The Effect of Mobility Trajectory Sparsity on Epidemic Modeling Outcomes

2026-05-29

AI Summary

GPS trajectory data are commonly characterized by high temporal sparsity, which can introduce substantial bias into epidemiological modeling—a critical issue that has long been overlooked. This study systematically quantifies, for the first time, the impact of trajectory sparsity on key epidemiological metrics and demonstrates that complex missingness patterns significantly underestimate epidemic intensity. To address this, we develop a benchmark framework based on near-complete GPS trajectories, simulating realistic missingness and constructing co-occurrence networks. We further propose a network-based inverse probability weighting correction method applied to network edges. Experimental results show that our approach effectively reduces estimation bias in commercial GPS data subsets, substantially enhancing model reliability and the accuracy of parameter inference.

Abstract

GPS mobility data are increasingly used in epidemic modeling, allowing the construction of co-location networks or population flows. These trajectories typically exhibit high temporal sparsity because data collection is opportunistic and tied to phone use. Despite growing awareness of this limitation, the analysis and treatment of biases derived from it have been largely overlooked in existing epidemic modeling studies, raising concerns about the robustness of downstream inferences. We introduce a principled framework to quantify the impact of trajectory sparsity on key epidemic modeling outcomes across different levels of missingness. Our approach leverages a highly-complete dataset that exhibits both near-complete and sparse GPS trajectories. Near-complete trajectories provide baseline epidemic outcomes, while sparse trajectories provide realistic missingness patterns that we impose on the baseline to measure bias. In this way, we show how missing records can result in substantial underestimation of key measures of epidemic intensity, explained not only by the amount of missing data, but by more complex features of data missingness that should be taken into account when designing correction methods. Finally, we propose and evaluate a correction based on inverse probability weighting of network edges before epidemic model calibration, which is shown to reduce bias and parameter misspecification. We also demonstrate this correction on a separate anonymized sample from a commercial GPS mobility dataset and report on its effect. Together, our findings provide a first rigorous quantification of trajectory-sparsity bias in epidemic modeling, offering initial guidance on the treatment of this issue.

Problem

• mobility trajectory sparsity
• epidemic modeling
• missing data bias
• GPS mobility data
• epidemic intensity

Innovation

• trajectory sparsity
• epidemic modeling
• inverse probability weighting
• mobility data bias
• co-location networks