Human Mobility in Epidemic Modeling

TL;DR

This work surveys how human mobility data and representations reshape epidemic modeling beyond homogeneous mixing, detailing data sources, trajectory–network representations, and links between movement and transmission. It synthesizes modeling frameworks—from compartmental and metapopulation to network-based, agent-based, and machine learning—showing how mobility enhances risk assessment, outbreak origin detection, and intervention design. The paper highlights practical gains in predicting spread and guiding NPIs, exemplified by frameworks like GLEaM and PandemicLLM, while acknowledging biases, privacy concerns, and data integration challenges. Its findings emphasize that integrating multi-source mobility data into diverse modeling paradigms enables more precise, timely, and context-sensitive epidemic responses, with important implications for public health policy and preparedness.

Abstract

Human mobility forms the backbone of contact patterns through which infectious diseases propagate, fundamentally shaping the spatio-temporal dynamics of epidemics and pandemics. While traditional models are often based on the assumption that all individuals have the same probability of infecting every other individual in the population, a so-called random homogeneous mixing, they struggle to capture the complex and heterogeneous nature of real-world human interactions. Recent advancements in data-driven methodologies and computational capabilities have unlocked the potential of integrating high-resolution human mobility data into epidemic modeling, significantly improving the accuracy, timeliness, and applicability of epidemic risk assessment, contact tracing, and intervention strategies. This review provides a comprehensive synthesis of the current landscape in human mobility-informed epidemic modeling. We explore diverse sources and representations of human mobility data, and then examine the behavioral and structural roles of mobility and contact in shaping disease transmission dynamics. Furthermore, the review spans a wide range of epidemic modeling approaches, ranging from classical compartmental models to network-based, agent-based, and machine learning models. And we also discuss how mobility integration enhances risk management and response strategies during epidemics. By synthesizing these insights, the review can serve as a foundational resource for researchers and practitioners, bridging the gap between epidemiological theory and the dynamic complexities of human interaction while charting clear directions for future research.

Figures (13)

• Figure 1: Chronological outline of major pandemics and epidemic events in human history.
• Figure 2: Broad overview of anonymized GPS data applications.
• Figure 3: Estimated spatiotemporal resolution of human mobility data sources.
• Figure 4: Trajectory data example.
• Figure 5: Example of the individual mobility network.