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How SARS changed the world in less than six months.

2003· article· en· W1506484293 on OpenAlexaboutno aff
Fiona Fleck

Bibliographic record

VenuePubMed · 2003
Typearticle
Languageen
FieldMedicine
TopicViral Infections and Outbreaks Research
Canadian institutionsnot available
Fundersnot available
KeywordsOutbreakChinaSocioeconomicsMedicineGeographyPandemicCoronavirus disease 2019 (COVID-19)DemographyDiseaseInfectious disease (medical specialty)VirologyPathologyArchaeology

Abstract

fetched live from OpenAlex

The global outbreak of severe acute respiratory syndrome (SARS) can be traced to one man and one night he spent in a Hong Kong hotel on 21 February 2003. Scientists are still baffled as to how Dr Liu Jianlun, a 64-year-old medical doctor from Chinas Guangdong province, where the mysterious virus originated, could have transferred to at least 16 other guests on the same floor during his brief stay. But there is no doubt those travellers fanned out across the triggering outbreaks in Singapore, Toronto in Canada, and Hanoi in Viet Nam as well as in Hong Kong itself. In less than four months, some 4000 cases and 550 deaths of outside China and Taiwan can be traced to Dr Jianlun's visit to Hong Kong; the Metropole Hotel is considering turning the ninth floor (he stayed in room 911) into a museum; and has proved that the worst-case scenario long mooted by infectious disease experts can come true; but also that such an outbreak, for all its speed and force, can be contained. has travelled more widely, swiftly and lethally than any other recent new disease so far. Near the end of June 2003, the total of cases was 8456 in 30 countries and areas, 809 of which had resulted in death. HIV/AIDS took two decades to cover the globe, owing partly to its incubation period of up to 10 years. Ebola, which has caused periodic outbreaks in Africa since 1976, and two new Asian diseases, caused by the Nipah and Hendra viruses, have not travelled extensively, in the case of Ebola, this is because the patient quickly becomes much too ill to travel; and for Nipah and Hendra it is because neither virus established efficient human-to-human transmission. SARS is the first new disease to show the damage possible in a globalized world, said Mary Kay Kindhauser, who writes updates for the WHO website, adding that has made people take the infectious diseases threat more seriously and made them realize that in today's closely interconnected and highly mobile every country is vulnerable. Rapid international travel helped to spread, but rapid communications helped to contain it. WHO issued its first of two global alerts on 12 March, after 55 cases of the new disease had been identified. Heightened awareness facilitated the early detection and isolation of. suspected cases, and a virtual lab network identified the coronavirus within a month. WHO's second alert, on 15 March, called the disease Severe Acute Respiratory Syndrome for the first time, and issued an emergency advisory notice for travellers and airlines. The warning made headlines across the world as authorities scrambled to contain the disease or prevent outbreaks from imported cases. Some were better prepared than others. After anthrax was distributed through the US postal service by suspected bioterrorists following the September 2001 attacks, the United States was ready to react and has had few cases and no deaths. Chinas failure to admit the true extent of the outbreak drew severe criticism from governments and flora WHO's Director-General Gro Harlem Brundtland. China has been hardest hit to date (24 June), with 5327 cases and 348 deaths reported from throughout the mainland. Beijing and Guangdong were the most severely affected, with 4033 of these cases and 250 of the deaths. But it took two months--after explosive outbreaks in Hong Kong, Singapore, Hanoi and Toronto, and the spread of exported cases to every continent--for China, under mounting international pressure, to allow WHO epidemiologists to enter Guangdong province on 3 April to assess the situation there and determine that the outbreak of atypical pneumonia was indeed SARS. …

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.562
Threshold uncertainty score0.327

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.076
GPT teacher head0.296
Teacher spread0.220 · 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.

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

Citations27
Published2003
Admission routes1
Has abstractyes

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