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

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...

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Autores principales: Sarría-Santamera, Antonio, Abdukadyrov, Nurlan, Glushkova, Natalya, Russell Peck, David, Colet, Paolo, Yeskendir, Alua, Asúnsolo, Angel, Ortega, Miguel A.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8880445/
https://www.ncbi.nlm.nih.gov/pubmed/35208577
http://dx.doi.org/10.3390/medicina58020253
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author Sarría-Santamera, Antonio
Abdukadyrov, Nurlan
Glushkova, Natalya
Russell Peck, David
Colet, Paolo
Yeskendir, Alua
Asúnsolo, Angel
Ortega, Miguel A.
author_facet Sarría-Santamera, Antonio
Abdukadyrov, Nurlan
Glushkova, Natalya
Russell Peck, David
Colet, Paolo
Yeskendir, Alua
Asúnsolo, Angel
Ortega, Miguel A.
author_sort Sarría-Santamera, Antonio
collection PubMed
description 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.
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spelling pubmed-88804452022-02-26 Towards an Accurate Estimation of COVID-19 Cases in Kazakhstan: Back-Casting and Capture–Recapture Approaches Sarría-Santamera, Antonio Abdukadyrov, Nurlan Glushkova, Natalya Russell Peck, David Colet, Paolo Yeskendir, Alua Asúnsolo, Angel Ortega, Miguel A. Medicina (Kaunas) Article 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. MDPI 2022-02-08 /pmc/articles/PMC8880445/ /pubmed/35208577 http://dx.doi.org/10.3390/medicina58020253 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Sarría-Santamera, Antonio
Abdukadyrov, Nurlan
Glushkova, Natalya
Russell Peck, David
Colet, Paolo
Yeskendir, Alua
Asúnsolo, Angel
Ortega, Miguel A.
Towards an Accurate Estimation of COVID-19 Cases in Kazakhstan: Back-Casting and Capture–Recapture Approaches
title Towards an Accurate Estimation of COVID-19 Cases in Kazakhstan: Back-Casting and Capture–Recapture Approaches
title_full Towards an Accurate Estimation of COVID-19 Cases in Kazakhstan: Back-Casting and Capture–Recapture Approaches
title_fullStr Towards an Accurate Estimation of COVID-19 Cases in Kazakhstan: Back-Casting and Capture–Recapture Approaches
title_full_unstemmed Towards an Accurate Estimation of COVID-19 Cases in Kazakhstan: Back-Casting and Capture–Recapture Approaches
title_short Towards an Accurate Estimation of COVID-19 Cases in Kazakhstan: Back-Casting and Capture–Recapture Approaches
title_sort towards an accurate estimation of covid-19 cases in kazakhstan: back-casting and capture–recapture approaches
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8880445/
https://www.ncbi.nlm.nih.gov/pubmed/35208577
http://dx.doi.org/10.3390/medicina58020253
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