Abstract
This introductory lecture intends to outline the methodology of the main epidemiological study models. The methods for quantitatively describing health phenomena, studying the associations between risk factors and diseases, and assessing the effectiveness and safety of health interventions will be illustrated starting from the classical objectives of epidemiology (descriptive, analytical and experimental). Frequency measures (prevalence and incidence) and the main measures of association will be covered. The potential and limitations of different epidemiological approaches will be analyzed by reviewing epidemiological studies published in the literature, such as the clinical trials that have documented the efficacy and safety of vaccines against COVID-19. The approach of systematic literature reviews and meta-analyses to synthesize scientific evidence on the efficacy of health interventions will also be illustrated as an introduction.

Abstract
The pervasiveness of Web and mobile technologies as well as the growing adoption of smart wearable sensors have significantly changed the landscape of epidemic intelligence data gathering with an unprecedented impact on global public health. In this double presentation, we will show how disease surveillance in public health works and how digital technologies have changed the way we monitor diseases.

Abstract
Digital approaches have the potential to improve non-pharmaceutical interventions. One of the most innovative approaches proposed during the current pandemic has been Digital Contact Tracing, i.e. a faster and more effective approach to record proximity between individuals and notify individuals who are likely to have been exposed to a pathogen due to their proximity to an infected individual. In practice, many countries have implemented Exposure Notification apps based on privacy-preserving proximity tracing via Bluetooth Low Energy. In this lecture, I will present an overview of Digital Contact Tracing in terms of technology and public health. I will show how it can improve Test-Trace-Isolate strategies and discuss its requirements and the epidemiological evidence for its impact. I will also present some of the lesson learned from a few successful implementations of the technology and many failed ones. Finally, I will discuss its potential in terms of epidemic management and epidemic surveillance.

Abstract
Vaccination hesitancy has been an important public health issue even before COVID-19. Studies have found that vaccine hesitancy is a complex, multi-faceted phenomenon that needs to be addressed with an interdisciplinary methodology. In particular, social media can drive this phenomenon through vocal influencers but can also help uncover the unknown determinants behind this global phenomenon. In this double presentation, we will present some studies aimed at leveraging social media platforms to address the problem of vaccine hesitancy with a European and Italian perspective.

Abstract
The information on transmission chains (who infected whom and when during an epidemic outbreak) provides precious insights on transmission heterogeneities and on critical quantities of epidemic dynamics, such as the distribution of generation times and transmission distances. In practice, epidemiological investigations can determine where and from whom an individual was infected only in rare cases. Bayesian inference models can exploit the spatial and/or temporal structure in epidemiological data to probabilistically reconstruct transmission chains and infer statistical properties of the transmission dynamics. We will show the general principles of these models and some applications to geo-referenced data from mosquito-borne surveillance and to contact tracing data from Hepatitis A Virus and SARS-CoV-2 outbreaks.