The DOI matches its Crossref record; 41 of the 41 references it cites were found in a registry or an index.
Using Mobile Phone Data to Predict the Spatial Spread of Cholera
Open accessAbstract
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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“In the realm of infectious diseases, ML and AI have been applied to predict disease outbreaks and identify high-risk areas [19, 20].”
From Integrating machine learning and artificial intelligence in life-course epidemiology: pathways to innovative public health solutions (2024) · cites this paper as [20]
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“Bengtsson et al. (2015) utilized mobile phone data and machine learning techniques to predict the spatial spread of cholera in Haiti following the 2010 earthquake [20].”
From Integrating machine learning and artificial intelligence in life-course epidemiology: pathways to innovative public health solutions (2024) · cites this paper as [20]
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Oliver, N., Lepri, B., Sterly, H., Lambiotte, R., Deletaille, S., De Nadai, M., Letouzé, E., Salah, A. A., Benjamins, R., Cattuto, C., et al. (2020) · Science Advances · doi:10.1126/sciadv.abc0764 · Cited by 631
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“In order to have a better estimation of the basic reproduction number and effective reproduction number, as well as predicting the trend of epidemic transmission, we not only need to know precise epidemiological determinants, but also need to improve the model itself by further considering the diversity in susceptibil…”
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Metadata
- 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
- Venue
- Scientific Reports
- Published
- 2015-03-09
- Volume
- 5
- Issue
- 1
- Pages
- 8923-8923
- ISSN
- 2045-2322 (ISSN-L)
- 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
- 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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