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

被引:7
作者
Cui, Qianqian [1 ]
Shi, Zhengli [2 ]
Yimamaidi, Duman [3 ,4 ,5 ]
Hu, Ben [2 ]
Zhang, Zhuo [6 ]
Saqib, Muhammad [7 ]
Zohaib, Ali [8 ]
Gulnara, Baikadamova [9 ]
Yersyn, Mukhanbetkaliyev [9 ]
Hu, Zengyun [3 ,4 ,5 ]
Li, Shizhu [10 ]
机构
[1] Ningxia Univ, Sch Math & Stat, Yinchuan 750021, Ningxia, Peoples R China
[2] Chinese Acad Sci, Wuhan Inst Virol, Key Lab Special Pathogens & Biosafety, Wuhan 430071, Peoples R China
[3] Chinese Acad Sci, Xinjiang Inst Ecol & Geog, State Key Lab Desert & Oasis Ecol, Urumqi 830011, Xinjiang, Peoples R China
[4] Chinese Acad Sci, Res Ctr Ecol & Environm Cent Asia, Urumqi 830011, Xinjiang, Peoples R China
[5] Univ Chinese Acad Sci, Beijing, Peoples R China
[6] Xinjiang Univ, Coll Geog & Remote Sensing Sci, Urumqi 830017, Peoples R China
[7] Univ Agr Faisalabad, Fac Vet Sci, Dept Clin Med & Surg, Faisalabad, Pakistan
[8] Islamia Univ Bahawalpur, Fac Vet & Anim Sci, Dept Microbiol, Bahawalpur, Pakistan
[9] Kazakh Agrotech Univ, Vet Med Dept, Astana, Kazakhstan
[10] Natl Ctr Int Res Trop Dis, Natl Inst Parasit Dis, Chinese Ctr Dis Control & Prevent,Chinese Ctr Tro, NHC Key Lab Parasite & Vector Biol,WHO Collaborat, Shanghai 200025, Peoples R China
关键词
COVID-19; Pandemic; Omicron; Daily new confirmed cases; Cumulative confirmed cases; Simulation; Prediction; QUARANTINE STRATEGIES; DISEASE; POPULATION; INFECTION; VACCINE;
D O I
10.1186/s40249-023-01072-5
中图分类号
R51 [传染病];
学科分类号
100401 ;
摘要
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.
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页数:12
相关论文
共 49 条
[11]   Forecasting the transmission trends of respiratory infectious diseases with an exposure-risk-based model at the microscopic level [J].
Cui, Ziwei ;
Cai, Ming ;
Xiao, Yao ;
Zhu, Zheng ;
Yang, Mofeng ;
Chen, Gongbo .
ENVIRONMENTAL RESEARCH, 2022, 212
[12]   Rapid increase in Omicron infections in England during December 2021: REACT-1 study [J].
Elliott, Paul ;
Bodinier, Barbara ;
Eales, Oliver ;
Wang, Haowei ;
Haw, David ;
Elliott, Joshua ;
Whitaker, Matthew ;
Jonnerby, Jakob ;
Tang, David ;
Walters, Caroline E. ;
Atchison, Christina ;
Diggle, Peter J. ;
Page, Andrew J. ;
Trotter, Alexander J. ;
Ashby, Deborah ;
Barclay, Wendy ;
Taylor, Graham ;
Ward, Helen ;
Darzi, Ara ;
Cooke, Graham S. ;
Chadeau-Hyam, Marc ;
Donnelly, Christl A. .
SCIENCE, 2022, 375 (6587) :1406-+
[13]  
Feikin DR, 2022, LANCET, V399, P924, DOI 10.1016/S0140-6736(22)00152-0
[14]  
FISHBEIN G, 1986, Genetic Engineering Letter, V6, P2
[15]   Estimating the effects of non-pharmaceutical interventions on COVID-19 in Europe [J].
Flaxman, Seth ;
Mishra, Swapnil ;
Gandy, Axel ;
Unwin, H. Juliette T. ;
Mellan, Thomas A. ;
Coupland, Helen ;
Whittaker, Charles ;
Zhu, Harrison ;
Berah, Tresnia ;
Eaton, Jeffrey W. ;
Monod, Melodie ;
Ghani, Azra C. ;
Donnelly, Christl A. ;
Riley, Steven ;
Vollmer, Michaela A. C. ;
Ferguson, Neil M. ;
Okell, Lucy C. ;
Bhatt, Samir .
NATURE, 2020, 584 (7820) :257-+
[16]   Untangling the changing impact of non-pharmaceutical interventions and vaccination on European COVID-19 trajectories [J].
Ge, Yong ;
Zhang, Wen-Bin ;
Wu, Xilin ;
Ruktanonchai, Corrine W. ;
Liu, Haiyan ;
Wang, Jianghao ;
Song, Yongze ;
Liu, Mengxiao ;
Yan, Wei ;
Yang, Juan ;
Cleary, Eimear ;
Qader, Sarchil H. ;
Atuhaire, Fatumah ;
Ruktanonchai, Nick W. ;
Tatem, Andrew J. ;
Lai, Shengjie .
NATURE COMMUNICATIONS, 2022, 13 (01)
[17]   Breakthrough SARS-CoV-2 infections during periods of delta and omicron predominance, South Africa [J].
Goga, Ameena ;
Bekker, Linda-Gail ;
Garrett, Nigel ;
Reddy, Tarylee ;
Yende-Zuma, Nonhlanhla ;
Fairall, Lara ;
Moultrie, Harry ;
Takalani, Azwidihwi ;
Trivella, Valentina ;
Faesen, Mark ;
Bailey, Veronique ;
Seocharan, Ishen ;
Gray, Glenda E. .
LANCET, 2022, 400 (10348) :269-271
[18]   Reconstruction of the full transmission dynamics of COVID-19 in Wuhan [J].
Hao, Xingjie ;
Cheng, Shanshan ;
Wu, Degang ;
Wu, Tangchun ;
Lin, Xihong ;
Wang, Chaolong .
NATURE, 2020, 584 (7821) :420-+
[19]   CCHZ-DISO: A Timely New Assessment System for Data Quality or Model Performance From Da Dao Zhi Jian [J].
Hu, Zengyun ;
Chen, Deliang ;
Chen, Xi ;
Zhou, Qiming ;
Peng, Yuzhou ;
Li, Jianfeng ;
Sang, Yanfang .
GEOPHYSICAL RESEARCH LETTERS, 2022, 49 (23)
[20]   Evaluation and prediction of the COVID-19 variations at different input population and quarantine strategies, a case study in Guangdong province, China [J].
Hu, Zengyun ;
Cui, Qianqian ;
Han, Junmei ;
Wang, Xia ;
Sha, Wei E. I. ;
Teng, Zhidong .
INTERNATIONAL JOURNAL OF INFECTIOUS DISEASES, 2020, 95 :231-240