The 1978 English boarding school influenza outbreak: where the classic SEIR model fails
Bibliographic record
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 multi-stage (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 ( <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" overflow="scroll"> <mml:mstyle displaystyle="true" scriptlevel="0"> <mml:mrow> <mml:msub> <mml:mrow> <mml:mi class="mathcal" mathvariant="script">R</mml:mi> </mml:mrow> <mml:mrow> <mml:mn>0</mml:mn> </mml:mrow> </mml:msub> </mml:mrow> </mml:mstyle> </mml:math> ) 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.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".