MAtHematical and Digital Epidemiology
We live as never before in an interconnected society. We can reach almost any part of the globe within 24 hours. While the economic benefits of these capabilities are extraordinary, they make us more fragile to the spread of infectious diseases. For example, the SARS-CoV-2 virus took just a few months to reach almost all the countries in the world. Since my Ph.D., I have been combining Network and Data Science with Epidemiology to model the spreading of infectious diseases at different geo-spatial scales.
During the H1N1 pandemic in 2009, we have provided short-term and long-term predictions at the global scale, characterizing the spreading potential of the disease and testing the efficiency of intervention policies [1, 2]. We have built a novel framework to forecast the unfolding of the seasonal flu at the country level by fusing health indicators extracted from online social media and digital participatory surveillance platforms with a metapopulation epidemic model [3]. During the current pandemic, we have developed a realistic epidemic model at the global scale to define the initial cryptic (i.e., largely undetected) spreading of SARS-CoV-2 [4]. We have also developed realistic models, at the national and sub-national scales, to measure the impact of the COVID-19 vaccination campaign [4] and predict the evolution of new variants of concern [5].
Beside the development of realistic epidemic models, my research agenda targets one of the main challenges in Epidemiology: the feedback loop linking infectious diseases and human behavior. Indeed, the spreading of infectious diseases might induce behavioral changes. Authorities might decide to close schools and ban public gatherings, or people might take individual actions to avoid crowded places changing their daily activities. In turn, such changes might drastically affect the spreading patterns of diseases. The current pandemic and the drastic non-pharmaceutical interventions (NPIs) put in place to curb the spreading provide a very vivid example. Understanding social disruption due to disease spreading is a long-standing problem in Epidemiology [5]. It represents one of the most challenging and important issues in the field as it defines clear limits in the predictability of all realistic models.
During the H1N1 pandemic in 2009, we have provided short-term and long-term predictions at the global scale, characterizing the spreading potential of the disease and testing the efficiency of intervention policies [1, 2]. We have built a novel framework to forecast the unfolding of the seasonal flu at the country level by fusing health indicators extracted from online social media and digital participatory surveillance platforms with a metapopulation epidemic model [3]. During the current pandemic, we have developed a realistic epidemic model at the global scale to define the initial cryptic (i.e., largely undetected) spreading of SARS-CoV-2 [4]. We have also developed realistic models, at the national and sub-national scales, to measure the impact of the COVID-19 vaccination campaign [4] and predict the evolution of new variants of concern [5].
Beside the development of realistic epidemic models, my research agenda targets one of the main challenges in Epidemiology: the feedback loop linking infectious diseases and human behavior. Indeed, the spreading of infectious diseases might induce behavioral changes. Authorities might decide to close schools and ban public gatherings, or people might take individual actions to avoid crowded places changing their daily activities. In turn, such changes might drastically affect the spreading patterns of diseases. The current pandemic and the drastic non-pharmaceutical interventions (NPIs) put in place to curb the spreading provide a very vivid example. Understanding social disruption due to disease spreading is a long-standing problem in Epidemiology [5]. It represents one of the most challenging and important issues in the field as it defines clear limits in the predictability of all realistic models.
