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Record W1982797147 · doi:10.1002/hec.1554

The possible macroeconomic impact on the UK of an influenza pandemic

2009· article· en· W1982797147 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

aboutThe title or abstract carries a Canadian signal from the geographic lexicon.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueHealth Economics · 2009
Typearticle
Languageen
FieldMathematics
TopicCOVID-19 epidemiological studies
Canadian institutionsnot available
FundersEuropean Commission
KeywordsPandemicEconomic impact analysisInfluenza pandemicQuarter (Canadian coin)PopulationEconomicsConsumption (sociology)Development economicsPandemic influenzaCoronavirus disease 2019 (COVID-19)DiseaseDemographyDemographic economicsGeographyEconomic growthMedicineEnvironmental healthInfectious disease (medical specialty)

Abstract

fetched live from OpenAlex

Little is known about the possible impact of an influenza pandemic on a nation's economy. We applied the UK macroeconomic model 'COMPACT' to epidemiological data on previous UK influenza pandemics, and extrapolated a sensitivity analysis to cover more extreme disease scenarios. Analysis suggests that the economic impact of a repeat of the 1957 or 1968 pandemics, allowing for school closures, would be short-lived, constituting a loss of 3.35 and 0.58% of GDP in the first pandemic quarter and year, respectively. A more severe scenario (with more than 1% of the population dying) could yield impacts of 21 and 4.5%, respectively. The economic shockwave would be gravest when absenteeism (through school closures) increases beyond a few weeks, creating policy repercussions for influenza pandemic planning as the most severe economic impact is due to policies to contain the pandemic rather than the pandemic itself.Accounting for changes in consumption patterns made in an attempt to avoid infection worsens the potential impact. Our mild disease scenario then shows first quarter/first year reductions in GDP of 9.5/2.5%, compared with our severe scenario reductions of 29.5/6%. These results clearly indicate the significance of behavioural change over disease parameters.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.121
Threshold uncertainty score0.409

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.301
GPT teacher head0.486
Teacher spread0.185 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it