Climate-Driven Statistical Models as Effective Predictors of Local Dengue Incidence in Costa Rica: A Generalized Additive Model and Random Forest Approach
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| 著者: | , , , |
|---|---|
| フォーマット: | artículo original |
| 出版日付: | 2019 |
| その他の書誌記述: | Climate has been an important factor in shaping the distribution and incidence of dengue cases in tropical and subtropical countries. In CostaRica, a tropical country with distinctive micro-climates, dengue has been endemic since its introduction in 1993, inflicting substantial economic, social, and public health repercussions. Using the number of dengue reported cases and climate data from 2007-2017, we fitted a prediction model ap-plying a Generalized Additive Model (GAM) and Random Forest (RF)approach, which allowed us to retrospectively predict the relative risk of dengue in five climatological diverse municipalities around the country. |
| 国: | Kérwá |
| 機関: | Universidad de Costa Rica |
| Repositorio: | Kérwá |
| 言語: | Inglés |
| OAI Identifier: | oai:kerwa.ucr.ac.cr:10669/83429 |
| オンライン･アクセス: | https://revistas.ucr.ac.cr/index.php/matematica/article/view/39931 https://hdl.handle.net/10669/83429 |
| キーワード: | Mosquito-borne diseases Dengue Climate variables Costa Rica Generalized additive models Random forests Enfermedades de trasmisión vectorial Variables climáticas Modelos aditivos generalizados Bosques aleatorios |