https://scholars.lib.ntu.edu.tw/handle/123456789/448985
標題: | A spatiotemporal dengue fever early warning model accounting for nonlinear associations with hydrological factors: a Bayesian maximum entropy approach | 作者: | HWA-LUNG YU Lee, Chieh-Han Chien, Lung-Chang |
關鍵字: | Bayesian maximum entropy; Dengue fever; Early warning; Nonlinear | 公開日期: | 2016 | 卷: | 30 | 期: | 8 | 起(迄)頁: | 2127-2141 | 來源出版物: | Stochastic Environmental Research and Risk Assessment | 摘要: | Dengue fever is identified as one of the most widespread vector-borne diseases in the world. In the last decade, high incidence of dengue fever has been observed in southern Taiwan on an annual basis. There is an urgent need to develop a dengue fever early warning model for the area. Previous studies showed that dengue fever transmission has complex space–time dynamics highly depending upon the activities of human and vectors and their interactions, as well as hydrological processes. This study developed a 1-week-ahead dengue fever warning system in southern Taiwan considering weekly-based nonlinear temporal lagged associations between dengue cases and hydrological factors across space and time by investigating the disease database during 1998–2011. The proposed model is based on an integration of distributed lag nonlinear model and spatiotemporal dependence structure under an epistemic framework of Bayesian maximum entropy method. This study identified that minimum temperature and maximum 24-h rainfall are the most significant to dengue fever incidences. Their associations to dengue fever risks are presented with respect to both hydrological measurement levels and temporal lags. A nested spatiotemporal covariance of transmissions was used to account for the disease diffusion across space and time. Results show that the proposed approach can provide early warnings of the dengue fever occurrences in both initial and peak epidemic stages in 2012, and obtain the spatiotemporal distribution of dengue fever epidemics. These features can be useful for local governmental agencies to seek more effective strategies for dengue fever prevention and control. ? 2016, Springer-Verlag Berlin Heidelberg. |
URI: | https://scholars.lib.ntu.edu.tw/handle/123456789/448985 | ISSN: | 1436-3240 | DOI: | 10.1007/s00477-016-1328-1 | SDG/關鍵字: | Risk assessment; Vector spaces; Bayesian maximum entropies; Bayesian maximum entropy methods; Dengue fevers; Early warning; Nonlinear; Prevention and controls; Spatiotemporal covariance; Spatiotemporal distributions; Maximum entropy methods; Bayesian analysis; dengue fever; disease vector; early warning system; hydrology; maximum entropy analysis; nonlinearity; spatiotemporal analysis; Taiwan |
顯示於: | 生物環境系統工程學系 |
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