Short-Term Infectious Diarrhea Prediction Using Weather and Search Data in Xiamen, China,Scientific Programming

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Short-Term Infectious Diarrhea Prediction Using Weather and Search Data in Xiamen, China
Scientific Programming Pub Date : 2020-09-28 , DOI: 10.1155/2020/8814222
Zhijin Wang ₁ , Yaohui Huang ₂ , Bingyan He ₁ , Ting Luo ₂ , Yongming Wang ₃ , Yonggang Fu ₁ , Chenxi Huang

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Infectious diarrhea has high morbidity and mortality around the world. For this reason, diarrhea prediction has emerged as an important problem to prevent and control outbreaks. Numerous studies have built disease prediction models using large-scale data. However, these methods perform poorly on diarrhea data. To address this issue, this paper proposes a parsimonious model (PM), which takes historical outpatient visit counts, meteorological factors (MFs) and Baidu search indices (BSIs) as inputs to perform prediction. An experimental evaluation was done to compare the short-term prediction performance of ten algorithms for four groups of inputs, using data collected in Xiamen, China. Results show that the proposed method is effective in improving the prediction accuracy.

更新日期：2020-09-28

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