  We developed a nonlinear differential equation model to explore the dynamics
of relapse phenomena. Our incidence rate function is formulated, taking
inspiration from recent adaptive algorithms. It incorporates contact behavior
for individuals in each health class. We use constant contact rates at each
health status for our analytical results and prove conditions for different
forward-backward bifurcation scenarios. The relationship between the different
contact rates influences these conditions. Numerical examples show the
sensitivity of the model toward initial conditions. In particular, we highlight
the effect of temporarily recovered individuals and infected initial
conditions.
