Modeling Epidemiological Dynamics Under Adversarial Data and User Deception

2026-02-23

AI Summary

This study addresses the distortion in epidemic modeling caused by strategic misreporting of self-reported behavioral data—such as mask-wearing and vaccination status—and proposes a novel integration of signaling games into epidemiological frameworks. It formulates a strategic interaction model between the public and public health authorities to systematically analyze how deceptive reporting affects the efficacy of non-pharmaceutical interventions. By synthesizing game theory, signaling models, and epidemic dynamics, the research demonstrates that infection levels can approach zero under a separating equilibrium. Moreover, even in a pooling equilibrium characterized by widespread deception, carefully designed mechanisms can still effectively contain outbreaks, offering a theoretical foundation for robust intervention strategies that remain effective despite informational inaccuracies.

Technology Category

• Game Theory and Economic Paradigms: Imperfect Information
• Multiagent Systems: Mechanism Design
• Application Domains: Misinformation & Fake News

Application Category

• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems
• Economics, Online Markets and Human Computation: Trust and reliance of crowd workers and data experts on GenAI
• Security and Privacy: Large-scale security measurements

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 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.

Problem

• epidemiological modeling
• strategic misreporting
• adversarial data
• user deception
• non-pharmaceutical interventions

Innovation

• signaling game
• adversarial data
• strategic misreporting
• epidemiological modeling
• behavioral deception