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Developing Machine Learning Algorithms for Tracking Epidemics in Time and Space

Epidemic diffusion is a space–time process, which can be considered as the movement of linked cases through space and time. Therefore, space-time locations of cases are key to identify any diffusion process. Dr. Tzai-Hung Wen, Professor at Department of Geography, National Taiwan University, borrowed the concepts of tracking the routes of typhoons for developing machine learning algorithms for profiling diffusion dynamics of disease clustering and epidemic propagation. The novel algorithms that utilize the temporal lag within the diffusion process and the spatial distance between cases to detect the spatial-temporal sub-clusters and to uncover the development of progression chains. The progression of dengue epidemics in south Taiwan from 1998 to 2015 are used for demonstrating the capability of the algorithms. Dr. Wen's work contributes a more detailed and in-depth understanding of the geographic diffusion process of epidemics. These results have been published in the international journals, including Scientific Reports 7:12565 and Annals of the American Association of Geographers 108(4):1168-1186. Dr. Wen suggests that health authorities can integrate the epidemic reporting system with the algorithms as an automatic early-warning decision tool for uncovering the evolution of disease transmission in time and space, such as dengue, measles and tuberculosis.




Tzai-Hung Wen, PhD

Department of Geography, National Taiwan University

E-mail: wenthung@ntu.edu.tw

Tel: +886-2-3366-5847

Last Modified : 2020/04/15