WFRF:(Gaudart J.)
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| Fältnamn | Indikatorer | Metadata |
|---|---|---|
| 000 | 03782naa a2200433 4500 | |
| 001 | oai:prod.swepub.kib.ki.se:130826558 | |
| 003 | SwePub | |
| 008 | 240701s2015 | |||||||||||000 ||eng| | |
| 009 | oai:hhs.se:1154977480006056 | |
| 024 | 7 | a http://kipublications.ki.se/Default.aspx?queryparsed=id:1308265582 URI |
| 024 | 7 | a https://doi.org/10.1038/srep089232 DOI |
| 024 | 7 | a https://research.hhs.se/esploro/outputs/journalArticle/Using-Mobile-Phone-Data-to-Predict/9910014794221060562 URI |
| 040 | a (SwePub)kid (SwePub)hhs | |
| 041 | a engb eng | |
| 042 | 9 SwePub | |
| 072 | 7 | a ref2 swepub-contenttype |
| 072 | 7 | a art2 swepub-publicationtype |
| 100 | 1 | a Bengtsson, Lu Karolinska Institutet,Flowminder Foundation (SE)4 aut |
| 245 | 1 0 | a Using mobile phone data to predict the spatial spread of cholera |
| 264 | c 2015-03-09 | |
| 264 | 1 | b Springer Science and Business Media LLC,c 2015 |
| 520 | a 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. | |
| 650 | 7 | a MEDICIN OCH HÄLSOVETENSKAPx Hälsovetenskapx Folkhälsovetenskap, global hälsa, socialmedicin och epidemiologi0 (SwePub)303022 hsv//swe |
| 650 | 7 | a MEDICAL AND HEALTH SCIENCESx Health Sciencesx Public Health, Global Health, Social Medicine and Epidemiology0 (SwePub)303022 hsv//eng |
| 653 | a 03 Good Health and Well-being | |
| 700 | 1 | a Gaudart, Ju Aix-Marseille University4 aut |
| 700 | 1 | a Lu, Xu Karolinska Institutet,National University of Defense Technology (CN)4 aut |
| 700 | 1 | a Moore, Su Arcadia University4 aut |
| 700 | 1 | a Wetter, Eriku Stockholm School of Economics,Handelshögskolan i Stockholm4 aut0 (Swepub:hhs)888@hhs.se |
| 700 | 1 | a Sallah, Ku Aix-Marseille University4 aut |
| 700 | 1 | a Rebaudet, Su Aix-Marseille University4 aut |
| 700 | 1 | a Piarroux, Ru Aix-Marseille University4 aut |
| 710 | 2 | a Karolinska Institutetb Flowminder Foundation (SE)4 org |
| 773 | 0 | t Scientific reportsd : Springer Science and Business Media LLCg 5, s. 8923-q 5<8923-x 2045-2322 |
| 856 | 4 | u https://www.nature.com/articles/srep08923.pdf |
| 856 | 4 | u https://research.hhs.se/esploro/outputs/journalArticle/Using-Mobile-Phone-Data-to-Predict/991001479422106056x primaryx Object in context |
| 856 | 4 8 | u http://kipublications.ki.se/Default.aspx?queryparsed=id:130826558 |
| 856 | 4 8 | u https://doi.org/10.1038/srep08923 |
| 856 | 4 8 | u https://research.hhs.se/esploro/outputs/journalArticle/Using-Mobile-Phone-Data-to-Predict/991001479422106056 |
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- Piarroux, R
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