The modeling of the spreading of infectious diseases has experienced significant advances in the last decades. This has been possible due to the proliferation of data and the development of new methods to gather, mine, and analyze it. A key role has also been played by the latest advances in new disciplines like network science. Nonetheless, any model can only be as good as the data used to build and calibrate it. In this talk, we will explore how integrating data coming from many different sources may provide answers to our questions, while raising many others. Even more important, we will discuss the limitations of these models, both due to their mathematical definition or to the availability of data. The dynamics of an epidemic are completely intertwined with the behavior of the population and, as such, we will only be able to provide answers as long as we can effectively predict the human component of the disease.
Registration is required: https://iu.zoom.us/webinar/register/WN_v-8nEgh0Rf20rJcbxu1jLQ
Alberto Aleta is a Postdoctoral Researcher at ISI Foundation in Turin, Italy. Alberto obtained his Ph.D. in theoretical physics at the University of Zaragoza, Spain. His research interests lie in the broad area of complex systems, network science, and data science. He has collaborated in projects of diverse disciplines from epidemiology to online videogames, but during the current pandemic his work has mostly focused on studying the spreading of COVID-19 using data-driven models.
