Empirical Trajectory Sparsity Biases Mobility-Informed Epidemic Modeling
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 data 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 the contact network 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.
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BibTeX
@misc{delussu2026empirical,
title = {{Empirical Trajectory Sparsity Biases Mobility-Informed Epidemic Modeling}},
author = {Federico Delussu and Francisco Barreras and Yuan Liao and Duncan J. Watts and Laura Alessandretti},
howpublished = {arXiv},
year = {2026},
month = sep,
doi = {10.48550/arxiv.2605.31282},
eprint = {2605.31282},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2605.31282},
note = {CC BY-NC-ND 4.0},
}
RIS
TY - UNPB
TI - Empirical Trajectory Sparsity Biases Mobility-Informed Epidemic Modeling
AU - Federico Delussu
AU - Francisco Barreras
AU - Yuan Liao
AU - Duncan J. Watts
AU - Laura Alessandretti
PY - 2026
DA - 2026/09/23
PB - arXiv
DO - 10.48550/arxiv.2605.31282
UR - https://arxiv.org/abs/2605.31282
AB - 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 data 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 the contact network 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.
ER -