Speaker
Description
Human behavior strongly shapes epidemic dynamics, yet responses to risk are typically based on spatially aggregated and temporally delayed information. While behavioral adaptation, spatial structure, and delays have been studied separately, their combined effects on networks remain poorly understood.
We investigate these effects using stochastic epidemic simulations on small-world contact networks with behavior driven by delayed, spatially aggregated risk perception. We find a robust and non-monotonic dependence of epidemic size and outbreak probability on the spatial scale of information, with intermediate scales minimizing burden. This reflects a fundamental trade-off: highly local information is precise but loses predictive value under delays, whereas highly aggregated information obscures heterogeneity and delays behavioral activation.
As delays increase, behavioral responses become increasingly misaligned with epidemic growth, and differences across spatial scales diminish, yielding dynamics similar to homogeneous mixing. Increased network connectivity further reduces spatial heterogeneity, weakening the benefits of localized information.
A complementary multi-patch model exhibits qualitatively similar patterns, supporting the generality of these findings. Overall, our results show that the effectiveness of behavior-driven epidemic control depends critically on the alignment of information scale and timing with underlying transmission dynamics.
