On the Use of Human Mobility Proxies for Modeling Epidemics
Publication Date
July 10, 2014
Journal
PLOS Computational Biology
Authors
Michele Tizzoni, Paolo Bajardi, Adeline Decuyper, Guillaume Kon Kam King, et al
Volume
10
Issue
7
Pages
e1003716
DOI
https://dx.plos.org/10.1371/journal.pcbi.1003716
Publisher URL
http://journals.plos.org/ploscompbiol/article?id=10.1371%2Fjournal.pcbi.1003716
PubMed
http://www.ncbi.nlm.nih.gov/pubmed/25010676
PubMed Central
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4091706
Europe PMC
http://europepmc.org/abstract/MED/25010676
Web of Science
000339890900031
Scopus
84905454884
Mendeley
http://www.mendeley.com/research/human-mobility-proxies-modeling-epidemics
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Mendeley | Further Information

{"title"=>"On the Use of Human Mobility Proxies for Modeling Epidemics", "type"=>"journal", "authors"=>[{"first_name"=>"Michele", "last_name"=>"Tizzoni", "scopus_author_id"=>"35115743400"}, {"first_name"=>"Paolo", "last_name"=>"Bajardi", "scopus_author_id"=>"35114469000"}, {"first_name"=>"Adeline", "last_name"=>"Decuyper", "scopus_author_id"=>"56310882900"}, {"first_name"=>"Guillaume", "last_name"=>"Kon Kam King", "scopus_author_id"=>"56306136600"}, {"first_name"=>"Christian M.", "last_name"=>"Schneider", "scopus_author_id"=>"57198962450"}, {"first_name"=>"Vincent", "last_name"=>"Blondel", "scopus_author_id"=>"7003475397"}, {"first_name"=>"Zbigniew", "last_name"=>"Smoreda", "scopus_author_id"=>"26640645700"}, {"first_name"=>"Marta C.", "last_name"=>"González", "scopus_author_id"=>"36913172600"}, {"first_name"=>"Vittoria", "last_name"=>"Colizza", "scopus_author_id"=>"8225873300"}], "year"=>2014, "source"=>"PLoS Computational Biology", "identifiers"=>{"sgr"=>"84905454884", "doi"=>"10.1371/journal.pcbi.1003716", "pui"=>"373701175", "pmid"=>"25010676", "scopus"=>"2-s2.0-84905454884", "issn"=>"15537358", "isbn"=>"1553-7358 (Electronic)\\r1553-734X (Linking)", "arxiv"=>"1309.7272"}, "id"=>"793c0c58-7500-3c1a-8c87-84b536ef39fa", "abstract"=>"Human mobility is a key component of large-scale spatial-transmission models of infectious diseases. Correctly modeling and quantifying human mobility is critical for improving epidemic control, but may be hindered by data incompleteness or unavailability. Here we explore the opportunity of using proxies for individual mobility to describe commuting flows and predict the diffusion of an influenza-like-illness epidemic. We consider three European countries and the corresponding commuting networks at different resolution scales, obtained from (i) official census surveys, (ii) proxy mobility data extracted from mobile phone call records, and (iii) the radiation model calibrated with census data. Metapopulation models defined on these countries and integrating the different mobility layers are compared in terms of epidemic observables. We show that commuting networks from mobile phone data capture the empirical commuting patterns well, accounting for more than 87% of the total fluxes. The distributions of commuting fluxes per link from mobile phones and census sources are similar and highly correlated, however a systematic overestimation of commuting traffic in the mobile phone data is observed. This leads to epidemics that spread faster than on census commuting networks, once the mobile phone commuting network is considered in the epidemic model, however preserving to a high degree the order of infection of newly affected locations. Proxies' calibration affects the arrival times' agreement across different models, and the observed topological and traffic discrepancies among mobility sources alter the resulting epidemic invasion patterns. Results also suggest that proxies perform differently in approximating commuting patterns for disease spread at different resolution scales, with the radiation model showing higher accuracy than mobile phone data when the seed is central in the network, the opposite being observed for peripheral locations. 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CrossRef

