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Record W3011072023 · doi:10.1101/2020.03.15.20036533

Is a 14-day quarantine period optimal for effectively controlling coronavirus disease 2019 (COVID-19)?

2020· preprint· en· W3011072023 on OpenAlexaff
Xue Jiang, Yawei Niu, Xiong Li, Li Lin, Wenxiang Cai, Yucan Chen, Bo Liao, Edwin Wang

Bibliographic record

VenuemedRxiv · 2020
Typepreprint
Languageen
FieldMathematics
TopicCOVID-19 epidemiological studies
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsQuarantineIncubation periodCoronavirus disease 2019 (COVID-19)OutbreakMedicinePandemicCohortSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2)DiseasePopulationTransmission (telecommunications)Isolation (microbiology)Viral sheddingPediatricsDemographyIncubationInternal medicineVirologyBiologyInfectious disease (medical specialty)Environmental healthVirusPathology

Abstract

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ABSTRACT Background The outbreak of a new coronavirus (SARS-CoV-2) disease (Covid-19) has become pandemic. To be more effectively controlling the disease, it is critical to set up an optimal quarantine period so that about 95% of the cases developing symptoms will be retained for isolation. At the moment, the WHO-established quarantine period is 14 days based on previous reports which had studied small sizes of hospitalized cases (10 and ∼100, respectively), however, over 80% of adult- and 95% of child-cases were not necessary to stay in hospitals, and therefore, had not been hospitalized. Therefore, we are questioning if the current-inferred median incubation time is representative for the whole Covid-19 population, and if the current quarantine period is optimal. Methods We compiled and analyzed the patient-level information of 2015 laboratory-confirmed Covid-19 cases including 99 children in 28 Chinese provinces. This cohort represents a wide-range spectrum of Covid-19 disease with both hospitalized and non-hospitalized cases. Results The full range of incubation periods of the Covid-19 cases ranged from 0 to 33 days among 2015 cases. There were 6 (0.13%) symptom-free cases including 4 females with a median age of 25.5 years and 2 males with a median age of 36 years. The median incubation period of both male and female adults was similar (7-day) but significantly shorter than that (9-day) of child cases (P=0.02). This cohort contained 4 transmission generations, and incubation periods of the cases between generations were not significantly different, suggesting that the virus has not been rapidly adapted to human beings. Interestingly, incubation periods of 233 cases (11.6%) were longer than the WHO-established quarantine period (14 days). Data modeling suggested that if adults take an extra 4-day or 7-day of isolation (i.e., a quarantine period of 18 or 21 days), 96.2% or 98.3%, respectively, of the people who are developing symptoms will be more effectively quarantined. Patients transmitted via lunch/dinner parties (i.e., gastrointestinal tract infection through oral transmission) had a significantly longer incubation period (9-day) than other adults transmitted via respiratory droplets or contaminated surfaces and objects (P<0.004). Conclusions The whole Covid-19 population including both hospitalized and non-hospitalized cases had a median incubation period of 7-day for adults, which is 1.8-day longer than the hospitalized cases reported previously. An extension of the adult quarantine period to 18 days or 21 days could be more effective in preventing virus-spreading and controlling the disease. The cases transmitted by lunch/dinner parties could be infected first in the gastrointestinal tract through oral transmission and then infected in the respiratory system so that they had a longer incubation period.

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)0.002
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.020
Threshold uncertainty score0.039

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.313
GPT teacher head0.461
Teacher spread0.149 · 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 designSimulation or modeling
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".

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Citations57
Published2020
Admission routes1
Has abstractyes

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