Predicting Epidemic Risk from Past Temporal Contact Data
Publication Date
March 12, 2015
Journal
PLOS Computational Biology
Authors
Eugenio Valdano, Chiara Poletto, Armando Giovannini, Diana Palma, et al
Volume
11
Issue
3
Pages
e1004152
DOI
http://doi.org/10.1371/journal.pcbi.1004152
Publisher URL
http://journals.plos.org/ploscompbiol/article?id=10.1371%2Fjournal.pcbi.1004152
PubMed
http://www.ncbi.nlm.nih.gov/pubmed/25763816
PubMed Central
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4357450
Europe PMC
http://europepmc.org/abstract/MED/25763816
Web of Science
000352195700041
Scopus
84926357287
Mendeley
http://www.mendeley.com/research/predicting-epidemic-risk-past-temporal-contact-data
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Mendeley | Further Information

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Scopus | Further Information

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Figshare

  • {"files"=>["https://ndownloader.figshare.com/files/1946102"], "description"=>"<p>List of variables and their description.</p>", "links"=>[], "tags"=>["Epidemic Risk", "epidemics spread", "livestock displacements trade network", "control measures", "risk assessment analysis", "prediction", "pattern properties", "Past Temporal Contact Data", "intervention strategies", "control outbreaks", "infection", "data", "contact", "High accuracy", "animal holdings"], "article_id"=>1333758, "categories"=>["Uncategorised"], "users"=>["Eugenio Valdano", "Chiara Poletto", "Armando Giovannini", "Diana Palma", "Lara Savini", "Vittoria Colizza"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1004152.t001", "stats"=>{"downloads"=>2, "page_views"=>10, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_List_of_variables_and_their_description_/1333758", "title"=>"List of variables and their description.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2015-03-12 02:52:45"}
  • {"files"=>["https://ndownloader.figshare.com/files/1946099"], "description"=>"<p>(<i>A</i>), (<i>B</i>): Probability distributions of the infections potentials for loyal (<i>π</i><sub><i>L</i></sub>, green) and disloyal nodes (<i>π</i><sub><i>D</i></sub>, orange), for the cattle trade network and the sexual contact network, respectively. Loyalty is set with a threshold <i>ϵ</i> = 0.1. Dashed lines show the fit with a Landau+exponential model (see Material and <a href=\"http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1004152#sec009\" target=\"_blank\">Methods</a>). (<i>C</i>), (<i>D</i>): Loyalty transition probabilities between loyal statuses (<i>T</i><sub><i>LL</i></sub>(<i>k</i>), green) and disloyal statuses (<i>T</i><sub><i>DD</i></sub>(<i>k</i>), orange) as functions of the degree <i>k</i> of the node, for the cattle trade network and the sexual contact network, respectively. Dashed lines represent the logarithmic models: <i>T</i><sub><i>DD</i></sub>(<i>k</i>) = 0.78−0.11log <i>k</i>, and <i>T</i><sub><i>LL</i></sub>(<i>k</i>) = 0.63+0.06log <i>k</i> for the cattle trade network; <i>T</i><sub><i>DD</i></sub>(<i>k</i>) = 0.94−0.10log <i>k</i>, and <i>T</i><sub><i>LL</i></sub>(<i>k</i>) = 0.25+0.17log <i>k</i> for the sexual contact network. Transition probabilities are computed as frequencies in the datasets under study. The error bars here represent one binomial standard deviation from these frequencies. In (<i>C</i>) the error bars are smaller than the size of the points. A single pair of configurations is considered here as example; the behavior observed is the same for all the pair of configurations.</p>", "links"=>[], "tags"=>["Epidemic Risk", "epidemics spread", "livestock displacements trade network", "control measures", "risk assessment analysis", "prediction", "pattern properties", "Past Temporal Contact Data", "intervention strategies", "control outbreaks", "infection", "data", "contact", "High accuracy", "animal holdings"], "article_id"=>1333755, "categories"=>["Uncategorised"], "users"=>["Eugenio Valdano", "Chiara Poletto", "Armando Giovannini", "Diana Palma", "Lara Savini", "Vittoria Colizza"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1004152.g003", "stats"=>{"downloads"=>1, "page_views"=>7, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Infection_potentials_and_loyalty_transitions_/1333755", "title"=>"Infection potentials and loyalty transitions.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2015-03-12 02:52:45"}