Scopus | Further Information

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Figshare

  • {"files"=>["https://ndownloader.figshare.com/files/1588608"], "description"=>"<p>The full invasion trees for are shown for Portugal (top row) and France (bottom row) in the cases of the census network (<b>a, d</b>), the mobile phone network (<b>b, e</b>) and the radiation network (<b>c, f</b>). Seeds of the simulations (black nodes) are Lisbon for Portugal and Barcelonnette for France. Nodes belonging to the first shell of the tree, i.e. those directly infected from the seed are fully colored. Grey nodes have been infected by secondary infected nodes.</p>", "links"=>[], "tags"=>["Computational biology", "Population modeling", "Infectious disease modeling"], "article_id"=>1100096, "categories"=>["Biological Sciences"], "users"=>["Michele Tizzoni", "Paolo Bajardi", "Adeline Decuyper", "Guillaume Kon Kam King", "Christian M. Schneider", "Vincent Blondel", "Zbigniew Smoreda", "Marta C. González", "Vittoria Colizza"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1003716.g006", "stats"=>{"downloads"=>0, "page_views"=>8, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Epidemic_invasion_trees_/1100096", "title"=>"Epidemic invasion trees.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-07-10 03:41:47"}
  • {"files"=>["https://ndownloader.figshare.com/files/1588589"], "description"=>"<p>Top: probability density distributions of the weights () of the census commuting network (grey) and the mobile phone commuting network (red) in Portugal (<b>a</b>), Spain (<b>b</b>) and France (<b>c</b>). Bottom: comparing weights in the mobile phone network () and weights in the census networks () in Portugal (<b>d</b>), Spain (<b>e</b>) and France (<b>f</b>). Grey points are scatter plot for each connection. Box plots indicate the 95% reference range of values within a bin.</p>", "links"=>[], "tags"=>["Computational biology", "Population modeling", "Infectious disease modeling", "weights", "census", "networks"], "article_id"=>1100076, "categories"=>["Biological Sciences"], "users"=>["Michele Tizzoni", "Paolo Bajardi", "Adeline Decuyper", "Guillaume Kon Kam King", "Christian M. Schneider", "Vincent Blondel", "Zbigniew Smoreda", "Marta C. González", "Vittoria Colizza"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1003716.g002", "stats"=>{"downloads"=>0, "page_views"=>4, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Comparing_the_weights_of_the_census_networks_and_the_mobile_phone_networks_/1100076", "title"=>"Comparing the weights of the census networks and the mobile phone networks.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-07-10 03:41:47"}
  • {"files"=>["https://ndownloader.figshare.com/files/1588650", "https://ndownloader.figshare.com/files/1588651"], "description"=>"<div><p>Human mobility is a key component of large-scale spatial-transmission models of infectious diseases. Correctly modeling and quantifying human mobility is critical for improving epidemic control, but may be hindered by data incompleteness or unavailability. Here we explore the opportunity of using proxies for individual mobility to describe commuting flows and predict the diffusion of an influenza-like-illness epidemic. We consider three European countries and the corresponding commuting networks at different resolution scales, obtained from (i) official census surveys, (ii) proxy mobility data extracted from mobile phone call records, and (iii) the radiation model calibrated with census data. Metapopulation models defined on these countries and integrating the different mobility layers are compared in terms of epidemic observables. We show that commuting networks from mobile phone data capture the empirical commuting patterns well, accounting for more than 87% of the total fluxes. The distributions of commuting fluxes per link from mobile phones and census sources are similar and highly correlated, however a systematic overestimation of commuting traffic in the mobile phone data is observed. This leads to epidemics that spread faster than on census commuting networks, once the mobile phone commuting network is considered in the epidemic model, however preserving to a high degree the order of infection of newly affected locations. Proxies' calibration affects the arrival times' agreement across different models, and the observed topological and traffic discrepancies among mobility sources alter the resulting epidemic invasion patterns. <a href=\"http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003716#s3\" target=\"_blank\">Results</a> also suggest that proxies perform differently in approximating commuting patterns for disease spread at different resolution scales, with the radiation model showing higher accuracy than mobile phone data when the seed is central in the network, the opposite being observed for peripheral locations. Proxies should therefore be chosen in light of the desired accuracy for the epidemic situation under study.