Cited by 281Data-Driven Disease SurveillanceHuman Mobility and Location-Based AnalysisVibrio bacteria research studies

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Using Mobile Phone Data to Predict the Spatial Spread of Cholera

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Bengtsson, Linus, Gaudart, Jean, Lu, Xin, Moore, Sandra, Wetter, Erik, Sallah, Kankoe, Rebaudet, Stanislas, Piarroux, Renaud·2015·Scientific Reports

DOI 10.1038/srep08923PMC PMC4352843

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Abstract

Effective response to infectious disease epidemics requires focused control measures in areas predicted to be at high risk of new outbreaks. We aimed to test whether mobile operator data could predict the early spatial evolution of the 2010 Haiti cholera epidemic. Daily case data were analysed for 78 study areas from October 16 to December 16, 2010. Movements of 2.9 million anonymous mobile phone SIM cards were used to create a national mobility network. Two gravity models of population mobility were implemented for comparison. Both were optimized based on the complete retrospective epidemic data, available only after the end of the epidemic spread. Risk of an area experiencing an outbreak within seven days showed strong dose-response relationship with the mobile phone-based infectious pressure estimates. The mobile phone-based model performed better (AUC 0.79) than the retrospectively optimized gravity models (AUC 0.66 and 0.74, respectively). Infectious pressure at outbreak onset was significantly correlated with reported cholera cases during the first ten days of the epidemic (p < 0.05). Mobile operator data is a highly promising data source for improving preparedness and response efforts during cholera outbreaks. Findings may be particularly important for containment efforts of emerging infectious diseases, including high-mortality influenza strains.

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Figure 1 Mobile phone mobility network. The average absolute number of mobile phones moving between the study areas (October 15 to December 19, 2010). Thicker, bluer lines indicate larger number of travelers. The original outbreak location (Mirebelais), the Artibonite River (dark blue) and Port-au-Prince (PAP) are depicted (visualisation using Gephi and ArcGIS).CC BY · PubMed Central

Figure 2 (a) Relationship between infectious pressure, calculated from the mobile phone data (Pphone), and the risk of areas experiencing a new outbreak within seven days. Ninety-five percent confidence intervals based on a binomial distribution are included. (b) ROC curve (sensitivity and specificity) for predicting outbreak occurrence within seven days at increasing thresholds of infectious pressure (red: Pphone, green: Pgrav1, black: Pgrav2). Random guesses would yield values along the diagonal line.CC BY · PubMed Central

Figure 3 Correlation (r) between infectious pressure at outbreak onset and average daily number of reported cases during the first D days of the outbreak (one to ten days from onset). Red: Pphone, solid green: Pgrav1, black: Pgrav2.CC BY · PubMed Central

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Populations
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Introduction
Rinaldo, A., Gatto, M., & Rodriguez-Iturbe, I. (2020) · Cambridge University Press eBooks · doi:10.1017/9781108775014.002
Species
Rinaldo, A., Gatto, M., & Rodriguez-Iturbe, I. (2020) · Cambridge University Press eBooks · doi:10.1017/9781108775014.003
Waterborne Disease
Rinaldo, A., Gatto, M., & Rodriguez-Iturbe, I. (2020) · Cambridge University Press eBooks · doi:10.1017/9781108775014.005
Afterthoughts and Outlook
Rinaldo, A., Gatto, M., & Rodriguez-Iturbe, I. (2020) · Cambridge University Press eBooks · doi:10.1017/9781108775014.006
Appendices
Rinaldo, A., Gatto, M., & Rodriguez-Iturbe, I. (2020) · Cambridge University Press eBooks · doi:10.1017/9781108775014.007
EPIDEMIOLOGIA DIGITAL
Pastor-Escuredo, D. (2020) · Revista Diecisiete Investigación Interdisciplinar para los Objetivos de Desarrollo Sostenible · doi:10.36852/2695-4427_2020_03.07
Preface
Rinaldo, A., Gatto, M., & Rodriguez-Iturbe, I. (2020) · Cambridge University Press eBooks · doi:10.1017/9781108775014.001
Index
Rinaldo, A., Gatto, M., & Rodriguez-Iturbe, I. (2020) · Cambridge University Press eBooks · doi:10.1017/9781108775014.008
Improving epidemic risk maps using mobility information from mobile network data
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Background
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Authors: Bengtsson, L., Gaudart, J., Lu, X., Moore, S., Wetter, E., Sallah, K., Rebaudet, S., & Piarroux, R.
Affiliations: Karolinska Institutet (SE)
Inserm (FR)
National University of Defense Technology (CN)
Aix-Marseille Université (FR)
Stockholm School of Economics (SE)
Corresponding author: Bengtsson, Linus
ORCID: Bengtsson, Linus
Gaudart, Jean
Lu, Xin
Moore, Sandra
Wetter, Erik
Rebaudet, Stanislas
Piarroux, Renaud
Venue: Scientific Reports
Publisher: Springer Science and Business Media LLC
Published: 2015-03-09
Volume: 5
Issue: 1
Pages: 8923-8923
ISSN: 2045-2322 (ISSN-L)
DOI: 10.1038/srep08923
PMC: PMC4352843
OpenAlex: W1993035486
Cited by: 281
References: 41
Citations by year: 2026: 6 · 2025: 15 · 2024: 18 · 2023: 20 · 2022: 25 · 2021: 46 · 2020: 66 · 2019: 17 · 2018: 20 · 2017: 20 · 2016: 27 · 2015: 1
FWCI: 18.06
Type: journal article
Language: en
Open access: gold
doi.org
Licence: cc-by
creativecommons.org/licenses/by/4.0 (tdm)
creativecommons.org/licenses/by/4.0 (vor)
Locations: Scientific Reports (publishedVersion, cc-by)
Europe PMC (PubMed Central) (publishedVersion, cc-by)
PubMed Central (publishedVersion, cc-by)
PubMed (publishedVersion)
HAL (submittedVersion, other-oa)
Funders: National Natural Science Foundation of China (71301165, 71101013)
Vetenskapsrådet
Topics: Health Sciences › Medicine › Epidemiology › Data-Driven Disease Surveillance
Social Sciences › Transportation › Human Mobility and Location-Based Analysis
Life Sciences › Immunology and Microbiology › Microbiology › Vibrio bacteria research studies
Keywords: cholera, mobile phone data, human mobility, gravity model, outbreak risk prediction, epidemic preparedness and response, emerging infectious diseases
MeSH terms: Cholera, Disease Outbreaks, Haiti, Humans, Population Surveillance, Sensitivity and Specificity, Reproducibility of Results, Geographic Information Systems, Cell Phone, Spatio-Temporal Analysis, Geographic Mapping

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Using Mobile Phone Data to Predict the Spatial Spread of Cholera

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