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• {"link"=>{"url"=>"http://www.citeulike.org/user/apurbana/article/8406498"}, "post_time"=>"2013-01-30 08:17:06", "tag"=>["airline", "transportation"], "linkout"=>{"type"=>"DOI", "url"=>"http://dx.doi.org/10.1371/journal.pmed.0030401"}, "username"=>"apurbana", "article_id"=>"8406498"}
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{"title"=>"Empirical evidence for the effect of airline travel on inter-regional influenza spread in the United States", "type"=>"journal", "authors"=>[{"first_name"=>"John S.", "last_name"=>"Brownstein", "scopus_author_id"=>"8872411400"}, {"first_name"=>"Cecily J.", "last_name"=>"Wolfe", "scopus_author_id"=>"7102520517"}, {"first_name"=>"Kenneth D.", "last_name"=>"Mandl", "scopus_author_id"=>"7003721828"}], "year"=>2006, "source"=>"PLoS Medicine", "identifiers"=>{"sgr"=>"33750882921", "doi"=>"10.1371/journal.pmed.0030401", "issn"=>"15491277", "pui"=>"44721821", "isbn"=>"1549-1676", "pmid"=>"16968115", "scopus"=>"2-s2.0-33750882921"}, "id"=>"7d8ae2db-aa13-3e92-94d5-3e795d85b56d", "abstract"=>"BACKGROUND: The influence of air travel on influenza spread has been the subject of numerous investigations using simulation, but very little empirical evidence has been provided. Understanding the role of airline travel in large-scale influenza spread is especially important given the mounting threat of an influenza pandemic. Several recent simulation studies have concluded that air travel restrictions may not have a significant impact on the course of a pandemic. Here, we assess, with empirical data, the role of airline volume on the yearly inter-regional spread of influenza in the United States. METHODS AND FINDINGS: We measured rate of inter-regional spread and timing of influenza in the United States for nine seasons, from 1996 to 2005 using weekly influenza and pneumonia mortality from the Centers for Disease Control and Prevention. Seasonality was characterized by band-pass filtering. We found that domestic airline travel volume in November (mostly surrounding the Thanksgiving holiday) predicts the rate of influenza spread (r(2) = 0.60; p = 0.014). We also found that international airline travel influences the timing of influenza mortality (r(2) = 0.59; p = 0.016). The flight ban in the US after the terrorist attack on September 11, 2001, and the subsequent depression of the air travel market, provided a natural experiment for the evaluation of flight restrictions; the decrease in air travel was associated with a delayed and prolonged influenza season. CONCLUSIONS: We provide the first empirical evidence for the role of airline travel in long-range dissemination of influenza. Our results suggest an important influence of international air travel on the timing of influenza introduction, as well as an influence of domestic air travel on the rate of inter-regional influenza spread in the US. Pandemic preparedness strategies should account for a possible benefit of airline travel restrictions on influenza spread.", "link"=>"http://www.mendeley.com/research/empirical-evidence-effect-airline-travel-interregional-influenza-spread-united-states", "reader_count"=>154, "reader_count_by_academic_status"=>{"Unspecified"=>3, "Professor > Associate Professor"=>18, "Librarian"=>2, "Researcher"=>34, "Student > Doctoral Student"=>8, "Student > Ph. D. Student"=>27, "Student > Postgraduate"=>4, "Other"=>8, "Student > Master"=>14, "Student > Bachelor"=>17, "Lecturer"=>2, "Lecturer > Senior Lecturer"=>6, "Professor"=>11}, "reader_count_by_user_role"=>{"Unspecified"=>3, "Professor > Associate Professor"=>18, "Librarian"=>2, "Researcher"=>34, "Student > Doctoral Student"=>8, "Student > Ph. D. 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• {"url"=>"http%3A%2F%2Fjournals.plos.org%2Fplosmedicine%2Farticle%3Fid%3D10.1371%252Fjournal.pmed.0030401", "share_count"=>0, "like_count"=>0, "comment_count"=>0, "click_count"=>0, "total_count"=>0}

