Optimal Seeding Strategies in Epidemic Networks: Balancing Simple and Complex Contagion Dynamics

Understanding optimal strategies for promoting protective behaviors in networked populations is crucial for effective epidemic containment. While diseases spread via simple contagion—where a single contact can transmit infection—protective behaviors like mask-wearing often spread through complex contagion, requiring reinforcement from multiple contacts. This dichotomy presents a strategic trade-off: seeding behavior adoption at nodes with high standard centrality (key in disease spread) may slow the epidemic but hinder the propagation of the protective behavior, whereas seeding at nodes with high complex centrality accelerates behavior adoption but may allow the epidemic to spread unchecked.

In this work, we couple a simple Susceptible-Infected-Susceptible (SIS) epidemic model with a complex contagion model of behavior adoption. We analyze various seeding strategies across single-layer and multiplex network frameworks to assess their effectiveness under different objectives, such as minimizing fatalities or delaying peak infection. Our findings reveal that the optimal seeding strategy is sensitive to the relative time scales of disease and behavior spread and often involves an interpolation between standard and complex centrality measures. These results offer nuanced insights into designing targeted interventions that balance rapid adoption of protective behaviors with effective suppression of epidemic spread.