  The symptoms of many infectious diseases influence their host to withdraw
from social activity limiting their own potential to spread. Successful
transmission therefore requires the onset of infectiousness to coincide with a
time when its host is socially active. Since social activity and infectiousness
are both temporal phenomena, we hypothesize that diseases are most pervasive
when these two processes are synchronized. We consider disease dynamics that
incorporate a behavioral response that effectively shortens the infectious
period of the disease. We apply this model to data collected from face-to-face
social interactions and look specifically at how the duration of the latent
period effects the reachability of the disease. We then simulate the spread of
the model disease on the network to test the robustness of our results.
Diseases with latent periods that synchronize with the temporal social behavior
of people, i.e. latent periods of 24 hours or 7 days, correspond to peaks in
the number of individuals who are potentially at risk of becoming infected. The
effect of this synchronization is present for a range of disease models with
realistic parameters. The relationship between the latent period of an
infectious disease and its pervasiveness is non-linear and depends strongly on
the social context in which the disease is spreading.
