  The co-location of individuals in specific environments is an important
prerequisite for exposure to infectious diseases on a social network. Standard
epidemic models fail to capture the potential complexity of this scenario by
(1) neglecting the hypergraph structure of contacts which typically occur
through environments like workplaces, restaurants, and households; and by (2)
assuming a linear relationship between the exposure to infected contacts and
the risk of infection. Here, we leverage a hypergraph model to embrace the
heterogeneity of different environments and the heterogeneity of individual
participation in these environments. We find that a bursty exposure to
environments can induce a nonlinear relationship between the number of infected
participants and infection risk. This allows us to connect complex contagions
based on nonlinear infection kernels and threshold models. We then demonstrate
how conventional epidemic wisdom can break down with the emergence of
discontinuous transitions, super-exponential spread, and regimes of hysteresis.
