{"doi":"10.1038/s43856-024-00714-5","title":"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, 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.","journal":"Communications Medicine","year":2025,"id":538417,"datarank":0.21889795302028173,"base_score":1.3862943611198906,"endowment":1.3862943611198906,"self_citation_contribution":0.20794415416798362,"citation_network_contribution":0.01095379885229812,"self_endowment_contribution":0.20794415416798362,"citer_contribution":0.01095379885229812,"corpus_percentile":37.201206776514276,"corpus_rank":8119,"citation_count":3,"citer_count":3,"citers_with_citation_signal":1,"citers_with_endowment":1,"datacite_reuse_total":0,"is_dataset":true,"is_dataset_confidence":0.8415,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2025-01-01","fair_score":12.5,"fair_percentile":28.767960868236013,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":264575,"name":"Michael A. Johansson","orcid":"0000-0002-5090-7722","position":1,"is_corresponding":false},{"id":1425026,"name":"Anir Chowdhury","orcid":"0000-0003-3907-8211","position":2,"is_corresponding":false},{"id":1425027,"name":"Shayan Chowdhury","orcid":"0000-0001-5153-9055","position":3,"is_corresponding":false},{"id":1425028,"name":"Kawsar Hossain Sojib","orcid":"0000-0002-1553-7113","position":4,"is_corresponding":false},{"id":1425396,"name":"Md. Tanvir Quader","orcid":null,"position":5,"is_corresponding":false},{"id":228279,"name":"Caroline O. Buckee","orcid":"0000-0002-8386-5899","position":6,"is_corresponding":false},{"id":614345,"name":"Ayesha S. Mahmud","orcid":"0000-0002-2282-8613","position":7,"is_corresponding":false},{"id":304719,"name":"Taylor Chin","orcid":"0000-0001-6852-1169","position":0,"is_corresponding":true}],"reference_count":34,"raw_metadata":null,"created_at":"2026-07-19T02:52:21.389196Z","pmid":"39774250","pmcid":"PMC11706981","fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":11.1111,"fair_a":6.25,"fair_i":20.0,"fair_r":25.0,"fair_zscore":-0.8688,"fair_rationale":{"fair_score":12.5,"has_llm":true,"taxonomy_version":"fair_taxonomy_v5","dimensions":{"F":{"name":"Findable","score":11.11,"criteria":[{"key":"f_dataset_pid","label":"Persistent identifier for the data","kind":"llm","weight":2.0,"fraction":0.0,"verdict":"no","evidence":null,"grounded":false,"rationale":"No persistent identifier string is given for the dataset.","anchors":["RDA-F1-01D — FAIR Data Maturity Model: 'Data is identified by a persistent identifier' (priorit","RDA-F1-02D — FAIR Data Maturity Model: 'Data is identified by a globally unique identifier'","FsF-F1-02D — F-UJI/FAIRsFAIR: 'Data is assigned a persistent identifier'"],"scored":true,"signal":null},{"key":"f_repository_named","label":"Named repository","kind":"llm","weight":2.0,"fraction":0.0,"verdict":"no","evidence":"The CDR data that support the findings of this study are available on request from a2i.","grounded":false,"rationale":"a2i is an institutional host, not a standard data repository. 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[majority verdict 'yes' (4/5 passes agreed)]","anchors":["RDA-I3-01M — '(meta)data include references to other (meta)data'","RDA-I3-03M — 'metadata includes qualified references to other metadata'","FsF-I3-01M — F-UJI: 'Metadata includes links between the data and its related entities'"],"scored":false,"signal":null}]},"R":{"name":"Reusable","score":25.0,"criteria":[{"key":"r_reuse_license","label":"Reuse licence","kind":"llm","weight":2.0,"fraction":0.0,"verdict":"no","evidence":null,"grounded":false,"rationale":"No license is stated for the data.","anchors":["RDA-R1.1-01M — 'Metadata includes information about the licence under which the data can be reu","RDA-R1.1-02M — 'Metadata refers to a standard reuse licence'","RDA-R1.1-03M — 'Metadata refers to a machine-understandable reuse licence'"],"scored":true,"signal":null},{"key":"r_provenance_methods","label":"Provenance of the data","kind":"llm","weight":1.0,"fraction":0.0,"verdict":"no","evidence":"subscribers were assigned to an upazila— one of 544 sub-district administrative units— each day in the analysis period, April 28, 2020 to September 1, 2020, based on their most frequently used cell tower for routing calls and SMS texts.","grounded":false,"rationale":"The data production is described in generic terms without naming any specific instrument or software version. 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A bare URL is not persistent: it is the single most common cause of a dead data link five years after publication. For clinical / human-subjects data, deposit in dbGaP or the European Genome-phenome Archive (EGA).","anchors":["yes","partial","no"],"verdict":"no","current":0.0,"evidence":null,"why":"No persistent identifier string is given for the dataset.","gain":16.67,"priority":"essential","scored":true},{"key":"f_repository_named","dimension":"F","label":"Named repository","action":"Deposit the data in a repository registered in re3data/FAIRsharing (a domain repository such as GEO, SRA, dbGaP, PRIDE, or a generalist such as Zenodo, Dryad, Dataverse) and name it explicitly in the paper. A lab website is not an archive: it has no retention commitment and no accession. 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[majority verdict 'no' (3/5 passes agreed)]","gain":0.0,"priority":"important","scored":false},{"key":"i_community_standard_vocabulary","dimension":"I","label":"Community standard / vocabulary","action":"Adopt and NAME your domain's data standard — the minimum-information checklist, metadata schema, or ontology your community uses (MIAME/MINSEQE, ISA-Tab, BIDS, an OBO ontology, HL7 FHIR/OMOP) — and say which one you followed. A reporting checklist standardises your paper; it does nothing for your data. In clinical / human-subjects, describe the data with OMOP CDM, CDISC SDTM or HL7 FHIR.","anchors":["yes","partial","no"],"verdict":"no","current":0.0,"evidence":null,"why":"No named data or metadata community standard is applied to the data.","gain":0.0,"priority":"important","scored":false},{"key":"r_provenance_methods","dimension":"R","label":"Provenance of the data","action":"Name the instruments, kits, and software — with versions — that produced the data, not just the verbs. 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Only a reference- list entry is machine-readable to Crossref/DataCite, and only a citation lets the data earn credit. Cite the clinical / human-subjects repository accession (e.g. from dbGaP or the European Genome-phenome Archive (EGA)) in the reference list."],"model":"deepseek/deepseek-v4-flash","agent_version":"fair_agent_v8","fulltext_source":"unpaywall_pdf"},"fair_model":"deepseek/deepseek-v4-flash","fair_agent_version":"fair_agent_v8","fair_fulltext_source":"unpaywall_pdf","fair_has_llm":true,"fair_computed_at":"2026-07-20T13:11:36.318889Z","clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}