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• {"type"=>"COMMENT", "annotationUri"=>"info:doi/10.1371/annotation/ec8e10bd-8836-43aa-80f3-63aa069688c5", "title"=>"Aircraft Air Quality", "body"=>"Author: David Cherbonnier\nPosition: Engineer\nInstitution: DLC Engineering, Singapore\nE-mail: dlc_engineering@yahoo.com\nSubmitted Date: September 16, 2006\nPublished Date: September 19, 2006\nThis comment was originally posted as a “Reader Response” on the publication date indicated above. All Reader Responses are now available as comments.\n\nAircraft air quality suffered with the ban on smoking.\n\nCabin air is provided by using engine bleed air and regulating its outflow. Nicotene and soot often fouled the outlet valves and required regular cleaning. Also the bleed air represented a albeit small, decrease in engine efficiency.\n\nWith the introduction of smoking ban the cabin air required less circulation (replacement) resulting in better efficiency and reduced fuel costs. The down side is that with less circulation the air became more stale and prolonged passenger exposure to airborne toxins.\n\nRather than reducing flights or passenger numbers, the solution lies in improving the air circulation methods without adversely affecting fuel efficiency.", "isRemoved"=>false, "created"=>"2009-03-31T00:00:02Z", "lastModified"=>"2009-03-31T00:00:02Z", "creator"=>{"userId"=>"172479"}, "highlightedText"=>"", "competingInterestStatement"=>{"creatorWasPrompted"=>true, "hasCompetingInterests"=>false}, "parentArticle"=>{"doi"=>"info:doi/10.1371/journal.pmed.0030401", "state"=>"published", "journals"=>{"PLoSMedicine"=>{"journalKey"=>"PLoSMedicine", "eIssn"=>"1549-1676", "title"=>"PLOS Medicine"}}}, "replyTreeSize"=>0, "mostRecentActivity"=>"2009-03-31T00:00:02Z", "replies"=>[]}
• {"type"=>"COMMENT", "annotationUri"=>"info:doi/10.1371/annotation/72f9df3a-2a6e-4659-82f4-f02fda2cd992", "title"=>"Response to Brownstein et al", "body"=>"Author: Cecile Viboud\nPosition: No occupation was given\nInstitution: Fogarty International Center, NIH, Bethesda, MD, USA\nE-mail: viboudc@mail.nih.gov\nAdditional Authors: Mark. A Miller, Bryan T. Grenfell, Ottar N. Bjornstad, Lone Simonsen\nSubmitted Date: October 03, 2006\nPublished Date: October 3, 2006\nThis comment was originally posted as a “Reader Response” on the publication date indicated above. All Reader Responses are now available as comments.\n\nLetter to the editor in response to Brownstein et al, Plos Med, Sep 2006\n\nAIR TRAVEL AND THE SPREAD OF INFLUENZA\n\nCecile Viboud [1], Mark A Miller [1], Bryan T Grenfell [1, 2], Ottar N. Bjornstad [1,2,3], Lone Simonsen [4]\n\n1 Fogarty International Center, National Institutes of Health, Bethesda, MD 20892, USA\n2 Center for Infectious Disease Dynamics, Department of Biology, Pennsylvania State University, University Park, Pennsylvania 16802, USA.\n3 Department of Entomology, Pennsylvania State University, University Park, Pennsylvania 16802, USA.\n4 National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, MD 20818, USA\n\nWhile 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 [4] 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.\n\nThe 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.\n\nGiven 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 [5], 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% [7]. 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 [8] 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 [7]. 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.\n\nIn 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) [5]. 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 [5]. 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 [5].\t\n\nIn 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 [9] - 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 [10]. 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.\n\nReferences:\n1. Cooper BS, Pitman RJ, Edmunds WJ and Gay NJ. Delaying the International Spread of Pandemic Influenza. PLoS Med 2006; 3(6):e212\n2. Ferguson NM, Cummings DA, Fraser C, Cajka JC, Cooley PC and Burke DS. Strategies for mitigating an influenza pandemic. Nature 2006; 442:448-52\n3. Germann TC, Kadau K, Longini IM, Jr. and Macken CA. Mitigation strategies for pandemic influenza in the United States. Proc Natl Acad Sci U S A 2006;103:5935-40\n4. Brownstein JS, Wolfe CJ and Mandl KD. Empirical Evidence for the Effect of Airline Travel on Inter-Regional Influenza Spread in the United States. PLoS Med 2006;3(10):e401 [Epub ahead of print]\n5. Viboud C, Bjornstad ON, Smith DL, Simonsen L, Miller MA and Grenfell BT. Synchrony, Waves, and Spatial Hierarchies in the Spread of Influenza. Science 2006; 312: 447-51\n6. Smith DJ, Lapedes AS, de Jong JC, et al. Mapping the antigenic and genetic evolution of influenza virus. Science 