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[2602.20134] Modeling Epidemiological Dynamics Under Adversarial Data and User Deception
Summary
This paper presents a game-theoretic model to analyze how adversarial data and user deception affect epidemiological dynamics, particularly in the context of public health reporting.
Why It Matters
Understanding the impact of strategic misreporting on epidemiological models is crucial for public health authorities. This research provides insights into designing effective interventions that can maintain epidemic control despite potential dishonesty in self-reported data.
Key Takeaways
- Introduces a game-theoretic framework for modeling public health data reporting.
- Demonstrates how deception in self-reported data can still allow for effective epidemic control.
- Identifies strategies for public health authorities to mitigate the effects of misinformation.
- Highlights the importance of understanding user behavior in epidemiological modeling.
- Offers analytical characterizations of equilibrium outcomes in the face of strategic misreporting.
Computer Science > Computer Science and Game Theory arXiv:2602.20134 (cs) [Submitted on 23 Feb 2026] Title:Modeling Epidemiological Dynamics Under Adversarial Data and User Deception Authors:Yiqi Su, Christo Kurisummoottil Thomas, Walid Saad, Bud Mishra, Naren Ramakrishnan View a PDF of the paper titled Modeling Epidemiological Dynamics Under Adversarial Data and User Deception, by Yiqi Su and 4 other authors View PDF HTML (experimental) Abstract: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 contro...
