Dynamic variations in COVID-19 with the SARS-CoV-2 Omicron variant in Kazakhstan and Pakistan

Qianqian Cui; Zhengli Shi; Duman Yimamaidi; Ben Hu; Zhuo Zhang; Muhammad Saqib; Ali Zohaib; Baikadamova Gulnara; Mukhanbetkaliyev Yersyn; Zengyun Hu; Shizhu Li

doi:10.1186/s40249-023-01072-5

Dynamic variations in COVID-19 with the SARS-CoV-2 Omicron variant in Kazakhstan and Pakistan

Infect Dis Poverty. 2023 Mar 15;12(1):18. doi: 10.1186/s40249-023-01072-5.

Authors

Qianqian Cui¹, Zhengli Shi², Duman Yimamaidi^{3

4

5}, Ben Hu², Zhuo Zhang⁶, Muhammad Saqib⁷, Ali Zohaib⁸, Baikadamova Gulnara⁹, Mukhanbetkaliyev Yersyn⁹, Zengyun Hu^{10

11

12}, Shizhu Li¹³

Affiliations

¹ School of Mathematics and Statistics, Ningxia University, Yinchuan, 750021, Ningxia, China.
² Chinese Academy of Sciences Key Laboratory of Special Pathogens and Biosafety, Wuhan Institute of Virology, Chinese Academy of Sciences, Wuhan, 430071, China.
³ State Key Laboratory of Desert and Oasis Ecology, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Ürümqi, 830011, Xinjiang, China.
⁴ Research Center for Ecology and Environment of Central Asia, Chinese Academy of Sciences, Ürümqi, 830011, Xinjiang, China.
⁵ University of Chinese Academy of Sciences, Beijing, China.
⁶ College of Geography and Remote Sensing Sciences, Xinjiang University, Ürümqi, 830017, China.
⁷ Department of Clinical Medicine and Surgery, Faculty of Veterinary Science, University of Agriculture Faisalabad, Faisalabad, Pakistan.
⁸ Department of Microbiology, Faculty of Veterinary and Animal Sciences, The Islamia University of Bahawalpur, Bahawalpur, Pakistan.
⁹ Veterinary Medicine Department, Kazakh Agrotechnical University, Astana, Kazakhstan.
¹⁰ State Key Laboratory of Desert and Oasis Ecology, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Ürümqi, 830011, Xinjiang, China. huzengyun@ms.xjb.ac.cn.
¹¹ Research Center for Ecology and Environment of Central Asia, Chinese Academy of Sciences, Ürümqi, 830011, Xinjiang, China. huzengyun@ms.xjb.ac.cn.
¹² University of Chinese Academy of Sciences, Beijing, China. huzengyun@ms.xjb.ac.cn.
¹³ National Institute of Parasitic Diseases, Chinese Centre for Disease Control and Prevention (Chinese Centre for Tropical Diseases Research), NHC Key Laboratory of Parasite and Vector Biology, WHO Collaborating Centre for Tropical Diseases, National Centre for International Research On Tropical Diseases, Shanghai, 200025, China. lisz@chinacdc.cn.

Abstract

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.

Keywords: COVID-19; Cumulative confirmed cases; Daily new confirmed cases; Omicron; Pandemic; Prediction; Simulation.

MeSH terms

COVID-19* / epidemiology
Humans
Kazakhstan / epidemiology
Pakistan / epidemiology
SARS-CoV-2

Supplementary concepts

SARS-CoV-2 variants