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Record W2996570652 · doi:10.3934/mbe.2020099

The spread of influenza-like-illness within the household in Shanghai, China

2019· article· en· W2996570652 on OpenAlexaff
Meili Li, Hong Wang, Baojun Song, Junling Ma

Bibliographic record

VenueMathematical Biosciences & Engineering · 2019
Typearticle
Languageen
FieldMedicine
TopicInfluenza Virus Research Studies
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsOutbreakChinaTransmission (telecommunications)Shanghai chinaEnvironmental healthGeographyDisease transmissionInfectious disease (medical specialty)Incidence (geometry)DemographySocioeconomicsMedicineDiseaseVirologyEconomics

Abstract

fetched live from OpenAlex

High-density urban habitats provide a hotbed for the rapid spread of infectious diseases. School children densely aggregate in classrooms. So schools are high incidence area of infectious diseases. This paper aims at investigating the transmission of influenza-like-illness within households with a school child using a survey study of fourth grade elementary school students in Shanghai, China. We found that the pairwise transmission probability within a household is only 0.172, which implies that the average number of infections caused by a single infectious individual in a household in Shanghai is only 0.304. Thus, the majority of transmission must occur outside of a household for a disease to cause an outbreak.

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.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.142
Threshold uncertainty score0.282

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.039
GPT teacher head0.310
Teacher spread0.271 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations6
Published2019
Admission routes1
Has abstractyes

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