2004;305:371-6\n7. http://www.bts.gov/programs/airline_information/air_carrier_traffic_statistics/ US Department of Transportation Air Traffic Statistics. Accessed Sep 14, 2006\n8. http://www.cdc.gov/flu/. CDC Influenza activity in the US. Accessed Oct 12, 2005\n9. Enserink M. Ground the planes during a flu pandemic? Studies disagree. Science 2006;313:1555\n10. Epstein, SE. Prior H1N1 influenza infection and susceptibility of Cleveland Family Study participants during the H2N2 pandemic of 1957: an experiment of nature. J Infect Dis. 2006; 193: 49-5", "isRemoved"=>false, "created"=>"2009-03-31T00:00:13Z", "lastModified"=>"2009-03-31T00:00:13Z", "creator"=>{"userId"=>"172479"}, "highlightedText"=>"", "competingInterestStatement"=>{"creatorWasPrompted"=>true, "hasCompetingInterests"=>true, "body"=>"I do not have any competing interest regarding this letter, and neither have my co-authors. CV"}, "parentArticle"=>{"doi"=>"info:doi/10.1371/journal.pmed.0030401", "state"=>"published", "journals"=>{"PLoSMedicine"=>{"journalKey"=>"PLoSMedicine", "eIssn"=>"1549-1676", "title"=>"PLOS Medicine"}}}, "replyTreeSize"=>0, "mostRecentActivity"=>"2009-03-31T00:00:13Z", "replies"=>[]}
• {"type"=>"COMMENT", "annotationUri"=>"info:doi/10.1371/annotation/29e390a5-9e0b-42ae-a0a4-3155b086bb5a", "title"=>"AIR TRAVEL AND THE SPREAD OF INFLUENZA", "body"=>"Author: Cecile Viboud\nPosition: Research Scientist\nInstitution: Fogarty International Center, National Institutes of Health, USA\nE-mail: viboudc@mail.nih.gov\nAdditional Authors: Mark A Miller, Bryan T Grenfell, Ottar N. Bjornstad, Lone Simonsen\nSubmitted Date: September 26, 2006\nPublished Date: October 3, 2006\nThis comment was originally posted as a “Reader Response” on the publication date indicated above. All Reader Responses are now available as comments.\n\nWhile 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 [1,2,3]. 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 [4] 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.\n\tThe core of Brownstein's et al results lies in the observation that the 2001 to 2002 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.\nGiven 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 [5], we identified four epidemics peaking in the month of March (13%), including the 2001 to 2002 epidemic following 9/11/2001, but also two epidemics in the 1970s and the more recent 1991 to 1992 epidemic [Figure 1A]. Furthermore, the average timing of influenza epidemics has not changed between 1972 and 2002 [Figure 1A] despite a concurrent and steady increase in air traffic volume by over 300% [7]. Indeed, during the earlier part of the last century when air traffic was minimal, influenza epidemics rapidly circulated around the world. Moreover, CDC's realtime influenza virus surveillance data [8] show that last winter's (2005to 2006) 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 [7]. Clearly, lateseason 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,\nIn 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 to 2005). In our previous work, we estimated the rate of influenza spread among all US states for 30 consecutive seasons (1972 to 2002) [5]. Our analysis shows that the epidemic following 9/11/2001 spread at a rate comparable to other epidemics [Figure 1B], even after adjusting for the subtype of circulating viruses [5]. 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 [5].\n\nIn 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 [9] 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 interpandemic 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 [10]. 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.\n\nReferences:\n1. Cooper BS, Pitman RJ, Edmunds WJ and Gay NJ. Delaying the International Spread of Pandemic Influenza. PLoS Med 2006;3(6):e212\n2. Ferguson NM, Cummings DA, Fraser C, Cajka JC, Cooley PC and Burke DS. Strategies for mitigating an influenza pandemic. Nature 2006;442:448-52\n3. Germann TC, Kadau K, Longini IM, Jr. and Macken CA. Mitigation strategies for pandemic influenza in the United States. Proc Natl Acad Sci U S A 2006;103:5935-40\n4. Brownstein JS, Wolfe CJ and Mandl KD. Empirical Evidence for the Effect of Airline Travel on Inter-Regional Influenza Spread in the United States. PLoS Med 2006;3(10):e401 [Epub ahead of print]\n5. Viboud C, Bjornstad ON, Smith DL, Simonsen L, Miller MA and Grenfell BT. Synchrony, Waves, and Spatial