Towards an Accurate Estimation of COVID-19 Cases in Kazakhstan: Back-Casting and Capture-Recapture Approaches

Antonio Sarría-Santamera, Nurlan Abdukadyrov, Natalya Glushkova, David Russell Peck, Paolo Colet, Alua Yeskendir, Angel Asúnsolo, Miguel A Ortega

Research output: Contribution to journalArticlepeer-review

5 Citations (Scopus)


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.

Original languageEnglish
Issue number2
Publication statusPublished - Feb 8 2022


  • COVID-19
  • Humans
  • Kazakhstan/epidemiology
  • Pandemics/prevention & control
  • SARS-CoV-2


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