Evidence map›Paper›PMID 42150823›Full record

ArticleBMJ open2026

Assessing community resilience during the first COVID-19 wave peak in a megacity in eastern China: a cross-sectional study using social media data.

Lanyue Zhang, Shouchuang Zhang, Yuehui Wei, Danijela Gasevic, Stella Talic, Jiayi Weng, Jun Zhang, Larry Z Liu, Weiyan Jian

Abstract read
In one paragraph

Article in BMJ open, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing 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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

9 authors.

Lanyue ZhangSchool of Public Health, Peking University Health Science Center, Beijing, China.
Shouchuang ZhangSchool of Public Health, Peking University Health Science Center, Beijing, China.
Yuehui WeiSchool of Public Health, Peking University Health Science Center, Beijing, China.
Danijela GasevicSchool of Public Health and Preventive Medicine, Monash University, Melbourne, Victoria, Australia.ORCID http://orcid.org/0000-0001-5976-4011
Stella TalicSchool of Public Health and Preventive Medicine, Monash University, Melbourne, Victoria, Australia.
Jiayi WengSchool of Economics and Management, Beijing Jiaotong University, Beijing, China.
Jun ZhangMSD R&D (China) Co., Ltd, Beijing, China.
Larry Z LiuMerck & Co., Inc, Rahway, New Jersey, USA.
Weiyan JianSchool of Public Health, Peking University Health Science Center, Beijing, China jianweiyan@bjmu.edu.cn.ORCID http://orcid.org/0000-0003-4886-7263

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectivesIn the face of pandemics resulting from infectious disease, improving community resilience has become increasingly vital. China's sudden exit from Zero-COVID policy in December 2022 triggered a surge in COVID-19 cases, compounded by medication shortages due to earlier restrictions, creating a public health crisis. This study assesses community resilience during the first post-Zero-COVID infection wave (8 December 2022 to 7 January 2023), focusing on adaptation mechanisms, resource mobilisation, protective behaviours and medicine access, using real-time social media data to capture these dynamics.

methodsThis cross-sectional study analysed all geotagged COVID-19-related posts on Sina Weibo-China's largest public microblogging platform-collected from a megacity in eastern China with over 10 million residents, covering 8 December 2022 to 7 January 2023. Posts were obtained through a data purchase agreement with Sina Weibo and comprised publicly available content. Machine learning and natural language processing were applied to classify posts across four dimensions: content, responder, response type and time. Community resilience was assessed using the community-level response ratio, response speed and sentiment expressed in interactions related to medicine-seeking posts.

results26 973 posts were analysed, of which 12 152 (45.05%) were help-seeking. Among these help-seeking posts, 11 236 (92.46%) specifically sought COVID-19 medicine, of which 8495 (75.61%) of these medicine-seeking posts received community support. Over 4 weeks, community responses comprised >70% of all replies (the rest were from market-based responders, government and NGOs). More than half of the community responses occurred within an hour, and the emotional state at the community level was the most stable and consistently positive, indicating a high level of prompt community engagement.

conclusionCommunities in the sample area consistently exhibited prompt and proactive responses during the health crisis, with community responses accounting for the majority of interactions on medicine-seeking posts. The power of community mutual aid can significantly enhance responsiveness to public health emergencies. Such insights suggest that strengthening community resilience is crucial in designing more effective disaster response strategies.

Indexed as

COVID-19PandemicsResilience, PsychologicalSocial MediaChinaCross-Sectional StudiesHumansPublic HealthSARS-CoV-2COVID-19Health policyInternetPublic healthSocial Media

Identifiers

PMID42150823
PMCPMC13185020

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
Read underepoch 390

Registered trials

None linked

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 OpenQuestion graph.