  Contacts' temporal ordering and dynamics are crucial for understanding the
transmission of infectious diseases. We introduce an interaction-driven model
of an airborne disease over contact networks. We demonstrate our
interaction-driven contagion model, instantiated for COVID-19, over
history-maintaining random temporal networks and real-world contacts. We use it
to evaluate temporal, spatiotemporal, and spatial social distancing policies.
We find that a spatial distancing policy is mainly beneficial at the early
stages of a disease.
  We then continue to evaluate temporal social distancing, that is, timeline
dilation that maintains the activity potential. We expand our model to consider
the exposure to viral load, which we correlate with meetings' duration. Using
real-life contact data, we demonstrate the beneficial effect of timeline
dilation on overall infection rates.
  Our results demonstrate that given the same transmission level, there is a
decrease in the disease's infection rate and overall prevalence under timeline
dilation conditions. We further show that slow-spreading pathogens (i.e.,
require more prolonged exposure to infect) spread roughly at the same rate as
fast-spreading ones in highly active communities. This is surprising since
slower pathogens follow paths that include longer meetings, while faster
pathogens can potentially follow paths that include shorter meetings, which are
more common. Our results demonstrate that the temporal dynamics of a community
have a more significant effect on the spread of the disease than the
characteristics of the spreading processes.
