Multimodal Visual Analysis of Vector-Borne Infectious Diseases

  • Xiaohui Qiu
  • Fengjun Zhang
  • Hongning Zhou
  • Longfei Du
  • Xin Wang
  • Geng Liang
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 875)


In this work, we mainly analyze an infectious disease – dengue, which is transmitted by Aedes aegypti. Here, we propose a visual analysis method based on multiple perspectives. At first, visual analysis is used to calculate the probability of occurrence of new cases of dengue and the relative risk of occurrence of dengue cases influenced by various variables causing dengue. As a result, we find that climatic variables (rainfall, maximum, minimum and average temperature), imported case and density of Aedes aegypti are three major factors affecting outbreak of dengue. Then, prediction model is built based on analysis to realize early warning. At last, various visual methods are used to show prediction results. This whole method is first used in Yunnan province, China. Compared with current methods, our method takes more factors into consideration and bases on machine learning to build prediction model, which can improve the accuracy of prediction.


Multi-view visual analysis Prediction model Visual display Dengue 



This work was supported by the National Natural Science Foundation of China (No. 61572479), the National Key Research and Development Program of China (No. 2016YFB1001403), the National Natural Science Foundation of China together with the National Research Foundation of Singapore (No. 61661146002), the Science and Technology Program of Guangzhou (Grant No. 201802020015), Key deployment project of the Chinese Academy of Sciences (No. KFZD-SW-316-3), the Strategy Priority Research Program of Chinese Academy of Sciences (No. XDA20080100).


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Copyright information

© Springer Nature Singapore Pte Ltd. 2018

Authors and Affiliations

  • Xiaohui Qiu
    • 1
    • 2
  • Fengjun Zhang
    • 1
  • Hongning Zhou
    • 3
  • Longfei Du
    • 3
  • Xin Wang
    • 1
  • Geng Liang
    • 1
  1. 1.Institute of Software Chinese Academy of SciencesBeijingChina
  2. 2.University of Chinese Academy of SciencesBeijingChina
  3. 3.Yunnan Institute of Parasitic DiseasesPu’er CityChina

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