From Risk Perception to Behavior Large Language Models-Based Simulation of Pandemic Prevention Behaviors

TL;DR

This paper introduces an LLM-based simulation framework to predict and analyze pandemic prevention behaviors, effectively handling scarce data and evolving policies by coupling static and dynamic modules for behavior prediction and risk perception updates.

Contribution

The study develops a novel LLM-based simulation framework that accurately predicts prevention behaviors and adapts to limited data and changing epidemic contexts.

Findings

Framework achieves up to 81.8% predictive accuracy.

Robust performance under zero-shot, few-shot, and transfer settings.

Behavioral responses vary significantly during policy relaxation.

Abstract

Individual prevention behaviors are a primary line of defense during the early stages of novel infectious disease outbreaks, yet their adoption is heterogeneous and difficult to forecast-especially when empirical data are scarce and epidemic-policy contexts evolve rapidly. To address this gap, we develop an LLM-based prevention-behavior simulation framework that couples (i) a static module for behavior-intensity prediction under a specified external context and (ii) a dynamic module that updates residents' perceived risk over time and propagates these updates into behavior evolution. The model is implemented via structured prompt engineering in a first-person perspective and is evaluated against two rounds of survey data from Beijing residents (R1: December 2020; R2: August 2021) under progressively realistic data-availability settings: zero-shot, few-shot, and cross-context transfer. Using Kolmogorov-Smirnov tests to compare simulated and observed behavior distributions (p > 0.001 as the validity criterion), the framework demonstrates robust performance and improves with limited reference examples; reported predictive accuracy increases from 72.7% (zero-shot) to 81.8% (few-shot), and remains high at 77.8% under transfer to novel contexts. We further apply the framework to simulate behavior changes during China's December 2022 policy relaxation and to stress-test behavioral responses across 120 systematically varied epidemic conditions (R0, CFR, and control-measure tiers). Results indicate broad behavioral loosening under relaxation but a distinctive counter-trend increase in drain-related disinfection, highlighting how low-cost, low-friction behaviors may persist or intensify even when external constraints recede-raising a potential environmental tradeoff.