International flight‐related transmission of pandemic influenza <scp>A</scp>(<scp>H</scp>1<scp>N</scp>1)pdm09: an historical cohort study of the first identified cases in the <scp>U</scp>nited <scp>K</scp>ingdom

Young, N. J.; Pebody, Richard; Smith, Gillian; Olowokure, Babatunde; Shankar, Giri; Höschler, Katja; Galiano, Mónica; Green, Helen; Wallensten, Anders; Hogan, Angela; Oliver, Isabel

doi:10.1111/irv.12181

Cited by 34 publications

(43 citation statements)

References 25 publications

(76 reference statements)

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“…Infection during flights has been found to have a significant probability. Even though aircraft are equipped with High-Efficiency Particulate Air (HEPA) filters, which reduce the risk of transmission through the air circulation system, several studies have demonstrated that infection rates can be as high as 4.3% in a 9-h flight, and that the risk does not depend on the relative seating of contagious passengers and passengers infected in-flight [29]. Mangili and Gendreau already discussed the need for additional work on in-flight transmissions [30], but insufficient data have been made available in the meanwhile to allow a more robust conclusion.…”

Section: Limitations To Predictive Capacitymentioning

confidence: 99%

The Predictive Capacity of Air Travel Patterns during the Global Spread of the COVID-19 Pandemic: Risk, Uncertainty and Randomness

Christidis

Aris

2020

IJERPH

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Air travel has a decisive role in the spread of infectious diseases at the global level. We present a methodology applied during the early stages of the COVID-19 pandemic that uses detailed aviation data at the final destination level in order to measure the risk of the disease spreading outside China. The approach proved to be successful in terms of identifying countries with a high risk of infected travellers and as a tool to monitor the evolution of the pandemic in different countries. The high number of undetected or asymptomatic cases of COVID-19, however, limits the capacity of the approach to model the full dynamics. As a result, the risk for countries with a low number of passengers from Hubei province appeared as low. Globalization and international aviation connectivity allow travel times that are much shorter than the incubation period of infectious diseases, a fact that raises the question of how to react in a potential new pandemic.Int. J. Environ. Res. Public Health 2020, 17, 3356 2 of 15 information and air travel networks to produce a user-defined global map of risk distribution [9]. A generic tool that allows the estimation of the median early disease arrival time from around the world using air transport schedules was proposed by [10]. Air travel patterns were also employed by Bajardi et al. [11] in order to theoretically model the potential impacts of travel restrictions on the spread of an epidemic. Nevertheless, while travel and trade have been shown as relevant for the international spread of the 2009 A/H1N1 influenza, the slow deployment of control measures in countries with lower healthcare capacities led to spatial imbalances [12]. Air transport data have been used indirectly to measure the effective distance and the relative arrival time of a disease outbreak, with promising results when tested on historical data [13]. Based on the same premises, producing near real-time or now-casting predictions on the international spread of COVID-19 appeared to be based on scientifically sound principles [14].However, most expectations about the ability to predict the global spread of COVID-19 proved misleading or of limited use.This includes our own work, which we present here in order to discuss the reasons why most projections missed important aspects of the disease propagation dynamics. In our opinion, and as a part of the scientific process, it is important to publish and discuss even the negative results of research activities. The lessons learned from unsuccessful applications can be valuable for future work, either by improving current practices or by re-examining risk assessment from a new perspective. Especially in the case of predictive modeling, the wide body of literature that presents successful ex-post examples of possible value may give the false impression that the spatial aspects of disease propagation are more easily predictable than what is actually possible in reality.The question this paper is trying to answer is whether air passenger traffic alone can provide early information...

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Section: Limitations To Predictive Capacitymentioning

confidence: 99%

The Predictive Capacity of Air Travel Patterns during the Global Spread of the COVID-19 Pandemic: Risk, Uncertainty and Randomness

Christidis

Aris

2020

IJERPH

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“…In general, these techniques are not entirely effective in preventing the spread of viral infectious disease because passengers, either with a fever or presenting symptoms with respiratory virus infections such as influenza virus, often shed viruses before they have fever. A notable example of the failure of screening methods occurred in 2009 during the spread of influenza A(H1N1) pdm09 virus, where passengers on a flight from Cancun Mexico were screened but still managed to infect other passengers during flight [2].…”

Section: Introductionmentioning

confidence: 99%

Molecular surveillance of respiratory viruses with bioaerosol sampling in an airport

Bailey

Choi

Zemke

et al. 2018

Trop Dis Travel Med Vaccines

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Recognizing that crowded, high-traffic airports and airplanes have been implicated in respiratory disease transmission, we partnered with administrators of Raleigh Durham International Airport (RDU) in conducting a pilot study of aerosol surveillance for respiratory viruses at RDU. From January to March 2018 we used NIOSH 2-stage samplers to collect 150 min aerosol samples in crowded areas at RDU. Four (17%) of the 24 samples were positive for known respiratory pathogens including influenza D virus and adenovirus. These results suggest the feasibility of employing bioaerosol surveillance techniques in public transportation areas, such as airports, as a noninvasive way to detect and characterize novel respiratory viruses.Electronic supplementary materialThe online version of this article (10.1186/s40794-018-0071-7) contains supplementary material, which is available to authorized users.

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“…With over three billion airline passengers annually, the risk of in-flight transmission of infectious disease is a vital global health concern [1, 2]. Over two dozen cases of in-flight transmission have been documented, including influenza [3][4][5][6][7], measles [8,9], meningococcal infections [10], norovirus [11], SARS [12,13], shigellosis [14], cholera [15], and multi-drug resistant tuberculosis [1,[16][17][18]. Studies of SARS [12,13] and pandemic influenza (H1N1p) [19] transmission on airplanes indicate that air travel can serve as a conduit for the rapid spread of newly emerging infections and pandemics.…”

Section: Introductionmentioning

confidence: 99%

The Airplane Cabin Microbiome

et al. 2018

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Serving over three billion passengers annually, air travel serves as a conduit for infectious disease spread, including emerging infections and pandemics. Over two dozen cases of in-flight transmissions have been documented. To understand these risks, a characterization of the airplane cabin microbiome is necessary. Our study team collected 229 environmental samples on ten transcontinental US flights with subsequent 16S rRNA sequencing. We found that bacterial communities were largely derived from human skin and oral commensals, as well as environmental generalist bacteria. We identified clear signatures for air versus touch surface microbiome, but not for individual types of touch surfaces. We also found large flight-to-flight beta diversity variations with no distinguishing signatures of individual flights, rather a high between-flight diversity for all touch surfaces and particularly for air samples. There was no systematic pattern of microbial community change from pre- to post-flight. Our findings are similar to those of other recent studies of the microbiome of built environments. In summary, the airplane cabin microbiome has immense airplane to airplane variability. The vast majority of airplane-associated microbes are human commensals or non-pathogenic, and the results provide a baseline for non-crisis-level airplane microbiome conditions.

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International flight‐related transmission of pandemic influenza A(H1N1)pdm09: an historical cohort study of the first identified cases in the United Kingdom

Cited by 34 publications

References 25 publications

The Predictive Capacity of Air Travel Patterns during the Global Spread of the COVID-19 Pandemic: Risk, Uncertainty and Randomness

The Predictive Capacity of Air Travel Patterns during the Global Spread of the COVID-19 Pandemic: Risk, Uncertainty and Randomness

Molecular surveillance of respiratory viruses with bioaerosol sampling in an airport

The Airplane Cabin Microbiome

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