Although treatment for cholera is well-known and cheap, outbreaks in epidemic regions stillexact high death tolls mostly due to the unpreparedness of health care infrastructures to faceunforeseen emergencies. In this context, mathematical models for the prediction of theevolution of an ongoing outbreak are of paramount importance. Here, we present a spatially-explicit scheme that accounts for the dynamics of susceptible, infected and recoveredindividuals hosted in different local communities connected through hydrologic and humanmobility 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 indeveloping countries. Mobile phone call data provides a new source of information whichallows the tracking of the evolution of mobility fluxes with high resolution in space and time. Weanalyze a dataset of mobile phone records of approximately 150,000 users in Senegal to extracthuman mobility fluxes and directly incorporate them into the epidemiological model. Ourfindings highlight the major influence that a mass gathering, which took place during the initialphase of the outbreak had on the course of the epidemic. Model results also show howconcentrated efforts towards disease control in a transmission hotspot could have an importanteffect on the large-scale progression of an outbreak and support the use of mathematicalmodels for emergency management and evaluation of alternative intervention strategies.
Enrico Bertuzzo is associate Professor at Ca’ Foscari University of Venice. He received a MSc inenvironmental engineering and a PhD in hydrology from University of Padua and was appointedas senior scientist at the Ecole Politechique Fédérale de Lausanne. His research focuses on largescale ecological processes and in particular on the development, validation and testing ofspatially distributed epidemiological models of waterborne diseases and zoonoses (includingcholera, schistosomiasis and proliferative kidney diseases) for real time prediction and for theevaluation of alternative intervention strategies (e.g. vaccines).