Spatiotemporal dengue fever hotspots associated with climatic factors in Taiwan including outbreak predictions based on machine-learning

  • Sumiko Anno | sumiko_anno@sophia.ac.jp Graduate School of Global Environmental Studies, Sophia University, Tokyo, Japan.
  • Takeshi Hara Department of Engineering, Gifu University, Gifu, Japan.
  • Hiroki Kai Department of Research Development, Remote Sensing Technology Centre of Japan, Tokyo, Japan.
  • Ming-An Lee Centre of Excellence for Ocean Engineering and Department of Environmental Biology and Fisheries Science, National Taiwan Ocean University, Keelung, Taiwan, Province of China.
  • Yi Chang Department of Hydraulic and Ocean Engineering and Institute of Ocean Technology and Marine Affairs, National Cheng Kung University, Tainan, Taiwan, Province of China.
  • Kei Oyoshi Earth Observation Research Centre, Japan Aerospace Exploration Agency, Ibaraki, Japan.
  • Yousei Mizukami Earth Observation Research Centre, Japan Aerospace Exploration Agency, Ibaraki, Japan.
  • Takeo Tadono Earth Observation Research Centre, Japan Aerospace Exploration Agency, Ibaraki, Japan.

Abstract

Early warning systems (EWS) have been proposed as a measure for controlling and preventing dengue fever outbreaks in countries where this infection is endemic. A vaccine is not available and has yet to reach the market due to the economic burden of development, introduction and safety concerns. Understanding how dengue spreads and identifying the risk factors will facilitate the development of a dengue EWS, for which a climate-based model is still needed. An analysis was conducted to examine emerging spatiotemporal hotspots of dengue fever at the township level in Taiwan, associated with climatic factors obtained from remotely sensed data in order to identify the risk factors. Machinelearning was applied to support the search for factors with a spatiotemporal correlation with dengue fever outbreaks. Three dengue fever hotspot categories were found in southwest Taiwan and shown to be spatiotemporally associated with five kinds of sea surface temperatures. Machine-learning, based on the deep AlexNet model trained by transfer learning, yielded an accuracy of 100% on an 8-fold cross-validation test dataset of longitudetime sea surface temperature images.

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Published
2019-11-06
Section
Original Articles
Keywords:
Dengue fever, Spatiotemporal hotspot analysis, Remote sensing, Machine-learning, Taiwan
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How to Cite
Anno, S., Hara, T., Kai, H., Lee, M.-A., Chang, Y., Oyoshi, K., Mizukami, Y., & Tadono, T. (2019). Spatiotemporal dengue fever hotspots associated with climatic factors in Taiwan including outbreak predictions based on machine-learning. Geospatial Health, 14(2). https://doi.org/10.4081/gh.2019.771