  • {"files"=>["https://ndownloader.figshare.com/files/1946098"], "description"=>"<p>(<i>A</i>) Visualization of the neighborhood of two different farms in the cattle trade network (orange node, characterized by low loyalty, and green node, characterized by high loyalty) and corresponding loyalty values computed on three consecutive configurations (2006, 2007, 2008). (<i>B</i>), (<i>C</i>): Loyalty distributions in the cattle trade network and in the sexual contact network, respectively. Histograms refer to the first pair of consecutive configurations for visualization purposes, all other distributions being reported in <a href=\"http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1004152#pcbi.1004152.s002\" target=\"_blank\">S1 Text</a> and showing stability across time.</p>", "links"=>[], "tags"=>["Epidemic Risk", "epidemics spread", "livestock displacements trade network", "control measures", "risk assessment analysis", "prediction", "pattern properties", "Past Temporal Contact Data", "intervention strategies", "control outbreaks", "infection", "data", "contact", "High accuracy", "animal holdings"], "article_id"=>1333754, "categories"=>["Uncategorised"], "users"=>["Eugenio Valdano", "Chiara Poletto", "Armando Giovannini", "Diana Palma", "Lara Savini", "Vittoria Colizza"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1004152.g002", "stats"=>{"downloads"=>4, "page_views"=>11, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Loyalty_/1333754", "title"=>"Loyalty.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2015-03-12 02:52:45"}
  • {"files"=>["https://ndownloader.figshare.com/files/1946113", "https://ndownloader.figshare.com/files/1946114"], "description"=>"<div><p>Understanding how epidemics spread in a system is a crucial step to prevent and control outbreaks, with broad implications on the system’s functioning, health, and associated costs. This can be achieved by identifying the elements at higher risk of infection and implementing targeted surveillance and control measures. One important ingredient to consider is the pattern of disease-transmission contacts among the elements, however lack of data or delays in providing updated records may hinder its use, especially for time-varying patterns. Here we explore to what extent it is possible to use past temporal data of a system’s pattern of contacts to predict the risk of infection of its elements during an emerging outbreak, in absence of updated data. We focus on two real-world temporal systems; a livestock displacements trade network among animal holdings, and a network of sexual encounters in high-end prostitution. We define the node’s loyalty as a local measure of its tendency to maintain contacts with the same elements over time, and uncover important non-trivial correlations with the node’s epidemic risk. We show that a risk assessment analysis incorporating this knowledge and based on past structural and temporal pattern properties provides accurate predictions for both systems. Its generalizability is tested by introducing a theoretical model for generating synthetic temporal networks. High accuracy of our predictions is recovered across different settings, while the amount of possible predictions is system-specific. The proposed method can provide crucial information for the setup of targeted intervention strategies.</p></div>", "links"=>[], "tags"=>["Epidemic Risk", "epidemics spread", "livestock displacements trade network", "control measures", "risk assessment analysis", "prediction", "pattern properties", "Past Temporal Contact Data", "intervention strategies", "control outbreaks", "infection", "data", "contact", "High accuracy", "animal holdings"], "article_id"=>1333768, "categories"=>["Uncategorised"], "users"=>["Eugenio Valdano", "Chiara Poletto", "Armando Giovannini", "Diana Palma", "Lara Savini", "Vittoria Colizza"], "doi"=>["https://dx.doi.org/10.1371/journal.pcbi.1004152.s001", "https://dx.doi.org/10.1371/journal.pcbi.1004152.s002"], "stats"=>{"downloads"=>3, "page_views"=>15, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Predicting_Epidemic_Risk_from_Past_Temporal_Contact_Data_/1333768", "title"=>"Predicting Epidemic Risk from Past Temporal Contact Data", "pos_in_sequence"=>0, "defined_type"=>4, "published_date"=>"2015-03-12 02:52:45"}