Hierarchies in the Spread of Influenza. Science 2006; 312: 447-51\n6. Smith DJ, Lapedes AS, de Jong JC, et al. Mapping the antigenic and genetic evolution of influenza virus. Science 2004;305:371-6\n7. US Department of Transportation Air Traffic Statistics website. Accessed Sep 14, 2006\n8. CDC Influenza activity in the US website. Accessed Oct 12, 2005\n9. Enserink M. Ground the planes during a flu pandemic? Studies disagree. Science 2006;313:1555\n10. Epstein, SE. Prior H1N1 influenza infection and susceptibility of Cleveland Family Study participants during the H2N2 pandemic of 1957: an experiment of nature. J Infect Dis. 2006; 193: 49-5", "isRemoved"=>false, "created"=>"2009-03-31T00:00:29Z", "lastModified"=>"2009-03-31T00:00:29Z", "creator"=>{"userId"=>"172479"}, "highlightedText"=>"", "competingInterestStatement"=>{"creatorWasPrompted"=>true, "hasCompetingInterests"=>true, "body"=>"I declare that I have no competing interests."}, "parentArticle"=>{"doi"=>"info:doi/10.1371/journal.pmed.0030401", "state"=>"published", "journals"=>{"PLoSMedicine"=>{"journalKey"=>"PLoSMedicine", "eIssn"=>"1549-1676", "title"=>"PLOS Medicine"}}}, "replyTreeSize"=>0, "mostRecentActivity"=>"2009-03-31T00:00:29Z", "replies"=>[]}
• {"type"=>"COMMENT", "annotationUri"=>"info:doi/10.1371/annotation/f678f89e-5a71-45af-969b-796073c2bcc7", "title"=>"Long-Term Trends in Air Travel and the Spread of Influenza: Letter to the editor in response to Viboud et al.", "body"=>"Author: John Brownstein\nPosition: No occupation was given\nInstitution: Children's Hospital Boston, Boston, MA\nE-mail: John.Brownstein@childrens.harvard.edu\nAdditional Authors: Cecily J. Wolfe, Kenneth D. Mandl\nSubmitted Date: October 17, 2006\nPublished Date: October 24, 2006\nThis comment was originally posted as a “Reader Response” on the publication date indicated above. All Reader Responses are now available as comments.\n\nViboud et al. offer thoughtful commentary on our paper opening a scientific exchange that we hope brings attention to a critical issue [1]. We reported the first empirical and quantitative evidence for the effect of airline travel on the rate of epidemic influenza spread. Though other investigators have also found this relationship [2,3,4,5], there is no consensus on effect size. We welcome scrutiny of our methods and results and believe the findings stand for the following reasons.\n\nViboud et al., taking a longer historical perspective, suggest that the slower spread and late peaking of the 2001-2002 season is not unique and highlight three other late seasons dominated by influenza B (1992-1993, 1973-1974 and 1976-1977). Recent studies, including one by Viboud and colleagues, find that B seasons have different epidemiological characteristics than A/H3N2 seasons, which may explain the late peaking in these years. [6,7] As 2001-2002 was dominated by influenza A/H3N2, late peaking in 2001-2002 cannot be explained by dominant subtype (nor climatic conditions). Our study period from 1996-2005 represents the longest stretch of A/H3N2 seasons in over 30 years, essentially controlling for subtype. Viboud et al. also point to the 2005-2006 season as particularly delayed. We examined peaking during that season using CDC mortality data, in order to compare to our estimates from prior seasons [8]. We find that mortality as well as morbidity was bimodal, with larger peaks in January and December, respectively. We thus reaffirm that the 2001-2002, being the latest peaking H3N2 season in over 30 years, is an aberrant season for which airline travel interruption remains the best explanation.\n\nViboud et al's letter does not take into account that our results are not simply based on an outlier year, nor are they based on a single data source. Rather, we have revealed an important correlation across nine influenza seasons. The impact of airline volume on flu spread does not depend on the 2001-2002 season and remains significant even after its exclusion. Since considering longer time series may provide insight, we repeated our methods on the 30 year mortality time series which Viboud et al also analyzed.[9] We employed the same spatial aggregation (nine geographic regions) and time series methods as described in [1]. We found substantial long-term log-linear trends toward earlier peaking and faster spreading influenza epidemics that are correlated with air travel volume (R2=0.460 p less than 0.001 and R2=0.265 p=0.004, respectively, Figure 1). Thus, our new analyses of longer-term data support an effect of airline travel volume.