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Brief Report
Air Evacuation of Passengers with Potential SARS-CoV-2 Infection Under the Guidelines for Appropriate Infection Control and Prevention
Jeong-Gil Kim, Seung Hak Lee, Hansuk Kim, Hong Sang Oh, Jacob Lee
Osong Public Health Res Perspect. 2020;11(5):334-338.   Published online October 22, 2020
DOI: https://doi.org/10.24171/j.phrp.2020.11.5.10
  • 6,767 View
  • 96 Download
  • 2 Web of Science
  • 2 Crossref
AbstractAbstract PDF

This report describes the air evacuation of 80 Koreans from Iran to Korea on March 19th, 2020, with a direct transfer of passengers between airplanes in Dubai. The passengers were potentially infected with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) so, strict precautions were taken for the duration of the flight, and the passengers were screened before embarking from Dubai and disembarking at Incheon International Airport in Korea. Passengers with fever or symptoms of SARS-CoV-2 were assessed by a quarantine doctor, and if they were suspected of being infected with SARS-CoV-2, they were categorized as a “patient under investigation (PUI)”. There was 1 passenger designated as a PUI before the departure from Dubai and another designated as a PUI upon arrival into Korea. The first PUI tested negative, but the second PUI tested positive for SARS-CoV-2. All those aboard the flight (passengers, aircrew, and medical staff) were screened for SARS-CoV-2 upon arrival into Korea and completed a mandatory 14-day medical quarantine. There were no additional cases of infection.

Citations

Citations to this article as recorded by  
  • Risk of COVID-19 transmission on long-haul flights: During the COVID-19 pandemic
    Jiyun Park, Gye jeong Yeom, Poowin Bunyavejchewin
    PLOS ONE.2024; 19(8): e0309044.     CrossRef
  • International travel-related control measures to contain the COVID-19 pandemic: a rapid review
    Jacob Burns, Ani Movsisyan, Jan M Stratil, Renke Lars Biallas, Michaela Coenen, Karl MF Emmert-Fees, Karin Geffert, Sabine Hoffmann, Olaf Horstick, Michael Laxy, Carmen Klinger, Suzie Kratzer, Tim Litwin, Susan Norris, Lisa M Pfadenhauer, Peter von Philip
    Cochrane Database of Systematic Reviews.2021;[Epub]     CrossRef
Original Article
Modeling for Estimating Influenza Patients from ILI Surveillance Data in Korea
Joo-Sun Lee, Sun-Hee Park, Jin-Woong Moon, Jacob Lee, Yong Gyu Park, Yong Kyun Roh
Osong Public Health Res Perspect. 2011;2(2):89-93.   Published online June 30, 2011
DOI: https://doi.org/10.1016/j.phrp.2011.08.001
  • 3,753 View
  • 21 Download
  • 8 Crossref
AbstractAbstract PDF
Objective Prediction of influenza incidence among outpatients from an influenza surveillance system is important for public influenza strategy.
Methods
We developed two influenza prediction models through influenza surveillance data of the Korea Centers for Disease Control and Prevention (each year, each province and metropolitan city; total reported patients with influenza-like illness stratified by age) for 6 years from 2005 to 2010 and disease-specific data (influenza code J09-J11, monthly number of influenza patients, total number of outpatients and hospital visits) from the Health Insurance Review and Assessment service.
Results
Incidence of influenza in each area, year, and month was estimated from our prediction models, which were validated by simulation processes. For example, in November 2009, Seoul and Joenbuk, the final number of influenza patients calculated by prediction models A and B underestimated actual reported cases by 64 and 833 patients, respectively, in Seoul and 6 and 9 patients, respectively, in Joenbuk. R-square demonstrated that prediction model A was more suitable than model B for estimating the number of influenza patients.
Conclusion
Our prediction models from the influenza surveillance system could estimate the nationwide incidence of influenza. This prediction will provide important basic data for national quarantine activities and distributing medical resources in future pandemics.

Citations

Citations to this article as recorded by  
  • Statistical Machine and Deep Learning Methods for Forecasting of Covid-19
    Mamta Juneja, Sumindar Kaur Saini, Harleen Kaur, Prashant Jindal
    Wireless Personal Communications.2024; 138(1): 497.     CrossRef
  • Assessment of Intensive Vaccination and Antiviral Treatment in 2009 Influenza Pandemic in Korea
    Chaeshin Chu, Sunmi Lee
    Osong Public Health and Research Perspectives.2015; 6(1): 47.     CrossRef
  • Doing Mathematics with Aftermath of Pandemic Influenza 2009
    Hae-Wol Cho, Chaeshin Chu
    Osong Public Health and Research Perspectives.2015; 6(1): 1.     CrossRef
  • Antiviral treatment of influenza in South Korea
    Young June Choe, Hyunju Lee, Hoan Jong Lee, Eun Hwa Choi
    Expert Review of Anti-infective Therapy.2015; 13(6): 741.     CrossRef
  • Years of Epidemics (2009–2011): Pandemic Influenza and Foot-and-Mouth Disease Epidemic in Korea
    Hae-Wol Cho, Chaeshin Chu
    Osong Public Health and Research Perspectives.2013; 4(3): 125.     CrossRef
  • How to Manage a Public Health Crisis and Bioterrorism in Korea
    Hae-Wol Cho, Chaeshin Chu
    Osong Public Health and Research Perspectives.2013; 4(5): 223.     CrossRef
  • Was the Mass Vaccination Effective During the Influenza Pandemic 2009–2010 in Korea?
    Hae-Wol Cho, Chaeshin Chu
    Osong Public Health and Research Perspectives.2013; 4(4): 177.     CrossRef
  • A Tale of Two Fields: Mathematical and Statistical Modeling of Infectious Diseases
    Hae-Wol Cho, Chaeshin Chu
    Osong Public Health and Research Perspectives.2011; 2(2): 73.     CrossRef

PHRP : Osong Public Health and Research Perspectives
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