Cargando…
Dynamic variations in COVID-19 with the SARS-CoV-2 Omicron variant in Kazakhstan and Pakistan
BACKGROUND: The ongoing coronavirus disease 2019 (COVID-19) pandemic caused by the severe acute respiratory syndrome-coronavirus 2 (SARS-CoV-2) and the Omicron variant presents a formidable challenge for control and prevention worldwide, especially for low- and middle-income countries (LMICs). Hence...
Autores principales: | , , , , , , , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2023
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10014408/ https://www.ncbi.nlm.nih.gov/pubmed/36918974 http://dx.doi.org/10.1186/s40249-023-01072-5 |
_version_ | 1784906989529202688 |
---|---|
author | Cui, Qianqian Shi, Zhengli Yimamaidi, Duman Hu, Ben Zhang, Zhuo Saqib, Muhammad Zohaib, Ali Gulnara, Baikadamova Yersyn, Mukhanbetkaliyev Hu, Zengyun Li, Shizhu |
author_facet | Cui, Qianqian Shi, Zhengli Yimamaidi, Duman Hu, Ben Zhang, Zhuo Saqib, Muhammad Zohaib, Ali Gulnara, Baikadamova Yersyn, Mukhanbetkaliyev Hu, Zengyun Li, Shizhu |
author_sort | Cui, Qianqian |
collection | PubMed |
description | BACKGROUND: The ongoing coronavirus disease 2019 (COVID-19) pandemic caused by the severe acute respiratory syndrome-coronavirus 2 (SARS-CoV-2) and the Omicron variant presents a formidable challenge for control and prevention worldwide, especially for low- and middle-income countries (LMICs). Hence, taking Kazakhstan and Pakistan as examples, this study aims to explore COVID-19 transmission with the Omicron variant at different contact, quarantine and test rates. METHODS: A disease dynamic model was applied, the population was segmented, and three time stages for Omicron transmission were established: the initial outbreak, a period of stabilization, and a second outbreak. The impact of population contact, quarantine and testing on the disease are analyzed in five scenarios to analysis their impacts on the disease. Four statistical metrics are employed to quantify the model’s performance, including the correlation coefficient (CC), normalized absolute error, normalized root mean square error and distance between indices of simulation and observation (DISO). RESULTS: Our model has high performance in simulating COVID-19 transmission in Kazakhstan and Pakistan with high CC values greater than 0.9 and DISO values less than 0.5. Compared with the present measures (baseline), decreasing (increasing) the contact rates or increasing (decreasing) the quarantined rates can reduce (increase) the peak values of daily new cases and forward (delay) the peak value times (decreasing 842 and forward 2 days for Kazakhstan). The impact of the test rates on the disease are weak. When the start time of stage II is 6 days, the daily new cases are more than 8 and 5 times the rate for Kazakhstan and Pakistan, respectively (29,573 vs. 3259; 7398 vs. 1108). The impact of the start times of stage III on the disease are contradictory to those of stage II. CONCLUSIONS: For the two LMICs, Kazakhstan and Pakistan, stronger control and prevention measures can be more effective in combating COVID-19. Therefore, to reduce Omicron transmission, strict management of population movement should be employed. Moreover, the timely application of these strategies also plays a key role in disease control. GRAPHICAL ABSTRACT: [Image: see text] SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s40249-023-01072-5. |
format | Online Article Text |
id | pubmed-10014408 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-100144082023-03-15 Dynamic variations in COVID-19 with the SARS-CoV-2 Omicron variant in Kazakhstan and Pakistan Cui, Qianqian Shi, Zhengli Yimamaidi, Duman Hu, Ben Zhang, Zhuo Saqib, Muhammad Zohaib, Ali Gulnara, Baikadamova Yersyn, Mukhanbetkaliyev Hu, Zengyun Li, Shizhu Infect Dis Poverty Research Article BACKGROUND: The ongoing coronavirus disease 2019 (COVID-19) pandemic caused by the severe acute respiratory syndrome-coronavirus 2 (SARS-CoV-2) and the Omicron variant presents a formidable challenge for control and prevention worldwide, especially for low- and middle-income countries (LMICs). Hence, taking Kazakhstan and Pakistan as examples, this study aims to explore COVID-19 transmission with the