  • {"files"=>["https://ndownloader.figshare.com/files/1946101"], "description"=>"<p>(<i>A</i>): Probability distributions of the node in-degree, in the low (<i>p</i><sub><i>α</i></sub> = 0.3) and high memory (<i>p</i><sub><i>α</i></sub> = 0.7) regimes. The slope of the distributions does not depend on <i>p</i><sub><i>α</i></sub>, and it is forced by the exponent <i>γ</i> of the <i>β</i><sub><i>in</i></sub> distribution (dashed line). (<i>B</i>): Probability distributions of the loyalty, in the low and high memory regimes. Distributions are color-coded as in panel (<i>a</i>). (<i>C</i>): Probability distributions of the risk ratio <i>ν</i>, in the low and high memory regimes. Lines represent the median values obtained from 50 realizations of the model; darker and lighter shaded areas represent the 50% and 95% confidence intervals. (<i>D</i>): Probability distributions of the predictive power <i>ω</i>, in the low and high memory regimes. Medians and confidence intervals are presented as in panel (<i>C</i>). Distributions are color-coded as in panel (<i>A</i>).</p>", "links"=>[], "tags"=>["Epidemic Risk", "epidemics spread", "livestock displacements trade network", "control measures", "risk assessment analysis", "prediction", "pattern properties", "Past Temporal Contact Data", "intervention strategies", "control outbreaks", "infection", "data", "contact", "High accuracy", "animal holdings"], "article_id"=>1333757, "categories"=>["Uncategorised"], "users"=>["Eugenio Valdano", "Chiara Poletto", "Armando Giovannini", "Diana Palma", "Lara Savini", "Vittoria Colizza"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1004152.g005", "stats"=>{"downloads"=>1, "page_views"=>15, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Memory_driven_dynamical_model_model_properties_and_validation_of_the_risk_assessment_analysis_/1333757", "title"=>"Memory driven dynamical model: model properties and validation of the risk assessment analysis.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2015-03-12 02:52:45"}
  • {"files"=>["https://ndownloader.figshare.com/files/1946100"], "description"=>"<p>(<i>A</i>), (<i>B</i>): Probability distributions of the risk ratio <i>ν</i> for the cattle trade network and the sexual contact network, respectively. Red lines are computed on training sets (2007–08 for cattle and s2-s3 for sexual contacts). The dashed lines peaking around 1 represent a null model based on reshuffling the infection statuses, i.e. randomly permuting the attribute <i>“actually being infected”</i> among the nodes for which risk assessment is performed. (<i>C</i>), (<i>D</i>): Probability distributions of the predictive power <i>ω</i> for the cattle trade network and the sexual contact network, respectively.</p>", "links"=>[], "tags"=>["Epidemic Risk", "epidemics spread", "livestock displacements trade network", "control measures", "risk assessment analysis", "prediction", "pattern properties", "Past Temporal Contact Data", "intervention strategies", "control outbreaks", "infection", "data", "contact", "High accuracy", "animal holdings"], "article_id"=>1333756, "categories"=>["Uncategorised"], "users"=>["Eugenio Valdano", "Chiara Poletto", "Armando Giovannini", "Diana Palma", "Lara Savini", "Vittoria Colizza"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1004152.g004", "stats"=>{"downloads"=>2, "page_views"=>12, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Validation_of_the_risk_assessment_analysis_/1333756", "title"=>"Validation of the risk assessment analysis.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2015-03-12 02:52:45"}
  • {"files"=>["https://ndownloader.figshare.com/files/1946097"], "description"=>"<p>(<i>A</i>), (<i>B</i>): premises in-degree distributions in the cattle trade network and sex customers degree distribution in the sexual contact network, respectively. Distributions for different configurations of the networks are superimposed in both cases. (<i>C</i>), (<i>D</i>): fraction of common edges contained in two configurations of the network, for the cattle trade network and the sexual contact network, respectively. In (<i>B</i>), (<i>D</i>) <i>s</i> stands for semester, the aggregation interval of each configuration.</p>", "links"=>[], "tags"=>["Epidemic Risk", "epidemics spread", "livestock displacements trade network", "control measures", "risk assessment analysis", "prediction", "pattern properties", "Past Temporal Contact Data", "intervention strategies", "control outbreaks", "infection", "data", "contact", "High accuracy", "animal holdings"], "article_id"=>1333753, "categories"=>["Uncategorised"], "users"=>["Eugenio Valdano", "Chiara Poletto", "Armando Giovannini", "Diana Palma", "Lara Savini", "Vittoria Colizza"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1004152.g001", "stats"=>{"downloads"=>1, "page_views"=>20, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Structural_and_temporal_properties_of_the_cattle_trade_network_and_of_the_sexual_contact_network_/1333753", "title"=>"Structural and temporal properties of the cattle trade network and of the sexual contact network.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2015-03-12 02:52:45"}

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Relative Metric

{"start_date"=>"2015-01-01T00:00:00Z", "end_date"=>"2015-12-31T00:00:00Z", "subject_areas"=>[]}
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