The Cost of Simplifying Air Travel When Modeling Disease Spread
Publication Date
February 06, 2009
Journal
PLOS ONE
Authors
Justin Lessler, James H. Kaufman, Daniel A. Ford & Judith V. Douglas
Volume
4
Issue
2
Pages
e4403
DOI
https://dx.plos.org/10.1371/journal.pone.0004403
Publisher URL
http://journals.plos.org/plosone/article?id=10.1371%2Fjournal.pone.0004403
PubMed
http://www.ncbi.nlm.nih.gov/pubmed/19197382
PubMed Central
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2633616
Europe PMC
http://europepmc.org/abstract/MED/19197382
Web of Science
000265483700011
Scopus
84887212420
Mendeley
http://www.mendeley.com/research/cost-simplifying-air-travel-modeling-disease-spread
Events
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Mendeley | Further Information

{"title"=>"The cost of simplifying air travel when modeling disease spread", "type"=>"journal", "authors"=>[{"first_name"=>"Justin", "last_name"=>"Lessler", "scopus_author_id"=>"22951309100"}, {"first_name"=>"James H.", "last_name"=>"Kaufman", "scopus_author_id"=>"56222980400"}, {"first_name"=>"Daniel A.", "last_name"=>"Ford", "scopus_author_id"=>"7401568469"}, {"first_name"=>"Judith V.", "last_name"=>"Douglas", "scopus_author_id"=>"56221001600"}], "year"=>2009, "source"=>"PLoS ONE", "identifiers"=>{"pui"=>"354236600", "scopus"=>"2-s2.0-84887212420", "isbn"=>"1932-6203 (Electronic)\r1932-6203 (Linking)", "pmid"=>"19197382", "sgr"=>"84887212420", "issn"=>"19326203", "doi"=>"10.1371/journal.pone.0004403"}, "id"=>"46d6e639-197c-351e-9434-d044a26da2a6", "abstract"=>"BACKGROUND: Air travel plays a key role in the spread of many pathogens. Modeling the long distance spread of infectious disease in these cases requires an air travel model. Highly detailed air transportation models can be over determined and computationally problematic. We compared the predictions of a simplified air transport model with those of a model of all routes and assessed the impact of differences on models of infectious disease. METHODOLOGY/PRINCIPAL FINDINGS: Using U.S. ticket data from 2007, we compared a simplified \"pipe\" model, in which individuals flow in and out of the air transport system based on the number of arrivals and departures from a given airport, to a fully saturated model where all routes are modeled individually. We also compared the pipe model to a \"gravity\" model where the probability of travel is scaled by physical distance; the gravity model did not differ significantly from the pipe model. The pipe model roughly approximated actual air travel, but tended to overestimate the number of trips between small airports and underestimate travel between major east and west coast airports. For most routes, the maximum number of false (or missed) introductions of disease is small (<1 per day) but for a few routes this rate is greatly underestimated by the pipe model. CONCLUSIONS/SIGNIFICANCE: If our interest is in large scale regional and national effects of disease, the simplified pipe model may be adequate. If we are interested in specific effects of interventions on particular air routes or the time for the disease to reach a particular location, a more complex point-to-point model will be more accurate. For many problems a hybrid model that independently models some frequently traveled routes may be the best choice. Regardless of the model used, the effect of simplifications and sensitivity to errors in parameter estimation should be analyzed.", "link"=>"http://www.mendeley.com/research/cost-simplifying-air-travel-modeling-disease-spread", "reader_count"=>31, "reader_count_by_academic_status"=>{"Professor > Associate Professor"=>2, "Librarian"=>1, "Researcher"=>7, "Student > Doctoral Student"=>2, "Student > Ph. D. Student"=>4, "Student > Postgraduate"=>1, "Student > Master"=>5, "Student > Bachelor"=>4, "Lecturer > Senior Lecturer"=>4, "Professor"=>1}, "reader_count_by_user_role"=>{"Professor > Associate Professor"=>2, "Librarian"=>1, "Researcher"=>7, "Student > Doctoral Student"=>2, "Student > Ph. D. Student"=>4, "Student > Postgraduate"=>1, "Student > Master"=>5, "Student > Bachelor"=>4, "Lecturer > Senior Lecturer"=>4, "Professor"=>1}, "reader_count_by_subject_area"=>{"Unspecified"=>3, "Environmental Science"=>3, "Mathematics"=>4, "Medicine and Dentistry"=>10, "Agricultural and Biological Sciences"=>5, "Arts and Humanities"=>1, "Physics and Astronomy"=>1, "Social Sciences"=>1, "Computer Science"=>1, "Earth and Planetary Sciences"=>1, "Economics, Econometrics and Finance"=>1}, "reader_count_by_subdiscipline"=>{"Medicine and Dentistry"=>{"Medicine and Dentistry"=>10}, "Social Sciences"=>{"Social Sciences"=>1}, "Physics and Astronomy"=>{"Physics and Astronomy"=>1}, "Earth and Planetary Sciences"=>{"Earth and Planetary Sciences"=>1}, "Economics, Econometrics and Finance"=>{"Economics, Econometrics and Finance"=>1}, "Agricultural and Biological Sciences"=>{"Agricultural and Biological Sciences"=>5}, "Computer Science"=>{"Computer Science"=>1}, "Mathematics"=>{"Mathematics"=>4}, "Unspecified"=>{"Unspecified"=>3}, "Environmental Science"=>{"Environmental Science"=>3}, "Arts and Humanities"=>{"Arts and Humanities"=>1}}, "reader_count_by_country"=>{"United States"=>4, "Italy"=>1, "United Kingdom"=>1, "Australia"=>1}, "group_count"=>4}

