A recording of this talk can be viewed here. (The passcode is @7V8F&Xu)

How large a gathering is too large during the COVID-19 pandemic? Who influences social media discussions? To answer these questions, group interactions and complex mechanisms of influence must be taken into account. I will argue that complex (nonlinear) contagions on higher-order networks not only provide a novel perspective for the spreading of social phenomena, but it turns out to be a useful modeling approach for infectious diseases as well.
‍
I will first introduce some of my works on group-based approximate master equations, showing how influential groups can be important to seed and sustain contagions, with important consequences on their control. I will then present a model of communicable diseases that can be reduced to a complex contagion on higher-order networks. This suggests that reinforcement mechanisms, typically associated with the spreading of social phenomena, should be included in biological contagions. With this perspective, we embrace a broader phenomenology for infectious diseases, allowing, for instance, the onset of superexponential spread.