  The detection and management of diseases become quite complicated when
pathogens contain asymptomatic phenotypes amongst their ranks, as evident
during the recent COVID-19 pandemic. Spreading of diseases has been studied
extensively under the paradigm of Susceptible - Infected - Recovered - Deceased
(SIRD) dynamics. Various game-theoretic approaches have also addressed disease
spread, many of which consider S, I, R, and D as strategies rather than as
states. Remarkably, most studies from the above approaches do not account for
the distinction between the symptomatic or asymptomatic aspect of the disease.
It is well-known that precautionary measures like washing hands, wearing masks
and social distancing significantly mitigate the spread of many contagious
diseases. Herein, we consider the adoption of such precautions as strategies
and treat S, I, R, and D as states. We also attempt to capture the differences
in epidemic spreading arising from symptomatic and asymptomatic diseases on
various network topologies. Through extensive computer simulations, we examine
that the cost of maintaining precautionary measures as well as the extent of
mass testing in a population affects the final fraction of socially responsible
individuals. We observe that the lack of mass testing could potentially lead to
a pandemic in case of asymptomatic diseases. Network topology also seems to
play an important role. We further observe that the final fraction of proactive
individuals depends on the initial fraction of both infected as well as
proactive individuals. Additionally, edge density can significantly influence
the overall outcome. Our findings are in broad agreement with the lessons
learnt from the ongoing COVID-19 pandemic.
