Research publishing this week in PLOS Computational Biology analyzes 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.
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 courtesy of Valdano et al.
Research publishing this week in PLOS Computational Biology analyzes 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.
Reference: 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
CDC Urges Vigilance: New Recommendations for Monitoring and Testing H5N1 Exposures
July 11th 2025With avian influenza A(H5N1) infections surfacing in both animals and humans, the CDC has issued updated guidance calling for aggressive monitoring and targeted testing to contain the virus and protect public health.
IP LifeLine: Layoffs and the Evolving Job Market Landscape for Infection Preventionists
July 11th 2025Infection preventionists, once hailed as indispensable during the pandemic, now face a sobering reality: budget pressures, hiring freezes, and layoffs are reshaping the field, leaving many IPs worried about their future and questioning their value within health care organizations.
A Helping Hand: Innovative Approaches to Expanding Hand Hygiene Programs in Acute Care Settings
July 9th 2025Who knew candy, UV lights, and a college kid in scrubs could double hand hygiene adherence? A Pennsylvania hospital’s creative shake-up of its infection prevention program shows that sometimes it takes more than soap to get hands clean—and keep them that way.