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*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

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 (S) – exposed (E) – infectious (I) – confined-to-bed (B) – convalescent (C) – recovered (R) model with time delay (constant residence time) in E and I compartments and multistage (Erlang-distributed residence time) in B and C compartments. We simultaneously fitted the reported B and C prevalence curves as well as the attack rate (proportion of children infected during the outbreak). The non-exponential residence times were crucial for good fits. The estimates of the generation time and the basic reproductive number (R0) were biologically reasonable. A simplified discrete-time model was built and fitted using the Bayesian procedure. Our work not only provided an answer to the open question, but also demonstrated an approach to constructive model generation.

Original languageEnglish
Article number20240394
JournalJournal of the Royal Society Interface
Volume21
Issue number220
DOIs
StatePublished - 20 Nov 2024

Funding

FundersFunder number
University of Alberta
Research Grants Council, University Grants CommitteeT24-508/22-N, C5079-21G
National Natural Science Foundation of China12250410241
Australian Research CouncilDP240102585

    Keywords

    • Bayesian epidemic model
    • children’s pandemic
    • delay differential equations
    • influenza progression model
    • modelling
    • residence time

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