My name is Eugenio Valdano. I am a physicist, and a PhD in Epidemiology and Public Health.
My research interests revolve around modeling the spread of infectious diseases on contact and transportation networks. I integrate theoretical and data-driven approaches, both aiming to get a general understanding of spreading processes, and at the same time being able to deal with real-world scenarios, specific diseases and contact structures.
I focus on studying the conditions that make diseases turn epidemic, and how to devise optimal prevention and intervention strategies.
New job @ UCLA
In September I will start a postdoc at the Semel Institute for Neuroscience and Human Behavior at University of California Los Angeles (UCLA), in Prof. Sally Blower's group.
Best poster prize @ NetSci2018
I have won the best poster prize at NetSci 2018, together with Michele Re Fiorentin, Alberto Antonioni, and Francesco Venuto.
New paper on arXiv
A mathematical model for the spread of multipartite viruses reveals their evolutionary potential, which I did together with Susanna Manrubia, Sergio Gómez and Alex Arenas.
New paper on Physical Review Letters
The work Epidemic Threshold in Continuous-Time Evolving Networks, which I did together with Michele Re Fiorentin, Chiara Poletto and Vittoria Colizza is now out!
Talk at Livestock Networks
Livestock Movement Networks and Infectious Diseases is a satellite meeting of NetSci 2018 and will take place on June 12, 2018 in Paris, France. I have been invited to talk, and will present a study on cattle trade networks in Europe.
Main Research topics
Spread on time-evolving contact networks
A wide range of physical, social and biological phenomena can be expressed in terms of spreading processes on networked systems. Think of the spread of infectious diseases through direct contacts, the spatial propagation of epidemics driven by mobility networks, the spread of cyber worms along computer connections, or the diffusion of opinion, ideas mediated by social interactions (online and offline). All these phenomena arise from a complex interplay between the spreading process and the network’s underlying topology and dynamics. Understanding how the time-evolving properties of the network impact the spread of the disease is a crucial step to setting up control and prevention strategies. The increasing availability of highly-resolved interaction data has made it possible to target a wide variety of settings and diseases, but at the same time new methodological challenges have arisen. In particular, a fundamental property of such phenomena is the presence of an epidemic threshold, i.e., a critical transmission probability above which large-scale propagation occurs, as opposed to quick extinction of the epidemic-like process. Computing this threshold is of utmost importance for epidemic containment and control of information diffusion. I have developed an analytical framework for the computation of the epidemic threshold for an arbitrary time-varying network. By reinterpreting the tensor formalism of multi-layer networks, this framework allows the analytical calculation of the epidemic threshold, without making any assumption on contact structure and evolution, and can be applied to a wide class of diseases.
Livestock infectious diseases
Diseases spreading among farmed animals pose a great threat to animal welfare, economy, and, most importantly human health, as several human diseases have a zoonotic origin. Livestock diseases may spread through direct contacts, animal trade, fomites, and vectors, like ticks or mosquitoes. Moreover, they may infect just one species, or involve several, and they typically spread on time evolving networks. In order to deal with this kind of complexity, I integrate the tools of complex systems and statistical physics, with techniques from epidemiology, computational sciences and data analysis. The ultimate goal of my work is to understand devise new method for risk assessment, prevention, early detection of outbreaks, and immunization strategies. The temporal dimension of the system is crucial, as the continuously changing nature of the pattern of disease-transmission contacts among hosts makes its use less practical in real public health emergencies (or otherwise highly resource-demanding when possible). I've shown that in such situations critical knowledge to assess the real-time risk of infection can be extracted from past temporal contact data.
Moreover, a comprehensive study, showing the impact of country-specific driving factors on network evolution and topology, is however still missing. For that, I am developing a collaborative platform for analyzing and comparing networks from several European countries. Using a bring code to the data approach, our platform overcomes the strict regulations preventing data sharing, and allows an effective comparative analysis. I will describe the framework, and present the result of this analysis, highlighting both properties that are characteristic of livestock markets, and country-specific features.