Evidence map›Paper›PMID 35991759›Full record

ArticleGovernment information quarterly2022

Does the internet help governments contain the COVID-19 pandemic? Multi-country evidence from online human behaviour.

Qi Zhang, Chee Wei Phang, Cheng Zhang

Abstract read
In one paragraph

Article in Government information quarterly, 2022. 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

3 authors.

Qi ZhangParty School of the Chengdu Committee of the Chinese Communist Party, Chengdu 610110, China.
Chee Wei PhangBusiness School, University of Nottingham Ningbo, Ningbo 315100, China.
Cheng ZhangSchool of Management, Fudan University, Shanghai 200433, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The effectiveness of social distancing and other public health interventions for containing the COVID-19 pandemic has been demonstrated. However, whether and how Internet use behaviours can lead to enhanced self-protection and reduced transmission when considered in conjunction with behavioural interventions remains unclear. This study investigated the strength of effective Internet behaviours and its interaction with global public health interventions for controlling the COVID-19 pandemic. We conducted an econometric analysis of multisource infection and policy information, Internet behaviour, and meteorological information from worldwide in a 3-month period. People's Internet behaviours may contribute crucially to pandemic containment. Furthermore, they may help enhance the effects of public health interventions, particularly behavioural interventions. We discussed plausible mechanisms through which Internet behaviours reduce epidemic spread independently or in tandem with behavioural interventions. Further investigation into the heterogeneity of the interventions demonstrates Internet behaviour's significance in heightening the effects of difficult-to-implement, primitive crisis orientation, and specific objectives of interventions. Governments should recognise the importance of the Internet and leverage it in managing social crises. Our findings serve as a reference for the formulation of global public health policy. Specifically, the insights provided herein can facilitate the implementation of strategies for containing ongoing secondary outbreaks of COVID-19 or outbreaks of other emergent infectious diseases.

Indexed as

COVID-19 pandemicInternet behaviourIntervention heterogeneityPublic intervention

Identifiers

PMID35991759
PMCPMC9374504

What OpenQuestion holds

Textmetadata
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.