Event-based spatiotemporal networks for modelling emergent phenomena in complex systems

TL;DR

This paper introduces event-based spatiotemporal networks, a novel framework for modeling emergent phenomena in complex systems by encoding processes as discrete space-time events, demonstrated through epidemiology and transportation applications.

Contribution

The paper presents a new flexible and efficient modeling approach that captures emergent behaviors from micro-level event data across various complex systems.

Findings

Enabled fine-grained tracking of infection transmission routes.

Modeled propagation of delays in public transportation.

Discussed applications in biology and ecology.

Abstract

Complex systems display emergent phenomena that vary significantly across spatial and temporal scales. These variations originate from fine-grained system processes, yet arriving at macroscopic dynamics from micro-level data -- particularly when large, high-resolution datasets are available -- remains a persistent challenge. Here we develop event-based spatiotemporal networks, a computational modelling framework that encodes system processes as discrete events anchored in space and time. Event-based spatiotemporal networks offer a unified, flexible and efficient approach to generate emergent behaviour in complex systems across space and time from these events. We demonstrate the effectiveness of event-based spatiotemporal networks through two illustrative real-world applications. First, following a local outbreak of a novel respiratory pathogen in the Netherlands, spatiotemporal networks enable fine-grained tracking of transmission routes and infection patterns through space and time. Second, we use spatiotemporal networks to model propagation of delays in a public transportation system (S-bahn) around Z\"urich, Switzerland. We also discuss broader uses of event-based spatiotemporal networks in fields like developmental biology and community ecology, where focusing on events rather than static system states can improve data analysis, simulation, and collection strategies.