SARS Response And Experiences Post The 2003 Outbreak And The Effect On Moving Rotating Staff To Offshore Operations
Bibliographic record
Abstract
SARS Response And Experiences Post The 2003 Outbreak And The Effect On Moving Rotating Staff To Offshore Operations Philippe Guibert Philippe Guibert International SOS Search for other works by this author on: This Site Google Scholar Paper presented at the SPE International Conference on Health, Safety, and Environment in Oil and Gas Exploration and Production, Calgary, Alberta, Canada, March 2004. Paper Number: SPE-86645-MS https://doi.org/10.2118/86645-MS Published: March 29 2004 Cite View This Citation Add to Citation Manager Share Icon Share Twitter LinkedIn Get Permissions Search Site Citation Guibert, Philippe. "SARS Response And Experiences Post The 2003 Outbreak And The Effect On Moving Rotating Staff To Offshore Operations." Paper presented at the SPE International Conference on Health, Safety, and Environment in Oil and Gas Exploration and Production, Calgary, Alberta, Canada, March 2004. doi: https://doi.org/10.2118/86645-MS Download citation file: Ris (Zotero) Reference Manager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex Search Dropdown Menu toolbar search search input Search input auto suggest filter your search All ContentAll ProceedingsSociety of Petroleum Engineers (SPE)SPE International Conference and Exhibition on Health, Safety, Environment, and Sustainability Search Advanced Search AbstractThe Severe Acute Respiratory Syndrome (SARS) in 2003 is an emerging disease that spread rapidly worldwide. Remote and offshore operations appeared particularly at risk for various reasons. Medical teams were not prepared for the management of outbreaks. The density of population made close contact transmission possible. Remoteness resulted in misinterpreted information, weak and delayed adapted support, and improbable medical evacuation.As a result, companies immediately set travel policies and screening procedures to decrease the probability of having someone on site presenting early signs of SARS. In cooperation with site management, alert level policies were implemented to plan responses adapted to pre-defined thresholds of risk. Information and training focused on prompt detection and isolation of cases, strict infection control in medical facilities, and the tracing and quarantine of contacts. Specific SARS management kits were elaborated to address the treatment of suspect cases, the organisation of isolation and the protection of medical teams. Medical evacuation of cases became a challenge due to medical transportation issues, operational limitations and international administrative constraints. Information dissemination services and email alerts were set up via a SARS dedicated web site in order to provide compiled data and operational information to medical directors.This paper will review the experiences of companies tackling the SARS outbreak in remote settings. In the absence of a vaccine, robust diagnostic tests and specific treatment, this medical issue appeared initially to have no medical, but only operational answers. Options chosen in terms of staff management, treatment abilities and evacuation capacities will be discussed. In conclusion, the importance of appropriate communication and accurate information will be analysed to help corporations make appropriate decisions in such challenging circumstances.IntroductionThe severe acute respiratory syndrome (SARS) is described as the first epidemic of the XXI century. SARS hit the world in November 2002 when the first cases of atypical pneumonia were reported from the Guangdong province, South China. The disease rapidly spread to Hong Kong, Vietnam and Singapore, and then reached other hemisphere and continents. In such a short notice, nobody was prepared to tackle it. When it came to the attention of Public Health authorities, and to the hands of companies decision makers as they had at that time very little knowledge of the disease.The agent itself was unknown, the dissemination was puzzling, transmission was unclear and the reservoir was unidentified. Epidemiologically, the incubation period was vague, thought to be of some days, and some rumours existed of 'superspreaders', able to contaminate a large number of other people. Clinically, the diagnosis was differential with other pulmonary infectious diseases, and no diagnostic means were available.It is under these circumstances that companies running remote sites had to take decisions, and to implement SARS management policies. For the first time companies' management understood that national employees were not 'the risk' (i.e. African employees with viral hemorrhagic fever) but the non-nationals were (Filipinos, Canadians), bringing an uncontrollable disease in an unprepared environment.Remoteness of offshore operations was not comparable, depending of their geographic location. For instance, the situation was indeed not comparable between those rigs located in the SARS transmission area, where everybody could have possibly been infected (i.e. offshore Vietnam), and those out of this area (i.e. offshore Africa), where contamination would have come from a rotator. Keywords: health & medicine, transmission, society of petroleum engineers, quarantine, contingency planning, medical facility, information, artificial intelligence, machine learning, isolation Subjects: HSSE & Social Responsibility Management, Health, Contingency planning and emergency response This content is only available via PDF. 2004. 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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".