<p>This report describes the air evacuation of 80 Koreans from Iran to Korea on March 19<sup>th</sup>, 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.</p>
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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
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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.
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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
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