Evidence map›Paper›PMID 36504934›Full record

ArticleFrontiers in public health2022

Impact of early phase COVID-19 precautionary behaviors on seasonal influenza in Hong Kong: A time-series modeling approach.

Chun-Pang Lin, Ilaria Dorigatti, Kwok-Leung Tsui, Min Xie, Man-Ho Ling, Hsiang-Yu Yuan

Open access · goldAbstract read
In one paragraph

Article in Frontiers in public health, 2022. 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
0.5field-weighted citation impact, top 39% of its field
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, 4 citations in OpenAlex.

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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 at 4 institutions in 3 countries.

Chun-Pang LinSchool of Data Science, City University of Hong Kong, Kowloon, Hong Kong SAR, China.
Ilaria DorigattiMRC Centre for Global Infectious Disease Analysis, School of Public Health, Imperial College London, London, United Kingdom.
Kwok-Leung TsuiGrado Department of Industrial and Systems Engineering, College of Engineering, Virginia Polytechnic Institute and State University, Blacksburg, VA, United States.
Min XieSchool of Data Science, City University of Hong Kong, Kowloon, Hong Kong SAR, China.
Man-Ho LingDepartment of Mathematics and Information Technology, Faculty of Liberal Arts and Social Sciences, The Education University of Hong Kong, Tai Po, Hong Kong SAR, China.
Hsiang-Yu YuanDepartment of Biomedical Sciences, Jockey Club College of Veterinary Medicine and Life Sciences, City University of Hong Kong, Kowloon, Hong Kong SAR, China.
City University of Hong Kong · HKEducation University of Hong Kong · HKImperial College London · GBVirginia Tech · US

Funding

Medical Research Council MR/R015600/1Wellcome Trust 213494/Z/18/Z
6 · The paper itself

Abstract

Background: Before major non-pharmaceutical interventions were implemented, seasonal incidence of influenza in Hong Kong showed a rapid and unexpected reduction immediately following the early spread of COVID-19 in mainland China in January 2020. This decline was presumably associated with precautionary behavioral changes (e.g., wearing face masks and avoiding crowded places). Knowing their effectiveness on the transmissibility of seasonal influenza can inform future influenza prevention strategies. Methods: We estimated the effective reproduction number ( Findings: The model-estimated mean Conclusion: Our model results indicate that wearing face masks and avoiding crowded places could have potentially significant suppressive impacts on influenza.

Indexed as

COVID-19Influenza, HumanBayes TheoremHumansMasksTime FactorsCOVID-19face maskinfectious disease modelinginfluenzasocial distancingtime-series analysis

Identifiers

PMID36504934
PMCPMC9728392
OpenAlexW4308901034

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.