COLLOQUIUM: 15 Novembre 2018 Ore 16:30 - Vittoria Colizza "Epidemic threshold on temporal networks"
Dove: Aula Newton Plesso Fisico
Relatore: Vittoria Colizza
Note sul Relatore : INSERM, Institut Pierre Louis d’ Epidémiologie et de Santé Publique and Sorbonne Université, Paris, France.
E-mail di riferimento dell'organizzatore: firstname.lastname@example.org
Our understanding of communicable diseases prevention and control is rooted in the theory of host population transmission dynamics. The network of host-to-host contacts along which transmission can occur drives the epidemiology of communicable diseases, determining how quickly they spread and who gets infected. A large body of epidemiological, mathematical and computational studies has provided a number of insights into the understanding of the process and the identification of efficient control strategies. The explosion of time resolved contact data has however opened the stage to new challenges. What are the structural and temporal aspects, and possibly their non-trivial interplay, that are critical for disease spread? To answer this question, I will introduce the infection propagator approach, a theoretical framework for the assessment of the degree of vulnerability of a host population to disease epidemics, once we account for the time variation of its contact pattern. By reinterpreting the tensor formalism of multilayer networks, this approach allows the analytical computation of the epidemic threshold for an arbitrary time-varying network of host contacts, i.e. the critical pathogen transmissibility above which large-scale propagation occurs. I will apply this framework to a set of empirical time-varying contact networks and show how it can be used to test different intervention strategies for infection prevention and control in realistic settings.
Some useful refs:
Valdano et al. PRX 5, 021005 (2015)
Valdano et al. Eur Phys J B (2015)
Vestergaard et al. Eur J Appl Maths (2016)
Valdano et al. PRL 120, 068302 (2018)
Darbon et al. Prev Vet Med 158, 25 (2018)
Darbon et al. https://www.biorxiv.org/content/early/2018/08/29/401158
More info at:www.epicx-lab.com