Epidemiological models increasingly rely on self-reported behavioral data such as vaccination status, mask usage, and social distancing adherence to forecast disease transmission and assess the impact of non-pharmaceutical interventions (NPIs). While such data provide valuable real-time insights, they are often subject to strategic misreporting, driven by individual incentives to avoid penalties, access benefits, or express distrust in public health authorities. To account for such human behavior, in this paper, we introduce a game-theoretic framework that models the interaction between the population and a public health authority as a signaling game. Individuals (senders) choose how to report their behaviors, while the public health authority (receiver) updates their epidemiological model(s) based on potentially distorted signals. Focusing on deception around masking and vaccination, we characterize analytically game equilibrium outcomes and evaluate the degree to which deception can be tolerated while maintaining epidemic control through policy interventions. Our results show that separating equilibria-with minimal deception-drive infections to near zero over time. Remarkably, even under pervasive dishonesty in pooling equilibria, well-designed sender and receiver strategies can still maintain effective epidemic control. This work advances the understanding of adversarial data in epidemiology and offers tools for designing more robust public health models in the presence of strategic user behavior.

翻译：流行病学模型日益依赖自我报告的行为数据（如疫苗接种状态、口罩使用情况、社交距离遵守情况）来预测疾病传播并评估非药物干预措施（NPIs）的影响。尽管此类数据能提供有价值的实时洞察，但它们常因个体为避免惩罚、获取利益或表达对公共卫生机构不信任的动机而遭受策略性误报。为纳入此类人类行为，本文引入一个博弈论框架，将人群与公共卫生机构之间的互动建模为信号博弈。个体（发送者）选择如何报告其行为，而公共卫生机构（接收者）则基于可能失真的信号更新其流行病学模型。聚焦于围绕口罩使用和疫苗接种的欺骗行为，我们解析地刻画了博弈均衡结果，并评估了在通过政策干预维持疫情控制的同时可容忍的欺骗程度。我们的结果表明，具有最小欺骗的分离均衡会随时间推移将感染率降至接近零。值得注意的是，即使在普遍不诚实的混同均衡下，设计良好的发送者与接收者策略仍能维持有效的疫情控制。这项工作增进了对流行病学中对抗性数据的理解，并为在存在策略性用户行为的情况下设计更稳健的公共卫生模型提供了工具。