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Bias in mobility datasets drives divergence in modeled outbreak dynamics.
Taylor Chin1, Michael A Johansson1,2, Anir Chowdhury3
1Center for Communicable Disease Dynamics, Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA.
Communications Medicine
|January 8, 2025
Summary
Mobile phone data (CDRs) can bias infectious disease outbreak predictions due to varying operator coverage. Comparing data sources reveals significant differences in mobility patterns and simulated disease spread, impacting model generalizability.
Area of Science:
- Epidemiology
- Computational modeling
- Mobile data analytics
Background:
- Digital data, including mobile phone call detail records (CDRs), are increasingly used for population mobility and infectious disease outbreak prediction.
- Geographic coverage disparities among mobile operators can introduce bias into mobility estimates.
Purpose of the Study:
- To compare mobility patterns derived from different digital data sources.
- To assess the impact of data source variability on simulated infectious disease outbreak dynamics.
Main Methods:
- Utilized a unique dataset combining CDRs from three mobile operators and Meta's Data for Good digital trace data in Bangladesh.
- Employed a metapopulation model to simulate outbreak trajectories using different mobility data sources.
- Compared model outputs against a benchmark incorporating data from all operators (~100 million subscribers).
Main Results:
- Mobility data sources exhibited significant variations in travel route coverage and geographic mobility patterns.
- Discrepancies in simulated outbreak dynamics were more pronounced at finer spatial scales and for outbreaks in isolated regions.
- A simple diffusion model sometimes outperformed sparser mobility sources in capturing outbreak timing and spread.
Conclusions:
- Parameterizing metapopulation models with non-population-representative data can lead to biased outbreak predictions.
- Models built on novel human behavioral data have limitations in generalizability due to potential data biases.
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