Developing a dengue forecast model using machine learning: A case study in China

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PLoSNTDs

PLoSNTDs

PLOS Neglected Tropical Diseases is the first open-access journal devoted to the world's most neglected tropical diseases (NTDs), such as elephantiasis, river blindness, leprosy, hookworm, schistosomiasis, and African sleeping sickness. Links to PLoSNTD content is shared via GHHub using the Creative Commons. http://www.plosntds.org/

by Pi Guo, Tao Liu, Qin Zhang, Li Wang, Jianpeng Xiao, Qingying Zhang, Ganfeng Luo, Zhihao Li, Jianfeng He, Yonghui Zhang, Wenjun Ma Background In China, dengue remains an important public health issue with expanded areas and increased incidence recently. Accurate and timely forecasts of dengue incidence in China are still lacking. We aimed to use the state-of-the-art machine learning algorithms to develop an accurate predictive model of dengue

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Developing a dengue forecast model using machine learning: A case study in China