Mathematical modeling for epidemiological inference and public health support

During the last decades public health policy makers have been increasingly turning to mathematical modeling to support their decisions. This trend has been calling for the introduction of a new class of models that not only are capable to explain qualitatively the dynamics of infectious diseases, but also have the capability to provide quantitatively reliable and accurate results. To this aim models are becoming more and more detailed and informed with data. However, there is still much to be done in order to capture the individual and population features that shape the spread of infectious diseases. This thesis addresses some issues in epidemiological modeling that warrant further investigation. In Chapter 1 we introduce an age-structured individual-based stochastic model of Varicella Zoster Virus (VZV) transmission, whose main novelty is the inclusion of realistic population dynamics over the last century. This chapter represents an attempt to answer the need pointed out by recent studies for a better understanding of the role of demographic processes in shaping the circulation of infectious diseases. In Chapter 2 we use the model for VZV transmission developed in Chapter 1 to evaluate the effectiveness of varicella and HZ vaccination programs in Italy. With a view to the support of public health decisions, the epidemiological model is coupled with a cost-effectiveness analysis. To the best of our knowledge, this work represents the first attempt to evaluate the post-vaccination trends in varicella and HZ, both from an epidemiological and economic perspective, in light of the underlying effect of demographic processes. Another novelty of this study is that we take into account the uncertainty regarding the mechanism of VZV reactivation, by comparing results obtained using two different modeling assumptions on exogenous boosting. In Chapter 3 we retrospectively analyze the spatiotemporal dynamics of the 2009 H1N1 influenza pandemic in England, by using a spatially-explicit model of influenza transmission, accounting for socio-demographic and disease natural history data. The aim of this work is to investigate whether the observed spatiotemporal dynamics of the epidemic was shaped by a spontaneous behavioral response to the pandemic threat. This chapter, represents an attempt to contribute to the challenge of understanding and quantifying the effect of human behavioral changes on the spread of epidemics. In Chapter 4 we investigate the current epidemiology of measles in Italy, by using a detailed computational model for measles transmission, informed with regional heterogeneities in the age-specific seroprevalence profiles. The analysis performed in this chapter tries to fill some of the existing gaps in the knowledge of the epidemiological features of vaccine preventable diseases in frameworks characterized by a low circulation of the virus.