Strategies for Controlling Non-Transmissible Infection Outbreaks Using a Large Human Movement Data Set
- Submitting institution
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University of Exeter
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 1794
- Type
- D - Journal article
- DOI
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10.1371/journal.pcbi.1003809
- Title of journal
- PLoS Computational Biology
- Article number
- ARTN e1003809
- First page
- -
- Volume
- 10
- Issue
- 9
- ISSN
- 1553-734X
- Open access status
- Out of scope for open access requirements
- Month of publication
- September
- Year of publication
- 2014
- URL
-
-
- Supplementary information
-
-
- Request cross-referral to
- -
- Output has been delayed by COVID-19
- No
- COVID-19 affected output statement
- -
- Forensic science
- No
- Criminology
- No
- Interdisciplinary
- Yes
- Number of additional authors
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6
- Research group(s)
-
-
- Citation count
- 4
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- We devised a methodology for using commercially available datasets of human movement to improve detection of the source of non-transmissible infections (Legionnaires disease). The work was undertaken in collaboration with Public Health England (PHE, Obaghe Edeghere), and it is expected that the results will inform public health policy. The work is currently informing methodologies for COVID-related research utilising human movement data (e.g. doi: 10.2196/24432, arXiv:2011.12958v1)
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -