Applied research on environment and health using machine/deep learning and big data

Society medicine
3:GOOD HEALTH AND WELL-BEING
9:INDUSTRY, INNOVATION AND INFRASTRUCTURE
13:CLIMATE ACTION

Graduate School of Global Environmental Studies / Master's (Doctoral) Program in Global Environmental Studies

Anno Sumiko Professor

Abstract

Our research contributes to crisis management of emerging and reemerging infectious diseases using big data such as satellite data, human location data collected by IoT sensors and devices, and behavioral history data, as well as machine and deep learning.

Related patents/papers

Sumiko Anno, Hirakawa Tsubasa, Satoru Sugita, Shinya Yasumoto, Ming-An Lee, Yoshinobu Sasaki & Kei Oyoshi (2023) Challenges and implications of predicting the spatiotemporal distribution of dengue fever outbreak in Chinese Taiwan using remote sensing data and deep learning, Geo-spatial Information Science, DOI: 10.1080/10095020.2022.2144770

Anno S, Hirakawa T, Sugita S and Yasumoto S (2022) A graph convolutional network for predicting COVID-19 dynamics in 190 regions/countries. Front. Public Health 10:911336. doi: 10.3389/fpubh.2022.911336

Anno S, Hara T, Kai H, Lee MA, Chang Y, Oyoshi K, Mizukami Y, Tadono T. Spatiotemporal dengue fever hotspots associated with climatic factors in Taiwan including outbreak predictions based on machine-learning. Geospat Health. 2019 Nov 6;14(2). doi: 10.4081/gh.2019.771. PMID: 31724367.

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