Evidence map›Paper›PMID 36777899›Full record

ArticleChina CDC weekly2023

Characterizing Human Collective Behaviors During COVID-19 - Hong Kong SAR, China, 2020.

Zhanwei Du, Xiao Zhang, Lin Wang, Sidan Yao, Yuan Bai, Qi Tan, Xiaoke Xu, Sen Pei, Jingyi Xiao, Tim K Tsang and 5 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in China CDC weekly, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

4 citing papers in PubMed.

  1. Neural Bases of Collective Social Behavior and Group Interactions across Species.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2026
    Review
  2. Article
  3. Article
  4. 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

15 authors.

Zhanwei DuWHO 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.
Xiao ZhangLaboratory of Data Discovery for Health, Hong Kong Science and Technology Park, New Territories, Hong Kong Special Administrative Region, China.
Lin WangDepartment of Genetics, University of Cambridge, Cambridge, CB2 3EH, UK.
Sidan YaoInstitute of High Performance Computing (IHPC), Agency for Science, Technology and Research (ASTAR), Singapore.
Yuan BaiWHO 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.
Qi TanWHO 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.
Xiaoke XuCollege of Information and Communication Engineering, Dalian Minzu University, Dalian City, Liaoning Province, China.
Sen PeiDepartment of Environmental Health Sciences, Mailman School of Public Health, Columbia University, New York City, NY, USA.
Jingyi XiaoWHO 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.
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.
Qiuyan LiaoWHO 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.
Eric H Y LauWHO 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.
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.
Chao GaoSchool of Artificial Intelligence, Optics, and Electronics (iOpen), Northwestern Polytechnical University, Xi'an City, Shaanxi Province, 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.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

What is already known about this topic?: People are likely to engage in collective behaviors online during extreme events, such as the coronavirus disease 2019 (COVID-19) crisis, to express awareness, take action, and work through concerns. What is added by this report?: This study offers a framework for evaluating interactions among individuals' emotions, perceptions, and online behaviors in Hong Kong Special Administrative Region (SAR) during the first two waves of COVID-19 (February to June 2020). Its results indicate a strong correlation between online behaviors, such as Google searches, and the real-time reproduction numbers. To validate the model's output of risk perception, this investigation conducted 10 rounds of cross-sectional telephone surveys on 8,593 local adult residents from February 1 through June 20 in 2020 to quantify risk perception levels over time. What are the implications for public health practice?: Compared to the survey results, the estimates of the risk perception of individuals using our network-based mechanistic model capture 80% of the trend of people's risk perception (individuals who are worried about being infected) during the studied period. We may need to reinvigorate the public by involving people as part of the solution that reduced the risk to their lives.

Indexed as

Collective BehaviorCOVID-19Hong Kong

Identifiers

PMID36777899
PMCPMC9902758

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-SA
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.