image: Nodes represent animal holdings and links represent cattle movements. Top white circles (left and center) map the contact networks in the past and allow the identification of loyal nodes (grey contour surrounding the nodes). A disease spreading simulation run on past contact data (top center circle and left map of Italy) provides the infected farms (yellow nodes) in the simulated scenario. Coupled with the loyalty analysis, this numerical information allows us to predict which farms would be at high risk (red, or low risk, green, top right circle and right map of Italy) of infection if an outbreak would occur with the same epidemiological conditions, without the need of knowledge of the current network of contacts (dashed lines). view more
Credit: Valdano et al.
Research publishing this week in PLOS Computational Biology analyses the livestock trade in Italy and sexual encounters in a Brazilian prostitution service to find a correlation between loyalty and infection risk.
The French team at Inserm, in collaboration with ISI Foundation and Istituto Zooprofilattico Abruzzo-Molise in Italy, has shown that it is possible to use past contact data to predict the risk of infection in emerging outbreaks.
The study analyzes contact data between animal premises in Italy following livestock trade movements, and time-stamped sexual encounters in a high-end prostitution service in Brazil. The first is relevant for the spread of livestock diseases, and the second underlies the potential spread of sexually transmitted diseases.
By introducing a measure of loyalty in the way farmers or prostitution clients tend to establish their contacts with the same individuals over time (for livestock trade or sexual behavior), the authors uncovered a significant correlation between the loyalty of an individual and his/her risk of infection.
Contact patterns for disease transmission contribute to a detailed understanding of the disease spread and risk of infection, however they are often not readily available for analysis due to their time-evolving nature.
In a transmissible disease epidemic, these findings can be used to guide the prioritization of available interventional resources to efficiently control the disease spread. The general nature of the study makes its results applicable to a wide spectrum of possible epidemic scenarios.
###
Image Caption: Predicting epidemic risk for premises in the Italian cattle trade network. Nodes represent animal holdings and links represent cattle movements. Top white circles (left and center) map the contact networks in the past and allow the identification of loyal nodes (grey contour surrounding the nodes). A disease spreading simulation run on past contact data (top center circle and left map of Italy) provides the infected farms (yellow nodes) in the simulated scenario. Coupled with the loyalty analysis, this numerical information allows us to predict which farms would be at high risk (red, or low risk, green, top right circle and right map of Italy) of infection if an outbreak would occur with the same epidemiological conditions, without the need of knowledge of the current network of contacts (dashed lines).
Image Credit: Valdano et al.
Image Link: http://www.plos.org/wp-content/uploads/2015/03/Colizza-5-Mar.jpg
All works published in PLOS Computational Biology are Open Access, which means that all content is immediately and freely available. Use this URL in your coverage to provide readers access to the paper upon publication: http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1004152
Contact: Vittoria Colizza
Address: INSERM
Pierre Louis Institute of Epidemiology and Public Health site St. Antoine
27 rue Chaligny
Paris, Cedex 12 75571
France
Phone: +33 1 44738459
Email: vittoria.colizza@inserm.fr
Citation: : Valdano E, Poletto C, Giovannini A, Palma D, Savini L, Colizza V (2015) Predicting Epidemic Risk from Past Temporal Contact Data. PLoS Comput Biol 11(3): e1004152. doi:10.1371/journal.pcbi.1004152
Funding: This work was funded by: EC-Health contract no. 278433 (PREDEMICS--Prevention of Emerging Zoonotic Viruses with Pandemic Potential using Multidisciplinary Approaches--predemics.biomedtrain.eu). ANR contract no. ANR- 12-MONU-0018 (HARMSFLU--Harmonising multiple scales for data-driven computational approaches to the modeling of influenza spread--http://www.harmsflu.org). Programme Galilee (http://www.campusfrance.org/fr/ galilee) contract no. 28144NH. Italian Ministry of Health (http://www.salute.gov.it) contract no. IZS AM 04/11RC; EC-ANIHWA (Animal Health and Welfare ERA- Net--http://www.anihwa.eu) contract no. ANR-13-ANWA-0007-03 (LIVEepi). "Pierre Louis" School of Public Health of UPMC (http://www.ed393.upmc.fr). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Competing Interests: The authors have declared that no competing interests exist.
About PLOS Computational Biology
PLOS Computational Biology features works of exceptional significance that further our understanding of living systems at all scales through the application of computational methods. All works published in PLOS Computational Biology are Open Access. All content is immediately available and subject only to the condition that the original authorship and source are properly attributed. Copyright is retained. For more information follow @PLOSCompBiol on Twitter or contact ploscompbiol@plos.org.
About PLOS
PLOS is a nonprofit publisher and advocacy organization founded to accelerate progress in science and medicine by leading a transformation in research communication. For more information, visit http://www.plos.org.
Journal
PLoS Computational Biology