Peng Liping, Ainslie Kylie E C, Huang Xiaotong, Cowling Benjamin J, Wu Peng, Tsang Tim K
WHO Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region, China.
Centre for Infectious Disease Control, National Institute for Public Health and Environment (RIVM), Bilthoven, the Netherlands.
Commun Med (Lond). 2025 May 20;5(1):188. doi: 10.1038/s43856-025-00906-7.
Prior research has suggested a positive correlation between human mobility and COVID-19 transmission at national or provincial levels, assuming constant correlations during outbreaks. However, the correlation strength at finer scales and potential changes in relationships during outbreaks have been scarcely investigated.
We gathered case and mobility data (within-city movement, inter-city inflow, and inter-city outflow) at the city level from Omicron outbreaks in mainland China between February and November 2022. For each outbreak, we calculated the time-varying effective reproduction number (R). Subsequently, we estimated the cross-correlation and rolling correlation between R and the mobility index, comparing them and identifying potential factors affecting these correlations.
We identify 57 outbreaks during Omicron wave 1 (February to June) and 171 outbreaks during Omicron wave 2 (July to December). Cross-correlation estimates vary between waves, with values ranging from 0.64 to 0.71 in wave 1 and 0.45 to 0.46 in wave 2. Oscillation models best fit the rolling correlation for almost all outbreaks, and there are significant differences between extreme values of rolling correlation and cross-correlation. Additionally, we estimate a positive relationship between the GRI and rolling correlation during the pre-peak stage, turning negative during the post-peak stage.
Our findings suggest a positive relationship between Omicron transmission and mobility at the city level. However, significant fluctuations in their relationship, as demonstrated by rolling correlation, indicate that assuming a constant correlation between transmission and mobility may lead to inaccurate predictions or decisions when using mobility as a proxy for transmission intensity.
先前的研究表明，在国家或省级层面，人员流动与新冠病毒传播之间存在正相关关系，且假设疫情期间相关性保持不变。然而，在更精细尺度上的相关强度以及疫情期间关系的潜在变化鲜有研究。
我们收集了2022年2月至11月中国内地奥密克戎疫情期间城市层面的病例和流动数据（市内移动、市际流入和市际流出）。对于每次疫情，我们计算了随时间变化的有效繁殖数（R）。随后，我们估计了R与流动指数之间的交叉相关性和滚动相关性，对它们进行比较并确定影响这些相关性的潜在因素。
我们识别出奥密克戎第一波疫情（2月至6月）期间有57次疫情，奥密克戎第二波疫情（7月至12月）期间有171次疫情。不同波次的交叉相关性估计值有所不同，第一波的值在0.64至0.71之间，第二波的值在0.45至0.46之间。振荡模型最能拟合几乎所有疫情的滚动相关性，滚动相关性的极值与交叉相关性之间存在显著差异。此外，我们估计在峰值前阶段，疫情相关指数（GRI）与滚动相关性呈正相关，在峰值后阶段变为负相关。
我们的研究结果表明，在城市层面，奥密克戎传播与人员流动之间存在正相关关系。然而，滚动相关性所显示的它们之间关系的显著波动表明，在将人员流动作为传播强度的替代指标时，假设传播与流动之间的相关性恒定可能会导致预测或决策不准确。
