ComMit: Blind Community-based Early Mitigation Strategy against Viral Spread

In the early stages of a pandemic, epidemiological knowledge of the disease is limited and no vaccination is available. This poses the problem of determining an Early Mitigation Strategy. Previous studies have tackled this problem through finding globally influential nodes that contribute the most to the spread. These methods are often not practical due to their assumptions that (1) accessing the full contact social network is possible; (2) there is an unlimited budget for the mitigation strategy; (3) healthy individuals can be isolated for indefinite amount of time, which in practice can have serious mental health and economic consequences. In this work, we study the problem of developing an early mitigation strategy from a community perspective and propose a dynamic Community-based Mitigation strategy, ComMit. The distinguishing features of ComMit are: (1) It is agnostic to the dynamics of the spread; (2) does not require prior knowledge of contact network; (3) it works within a limited budget; and (4) it enforces bursts of short-term restriction on small communities instead of long-term isolation of healthy individuals. ComMit relies on updated data from test-trace reports and its strategy evolves over time. We have tested ComMit on several real-world social networks. The results of our experiments show that, within a small budget, ComMit can reduce the peak of infection by 73% and shorten the duration of infection by 90%, even for spreads that would reach a steady state of non-zero infections otherwise (e.g., SIS contagion model).

在大流行的早期阶段，对该疾病的流行病学知识有限，且没有可用的疫苗。这就带来了确定早期缓解策略的问题。先前的研究通过寻找对传播贡献最大的全球有影响力的节点来解决这个问题。由于这些方法存在以下假设，它们往往不实用：（1）有可能获取完整的接触社交网络；（2）缓解策略有无限的预算；（3）健康个体可以被无限期隔离，而在实际中这可能会产生严重的心理健康和经济后果。在这项工作中，我们从社区角度研究制定早期缓解策略的问题，并提出一种基于社区的动态缓解策略ComMit。ComMit的显著特点是：（1）它与传播动态无关；（2）不需要事先了解接触网络；（3）它在有限预算内起作用；（4）它对小社区实施短期限制的突发措施，而不是对健康个体进行长期隔离。ComMit依赖于检测追踪报告的更新数据，其策略随时间演变。我们已经在几个现实世界的社交网络上对ComMit进行了测试。我们的实验结果表明，在预算较小的情况下，ComMit可以将感染高峰降低73%，并将感染持续时间缩短90%，即使对于否则会达到非零感染稳定状态的传播情况（例如，SIS传染模型）也是如此。