  The COVID-19 pandemic has witnessed the role of online social networks (OSNs)
in the spread of infectious diseases. The rise in severity of the epidemic
augments the need for proper guidelines, but also promotes the propagation of
fake news-items. The popularity of a news-item can reshape the public health
behaviors and affect the epidemic processes. There is a clear inter-dependency
between the epidemic process and the spreading of news-items. This work creates
an integrative framework to understand the interplay. We first develop a
population-dependent `saturated branching process' to continually track the
propagation of trending news-items on OSNs. A two-time scale dynamical system
is obtained by integrating the news-propagation model with SIRS epidemic model,
to analyze the holistic system. It is observed that a pattern of periodic
infections emerges under a linear behavioral influence, which explains the
waves of infection and reinfection that we have experienced in the pandemic. We
use numerical experiments to corroborate the results and use Twitter and
COVID-19 data-sets to recreate the historical infection curve using the
integrative model.
