Evaluating Official Health Communication Strategies with an Empirically Parameterized Information-Epidemic Coupled Model

Abstract

With the rapid proliferation of information and communication technologies (ICT), epidemic dynamics are now inseparably entangled with information diffusion: public health messages alter individual behavior and thereby reshape transmission pathways, while rising case counts in turn drive demand for epidemic-related information. Despite growing recognition of this bidirectional coupling, most existing models either assume ad hoc behavioral parameters or confine analysis to a single intervention dimension, limiting their practical utility for guiding official communication campaigns. In this paper, we present an empirically parameterized agent-based model that couples information diffusion with epidemic transmission on a synthetic population of Yichang, China. The information-to-epidemic and epidemic-to-information coupling parameters are calibrated from real-world survey data. Using this framework, we conduct four sets of controlled experiments evaluating official health communication strategies across four dimensions: release timing, spatial targeting, release frequency, and target population. Results show that release timing is the most consequential dimension, reducing the final attack rate by up to 7.8 percentage points when the advisory is issued on Day 1 versus Day 45. Target population is the second most important lever: seeding the advisory among workers yields the lowest attack rate, whereas targeting the elderly-a group often prioritized in practice-produces the highest, because low-degree nodes in the information network diffuse the advisory too slowly to outpace the epidemic. Release frequency has a moderate effect, and spatial targeting produces negligible differences at the city scale tested. These findings offer concrete, evidence-grounded guidance for designing official health communication strategies during epidemic outbreaks.