Scopus | Further Information

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Figshare

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  • {"files"=>["https://ndownloader.figshare.com/files/909744"], "description"=>"<p>Legend: In all four graphs origins are ordered left to right by increasing airport traffic, and destinations are ordered bottom to top by increasing airport traffic. (A) The log-probability a trip from a given origin airport is to a particular destination airport under the saturated model. (B) The log-probability a trip from a given origin airport is to a particular destination airport under the pipe model. (C) The log probability ratio of the pipe model versus the saturated model. (D) Trips for which the rate of disease introductions from a fully infected location is overestimated by at least one individual per day (red) or underestimated by one individual per day (blue) under the pipe model.</p>", "links"=>[], "tags"=>["saturated", "models"], "article_id"=>580195, "categories"=>["Computational Biology", "Cancer", "Biotechnology", "Information And Computing Sciences"], "users"=>["Justin Lessler", "James H. Kaufman", "Daniel A. Ford", "Judith V. Douglas"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0004403.g001", "stats"=>{"downloads"=>1, "page_views"=>7, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Comparison_of_the_pipe_and_saturated_models_of_air_transport_/580195", "title"=>"Comparison of the pipe and saturated models of air transport.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-02-21 07:00:35"}
  • {"files"=>["https://ndownloader.figshare.com/files/909928"], "description"=>"<p>Legend: The Euclidian distance between the vectors representing the probability that a particular arrival at an airport is from a particular origin under the pipe-transport and saturated models versus the estimated number of yearly arrivals at the airport. For airports with larger numbers of arrivals the pipe model more accurately approximates the true distribution of arrivals.</p>", "links"=>[], "tags"=>["arriving", "passengers"], "article_id"=>580388, "categories"=>["Computational Biology", "Cancer", "Biotechnology", "Information And Computing Sciences"], "users"=>["Justin Lessler", "James H. Kaufman", "Daniel A. Ford", "Judith V. Douglas"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0004403.g002", "stats"=>{"downloads"=>3, "page_views"=>4, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Comparison_of_origin_for_arriving_passengers_under_the_two_models_/580388", "title"=>"Comparison of origin for arriving passengers under the two models.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-02-21 07:01:45"}

PMC Usage Stats | Further Information

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Relative Metric

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