Calculating the potential for within-flight transmission of influenza A (H1N1)

Center for Biomedical Modelling, Semel Institute of Neuroscience and Human Behavior, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA.
BMC Medicine (Impact Factor: 7.25). 12/2009; 7(1):81. DOI: 10.1186/1741-7015-7-81
Source: PubMed

ABSTRACT Clearly air travel, by transporting infectious individuals from one geographic location to another, significantly affects the rate of spread of influenza A (H1N1). However, the possibility of within-flight transmission of H1N1 has not been evaluated; although it is known that smallpox, measles, tuberculosis, SARS and seasonal influenza can be transmitted during commercial flights. Here we present the first quantitative risk assessment to assess the potential for within-flight transmission of H1N1.
We model airborne transmission of infectious viral particles of H1N1 within a Boeing 747 using methodology from the field of quantitative microbial risk assessment.
The risk of catching H1N1 will essentially be confined to passengers travelling in the same cabin as the source case. Not surprisingly, we find that the longer the flight the greater the number of infections that can be expected. We calculate that H1N1, even during long flights, poses a low to moderate within-flight transmission risk if the source case travels First Class. Specifically, 0-1 infections could occur during a 5 hour flight, 1-3 during an 11 hour flight and 2-5 during a 17 hour flight. However, within-flight transmission could be significant, particularly during long flights, if the source case travels in Economy Class. Specifically, two to five infections could occur during a 5 hour flight, 5-10 during an 11 hour flight and 7-17 during a 17 hour flight. If the aircraft is only partially loaded, under certain conditions more infections could occur in First Class than in Economy Class. During a 17 hour flight, a greater number of infections would occur in First Class than in Economy if the First Class Cabin is fully occupied, but Economy class is less than 30% full.
Our results provide insights into the potential utility of air travel restrictions on controlling influenza pandemics in the winter of 2009/2010. They show travel by one infectious individual, rather than causing a single outbreak of H1N1, could cause several simultaneous outbreaks. These results imply that, during a pandemic, quarantining passengers who travel in Economy on long-haul flights could potentially be an important control strategy. Notably, our results show that quarantining passengers who travel First Class would be unlikely to be an effective control strategy.

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Available from: Sally Blower, Feb 28, 2014
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    • "Previous studies have concluded that the net benefits to society of air travel restrictions are at best small [11]–[13], but most models rely on assumptions that have not been confronted with data [14]. What does seem clear is that flying increases the likelihood of air travelers contracting an infectious disease conditional on there being on infectious person on the airplane [15], [16], and that the public links flying with infection risk. Air travel may increase private infection risk beyond an acceptable level, and public health interventions related to travel and airport surveillance programs may benefit travelers [17], [18]. "
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    ABSTRACT: Theory suggests that human behavior has implications for disease spread. We examine the hypothesis that individuals engage in voluntary defensive behavior during an epidemic. We estimate the number of passengers missing previously purchased flights as a function of concern for swine flu or A/H1N1 influenza using 1.7 million detailed flight records, Google Trends, and the World Health Organization's FluNet data. We estimate that concern over "swine flu," as measured by Google Trends, accounted for 0.34% of missed flights during the epidemic. The Google Trends data correlates strongly with media attention, but poorly (at times negatively) with reported cases in FluNet. Passengers show no response to reported cases. Passengers skipping their purchased trips forwent at least $50 M in travel related benefits. Responding to actual cases would have cut this estimate in half. Thus, people appear to respond to an epidemic by voluntarily engaging in self-protection behavior, but this behavior may not be responsive to objective measures of risk. Clearer risk communication could substantially reduce epidemic costs. People undertaking costly risk reduction behavior, for example, forgoing nonrefundable flights, suggests they may also make less costly behavior adjustments to avoid infection. Accounting for defensive behaviors may be important for forecasting epidemics, but linking behavior with epidemics likely requires consideration of risk communication.
    PLoS ONE 03/2013; 8(3):e58249. DOI:10.1371/journal.pone.0058249 · 3.23 Impact Factor
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    • "After the passengers departed the aircraft, subsequent exposure measurement became inefficient and difficult (Baker et al., 2010). Thus, in-flight measurement of the potential risks of H1N1 transmission was proposed (Wagner et al., 2009). As an efficient means for spreading infectious diseases, air transport is also of concern regarding its influence on risks of transmission of other infectious diseases, including tuberculosis (Abubakar, 2010; Dowdall et al., 2010; Kornylo-Duong et al., 2010), malaria (Bradley, 1989; Tatem et al., 2006), plague (Pascali, 1982), yellow fever (Oliva, 1979), cholera (Rondle et al., 1978), dengue fever, Norwalk virus (Kirking et al., 2010), and epidemic meningococcal diseases (Rachael et al., 2009). "
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    Science China Earth Science 02/2013; 57(2). DOI:10.1007/s11430-013-4635-0 · 1.49 Impact Factor
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    • "Yet, the simple combination of the previous models will not generate what is needed because they focus on contacts (people or animals) and, at the earliest epidemic phase, the number of infected individuals is very low. While air-borne epidemics have been investigated [12], [37], they are atypical because their connecting structure is mobile, and, in air travel-mediated epidemics, reduced to the few yards that separate passengers sharing the same aircraft. "
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    ABSTRACT: To effectively control the geographical dissemination of infectious diseases, their properties need to be determined. To test that rapid microbial dispersal requires not only susceptible hosts but also a pre-existing, connecting network, we explored constructs meant to reveal the network properties associated with disease spread, which included the road structure. Using geo-temporal data collected from epizoonotics in which all hosts were susceptible (mammals infected by Foot-and-mouth disease virus, Uruguay, 2001; birds infected by Avian Influenza virus H5N1, Nigeria, 2006), two models were compared: 1) 'connectivity', a model that integrated bio-physical concepts (the agent's transmission cycle, road topology) into indicators designed to measure networks ('nodes' or infected sites with short- and long-range links), and 2) 'contacts', which focused on infected individuals but did not assess connectivity. THE CONNECTIVITY MODEL SHOWED FIVE NETWORK PROPERTIES: 1) spatial aggregation of cases (disease clusters), 2) links among similar 'nodes' (assortativity), 3) simultaneous activation of similar nodes (synchronicity), 4) disease flows moving from highly to poorly connected nodes (directionality), and 5) a few nodes accounting for most cases (a "20:80" pattern). In both epizoonotics, 1) not all primary cases were connected but at least one primary case was connected, 2) highly connected, small areas (nodes) accounted for most cases, 3) several classes of nodes were distinguished, and 4) the contact model, which assumed all primary cases were identical, captured half the number of cases identified by the connectivity model. When assessed together, the synchronicity and directionality properties explained when and where an infectious disease spreads. Geo-temporal constructs of Network Theory's nodes and links were retrospectively validated in rapidly disseminating infectious diseases. They distinguished classes of cases, nodes, and networks, generating information usable to revise theory and optimize control measures. Prospective studies that consider pre-outbreak predictors, such as connecting networks, are recommended.
    PLoS ONE 06/2012; 7(6):e39778. DOI:10.1371/journal.pone.0039778 · 3.23 Impact Factor
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