  During the COVID-19 pandemic, the behavioral response to reported case
numbers changed drastically over time. While a few dozen cases were enough to
trigger government-induced and voluntary contact reduction in early 2020, less
than a year later, much higher case numbers were required to induce behavioral
change. Little attention has been paid to understand, and mathematically model,
this effect of decreasing risk perception over longer time-scales. Here, first
we show that weighing the number of cases with a time-varying factor of the
form $t^{a}\;,\;a<0$ explains real-world mobility patterns from several
European countries during 2020 when introduced into a very simple behavior
model. Subsequently, we couple our behavior model with an SIR epidemic model.
Remarkably, decreasing risk perception can produce complex dynamics, including
multiple waves of infection. We find two regimes for the total number of
infected individuals that are explained by the interplay of initial attention
and the rate of attention decrease. Our results show that including adaption
into non-equilibrium models is necessary to understand behavior change over
long time scales and the emergence of non-trivial infection dynamics.
