Bias in mobility datasets drives divergence in modeled outbreak dynamics
Abstract Background Digital data sources such as mobile phone call detail records (CDRs) are increasingly being used to estimate population mobility fluxes and to predict the spatiotemporal dynamics of infectious disease outbreaks. Differences in mobile phone operators’ geographic coverage, however,...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-01-01
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| Series: | Communications Medicine |
| Online Access: | https://doi.org/10.1038/s43856-024-00714-5 |
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| author | Taylor Chin Michael A. Johansson Anir Chowdhury Shayan Chowdhury Kawsar Hosan Md Tanvir Quader Caroline O. Buckee Ayesha S. Mahmud |
| author_facet | Taylor Chin Michael A. Johansson Anir Chowdhury Shayan Chowdhury Kawsar Hosan Md Tanvir Quader Caroline O. Buckee Ayesha S. Mahmud |
| author_sort | Taylor Chin |
| collection | DOAJ |
| description | Abstract Background Digital data sources such as mobile phone call detail records (CDRs) are increasingly being used to estimate population mobility fluxes and to predict the spatiotemporal dynamics of infectious disease outbreaks. Differences in mobile phone operators’ geographic coverage, however, may result in biased mobility estimates. Methods We leverage a unique dataset consisting of CDRs from three mobile phone operators in Bangladesh and digital trace data from Meta’s Data for Good program to compare mobility patterns across these sources. We use a metapopulation model to compare the sources’ effects on simulated outbreak trajectories, and compare results with a benchmark model with data from all three operators, representing around 100 million subscribers across the country. Results We show that mobility sources can vary significantly in their coverage of travel routes and geographic mobility patterns. Differences in projected outbreak dynamics are more pronounced at finer spatial scales, especially if the outbreak is seeded in smaller and/or geographically isolated regions. In some instances, a simple diffusion (gravity) model was better able to capture the timing and spatial spread of the outbreak compared to the sparser mobility sources. Conclusions Our results highlight the potential biases in predicted outbreak dynamics from a metapopulation model parameterized with non-population representative data, and the limits to the generalizability of models built on these types of novel human behavioral data. |
| format | Article |
| id | doaj-art-43037bba13f741f9b179e96f35c69778 |
| institution | Kabale University |
| issn | 2730-664X |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Communications Medicine |
| spelling | doaj-art-43037bba13f741f9b179e96f35c697782025-01-12T12:37:18ZengNature PortfolioCommunications Medicine2730-664X2025-01-015111110.1038/s43856-024-00714-5Bias in mobility datasets drives divergence in modeled outbreak dynamicsTaylor Chin0Michael A. Johansson1Anir Chowdhury2Shayan Chowdhury3Kawsar Hosan4Md Tanvir Quader5Caroline O. Buckee6Ayesha S. Mahmud7Center for Communicable Disease Dynamics, Department of Epidemiology, Harvard T.H. Chan School of Public HealthCenter for Communicable Disease Dynamics, Department of Epidemiology, Harvard T.H. Chan School of Public Healtha2ia2ia2ia2iCenter for Communicable Disease Dynamics, Department of Epidemiology, Harvard T.H. Chan School of Public HealthDepartment of Demography, University of California, BerkeleyAbstract Background Digital data sources such as mobile phone call detail records (CDRs) are increasingly being used to estimate population mobility fluxes and to predict the spatiotemporal dynamics of infectious disease outbreaks. Differences in mobile phone operators’ geographic coverage, however, may result in biased mobility estimates. Methods We leverage a unique dataset consisting of CDRs from three mobile phone operators in Bangladesh and digital trace data from Meta’s Data for Good program to compare mobility patterns across these sources. We use a metapopulation model to compare the sources’ effects on simulated outbreak trajectories, and compare results with a benchmark model with data from all three operators, representing around 100 million subscribers across the country. Results We show that mobility sources can vary significantly in their coverage of travel routes and geographic mobility patterns. Differences in projected outbreak dynamics are more pronounced at finer spatial scales, especially if the outbreak is seeded in smaller and/or geographically isolated regions. In some instances, a simple diffusion (gravity) model was better able to capture the timing and spatial spread of the outbreak compared to the sparser mobility sources. Conclusions Our results highlight the potential biases in predicted outbreak dynamics from a metapopulation model parameterized with non-population representative data, and the limits to the generalizability of models built on these types of novel human behavioral data.https://doi.org/10.1038/s43856-024-00714-5 |
| spellingShingle | Taylor Chin Michael A. Johansson Anir Chowdhury Shayan Chowdhury Kawsar Hosan Md Tanvir Quader Caroline O. Buckee Ayesha S. Mahmud Bias in mobility datasets drives divergence in modeled outbreak dynamics Communications Medicine |
| title | Bias in mobility datasets drives divergence in modeled outbreak dynamics |
| title_full | Bias in mobility datasets drives divergence in modeled outbreak dynamics |
| title_fullStr | Bias in mobility datasets drives divergence in modeled outbreak dynamics |
| title_full_unstemmed | Bias in mobility datasets drives divergence in modeled outbreak dynamics |
| title_short | Bias in mobility datasets drives divergence in modeled outbreak dynamics |
| title_sort | bias in mobility datasets drives divergence in modeled outbreak dynamics |
| url | https://doi.org/10.1038/s43856-024-00714-5 |
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