Omicron variant at different contact, quarantine and test rates. METHODS: A disease dynamic model was applied, the population was segmented, and three time stages for Omicron transmission were established: the initial outbreak, a period of stabilization, and a second outbreak. The impact of population contact, quarantine and testing on the disease are analyzed in five scenarios to analysis their impacts on the disease. Four statistical metrics are employed to quantify the model’s performance, including the correlation coefficient (CC), normalized absolute error, normalized root mean square error and distance between indices of simulation and observation (DISO). RESULTS: Our model has high performance in simulating COVID-19 transmission in Kazakhstan and Pakistan with high CC values greater than 0.9 and DISO values less than 0.5. Compared with the present measures (baseline), decreasing (increasing) the contact rates or increasing (decreasing) the quarantined rates can reduce (increase) the peak values of daily new cases and forward (delay) the peak value times (decreasing 842 and forward 2 days for Kazakhstan). The impact of the test rates on the disease are weak. When the start time of stage II is 6 days, the daily new cases are more than 8 and 5 times the rate for Kazakhstan and Pakistan, respectively (29,573 vs. 3259; 7398 vs. 1108). The impact of the start times of stage III on the disease are contradictory to those of stage II. CONCLUSIONS: For the two LMICs, Kazakhstan and Pakistan, stronger control and prevention measures can be more effective in combating COVID-19. Therefore, to reduce Omicron transmission, strict management of population movement should be employed. Moreover, the timely application of these strategies also plays a key role in disease control. GRAPHICAL ABSTRACT: [Image: see text] SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s40249-023-01072-5. BioMed Central 2023-03-15 /pmc/articles/PMC10014408/ /pubmed/36918974 http://dx.doi.org/10.1186/s40249-023-01072-5 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data. |
spellingShingle | Research Article Cui, Qianqian Shi, Zhengli Yimamaidi, Duman Hu, Ben Zhang, Zhuo Saqib, Muhammad Zohaib, Ali Gulnara, Baikadamova Yersyn, Mukhanbetkaliyev Hu, Zengyun Li, Shizhu Dynamic variations in COVID-19 with the SARS-CoV-2 Omicron variant in Kazakhstan and Pakistan |
title | Dynamic variations in COVID-19 with the SARS-CoV-2 Omicron variant in Kazakhstan and Pakistan |
title_full | Dynamic variations in COVID-19 with the SARS-CoV-2 Omicron variant in Kazakhstan and Pakistan |
title_fullStr | Dynamic variations in COVID-19 with the SARS-CoV-2 Omicron variant in Kazakhstan and Pakistan |
title_full_unstemmed | Dynamic variations in COVID-19 with the SARS-CoV-2 Omicron variant in Kazakhstan and Pakistan |
title_short | Dynamic variations in COVID-19 with the SARS-CoV-2 Omicron variant in Kazakhstan and Pakistan |
title_sort | dynamic variations in covid-19 with the sars-cov-2 omicron variant in kazakhstan and pakistan |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10014408/ https://www.ncbi.nlm.nih.gov/pubmed/36918974 http://dx.doi.org/10.1186/s40249-023-01072-5 |
work_keys_str_mv | AT cuiqianqian dynamicvariationsincovid19withthesarscov2omicronvariantinkazakhstanandpakistan AT shizhengli dynamicvariationsincovid19withthesarscov2omicronvariantinkazakhstanandpakistan AT yimamaididuman dynamicvariationsincovid19withthesarscov2omicronvariantinkazakhstanandpakistan AT huben dynamicvariationsincovid19withthesarscov2omicronvariantinkazakhstanandpakistan AT zhangzhuo dynamicvariationsincovid19withthesarscov2omicronvariantinkazakhstanandpakistan AT saqibmuhammad dynamicvariationsincovid19withthesarscov2omicronvariantinkazakhstanandpakistan AT zohaibali dynamicvariationsincovid19withthesarscov2omicronvariantinkazakhstanandpakistan AT gulnarabaikadamova dynamicvariationsincovid19withthesarscov2omicronvariantinkazakhstanandpakistan AT yersynmukhanbetkaliyev dynamicvariationsincovid19withthesarscov2omicronvariantinkazakhstanandpakistan AT huzengyun dynamicvariationsincovid19withthesarscov2omicronvariantinkazakhstanandpakistan AT lishizhu dynamicvariationsincovid19withthesarscov2omicronvariantinkazakhstanandpakistan |