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Response to Brownstein et al
Posted by plosmedicine on 31 Mar 2009 at 00:00 GMT
Author: Cecile Viboud
Position: No occupation was given
Institution: Fogarty International Center, NIH, Bethesda, MD, USA
Additional Authors: Mark. A Miller, Bryan T. Grenfell, Ottar N. Bjornstad, Lone Simonsen
Submitted Date: October 03, 2006
Published Date: October 3, 2006
This comment was originally posted as a “Reader Response” on the publication date indicated above. All Reader Responses are now available as comments.
Letter to the editor in response to Brownstein et al, Plos Med, Sep 2006
AIR TRAVEL AND THE SPREAD OF INFLUENZA
Cecile Viboud , Mark A Miller , Bryan T Grenfell [1, 2], Ottar N. Bjornstad [1,2,3], Lone Simonsen 
1 Fogarty International Center, National Institutes of Health, Bethesda, MD 20892, USA
2 Center for Infectious Disease Dynamics, Department of Biology, Pennsylvania State University, University Park, Pennsylvania 16802, USA.
3 Department of Entomology, Pennsylvania State University, University Park, Pennsylvania 16802, USA.
4 National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, MD 20818, USA
While air travel contributes to the spread of influenza epidemics, the magnitude of impact is not clear compared to other factors -- a crucial issue when considering a flight ban in the context of pandemic planning. Recent modelling efforts simulating the spread of pandemic influenza conclude that such an intervention would matter little relative to other interventions [1-3]. But this assessment has now been challenged by an observational study of influenza in the winter following the post-9/11/2001 depression in air traffic. Brownstein's et al study published in the September issue of PLos Med  correlates variations in air traffic volume with patterns of timing and spread in influenza epidemics, based on US mortality data from nine epidemic seasons between 1996 and 2005. While we find the study interesting, we have identified several important caveats and question the robustness of the conclusions.
The core of Brownstein's et al results lies in the observation that the 2001-02 influenza epidemic immediately following 9/11/2001 was late in the season and peaked in March (week 11), whereas the eight surrounding epidemics peaked between the end of December and the end of February (weeks 52 to 9). The authors attribute this delay to the 27%-decline in air traffic that followed 9/11/2001.
Given the complexities of influenza virus subtype cycling and antigenic drift [5, 6], it is essential to consider longer-term disease data spanning much more than nine years to interpret the "lateness" of the 2001-02 epidemic. Using US national vital statistics data covering 30 winters from 1972 to 2002 , we identified four epidemics peaking in the month of March (13%), including the 2001-02 epidemic following 9/11/2001, but also two epidemics in the 1970s and the more recent 1991-92 epidemic. Furthermore, the average timing of influenza epidemics has not changed between 1972 and 2002 - despite a concurrent and steady increase in air traffic volume by over 300% . Indeed, during the earlier part of the last century when air traffic was minimal, influenza epidemics rapidly circulated around the world. Moreover, CDC's real-time influenza virus surveillance data  show that last winter's (2005-06) epidemic was even more delayed than the epidemic following 9/11/2001, despite a 20%-increase in air passenger traffic compared to the situation before 9/11/2001 . Clearly, late-season influenza epidemics have occurred and are still occurring even in the absence of restrictions on air travel. Hence a longer time perspective, with observations from both prior and more recent data, challenges Brownstein et al's conclusions.
In addition to comparing the timing of influenza epidemics across different seasons, Brownstein et al analyzed the rate of disease spread among US administrative regions for their 9 seasons of interest (1996-2005). In our previous work, we estimated the rate of influenza spread among all US states for 30 consecutive seasons (1972-2002) . Our analysis shows that the epidemic following 9/11/2001 spread at a rate comparable to other epidemics, even after adjusting for the subtype of circulating viruses . To increase our understanding of the spread of influenza, it is essential to quantify the relative importance of different modes of transportation. As an example, our recent study considered multiple modes of transportation (including air travel) and identified travel to and from work as a key determinant of the regional spread of epidemics .
In conclusion, Brownstein et al's analysis of the "natural experiment" of the post-9/11 season is innovative and ingenious - but in and of itself could not demonstrate a robust association or a causal link between the decrease in air traffic and delayed timing of influenza epidemics. Even if there in fact had been a delay as hypothesized, the study lacked power to address the hypothesis, because this single "natural experiment" was set in a background of considerable variability in influenza epidemic patterns. Extrapolations from Brownstein et al's findings predict that a flight ban could delay a pandemic by 2 months  - but we have shown here that this prediction is not supported by the analysis of more extensive disease data and transportation statistics. It is also unclear how a "natural experiment" conducted in the inter-pandemic period is applicable to a pandemic situation, where novel influenza viruses have higher transmissibility and circulate in fully susceptible populations, and may cause different age-pattern of transmission . While Brownstein et al's study represents an intriguing starting point, this study alone does not provide the critical quantitative evidence needed to evaluate the impact of travel restrictions on future pandemics.
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