Targeted interventions suppress epidemic outbreaks in spatial higher-order activity-driven networks

Traditional pairwise epidemic models fail to capture group-based infection risks in dense urban environments, where higher-order interactions in shared spaces drive explosive contagion. We develop a spatial activity-driven higher-order framework integrating agent-based mobility modeling with the microscopic Markov chain approach to simulate these complex contagion processes. Our results demonstrate that higher-order interactions significantly advance outbreak peaks by intensifying localized infection pressure, triggering explosive spreading patterns driven by cumulative infection pressure. Through systematic analysis of screening bias and testing scale, we find that targeted interventions based on activity levels are essential for containment, with their efficacy further amplified by the test scale. Crucially, the impact of isolation is highly contingent on screening bias: even the strictest isolation may fail if screening is poorly targeted. The derived basic reproduction number reveals three coupled dimensions governing epidemic thresholds: biological weight, higher-order enhancement, and structural spectral radius. This research establishes that the efficacy of strict isolation critically depends on the coupling between testing scale and screening bias in higher-order networks. By synchronizing high-resource allocation with precise targeting of spatial social hubs, public health authorities can effectively counteract the accelerated outbreak peaks induced by collective group interactions. Our findings provide fundamental insights into epidemic spreading in complex social systems and offer quantitative guidance for designing optimal intervention strategies against epidemic outbreaks.