</p></div>", "links"=>[], "tags"=>["Computational biology", "Population modeling", "Infectious disease modeling", "mobility", "proxies", "modeling"], "article_id"=>1100103, "categories"=>["Biological Sciences"], "users"=>["Michele Tizzoni", "Paolo Bajardi", "Adeline Decuyper", "Guillaume Kon Kam King", "Christian M. Schneider", "Vincent Blondel", "Zbigniew Smoreda", "Marta C. González", "Vittoria Colizza"], "doi"=>["https://dx.doi.org/10.1371/journal.pcbi.1003716.s001", "https://dx.doi.org/10.1371/journal.pcbi.1003716.s002"], "stats"=>{"downloads"=>16, "page_views"=>9, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_On_the_Use_of_Human_Mobility_Proxies_for_Modeling_Epidemics_/1100103", "title"=>"On the Use of Human Mobility Proxies for Modeling Epidemics", "pos_in_sequence"=>0, "defined_type"=>4, "published_date"=>"2014-07-10 03:41:47"}
  • {"files"=>["https://ndownloader.figshare.com/files/1588597"], "description"=>"<p>Comparing the epidemic behavior on the census network and two proxy networks, mobile phone (red symbols) and radiation model (blue symbols), in Portugal (top panels), Spain (middle) and France (bottom). <b>a, d, g</b> Jaccard similarity index measured between the epidemic infection tree of the census network and the infection tree of the proxy network, for three values of the basic reproduction number . Each symbol corresponds to a different initial infection seed, displayed on the map (right panels). <b>b, e, h</b> Differences between the arrival times in the census network and in the proxy network, for different values of and infection seed. Box plots indicate the 90% reference range, measured on all the network nodes. <b>c, f, i</b> Comparing the arrival times in the mobile phone network with those in the census network , for and the epidemic starting from the capital city. Red points are scatter plot for each node of the network and we subtracted the average systematic difference from each .</p>", "links"=>[], "tags"=>["Computational biology", "Population modeling", "Infectious disease modeling"], "article_id"=>1100084, "categories"=>["Biological Sciences"], "users"=>["Michele Tizzoni", "Paolo Bajardi", "Adeline Decuyper", "Guillaume Kon Kam King", "Christian M. Schneider", "Vincent Blondel", "Zbigniew Smoreda", "Marta C. González", "Vittoria Colizza"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1003716.g004", "stats"=>{"downloads"=>0, "page_views"=>6, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Epidemic_spreading_/1100084", "title"=>"Epidemic spreading.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-07-10 03:41:47"}
  • {"files"=>["https://ndownloader.figshare.com/files/1588611"], "description"=>"<p>Number of nodes, of links, and of commuters for each commuting network under study, without considering self-loops. Rows correspond to different countries and geographical subdivisions within a country. Columns indicate values from the census dataset and the mobile phone dataset. Commuters for the mobile phone dataset correspond to the values obtained directly from the samples, prior to the normalization procedure, and after the basic normalization procedure. Values obtained with the refined normalization are not reported as they are equal to those of the census dataset, by definition.</p>", "links"=>[], "tags"=>["Computational biology", "Population modeling", "Infectious disease modeling", "commuting"], "article_id"=>1100098, "categories"=>["Biological Sciences"], "users"=>["Michele Tizzoni", "Paolo Bajardi", "Adeline Decuyper", "Guillaume Kon Kam King", "Christian M. Schneider", "Vincent Blondel", "Zbigniew Smoreda", "Marta C. González", "Vittoria Colizza"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1003716.t001", "stats"=>{"downloads"=>0, "page_views"=>4, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Basic_properties_of_the_commuting_networks_/1100098", "title"=>"Basic properties of the commuting networks.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2014-07-10 03:41:47"}
  • {"files"=>["https://ndownloader.figshare.com/files/1588592"], "description"=>"<p>Panels show the ratio between the weights of the mobile phone networks and the census networks in Portugal (top panels), Spain (middle) and France (bottom), as function of the Euclidean distance between nodes (<b>a, d and g</b>), the population of origin (<b>b, e, and h</b>) and the population of destination (<b>c, f and i</b>). The solid red line indicates the unit value.</p>", "links"=>[], "tags"=>["Computational biology", "Population modeling", "Infectious disease modeling", "geography", "demography", "commuting"], "article_id"=>1100079, "categories"=>["Biological Sciences"], "users"=>["Michele Tizzoni", "Paolo Bajardi", "Adeline Decuyper", "Guillaume Kon Kam King", "Christian M. Schneider", "Vincent Blondel", "Zbigniew Smoreda", "Marta C. González", "Vittoria Colizza"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1003716.g003", "stats"=>{"downloads"=>0, "page_views"=>10, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Effects_of_geography_and_demography_on_commuting_fluxes_/1100079", "title"=>"Effects of geography and demography on commuting fluxes.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-07-10 03:41:47"}
