Computational epidemiology group

Leader : Prof. Pietro Coletti

Tobias Röspel (PhD Student)

Brigitte Umutoni (PhD Student)

Our research group focuses on modeling infectious diseases in a society facing global challenges. We are dedicated to advancing the understanding of infectious disease dynamics through data-informed modeling, biostatistics, and data science. By integrating diverse datasets—from disease surveillance and population immunity to climate and behavioral data—we develop predictive models that inform public health strategies. 

Our research is structured around three key axes : 

1. Impact of Climate Change on Infectious Diseases Climate change is reshaping disease transmission patterns through rising temperatures, extreme weather events, and environmental degradation. Our work aims to quantify these effects by leveraging heterogeneous datasets, including remote sensing, mobility data, and social mixing patterns, to predict how climate-driven disruptions influence disease spread and health system resilience. 

2. Behavioral Differences in Disease Modelling Human behavior plays a crucial role in disease transmission, yet its integration into epidemiological models remains challenging. We investigate how behavioral variations—such as intervention adherence or social contact patterns—impact infection dynamics, with the goal of developing more precise, population-specific public health strategies. 

3. Innovative Methodologies for Infectious Disease Modelling Advances in AI and network science are transforming epidemiological modeling. We explore novel approaches, to evaluate their potentials and their pitfalls. These methods will enhance our ability to identify at-risk populations and design targeted, effective interventions. 

By combining mechanistic modeling with cutting-edge data science, our group aims to provide actionable insights for disease prevention and control in an era shaped by climate change and evolving human behaviors.