Modeling Epidemiological Dynamics Under Adversarial Data and User Deception

TL;DR

This paper introduces a game-theoretic model to analyze how strategic misreporting of health behaviors affects epidemic control and demonstrates that well-designed strategies can mitigate deception's impact.

Contribution

It develops a novel signaling game framework to understand and counteract strategic misreporting in epidemiological data, enhancing model robustness.

Findings

Separating equilibria can drive infections to near zero.

Pooling equilibria with deception still allow effective epidemic control.

Robust strategies mitigate the impact of widespread dishonesty.

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.