  • {"files"=>["https://ndownloader.figshare.com/files/1588585"], "description"=>"<p>Map showing the ratio for each region of the countries under study. indicates the population in the mobile phone dataset estimated as (see <a href=\"http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003716#s2\" target=\"_blank\">Materials and Methods</a>), and represents the official census population. Values close to unity (in grey) indicate that the coverage of the mobile phone dataset is similar to the national coverage; larger (in red) or smaller values (in blue) indicate that the mobile phone dataset is over or under sampling those regions, respectively, compared to the national average. The map was made exclusively for this manuscript and is not subject to copyright.</p>", "links"=>[], "tags"=>["Computational biology", "Population modeling", "Infectious disease modeling", "differences", "phones", "census"], "article_id"=>1100074, "categories"=>["Biological Sciences"], "users"=>["Michele Tizzoni", "Paolo Bajardi", "Adeline Decuyper", "Guillaume Kon Kam King", "Christian M. Schneider", "Vincent Blondel", "Zbigniew Smoreda", "Marta C. González", "Vittoria Colizza"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1003716.g001", "stats"=>{"downloads"=>0, "page_views"=>13, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Spatial_differences_in_coverage_of_the_mobile_phones_and_census_datasets_/1100074", "title"=>"Spatial differences in coverage of the mobile phones and census datasets.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-07-10 03:41:47"}
  • {"files"=>["https://ndownloader.figshare.com/files/1588601"], "description"=>"<p>Comparing the epidemic behavior of mobile phone proxy vs. census, when basic and refined normalization are considered. Only the case of France is shown. <b>a</b> Jaccard similarity index of the epidemic infection tree. Each symbol corresponds to a different initial infection seed, displayed on the map (on the right). <b>b</b> Differences between the arrival times in the census network and in the proxy network, for different values of and infection seed. Box plots indicate the 90% reference range, measured on all the network nodes. <b>c</b> Arrival times in the mobile phone network compared with those in the census network , for and the epidemic starting from the capital city. Red points are scatter plot for each node of the network and we subtracted the average systematic difference from each .</p>", "links"=>[], "tags"=>["Computational biology", "Population modeling", "Infectious disease modeling", "spreading", "considering", "refined"], "article_id"=>1100088, "categories"=>["Biological Sciences"], "users"=>["Michele Tizzoni", "Paolo Bajardi", "Adeline Decuyper", "Guillaume Kon Kam King", "Christian M. Schneider", "Vincent Blondel", "Zbigniew Smoreda", "Marta C. González", "Vittoria Colizza"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1003716.g005", "stats"=>{"downloads"=>0, "page_views"=>18, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Epidemic_spreading_considering_the_refined_normalization_/1100088", "title"=>"Epidemic spreading considering the refined normalization.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-07-10 03:41:47"}
  • {"files"=>["https://ndownloader.figshare.com/files/1588610"], "description"=>"<p>Values of the Lin's concordance coefficients after a log transformation of variables, and Spearman's coefficient measured between the mobile phone network and the census network for the weights () and the nodes' total fluxes for incoming and outgoing commuters. Rows correspond to different countries and geographical subdivisions within a country.</p>", "links"=>[], "tags"=>["Computational biology", "Population modeling", "Infectious disease modeling", "census"], "article_id"=>1100097, "categories"=>["Biological Sciences"], "users"=>["Michele Tizzoni", "Paolo Bajardi", "Adeline Decuyper", "Guillaume Kon Kam King", "Christian M. Schneider", "Vincent Blondel", "Zbigniew Smoreda", "Marta C. González", "Vittoria Colizza"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1003716.t002", "stats"=>{"downloads"=>0, "page_views"=>5, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Statistical_comparison_between_census_and_mobile_phone_data_/1100097", "title"=>"Statistical comparison between census and mobile phone data.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2014-07-10 03:41:47"}

PMC Usage Stats | Further Information

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Relative Metric

{"start_date"=>"2014-01-01T00:00:00Z", "end_date"=>"2014-12-31T00:00:00Z", "subject_areas"=>[{"subject_area"=>"/Engineering and technology", "average_usage"=>[282]}, {"subject_area"=>"/Engineering and technology/Equipment", "average_usage"=>[287, 444]}, {"subject_area"=>"/People and places", "average_usage"=>[302]}]}
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