The evolution and impact of group collaboration in crisis response

Collaboration among social groups influences how communities respond to crises. However, there is limited understanding of how cross-group collaboration evolves during crises and how it affects network effectiveness. Here we propose a topological framework based on zigzag persistence to track higher-order group collaborations as time-varying simplicial complexes. Using 6.6 million volunteer activity records from Shenzhen, China, we characterize how pandemic waves and urban characteristics affect higher-order collaboration. We then evaluate these structures against an effectiveness metric, using a Shenzhen-based agent-based model and the global MapSwipe crisis-mapping platform for validation, finding that collaboration structures beyond pairwise interactions are more strongly associated with effectiveness than standard connectivity measures. These findings suggest that higher-order collaborations are a generic feature of crisis response and can inform strategies to improve collective action during large-scale emergencies. Effective cross-group collaboration is crucial during crises, yet its evolving dynamics and impact on network effectiveness remain poorly understood. Here, the authors employ a topological framework based on zigzag persistence applied to city-scale datasets associated with pandemic waves in Shenzhen, China, and reveal that higher-order collaborations are a generic feature of effective crisis response, offering insights for optimizing collective action in emergencies.