New-generation individual based models for infectious diseases transmission

Mathematical models are powerful tools for simulating plausible epidemic spread scenarios and for evaluating the impact of control policies. They represent the scientific basis on which public health policy makers should take their decisions on the intervention strategies that should be performed at local, national and international scale. In this context, Individual-Based simulation Models (IBM) have become one of the much relevant approaches. The crucial point of this thesis project is to override some of the limit of the current generation of IBM. Specifically, highly detailed models of the sociodemography and mobility of the Italian and European population have been developed; a model of individuals and households demographics, which leads the network of contacts among individual to evolve over time, has been introduced; an analysis of the role of different assumptions on the ``random''contacts among the individuals of a population on the spread of epidemics has been performed. Results such as the development, for the first time in literature, of an IBM working on a continental scale and of an IBM suitable for the investigation of endemic diseases represent a crucial improvement for the community of epidemic modelers. Moreover, the achieved results in terms of evaluation of the effectiveness of (individually-targeted) public health control measures have had a practical application. In fact, they have been used by the Italian Ministry of Health for assessing the efficacy of the Italian pandemic preparedness plan and for planning the mitigation strategies for the 2009 A(H1N1) influenza pandemic.