Evaluating the association between COVID-19 transmission and mobility in omicron outbreaks in China
Peng, Liping Ainslie, Kylie EC Huang, Xiaotong Cowling, Benjamin J Wu, Peng Tsang, Tim K
Peng, Liping
Ainslie, Kylie EC
Huang, Xiaotong
Cowling, Benjamin J
Wu, Peng
Tsang, Tim K
Series / Report no.
Open Access
Type
Journal Article
Article
Article
Language
en
Date of publication
2025-05-20
Year of publication
Research Projects
Organizational Units
Journal Issue
Title
Evaluating the association between COVID-19 transmission and mobility in omicron outbreaks in China
Translated Title
Published in
Commun Med-London 2025; 5(1):188
Abstract
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.
It is a general belief that a higher rate of population mobility leads to an increase in transmission of COVID-19, but the underlying relationship can be more complex. To understand this relationship better, we analyzed data from various cities in mainland China that experienced Omicron outbreaks in 2022. Our findings showed a generally positive correlation between population mobility and transmission. However, this relationship can change over time and vary between different outbreaks. The level of government response has an impact on this relationship. Our findings can guide the potential of utilizing mobility as a predictor for nowcasting and forecasting epidemics and emphasize that such usage requires careful consideration of different factors.
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.
It is a general belief that a higher rate of population mobility leads to an increase in transmission of COVID-19, but the underlying relationship can be more complex. To understand this relationship better, we analyzed data from various cities in mainland China that experienced Omicron outbreaks in 2022. Our findings showed a generally positive correlation between population mobility and transmission. However, this relationship can change over time and vary between different outbreaks. The level of government response has an impact on this relationship. Our findings can guide the potential of utilizing mobility as a predictor for nowcasting and forecasting epidemics and emphasize that such usage requires careful consideration of different factors.