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 \(\mathcal{S}\), \(\mathcal{I}\), \(\mathcal{R}\), and \(\mathcal{D}\) 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 \(\mathcal{S}\), \(\mathcal{I}\), \(\mathcal{R}\), and \(\mathcal{D}\) 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.

当病原体在其队列中包含无症状表型时，疾病的检测和管理变得相当复杂，这在最近的 COVID-19 大流行期间很明显。在易感-感染-康复-死亡 (SIRD) 动力学范式下，疾病的传播已得到广泛研究。各种博弈论方法也解决了疾病传播问题，其中许多考虑了\(\mathcal{S}\)、\(\mathcal{I}\)、\(\mathcal{R}\)和\(\mathcal {D}\)作为策略而不是状态。值得注意的是，上述方法的大多数研究都没有考虑到疾病有症状或无症状方面的区别。众所周知，洗手、戴口罩和保持社交距离等预防措施可显着减轻许多传染病的传播。在此，我们考虑采用此类预防措施作为策略，并将\(\mathcal{S}\)、\(\mathcal{I}\)、\(\mathcal{R}\)和\(\mathcal{D }\)作为状态。我们还试图捕捉各种网络拓扑上有症状和无症状疾病引起的流行病传播的差异。通过广泛的计算机模拟，我们研究了维持预防措施的成本以及人群中大规模检测的程度会影响社会责任感个人的最后一部分。我们观察到，在无症状疾病的情况下，缺乏大规模检测可能会导致大流行。网络拓扑似乎也起着重要作用。我们进一步观察到，主动个体的最终部分取决于感染者和主动个体的初始部分。此外，边缘密度可以显着影响整体结果。