米岡 大輔/Daisuke Yoneoka

Daisuke Yoneoka


Associate Professor, Graduate School of Public Health, St. Luke’s International University

Areas of Expertise

  • Biostatistics
  • machine learning
  • theoretical epidemiology
  • spatial statistics
  • public health

Research Program

Using Health Metrics to Monitor and Evaluate the Impact of Health Policies


Daisuke Yoneoka is an independent research scientist at St. Luke’s International University, Japan, working on theoretical aspects of biostatistics and machine learning for personalized medicine. He received his PhD (statistics) in 2016 from the Graduate University for Advanced Studies (SOKENDAI) and began his postdoc in the United States at St. Jude Children’s Research Hospital and in Switzerland at ETH Zurich. He is broadly interested in the field of biostatistics and machine learning, with a special focus on the application of personalized medicine, and is keen on applying mathematics/statistics to medicine/epidemiology/public health.

Select Publications

  1. Daisuke Yoneoka, Takayuki Kawashima, Koji Makiyama, Yuta Tanoue, Shuhei Nomura, and Akifumi Eguchi. Geographically weighted generalized Farrington algorithm for rapid outbreak detection over short data accumulation periods. Statistics in Medicine. 2021; 1– 18.
  2. Daisuke Yoneoka (co-first), Tomohide Yamada, Yuta Hiraike, Kimihiro Hino, Hiroyoshi Toyoshiba, Akira Shishido, Hisashi Noma, Nobuhiro Shojima, and Toshimasa Yamauchi. Deep neural network-based machine learning reduces the screening workload for systematic review: Investigation based on recentclinical guidelines. Journal of Medical Internet Research, 2020.
  3. Shoi Shi, Shiori Tanaka, Ryo Ueno, Stuart Gilmour, Yuta Tanoue, Takayuki Kawashima, Shuhei Nomura, Akifumi Eguchi, Hiroaki Miyata, and Daisuke Yoneoka. Travel restrictions and SARS-CoV-2 transmission: An effective distance approach to estimate impact. Bulletin of the World Health Organization, 98(8):518, 2020.
  4. Daisuke Yoneoka (co-first), Shuhei Nomura, Shoi Shi, Yuta Tanoue, Takayuki Kawashima, Akifumi Eguchi, Kentaro Matsuura, Koji Makiyama, Keisuke Ejima, Toshibumi Taniguchi, Haruka Sakamoto, Hiroyuki Kunishima, