Evidence map›Paper›PMID 37012350›Full record

ArticleScientific reports2023

Transmission dynamics of seasonal influenza in a remote island population.

Su Myat Han, Alexis Robert, Shingo Masuda, Takahiro Yasaka, Satoshi Kanda, Kazuhiri Komori, Nobuo Saito, Motoi Suzuki, Akira Endo, Marc Baguelin and 1 more

Open access · goldAbstract read
In one paragraph

Article in Scientific reports, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
1.3field-weighted citation impact, top 20% 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

3 citing papers in PubMed, 6 citations in OpenAlex.

  1. Article
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  3. 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

11 authors at 3 institutions in 2 countries.

Su Myat HanSchool of Tropical Medicine and Global Health, Nagasaki University, Nagasaki, Japan. pearl.june@gmail.com.
Alexis RobertDepartment of Infectious Disease Epidemiology, Faculty of Epidemiology and Population Health, London School of Hygiene and Tropical Medicine, London, UK.
Shingo MasudaSchool of Tropical Medicine and Global Health, Nagasaki University, Nagasaki, Japan.
Takahiro YasakaDepartment of Internal Medicine, Kamigoto Hospital, Kamigoto, Japan.
Satoshi KandaDepartment of Internal Medicine, Kamigoto Hospital, Kamigoto, Japan.
Kazuhiri KomoriDepartment of Internal Medicine, Kamigoto Hospital, Kamigoto, Japan.
Nobuo SaitoDepartment of Microbiology, Faculty of Medicine, Oita University, Yufu, Japan.
Motoi SuzukiSchool of Tropical Medicine and Global Health, Nagasaki University, Nagasaki, Japan.
Akira EndoSchool of Tropical Medicine and Global Health, Nagasaki University, Nagasaki, Japan.
Marc BaguelinDepartment of Infectious Disease Epidemiology, Faculty of Epidemiology and Population Health, London School of Hygiene and Tropical Medicine, London, UK.
Koya AriyoshiSchool of Tropical Medicine and Global Health, Nagasaki University, Nagasaki, Japan.
Nagasaki University · JPLondon School of Hygiene & Tropical Medicine · GBNational Institute of Infectious Diseases · JP

Funding

Department of Health NIHR200908
6 · The paper itself

Abstract

Seasonal influenza outbreaks remain an important public health concern, causing large numbers of hospitalizations and deaths among high-risk groups. Understanding the dynamics of individual transmission is crucial to design effective control measures and ultimately reduce the burden caused by influenza outbreaks. In this study, we analyzed surveillance data from Kamigoto Island, Japan, a semi-isolated island population, to identify the drivers of influenza transmission during outbreaks. We used rapid influenza diagnostic test (RDT)-confirmed surveillance data from Kamigoto island, Japan and estimated age-specific influenza relative illness ratios (RIRs) over eight epidemic seasons (2010/11 to 2017/18). We reconstructed the probabilistic transmission trees (i.e., a network of who-infected-whom) using Bayesian inference with Markov-chain Monte Carlo method and then performed a negative binomial regression on the inferred transmission trees to identify the factors associated with onwards transmission risk. Pre-school and school-aged children were most at risk of getting infected with influenza, with RIRs values consistently above one. The maximal RIR values were 5.99 (95% CI 5.23, 6.78) in the 7-12 aged-group and 5.68 (95%CI 4.59, 6.99) in the 4-6 aged-group in 2011/12. The transmission tree reconstruction suggested that the number of imported cases were consistently higher in the most populated and busy districts (Tainoura-go and Arikawa-go) ranged from 10-20 to 30-36 imported cases per season. The number of secondary cases generated by each case were also higher in these districts, which had the highest individual reproduction number (R

Indexed as

Influenza, HumanInfluenza VaccinesAdolescentAgedBayes TheoremChildChild, PreschoolHumansSeasonsVaccinationInfluenza Vaccines

Identifiers

PMID37012350
PMCPMC10068240
OpenAlexW4362522202

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.