School of Mathematics and Statistics, Ningxia University, Yinchuan, 750021, Ningxia, China.
Chinese Academy of Sciences Key Laboratory of Special Pathogens and Biosafety, Wuhan Institute of Virology, Chinese Academy of Sciences, Wuhan, 430071, China.
Infect Dis Poverty. 2023 Mar 15;12(1):18. doi: 10.1186/s40249-023-01072-5.
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.
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).
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.
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.
由严重急性呼吸系统综合征冠状病毒 2 型（SARS-CoV-2）引起的持续的 2019 年冠状病毒病（COVID-19）大流行和奥密克戎变异株对全球的控制和预防构成了巨大挑战，特别是对中低收入国家（LMICs）而言。因此，以哈萨克斯坦和巴基斯坦为例，本研究旨在探讨不同接触、隔离和检测率下奥密克戎变异株的 COVID-19 传播情况。
应用疾病动力学模型，对人群进行细分，建立奥密克戎传播的三个时间阶段：初始爆发期、稳定期和二次爆发期。分析了人口接触、隔离和检测对疾病的影响，并在五种情景下进行了分析，以分析其对疾病的影响。采用四个统计指标来量化模型的性能，包括相关系数（CC）、归一化绝对误差、归一化均方根误差和模拟与观测指数之间的距离（DISO）。
我们的模型在模拟哈萨克斯坦和巴基斯坦的 COVID-19 传播方面表现出色，相关系数（CC）值大于 0.9，DISO 值小于 0.5。与当前措施（基线）相比，降低（增加）接触率或增加（降低）隔离率可以降低（增加）每日新增病例的峰值和提前（延迟）峰值时间（哈萨克斯坦分别降低 842 次和提前 2 天）。检测率对疾病的影响较弱。当第二阶段的开始时间为 6 天时，每日新增病例数分别是哈萨克斯坦和巴基斯坦的 8 倍和 5 倍以上（29573 对 3259；7398 对 1108）。第三阶段开始时间对疾病的影响与第二阶段的影响相反。
对于这两个中低收入国家，哈萨克斯坦和巴基斯坦，更强有力的控制和预防措施可以更有效地对抗 COVID-19。因此，为了减少奥密克戎的传播，应严格管理人口流动。此外，及时应用这些策略在疾病控制中也起着关键作用。
