My name is Eugenio Valdano. I am a researcher at the Pierre Louis Institute of Epidemiology and Public Health (IPLESP) of the French National Institute of Health and Medical Research (INSERM), and Sorbonne Université, in Paris, France.
I am an infectious disease epidemiologist with a background in theoretical physics. I develop data-rich mathematical models to study how infectious disease spread, in human and animal populations. Patterns of human mobility and mixing influence the likelihood of epidemic outbreaks, drive their evolution, and determine the condition for disease containment and elimination. I design theoretical models to combine data on human behavior and epidemiological data to understand epidemics, make scenarios, help guide public health interventions.
Recently, I have been working on COVID-19, and HIV/AIDS. My next goal is to study the impact of climate change on epidemics.
This website is under construction. I’m in the process of uploading content, so come back in a few weeks for more!
PhD in Epidemiology and Public Health, 2015
Sorbonne Université, Paris, France
MSc in Theoretical physics, 2012
University of Turin, Italy
BSc in Physics, 2010
University of Turin, Italy
Talks older than 2022 are here
Twenty-six million people are living with HIV in sub-Saharan Africa; epidemics are widely dispersed, due to high levels of mobility. However, global elimination strategies do not consider mobility. We use Call Detail Records from 9 billion calls/texts to model mobility in Namibia; we quantify the epidemic-level impact by using a mathematical framework based on spatial networks. We find complex networks of risk flows dispersed risk countrywide: increasing the risk of acquiring HIV in some areas, decreasing it in others. Overall, 40% of risk was mobility-driven. Networks contained multiple risk hubs. All constituencies (administrative units) imported and exported risk, to varying degrees. A few exported very high levels of risk: their residents infected many residents of other constituencies. Notably, prevalence in the constituency exporting the most risk was below average. Large-scale networks of mobility-driven risk flows underlie generalized HIV epidemics in sub-Saharan Africa. In order to eliminate HIV, it is likely to become increasingly important to implement innovative control strategies that focus on disrupting risk flows.
The time variation of contacts in a networked system may fundamentally alter the properties of spreading processes and affect the condition for large-scale propagation, as encoded in the epidemic threshold. Despite the great interest in the problem for the physics, applied mathematics, computer science, and epidemiology communities, a full theoretical understanding is still missing and currently limited to the cases where the time-scale separation holds between spreading and network dynamics or to specific temporal network models. We consider a Markov chain description of the susceptible-infectious-susceptible process on an arbitrary temporal network. By adopting a multilayer perspective, we develop a general analytical derivation of the epidemic threshold in terms of the spectral radius of a matrix that encodes both network structure and disease dynamics. The accuracy of the approach is confirmed on a set of temporal models and empirical networks and against numerical results. In addition, we explore how the threshold changes when varying the overall time of observation of the temporal network, so as to provide insights on the optimal time window for data collection of empirical temporal networked systems. Our framework is of both fundamental and practical interest, as it offers novel understanding of the interplay between temporal networks and spreading dynamics.