-19), which poses a serious threat
to the collective health of the human race. Mathematical modeling of epidemics
is critical for developing such policies, especially during these uncertain
times. In this study, the
reproduction number and model parameters were predicted using AR(1)
(autoregressive time-series model of order 1) and the adaptive Kalman filter
(AKF). The data sample used in the study consists of the weekly and daily
number of cases amongst the Ziraat Bank personnel between March 11, 2020, and
April 19, 2021. This sample was modeled in the state space, and the AKF was
used to estimate the number of cases per day. It is quite simple to model the
daily and weekly case number time series with the time-varying parameter AR(1)
stochastic process and to estimate the time-varying parameter with online AKF.
Overall, we found that the weekly case number prediction was more accurate than
the daily case number (R2 = 0.97), especially in regions with a low
number of cases. We suggest that the simplest method for reproduction number
estimation can be obtained by modeling the daily cases using an AR(1) model.
JEL classification numbers: C02, C22, C32.
Keywords: COVID-19, Modeling, Reproduction number estimation, AR(1),