\n\nWe strongly caution that other factors may influence these trends, including population density, air passenger demographics, ground transportation, and climate. Our design relies on a shorter, more recent time series to avoid confounding by longer term secular trends that may be evident in the 30-year time series. Given the three year backlog of the 30-year dataset, the data Viboud et al use do not permit the interrupted time series analysis at the core of our investigation.\n\nReconciliation of our different time series methodologies and datasets should be considered in future research. Nonetheless, because our results were confirmed with viral surveillance data, we remain confident in the robustness of our analysis. We agree that other modes of transportation are important influences; our paper makes no claim that air travel is the only mechanism of spread, and we explicitly report that our model explains a portion of the variation in yearly influenza spread and peak.\n\nFinally, Viboud et al. emphasize the limited applicability of our findings to pandemics. We agree and have highlighted this limitation in our paper. The decision to restrict travel should be multi-factorial. We do hope that it will be evidence-based. Our analyses (including those presented here) provide empirical insight into the previously uncharacterized effect of air travel fluctuation on influenza spread. They are one contribution to a small body of investigations that are forming the basis of global policy on flu preparedness. Though the effect we observe might be smaller under pandemic conditions, the benefit of a delay is worthy of consideration by scientists and policy makers where lives are at stake and even a short lead time may be of enormous public health value.\n\nWe are pleased that Viboud et al have engaged in a discourse that we hope will strengthen the scientific basis of pandemic preparedness. We call on governments, industry, and healthcare to create a more accessible, freely available and well-documented data repository for geographically and temporally detailed data on influenza [10] and encourage empirical analyses of the dynamics and mechanisms of influenza spread.\n\nReferences\n\n1. Brownstein JS, Wolfe CJ, Mandl KD (2006) Empirical Evidence for the Effect of Airline Travel on Inter-Regional Influenza Spread in the United States. PLoS Med 3.\n2. Bonabeau E, Toubiana L, Flahault A (1998) The geographical spread of influenza. Proc Biol Sci 265: 2421-2425.\n3. Grais RF, Ellis JH, Kress A, Glass GE (2004) Modeling the spread of annual influenza epidemics in the U.S.: the potential role of air travel. Health Care Manag Sci 7: 127-134.\n4. Longini IM, Jr., Fine PE, Thacker SB (1986) Predicting the global spread of new infectious agents. Am J Epidemiol 123: 383-391.\n5. Rvachev L, Longini I (1985) A mathematical model for the global spread of influenza. Mathematical Biosciences 75: 3-22.\n6. Greene SK, Ionides EL, Wilson ML (2006) Patterns of Influenza-associated Mortality among US Elderly by Geographic Region and Virus Subtype, 1968-1998. Am J Epidemiol 163: 316-326.\n7. Viboud C, Bjornstad ON, Smith DL, Simonsen L, Miller MA, et al. (2006) Synchrony, waves, and spatial hierarchies in the spread of influenza. Science 312: 447-451.\n8. Centers for Disease Control and Prevention (2005) Morbidity and Mortality Weekly Report Table III (Mortality).\n9. National Center for Health Statistics (2005) Multiple cause-of-death files on CD-ROM. Hyattsville, MD: US Department of Health and Human Services.\n10. Salzberg S, Ghedin E, Spiro D (2006) Shared data are key to beating threat from flu. Nature 440: 605.", "isRemoved"=>false, "created"=>"2009-03-31T00:01:13Z", "lastModified"=>"2009-03-31T00:01:13Z", "creator"=>{"userId"=>"172479"}, "highlightedText"=>"", "competingInterestStatement"=>{"creatorWasPrompted"=>true, "hasCompetingInterests"=>true, "body"=>"No competing interests declared"}, "parentArticle"=>{"doi"=>"info:doi/10.1371/journal.pmed.0030401", "state"=>"published", "journals"=>{"PLoSMedicine"=>{"journalKey"=>"PLoSMedicine", "eIssn"=>"1549-1676", "title"=>"PLOS Medicine"}}}, "replyTreeSize"=>0, "mostRecentActivity"=>"2009-03-31T00:01:13Z", "replies"=>[]}

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Figshare

