Network Topology Matters, But Not Always: Mobility Networks in Epidemic Forecasting

2025-10-22

AI Summary

This study investigates the relative importance of mobile network topology versus human mobility volume in short-term epidemic forecasting. Leveraging anonymized smartphone data from Massachusetts, we construct a dynamic, directed inter-municipal mobility network and integrate macro-level incidence with network-position interaction features to propose a prediction-enhancement framework for data-scarce settings. Our contributions are threefold: (1) theoretical and empirical demonstration that incorporating network–epidemic interaction terms significantly improves forecast accuracy (Predict-R² increases from 0.60 to 0.83–0.89) when fine-grained local case data are unavailable; (2) identification of boundary conditions—when timely and reliable historical local case data exist, autoregressive models capture most predictability, rendering mobility network features marginally beneficial (≈0.5 percentage-point gain); and (3) provision of actionable guidelines for feature selection. Methodologically, we combine out-of-sample evaluation, agent-based simulation, and analytical decomposition, establishing a novel paradigm for multi-source, heterogeneous data-driven epidemiological modeling.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Reasoning under Uncertainty: Relational Probabilistic Models
• Data Mining & Knowledge Management: Graph Mining, Social Network Analysis & Community

Application Category

• Social Networks and Social Media: Influence propagation, information diffusion, and the prediction on networks
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Web Mining and Content Analysis: Large pretrained models with web data

Abstract

Short-horizon epidemic forecasts guide near-term staffing, testing, and messaging. Mobility data are now routinely used to improve such forecasts, yet work diverges on whether the volume of mobility or the structure of mobility networks carries the most predictive signal. We study Massachusetts towns (April 2020-April 2021), build a weekly directed mobility network from anonymized smartphone traces, derive dynamic topology measures, and evaluate their out-of-sample value for one-week-ahead COVID-19 forecasts. We compare models that use only macro-level incidence, models that add mobility network features and their interactions with macro incidence, and autoregressive (AR) models that include town-level recent cases. Two results emerge. First, when granular town-level case histories are unavailable, network information (especially interactions between macro incidence and a town's network position) yields large out-of-sample gains (Predict-R2 rising from 0.60 to 0.83-0.89). Second, when town-level case histories are available, AR models capture most short-horizon predictability; adding network features provides only minimal incremental lift (about +0.5 percentage points). Gains from network information are largest during epidemic waves and rising phases, when connectivity and incidence change rapidly. Agent-based simulations reproduce these patterns under controlled dynamics, and a simple analytical decomposition clarifies why network interactions explain a large share of cross-sectional variance when only macro-level counts are available, but much less once recent town-level case histories are included. Together, the results offer a practical decision rule: compute network metrics (and interactions) when local case histories are coarse or delayed; rely primarily on AR baselines when granular cases are timely, using network signals mainly for diagnostic targeting.

Problem

• Evaluating whether mobility network volume or topology better predicts epidemic forecasts
• Assessing network information value when local case histories are unavailable versus available
• Determining practical conditions for using network metrics versus autoregressive models

Innovation

• Using mobility network topology for epidemic forecasting
• Combining network features with macro incidence data
• Applying autoregressive models when local data available