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Opinion-driven risk perception and reaction in SIS epidemics

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arxiv 2410.12993 v2 pith:C3LQEHWR submitted 2024-10-16 math.DS physics.soc-phq-bio.PE

classification math.DSphysics.soc-phq-bio.PE
keywords riskmodelpopulationbehavioraversionbaselineepidemicinfection
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We present and analyze a mathematical model to study the feedback between behavior and epidemic spread in a population that is actively assessing and reacting to risk of infection. In our model, a population dynamically forms an opinion that reflects its willingness to engage in risky behavior (e.g., not wearing a mask in a crowded area) or reduce it (e.g., social distancing). We consider SIS epidemic dynamics in which the contact rate within a population adapts as a function of its opinion. For the new coupled model, we prove the existence of two distinct parameter regimes. One regime corresponds to a low baseline infectiousness, and the equilibria of the epidemic spread are identical to those of the standard SIS model. The other regime corresponds to a high baseline infectiousness, and there is a bistability between two new endemic equilibria that reflect an initial preference towards either risk seeking behavior or risk aversion. We prove that risk seeking behavior increases the steady-state infection level in the population compared to the baseline SIS model, whereas risk aversion decreases it. When a population is highly reactive to extreme opinions, we show how risk aversion enables the complete eradication of infection in the population. Extensions of the model to a network of populations or individuals are explored numerically.

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  1. Resolving Oversmoothing with Opinion Dissensus

    cs.LG 2025-01 conditional novelty 6.0 of 10

    BIMP, a continuous-depth GNN based on nonlinear opinion dynamics, is shown to avoid oversmoothing when its constant input has unique entries, and it outperforms baselines on ten node-classification datasets.

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