State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, School of Public Health, Xiamen University, Xiamen, People's Republic of China.
Department of Nephrology, Institute of Nephrology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, People's Republic of China.
Infect Dis Poverty. 2022 Nov 26;11(1):115. doi: 10.1186/s40249-022-01039-y.
There is a raising concern of a higher infectious Omicron BA.2 variant and the latest BA.4, BA.5 variant, made it more difficult in the mitigation process against COVID-19 pandemic. Our study aimed to find optimal control strategies by transmission of dynamic model from novel invasion theory.
Based on the public data sources from January 31 to May 31, 2022, in four cities (Nanjing, Shanghai, Shenzhen and Suzhou) of China. We segmented the theoretical curves into five phases based on the concept of biological invasion. Then, a spatial autocorrelation analysis was carried out by detecting the clustering of the studied areas. After that, we choose a mathematical model of COVID-19 based on system dynamics methodology to simulate numerous intervention measures scenarios. Finally, we have used publicly available migration data to calculate spillover risk.
Epidemics in Shanghai and Shenzhen has gone through the entire invasion phases, whereas Nanjing and Suzhou were all ended in the establishment phase. The results indicated that Rt value and public health and social measures (PHSM)-index of the epidemics were a negative correlation in all cities, except Shenzhen. The intervention has come into effect in different phases of invasion in all studied cities. Until the May 31, most of the spillover risk in Shanghai remained above the spillover risk threshold (18.81-303.84) and the actual number of the spillovers (0.94-74.98) was also increasing along with the time. Shenzhen reported Omicron cases that was only above the spillover risk threshold (17.92) at the phase of outbreak, consistent with an actual partial spillover. In Nanjing and Suzhou, the actual number of reported cases did not exceed the spillover alert value.
Biological invasion is positioned to contribute substantively to understanding the drivers and mechanisms of the COVID-19 spread and outbreaks. After evaluating the spillover risk of cities at each invasion phase, we found the dynamic zero-COVID strategy implemented in four cities successfully curb the disease epidemic peak of the Omicron variant, which was highly correlated to the way to perform public health and social measures in the early phases right after the invasion of the virus.
新型奥密克戎 BA.2 变体和最新的 BA.4、BA.5 变体的传染性更高，这使得 COVID-19 大流行的缓解工作更加困难。我们的研究旨在通过从新的入侵理论中传输动态模型来找到最佳的控制策略。
基于 2022 年 1 月 31 日至 5 月 31 日在中国四个城市（南京、上海、深圳和苏州）的公共数据源。我们根据生物入侵的概念将理论曲线分为五个阶段。然后，通过检测研究区域的聚类进行空间自相关分析。之后，我们选择基于系统动力学方法的 COVID-19 数学模型来模拟大量干预措施场景。最后，我们利用公开的移民数据来计算溢出风险。
上海和深圳的疫情已经经历了整个入侵阶段，而南京和苏州都已经结束了建立阶段。结果表明，除深圳外，所有城市的 Rt 值和公共卫生和社会措施（PHSM）指数与疫情呈负相关。干预措施已在所有研究城市的不同入侵阶段生效。截至 5 月 31 日，上海的大部分溢出风险仍高于溢出风险阈值（18.81-303.84），随着时间的推移，实际溢出数量（0.94-74.98）也在增加。深圳报告的奥密克戎病例仅在疫情爆发阶段高于溢出风险阈值（17.92），与实际部分溢出一致。在南京和苏州，报告的实际病例数量没有超过溢出警报值。
生物入侵理论有望为理解 COVID-19 传播和爆发的驱动因素和机制做出实质性贡献。在评估每个入侵阶段的城市溢出风险后，我们发现四个城市实施的动态零 COVID 策略成功遏制了奥密克戎变体的疾病疫情高峰，这与病毒入侵后早期实施公共卫生和社会措施的方式高度相关。