• {"files"=>["https://ndownloader.figshare.com/files/956192"], "description"=>"<p>The sentinel cities that report mortality due to P&I used in the Centers for Disease Control and Prevention 122 Cities Mortality Reporting System are displayed (black dots). Because the strength of the seasonal influenza cycle is weak for cities with small case counts and because some city data contain missing data points, we aggregated the raw city-level data to obtain composite waveforms by major geographic region, the aerial unit of analysis for this study.</p>", "links"=>[], "tags"=>["geographic", "regions", "united"], "article_id"=>626474, "categories"=>["Biotechnology", "Mathematics", "Medicine", "Infectious Diseases"], "users"=>["John S Brownstein", "Cecily J Wolfe", "Kenneth D Mandl"], "doi"=>"https://dx.doi.org/10.1371/journal.pmed.0030401.g002", "stats"=>{"downloads"=>2, "page_views"=>6, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Major_Geographic_Regions_of_the_United_States_/626474", "title"=>"Major Geographic Regions of the United States", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-02-21 13:11:18"}
• {"files"=>["https://ndownloader.figshare.com/files/956076"], "description"=>"<div><p>(A) The black line represents the aggregated national data of P&I weekly mortality. The blue line represents the seasonal influenza curve, derived by band-pass filtering the demeaned data (two-pole, two-pass Butterworth, 1/64–1/40 frequency range). For comparison with the raw data, the mean is added after filtering. The filtered time series plus mean accounts for 99.8% of the mortality, indicating that most deaths are from the mean and seasonal variation and not the high-frequency cycles.</p>\n <p>(B) Lines represent the raw time series data for each of the nine geographic regions of the US.</p>\n <p>(C) Lines represent the seasonal influenza curves for each of the nine geographic regions of the US, derived by band-pass filtering.</p></div>", "links"=>[], "tags"=>["united", "states"], "article_id"=>626355, "categories"=>["Biotechnology", "Mathematics", "Medicine", "Infectious Diseases"], "users"=>["John S Brownstein", "Cecily J Wolfe", "Kenneth D Mandl"], "doi"=>"https://dx.doi.org/10.1371/journal.pmed.0030401.g001", "stats"=>{"downloads"=>0, "page_views"=>4, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Filtering_of_Weekly_P_amp_I_Mortality_in_the_United_States_1996_8211_2005_/626355", "title"=>"Filtering of Weekly P&I Mortality in the United States (1996–2005)", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-02-21 13:10:24"}
• {"files"=>["https://ndownloader.figshare.com/files/956316"], "description"=>"<div><p>For each influenza year, phase shifts are calculated as the maximum value from cross-correlation of the band-pass filtered weekly P&I mortality data.</p>\n <p>(A) Contour plot of raw phase shifts between regions for each season, which displays shifts in the absolute timing of influenza mortality peaks from year to year. The plot shows the shifts in the yearly phase, with the 1999–2000 season exhibiting an overall earlier peak and the 2001–2002 season (following September 11, 2001) exhibiting an overall later peak across all regions.</p>\n <p>(B) Contour plot of demeaned phase shifts, which displays typical regional patterns and relative time to transnational spread. For each season, demeaned phase shifts were calculated by subtracting the mean peak date. The plot reveals increased variation in phase shifts (time to transnational spread) during the earliest influenza seasons, 1996–1997 and 1997–1998, as well as the increased variation during the 2001–2002 influenza season.</p></div>", "links"=>[], "tags"=>["influenza", "geographic", "regions", "united", "states"], "article_id"=>626590, "categories"=>["Biotechnology", "Mathematics", "Medicine", "Infectious Diseases"], "users"=>["John S Brownstein", "Cecily J Wolfe", "Kenneth D Mandl"], "doi"=>"https://dx.doi.org/10.1371/journal.pmed.0030401.g003", "stats"=>{"downloads"=>1, "page_views"=>5, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Timing_of_Influenza_Illness_across_the_Nine_Major_Geographic_Regions_of_the_United_States_1996_8211_2005_/626590", "title"=>"Timing of Influenza Illness across the Nine Major Geographic Regions of the United States (1996–2005)", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-02-21 13:12:13"}
