Evidence map›Paper›PMID 39563495›Full record

ArticleJournal of the Royal Society, Interface2024

The 1978 English boarding school influenza outbreak: where the classic SEIR model fails.

Konstantin K Avilov, Qiong Li, Lixin Lin, Haydar Demirhan, Lewi Stone, Daihai He

Abstract readHistorical Article
In one paragraph

Article in Journal of the Royal Society, Interface, 2024. 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. Article
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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.

Konstantin K AvilovDepartment of Applied Mathematics, The Hong Kong Polytechnic University, Hong Kong SAR, People's Republic of China.ORCID 0000-0003-1711-5659
Qiong LiGuangdong Provincial Key Laboratory of Interdisciplinary Research and Application for Data Science, BNU-HKBU United International College, Zhuhai, People's Republic of China.ORCID 0000-0001-6199-9039
Lixin LinMathematical Sciences, School of Science, Royal Melbourne Institute of Technology (RMIT) University, Melbourne, Victoria, Australia.ORCID 0000-0001-8339-5965
Haydar DemirhanMathematical Sciences, School of Science, Royal Melbourne Institute of Technology (RMIT) University, Melbourne, Victoria, Australia.ORCID 0000-0002-8565-4710
Lewi StoneMathematical Sciences, School of Science, Royal Melbourne Institute of Technology (RMIT) University, Melbourne, Victoria, Australia.
Daihai HeDepartment of Applied Mathematics, The Hong Kong Polytechnic University, Hong Kong SAR, People's Republic of China.ORCID 0000-0003-3253-654X

Funding

Australian Research CouncilCollaborative Research Grantthe Hong Kong Research Grants Council Theme-based Research Scheme fundthe National Science Foundation of China
6 · The paper itself

Abstract

Previous work has failed to fit classic SEIR epidemic models satisfactorily to the prevalence data of the famous English boarding school 1978 influenza A/H1N1 outbreak during the children's pandemic. It is still an open question whether a biologically plausible model can fit the prevalence time series and the attack rate correctly. To construct the final model, we first used an intentionally very flexible and overfitted discrete-time epidemiologic model to learn the epidemiological features from the data. The final model was a susceptible (

Indexed as

Disease OutbreaksInfluenza A Virus, H1N1 SubtypeInfluenza, HumanSchoolsChildEnglandEpidemiological ModelsHistory, 20th CenturyHumansBayesian epidemic modelchildren’s pandemicdelay differential equationsinfluenza progression modelmodellingresidence time

Identifiers

PMID39563495
PMCPMC11576841

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.