Cargando…
Forecast of Dengue Cases in 20 Chinese Cities Based on the Deep Learning Method
Dengue fever (DF) is one of the most rapidly spreading diseases in the world, and accurate forecasts of dengue in a timely manner might help local government implement effective control measures. To obtain the accurate forecasting of DF cases, it is crucial to model the long-term dependency in time...
Autores principales: | Xu, Jiucheng, Xu, Keqiang, Li, Zhichao, Meng, Fengxia, Tu, Taotian, Xu, Lei, Liu, Qiyong |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2020
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7014037/ https://www.ncbi.nlm.nih.gov/pubmed/31936708 http://dx.doi.org/10.3390/ijerph17020453 |
Ejemplares similares
-
Association between meteorological factors and the prevalence dynamics of Japanese encephalitis
por: Tu, Taotian, et al.
Publicado: (2021) -
Economic burden of dengue fever in China: A retrospective research study
por: Xu, Meng, et al.
Publicado: (2022) -
Improving Dengue Forecasts by Using Geospatial Big Data Analysis in Google Earth Engine and the Historical Dengue Information-Aided Long Short Term Memory Modeling
por: Li, Zhichao, et al.
Publicado: (2022) -
Deep learning models for forecasting dengue fever based on climate data in Vietnam
por: Nguyen, Van-Hau, et al.
Publicado: (2022) -
Daily forecast of dengue fever incidents for urban villages in a city
por: Chan, Ta-Chien, et al.
Publicado: (2015)