Evidence map›Paper›PMID 40394170›Full record

ArticleCommunications medicine2025

Evaluating the association between COVID-19 transmission and mobility in omicron outbreaks in China.

Liping Peng, Kylie E C Ainslie, Xiaotong Huang, Benjamin J Cowling, Peng Wu, Tim K Tsang

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Communications medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Liping PengWHO 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.
Kylie E C AinslieWHO 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.ORCID http://orcid.org/0000-0001-5419-7206
Xiaotong HuangWHO 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.
Benjamin J CowlingWHO 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.ORCID http://orcid.org/0000-0002-6297-7154
Peng WuWHO 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.ORCID http://orcid.org/0000-0003-1157-9401
Tim K TsangWHO 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. timtsang@connect.hku.hk.ORCID http://orcid.org/0000-0001-5037-6776

Funding

NIAID Centers of Excellence for Influenza Research and Response: Universal Influenza Vaccine Research Activities75N93021C00016 · NIAID · ST. JUDE CHILDREN'S RESEARCH HOSPITAL · PI WEBBY, RICHARD · 2021 to 2025
$91.4M
NIAID NIH HHS 75N93021C00016
6 · The paper itself

Abstract

backgroundPrior 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.

methodsWe 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

resultsWe 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.

conclusionsOur 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.

Identifiers

PMID40394170
PMCPMC12092746

What Socratic holds

Textmetadata
LicenceCC BY-NC-ND
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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.