Application of big-data analysis and machine learning in epidemiological research to promote primordial prevention effects of green infrastructure ~ Third report~
発表者／presenter’s name：〇Yoshitaka OTSUKA 1 2，Junichi IMANISHI 1 2，Mamoru NASU 2，Yutaka IWASAKI 2
所属／Affiliation：1 Osaka Metropolitan Univ., and Osaka International Research Center for Infectious Diseases., Japan，2 Graduate School of Horticulture, Chiba Univ., Japan

要旨／Abstract

　This study is the third report of the same project presented at the ICLEE 2021 and 2022. In the first report, we provided background on the importance of primordial prevention (i.e., creating communities that prevent people from getting diseases in the first place) through community development focused on green infrastructure, from the perspective of socioeconomic-based health disparities. We also outlined the methodologies employed (machine learning, big-data analysis, and epidemiological study design). 

　In the second report, we described the basic paradigm of philosophy and methodology in more detail. We outlined the grand design and positioning of the project. This project specifically illustrated a philosophical model based on the hierarchical model of social determinants of health proposed by the World Health Organization. In addition, we explained the philosophical premises and methodology of the artificial intelligence-based analysis that forms the backbone of this study, and presented specific examples of it using our previous research.

　In this third report, we present the results of an epidemiological study using artificial intelligence with panel data before and after the COVID-19 pandemic conducted in Koto Ward, Tokyo, in 2014 and 2020. This study corresponds to a preliminary survey for a series of new epidemiological research projects currently conducted by us in Osaka City. Currently, the project has completed the first, second and third survey (March 2021, March 2022 and March 20023) and has started to generate panel data at these three time points. Preparations are also underway to conduct the forth and subsequent surveys after November 2023.

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