Status: Bibliographieeintrag
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| |  Online-Ressource |
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| Verfasst von: | Sewe, Maquins Odhiambo [VerfasserIn]  |
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| | Tozan, Yeşim [VerfasserIn] |
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| | Rocklöv, Joacim [VerfasserIn] |
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| Titel: | Using remote sensing environmental data to forecast malaria incidence at a rural district hospital in Western Kenya |
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| Verf.angabe: | Maquins Odhiambo Sewe, Yesim Tozan, Clas Ahlm and Joacim Rocklöv |
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| E-Jahr: | 2017 |
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| Jahr: | 01 June 2017 |
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| Umfang: | 10 S. |
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| Fussnoten: | Published online 01 June 2017 ; Gesehen am 18.07.2018 |
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| Titel Quelle: | Enthalten in: Scientific reports |
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| Ort Quelle: | [London] : Macmillan Publishers Limited, part of Springer Nature, 2011 |
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| Jahr Quelle: | 2017 |
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| Band/Heft Quelle: | 7(2017) Artikel-Nummer 2589, 10 Seiten |
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| ISSN Quelle: | 2045-2322 |
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| Abstract: | Malaria surveillance data provide opportunity to develop forecasting models. Seasonal variability in environmental factors correlate with malaria transmission, thus the identification of transmission patterns is useful in developing prediction models. However, with changing seasonal transmission patterns, either due to interventions or shifting weather seasons, traditional modelling approaches may not yield adequate predictive skill. Two statistical models,a general additive model (GAM) and GAMBOOST model with boosted regression were contrasted by assessing their predictive accuracy in forecasting malaria admissions at lead times of one to three months. Monthly admission data for children under five years with confirmed malaria at the Siaya district hospital in Western Kenya for the period 2003 to 2013 were used together with satellite derived data on rainfall, average temperature and evapotranspiration(ET). There was a total of 8,476 confirmed malaria admissions. The peak of malaria season changed and malaria admissions reduced overtime. The GAMBOOST model at 1-month lead time had the highest predictive skill during both the training and test periods and thus can be utilized in a malaria early warning system. |
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| DOI: | doi:10.1038/s41598-017-02560-z |
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| URL: | Bitte beachten Sie: Dies ist ein Bibliographieeintrag. Ein Volltextzugriff für Mitglieder der Universität besteht hier nur, falls für die entsprechende Zeitschrift/den entsprechenden Sammelband ein Abonnement besteht oder es sich um einen OpenAccess-Titel handelt.
kostenfrei: Volltext: http://dx.doi.org/10.1038/s41598-017-02560-z |
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| | kostenfrei: Volltext: https://www.nature.com/articles/s41598-017-02560-z |
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| | DOI: https://doi.org/10.1038/s41598-017-02560-z |
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| Datenträger: | Online-Ressource |
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| Sprache: | eng |
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| K10plus-PPN: | 1577704029 |
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| Verknüpfungen: | → Zeitschrift |
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Using remote sensing environmental data to forecast malaria incidence at a rural district hospital in Western Kenya / Sewe, Maquins Odhiambo [VerfasserIn]; 01 June 2017 (Online-Ressource)
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