Sinha Saptarshi, Nath Deep, Roy Soumen
Department of Physics, Bose Institute, 93/1 Acharya Prafulla Chandra Road, Kolkata, 700009 India.
J Indian Inst Sci. 2021;101(3):371-380. doi: 10.1007/s41745-021-00247-z. Epub 2021 Aug 6.
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 , , , and 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 , , , and 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.
当病原体群体中包含无症状表型时,疾病的检测和管理就会变得相当复杂,最近的新冠疫情期间就很明显。在易感-感染-康复-死亡(SIRD)动态模型的范式下,疾病传播已经得到了广泛研究。各种博弈论方法也探讨了疾病传播,其中许多方法将 、 、 和 视为策略而非状态。值得注意的是,上述方法中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能显著减轻许多传染病的传播。在此,我们将采取此类预防措施视为策略,并将 、 、 和 视为状态。我们还试图捕捉在各种网络拓扑结构上,有症状和无症状疾病在疫情传播方面的差异。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的大规模检测程度如何影响有社会责任感个体的最终比例。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引发大流行。网络拓扑结构似乎也起着重要作用。我们进一步观察到,积极主动个体的最终比例取决于感染个体和积极主动个体的初始比例。此外,边密度会显著影响总体结果。我们的研究结果与从正在进行的新冠疫情中吸取的教训大致相符。