Assessing and Forecasting the COVID-19 Pandemic Profile with Time-Dependent Control Actions in Kazakhstan

This paper presents the assessment and forecasts of the transmission dynamics of COVID-19 with the effects of time-dependent control actions using the data in Kazakhstan as a case study. First, a modified SEIRD (susceptible, exposed, infectious, recovered, and death) model is introduced. The SEIRD model is developed to take into account the time-dependent characteristics of the model parameters, especially on the ever-evolving value of the reproduction number, which is one of the critical measurements used to describe the transmission dynamics of this epidemic. The reproduction number alongside other key parameters of the model can be estimated by fitting the model to real-world data using numerical optimisation techniques. In this paper, the trust-region-reflective (TTR) algorithm is used to fit the model to estimate both time-invariant and time-varying (with bounded contsraints) parameters. The model is verified using a case study based on the data in Kazakhstan, which is a country badly affected by this pandemic but has not been receiving much attention by the research community thus far. Finally, some forecasts are made using five scenarios with time-dependent control measures such that the effects of the control actions onto the transmission dynamics of the pandemic can be quantified and better understood.