The COVID-19 pandemic was accompanied by an "infodemic," i.e., an excess of information about the virus, protective measures, and government intervention. In particular, misinformation and conspiracy theories disseminated on the Internet were blamed for increasing polarization of opinion, radicalization, and declining trust in institutions. It was warned that this had fueled the pandemic and made it even more difficult to manage. However, models of disease spread, such as those used to predict pandemic dynamics, ignore the fact that information and the pandemic form a complex interaction. While these models take into account that, for example, low vaccination rates increase hospitalizations, they fail to consider that knowledge of increasing hospitalizations motivates people to get vaccinated. The main goal in infoXpand is to understand this feedback loop between pandemic and information dissemination and to derive suggestions for future decision makers. To this end, we have formed an interdisciplinary consortium with unique expertise in pandemic modeling, opinion dynamics, mobility, and human behavior. We closely develop and analyze agent-based models and compartmental models that capture both classical disease dynamics and opinion dynamics. We calibrate critical model assumptions with data from social science survey studies and behavioral experiments, as well as extensive mobility data.