The COVID-19 pandemic was accompanied by an “infodemic”, an overabundance of information about the virus, protective behavior, and government interventions. In particular, misinformation and conspiracy theories spreading online have been held responsible for growing opinion polarization, radicalization, and decreasing trust in institutions. It has been warned that this fueled the pandemic and made it even harder to manage it. Models of disease spread, however, fail to consider that information creates a complex loop. These models do include that, e.g., low vaccination rates increase hospitalization, but they ignore that learning about growing hospitalization motivates individuals to get vaccinated. Our core objective in infoXpand is to understand this feedback loop between pandemic and information spread, and to derive advice for future decision-makers. To that end, we set up an interdisciplinary consortium with unique expertise in pandemic modeling, opinion dynamics,
mobility, and human behavior. We develop and analyze in close entanglement agent-based models and compartmental models that capture classical disease dynamics as well as opinion dynamics. We calibrate critical model assumptions with data from social-science survey studies and behavioral experiments, as well as rich mobility data.