• {"files"=>["https://ndownloader.figshare.com/files/470689"], "description"=>"<div><h3>Background</h3><p>The influence of air travel on influenza spread has been the subject of numerous investigations using simulation, but very little empirical evidence has been provided. Understanding the role of airline travel in large-scale influenza spread is especially important given the mounting threat of an influenza pandemic. Several recent simulation studies have concluded that air travel restrictions may not have a significant impact on the course of a pandemic. Here, we assess, with empirical data, the role of airline volume on the yearly inter-regional spread of influenza in the United States.</p> <h3>Methods and Findings</h3><p>We measured rate of inter-regional spread and timing of influenza in the United States for nine seasons, from 1996 to 2005 using weekly influenza and pneumonia mortality from the Centers for Disease Control and Prevention. Seasonality was characterized by band-pass filtering. We found that domestic airline travel volume in November (mostly surrounding the Thanksgiving holiday) predicts the rate of influenza spread (<em>r</em>² = 0.60; <em>p =</em> 0.014). We also found that international airline travel influences the timing of influenza mortality (<em>r</em>² = 0.59; <em>p =</em> 0.016). The flight ban in the US after the terrorist attack on September 11, 2001, and the subsequent depression of the air travel market, provided a natural experiment for the evaluation of flight restrictions; the decrease in air travel was associated with a delayed and prolonged influenza season.</p> <h3>Conclusions</h3><p>We provide the first empirical evidence for the role of airline travel in long-range dissemination of influenza. Our results suggest an important influence of international air travel on the timing of influenza introduction, as well as an influence of domestic air travel on the rate of inter-regional influenza spread in the US. Pandemic preparedness strategies should account for a possible benefit of airline travel restrictions on influenza spread.</p> </div>", "links"=>[], "tags"=>["empirical", "inter-regional", "influenza", "united", "states"], "article_id"=>152822, "categories"=>["Mathematics", "Cancer", "Medicine", "Biotechnology"], "users"=>["John S Brownstein", "Cecily J Wolfe", "Kenneth D Mandl"], "doi"=>"https://dx.doi.org/10.1371/journal.pmed.0030401", "stats"=>{"downloads"=>0, "page_views"=>15, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/Empirical_Evidence_for_the_Effect_of_Airline_Travel_on_Inter_Regional_Influenza_Spread_in_the_United_States/152822", "title"=>"Empirical Evidence for the Effect of Airline Travel on Inter-Regional Influenza Spread in the United States", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2006-09-12 00:47:02"}
• {"files"=>["https://ndownloader.figshare.com/files/956417"], "description"=>"<div><p>(A) November domestic air travel volume (red line) is estimated by the total number of passengers on domestic flights. Duration to transnational spread of influenza (blue line) is estimated as the 99% confidence intervals for differences between the estimated seasonal curves of influenza mortality for each of nine major geographic regions of the United States.</p>\n <p>(B) The association between domestic airline travel in November and transnational spread is displayed. The numbers of traveling domestic passengers in November significantly predicts transnational influenza spread (<i>f</i> = 10.6; <i>r</i>² = 0.60; slope = −0.94 days/million passengers; <i>p =</i> 0.014).</p>\n <p>(C) September international air travel volume (red line) is estimated by the total number of passengers on international flights. The timing of seasonal national influenza mortality (blue line) is estimated as the peak date of influenza mortality from the filtered national curve. The timing displayed is relative to the average date of February 17.</p>\n <p>(D) The association between international airline travel in September and the timing of the US influenza peak is displayed. The numbers of traveling international passengers in September significantly predicts the timing of seasonal influenza mortality (<i>f</i> = 10.0; <i>r</i>² = 0.59; slope = −11.3 days/million passengers; <i>p =</i> 0.016).</p></div>", "links"=>[], "tags"=>["united", "states", "influenza", "yearly"], "article_id"=>626693, "categories"=>["Biotechnology", "Mathematics", "Medicine", "Infectious Diseases"], "users"=>["John S Brownstein", "Cecily J Wolfe", "Kenneth D Mandl"], "doi"=>"https://dx.doi.org/10.1371/journal.pmed.0030401.g004", "stats"=>{"downloads"=>3, "page_views"=>10, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Influence_of_United_States_Airline_Volume_on_Influenza_Spread_and_the_Timing_of_Yearly_Transmission_/626693", "title"=>"Influence of United States Airline Volume on Influenza Spread and the Timing of Yearly Transmission", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-02-21 13:13:06"}

