Agenda

Speaker: Enrico Bertuzzo, University of Venice

Abstract
Although treatment for cholera is well-known and cheap, outbreaks in epidemic regions still exact high death tolls mostly due to the unpreparedness of health care infrastructures to face unforeseen emergencies. In this context, mathematical models for the prediction of the evolution of an ongoing outbreak are of paramount importance. Here, we present a spatially-explicit scheme that accounts for the dynamics of susceptible, infected and recovered individuals hosted in different local communities connected through hydrologic and human mobility networks. The movement of people is a key driver of any infectious disease. However, understanding its dynamics is usually frustrated by the lack of accurate data, especially in developing countries. Mobile phone call data provides a new source of information which allows the tracking of the evolution of mobility fluxes with high resolution in space and time. We analyze a dataset of mobile phone records of approximately 150,000 users in Senegal to extract human mobility fluxes and directly incorporate them into the epidemiological model. Our findings highlight the major influence that a mass gathering, which took place during the initial phase of the outbreak had on the course of the epidemic. Model results also show how concentrated efforts towards disease control in a transmission hotspot could have an important effect on the large-scale progression of an outbreak and support the use of mathematical models for emergency management and evaluation of alternative intervention strategies.

Bio sketch
Enrico Bertuzzo is associate Professor at Ca’ Foscari University of Venice. He received a MSc in environmental engineering and a PhD in hydrology from University of Padua and was appointed as senior scientist at the Ecole Politechique Fédérale de Lausanne. His research focuses on large scale ecological processes and in particular on the development, validation and testing of spatially distributed epidemiological models of waterborne diseases and zoonoses (including cholera, schistosomiasis and proliferative kidney diseases) for real time prediction and for the evaluation of alternative intervention strategies (e.g. vaccines).