Assessing the effects of time-dependent restrictions and control actions to flatten the curve of COVID-19 in Kazakhstan

TL;DR

This study develops a deterministic model to evaluate the impact of time-dependent restrictions on COVID-19 transmission in Kazakhstan, aiding future pandemic planning and control strategies.

Contribution

It introduces a novel time-dependent population model for COVID-19, incorporating evolving reproduction numbers and fitting it to real data for Kazakhstan's first wave.

Findings

Model accurately fits first wave data

Time-varying parameters improve forecast accuracy

Certain control scenarios align closely with observed data

Abstract

This paper presents the assessment of time-dependent national-level restrictions and control actions and their effects in fighting the COVID-19 pandemic. By analysing the transmission dynamics during the first wave of COVID-19 in the country, the effectiveness of the various levels of control actions taken to flatten the curve can be better quantified and understood. This in turn can help the relevant authorities to better plan for and control the subsequent waves of the pandemic. To achieve this, a deterministic population model for the pandemic is firstly developed to take into consideration the time-dependent characteristics of the model parameters, especially on the ever-evolving value of the reproduction number, which is one of the critical measures used to describe the transmission dynamics of this pandemic. The reproduction number alongside other key parameters of the model can then be estimated by fitting the model to real-world data using numerical optimisation techniques or by inducing ad-hoc control actions as recorded in the news platforms. In this paper, the model is verified using a case study based on the data from the first wave of COVID-19 in the Republic of Kazakhstan. The model is fitted to provide estimates for two settings in simulations; time-invariant and time-varying (with bounded constraints) parameters. Finally, some forecasts are made using four scenarios with time-dependent control measures so as to determine which would reflect on the actual situations better.