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Epidemiological Characteristics and Forecast of COVID-19 Outbreak in the Republic of Kazakhstan
BACKGROUND: Coronavirus disease 2019 (COVID-19) pandemic entered Kazakhstan on 13 March 2020 and quickly spread over its territory. This study aimed at reporting on the rates of COVID-19 in the country and at making prognoses on cases, deaths, and recoveries through predictive modeling. Also, we att...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
The Korean Academy of Medical Sciences
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7308140/ https://www.ncbi.nlm.nih.gov/pubmed/32567261 http://dx.doi.org/10.3346/jkms.2020.35.e227 |
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author | Semenova, Yuliya Glushkova, Natalya Pivina, Lyudmila Khismetova, Zaituna Zhunussov, Yersin Sandybaev, Marat Ivankov, Alexandr |
author_facet | Semenova, Yuliya Glushkova, Natalya Pivina, Lyudmila Khismetova, Zaituna Zhunussov, Yersin Sandybaev, Marat Ivankov, Alexandr |
author_sort | Semenova, Yuliya |
collection | PubMed |
description | BACKGROUND: Coronavirus disease 2019 (COVID-19) pandemic entered Kazakhstan on 13 March 2020 and quickly spread over its territory. This study aimed at reporting on the rates of COVID-19 in the country and at making prognoses on cases, deaths, and recoveries through predictive modeling. Also, we attempted to forecast the needs in professional workforce depending on implementation of quarantine measures. METHODS: We calculated both national and local incidence, mortality and case-fatality rates, and made forecast modeling via classic susceptible-exposed-infected-removed (SEIR) model. The Health Workforce Estimator tool was utilized for forecast modeling of health care workers capacity. RESULTS: The vast majority of symptomatic patients had mild disease manifestations and the proportion of moderate disease was around 10%. According to the SEIR model, there will be 156 thousand hospitalized patients due to severe illness and 15.47 thousand deaths at the peak of an outbreak if no measures are implemented. Besides, this will substantially increase the need in professional medical workforce. Still, 50% compliance with quarantine may possibly reduce the deaths up to 3.75 thousand cases and the number of hospitalized up to 9.31 thousand cases at the peak. CONCLUSION: The outcomes of our study could be of interest for policymakers as they help to forecast the trends of COVID-19 outbreak, the demands for professional workforce, and to estimate the consequences of quarantine measures. |
format | Online Article Text |
id | pubmed-7308140 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | The Korean Academy of Medical Sciences |
record_format | MEDLINE/PubMed |
spelling | pubmed-73081402020-06-24 Epidemiological Characteristics and Forecast of COVID-19 Outbreak in the Republic of Kazakhstan Semenova, Yuliya Glushkova, Natalya Pivina, Lyudmila Khismetova, Zaituna Zhunussov, Yersin Sandybaev, Marat Ivankov, Alexandr J Korean Med Sci Original Article BACKGROUND: Coronavirus disease 2019 (COVID-19) pandemic entered Kazakhstan on 13 March 2020 and quickly spread over its territory. This study aimed at reporting on the rates of COVID-19 in the country and at making prognoses on cases, deaths, and recoveries through predictive modeling. Also, we attempted to forecast the needs in professional workforce depending on implementation of quarantine measures. METHODS: We calculated both national and local incidence, mortality and case-fatality rates, and made forecast modeling via classic susceptible-exposed-infected-removed (SEIR) model. The Health Workforce Estimator tool was utilized for forecast modeling of health care workers capacity. RESULTS: The vast majority of symptomatic patients had mild disease manifestations and the proportion of moderate disease was around 10%. According to the SEIR model, there will be 156 thousand hospitalized patients due to severe illness and 15.47 thousand deaths at the peak of an outbreak if no measures are implemented. Besides, this will substantially increase the need in professional medical workforce. Still, 50% compliance with quarantine may possibly reduce the deaths up to 3.75 thousand cases and the number of hospitalized up to 9.31 thousand cases at the peak. CONCLUSION: The outcomes of our study could be of interest for policymakers as they help to forecast the trends of COVID-19 outbreak, the demands for professional workforce, and to estimate the consequences of quarantine measures. The Korean Academy of Medical Sciences 2020-06-15 /pmc/articles/PMC7308140/ /pubmed/32567261 http://dx.doi.org/10.3346/jkms.2020.35.e227 Text en © 2020 The Korean Academy of Medical Sciences. https://creativecommons.org/licenses/by-nc/4.0/ This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (https://creativecommons.org/licenses/by-nc/4.0/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Original Article Semenova, Yuliya Glushkova, Natalya Pivina, Lyudmila Khismetova, Zaituna Zhunussov, Yersin Sandybaev, Marat Ivankov, Alexandr Epidemiological Characteristics and Forecast of COVID-19 Outbreak in the Republic of Kazakhstan |
title | Epidemiological Characteristics and Forecast of COVID-19 Outbreak in the Republic of Kazakhstan |
title_full | Epidemiological Characteristics and Forecast of COVID-19 Outbreak in the Republic of Kazakhstan |
title_fullStr | Epidemiological Characteristics and Forecast of COVID-19 Outbreak in the Republic of Kazakhstan |
title_full_unstemmed | Epidemiological Characteristics and Forecast of COVID-19 Outbreak in the Republic of Kazakhstan |
title_short | Epidemiological Characteristics and Forecast of COVID-19 Outbreak in the Republic of Kazakhstan |
title_sort | epidemiological characteristics and forecast of covid-19 outbreak in the republic of kazakhstan |
topic | Original Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7308140/ https://www.ncbi.nlm.nih.gov/pubmed/32567261 http://dx.doi.org/10.3346/jkms.2020.35.e227 |
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