Abstract:
Background and Objectives: Coronavirus disease 19 (COVID-19) has emerged as the most
devastating syndemic of the 21st century, with worrisome and sustained consequences for the entire
society. Despite the relative success of vaccination programs, the global threat of the novel coronavirus
SARS-CoV-2 is still present and further efforts are needed for its containment and control. Essential
for its control and containment is getting closer to understanding the actual extent of SARS-CoV-2
infections. Material and Methods: We present a model based on the mortality data of Kazakhstan for
the estimation of the underlying epidemic dynamic—with both the lag time from infection to death
and the infection fatality rate. For the estimation of the actual number of infected individuals in
Kazakhstan, we used both back-casting and capture–recapture methods. Results: Our results suggest
that despite the increased testing capabilities in Kazakhstan, official case reporting undercounts
the number of infections by at least 60%. Even though our count of deaths may be either over or
underestimated, our methodology could be a more accurate approach for the following: the estimation
of the actual magnitude of the pandemic; aiding the identification of different epidemiological values;
and reducing data bias. Conclusions: For optimal epidemiological surveillance and control efforts, our
study may lead to an increased awareness of the effect of COVID-19 in this region and globally, and
aid in the implementation of more effective screening and diagnostic measures