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Record W3131800306 · doi:10.1101/2021.02.23.21252287

Household secondary attack rate of COVID-19 by household size and index case characteristics

2021· preprint· en· W3131800306 on OpenAlexaffabout
Semra Tibebu, Kevin A. Brown, Nick Daneman, Lauren A. Paul, Sarah A. Buchan

Bibliographic record

VenuemedRxiv · 2021
Typepreprint
Languageen
FieldMathematics
TopicCOVID-19 epidemiological studies
Canadian institutionsPublic Health OntarioToronto Public Health
Fundersnot available
KeywordsIndex (typography)Coronavirus disease 2019 (COVID-19)GeographyDemographyPopulationSocioeconomicsIncidence (geometry)Environmental healthMedicineEconomics

Abstract

fetched live from OpenAlex

Abstract In this population-wide study in Ontario, Canada, we investigated the household secondary attack rate (SAR) to understand its relationship to household size and index case characteristics. We identified all patients with confirmed COVID-19 between July 1 and November 30, 2020. Cases within households were matched based on reported residential address; households were grouped based on the number of household contacts. The majority of households (68.2%) had a SAR of 0%, while 3,442 (11.7%) households had a SAR ≥75%. Overall household SAR was 19.5% and was similar across household sizes, but varied across index case characteristics. Households where index cases had longer delays between symptom onset and test seeking, households with older index cases, households with symptomatic index cases, and larger households located in diverse neighborhoods, were associated with greater household SAR. Our findings present characteristics associated with greater household SARs and proposes immediate testing as a method to reduce household transmission and incidence of COVID-19.

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 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)0.003
metaresearch head score (Gemma)0.032
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.168
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.032
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.003
Research integrity0.0010.002
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 head0.244
GPT teacher head0.391
Teacher spread0.147 · 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 teacher head, not a consensus.

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

Citations17
Published2021
Admission routes2
Has abstractyes

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