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Mobility data resolution needed to inform predictive models of spatial epidemic spread from mobile phone data
Giulia Pullano1, Shweta Bansal1, Stefania Rubrichi2
1Department of Biology, Georgetown University, Washington, District of Columbia, United States of America.
Plos Computational Biology
|July 2, 2026
Summary
Detailed human mobility data is not always necessary for accurate infectious disease spread prediction. Lower-resolution mobile phone data, focusing on time spent at key locations, sufficiently informs epidemic models and enhances data privacy.
Area of Science:
- Epidemiology and Public Health
- Computational Social Science
- Network Science
Background:
- Human mobility is a key driver of infectious disease spread.
- The required resolution of mobility data for accurate epidemic modeling is not well-defined.
- Mobile phone data offers high-resolution insights into population movements.
Purpose of the Study:
- To determine which aspects of human mobility are most epidemiologically relevant.
- To assess the necessary data resolution of mobility patterns for capturing spatial disease invasion dynamics.
- To compare different aggregation methods of mobile phone data for epidemic modeling.
Main Methods:
- Utilized mobile phone records from 9.5 million users in Senegal (80% of the population).
- Systematically compared three mobility data aggregation approaches: high-resolution (HR) individual displacements, medium-resolution (MR) time spent at locations, and low-resolution (LR) most-visited locations.
- Integrated these mobility representations into a metapopulation epidemic model simulating diseases with varying transmissibility.
Main Results:
- High-resolution tracking of individual displacements did not necessarily improve epidemiological relevance and fragmented long-range travel patterns.
- Mobility data capturing where individuals spend most of their time (e.g., home, work) more accurately reproduced spatial invasion patterns.
- Accounting for additional daily activities beyond primary locations provided minimal additional epidemiological information.
Conclusions:
- Lower-resolution mobility indicators, focusing on time spent at key locations, are sufficient for informing predictive epidemic models.
- Mobile phone data can be aggregated to reduce privacy concerns while retaining essential information for spatial disease transmission modeling.
- Findings impact epidemic modeling strategies and data governance for public health surveillance.
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