During pandemic events, strategies such as social distancing can be
fundamental to curb viral spreading. Such actions can reduce the number of
simultaneous infections and mitigate the disease spreading, which is relevant
to the risk of a healthcare system collapse. Although these strategies can be
suggested, their actual implementation may depend on the population perception
of the disease risk. The current COVID-19 crisis, for instance, is showing that
some individuals are much more prone than others to remain isolated, avoiding
unnecessary contacts. With this motivation, we propose an epidemiological SIR
model that uses evolutionary game theory to take into account dynamic
individual quarantine strategies, intending to combine in a single process
social strategies, individual risk perception, and viral spreading. The disease
spreads in a population whose agents can choose between self-isolation and a
lifestyle careless of any epidemic risk. The strategy adoption is individual
and depends on the perceived disease risk compared to the quarantine cost. The
game payoff governs the strategy adoption, while the epidemic process governs
the agent's health state. At the same time, the infection rate depends on the
agent's strategy while the perceived disease risk depends on the fraction of
infected agents. Results show recurrent infection waves, which were seen in
previous epidemic scenarios with quarantine. Notably, the risk perception is
found to be fundamental for controlling the magnitude of the infection peak,
while the final infection size is mainly dictated by the infection rates. Low
awareness leads to a single and strong infection peak, while a greater disease
risk leads to shorter, although more frequent, peaks. The proposed model
spontaneously captures relevant aspects of a pandemic event, highlighting the
fundamental role of social strategies