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Relative Metric

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F1000Prime | Further Information

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• {"year"=>2015, "month"=>1, "doi"=>"10.1371/journal.pmed.0030401", "f1000_id"=>"717978498", "url"=>"http://f1000.com/prime/717978498", "score"=>1, "classifications"=>["new_finding"], "updated_at"=>"2015-01-26T09:40:58Z"}
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• {"year"=>2017, "month"=>3, "doi"=>"10.1371/journal.pmed.0030401", "f1000_id"=>"717978498", "url"=>"http://f1000.com/prime/717978498", "score"=>1, "classifications"=>["new_finding"], "updated_at"=>"2017-03-27T08:40:42Z"}
• {"year"=>2017, "month"=>4, "doi"=>"10.1371/journal.pmed.0030401", "f1000_id"=>"717978498", "url"=>"http://f1000.com/prime/717978498", "score"=>1, "classifications"=>["new_finding"], "updated_at"=>"2017-04-24T08:40:58Z"}
• {"year"=>2017, "month"=>5, "doi"=>"10.1371/journal.pmed.0030401", "f1000_id"=>"717978498", "url"=>"http://f1000.com/prime/717978498", "score"=>1, "classifications"=>["new_finding"], "updated_at"=>"2017-05-29T08:40:57Z"}
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• {"year"=>2017, "month"=>10, "doi"=>"10.1371/journal.pmed.0030401", "f1000_id"=>"717978498", "url"=>"https://f1000.com/prime/717978498", "score"=>1, "classifications"=>["new_finding"], "updated_at"=>"2017-10-30T08:41:26Z"}
• {"year"=>2017, "month"=>11, "doi"=>"10.1371/journal.pmed.0030401", "f1000_id"=>"717978498", "url"=>"https://f1000.com/prime/717978498", "score"=>1, "classifications"=>["new_finding"], "updated_at"=>"2017-11-27T09:41:07Z"}
• {"year"=>2017, "month"=>12, "doi"=>"10.1371/journal.pmed.0030401", "f1000_id"=>"717978498", "url"=>"https://f1000.com/prime/717978498", "score"=>1, "classifications"=>["new_finding"], "updated_at"=>"2017-12-25T09:40:55Z"}
• {"year"=>2018, "month"=>1, "doi"=>"10.1371/journal.pmed.0030401", "f1000_id"=>"717978498", "url"=>"https://f1000.com/prime/717978498", "score"=>1, "classifications"=>["new_finding"], "updated_at"=>"2018-01-29T09:40:41Z"}
• {"year"=>2018, "month"=>2, "doi"=>"10.1371/journal.pmed.0030401", "f1000_id"=>"717978498", "url"=>"https://f1000.com/prime/717978498", "score"=>1, "classifications"=>["new_finding"], "updated_at"=>"2018-02-26T09:40:45Z"}
• {"year"=>2018, "month"=>3, "doi"=>"10.1371/journal.pmed.0030401", "f1000_id"=>"717978498", "url"=>"https://f1000.com/prime/717978498", "score"=>1, "classifications"=>["new_finding"], "updated_at"=>"2018-03-26T08:40:46Z"}
• {"year"=>2018, "month"=>4, "doi"=>"10.1371/journal.pmed.0030401", "f1000_id"=>"717978498", "url"=>"https://f1000.com/prime/717978498", "score"=>1, "classifications"=>["new_finding"], "updated_at"=>"2018-04-30T08:40:43Z"}
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• {"year"=>2018, "month"=>10, "doi"=>"10.1371/journal.pmed.0030401", "f1000_id"=>"717978498", "url"=>"https://f1000.com/prime/717978498", "score"=>1, "classifications"=>["new_finding"], "updated_at"=>"2018-10-29T08:40:55Z"}
• {"year"=>2018, "month"=>11, "doi"=>"10.1371/journal.pmed.0030401", "f1000_id"=>"717978498", "url"=>"https://f1000.com/prime/717978498", "score"=>1, "classifications"=>["new_finding"], "updated_at"=>"2018-11-26T09:40:47Z"}
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• {"year"=>2019, "month"=>1, "doi"=>"10.1371/journal.pmed.0030401", "f1000_id"=>"717978498", "url"=>"https://f1000.com/prime/717978498", "score"=>1, "classifications"=>["new_finding"], "updated_at"=>"2019-01-28T09:40:49Z"}
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• {"year"=>2019, "month"=>3, "doi"=>"10.1371/journal.pmed.0030401", "f1000_id"=>"717978498", "url"=>"https://f1000.com/prime/717978498", "score"=>1, "classifications"=>["new_finding"], "updated_at"=>"2019-03-25T08:40:46Z"}
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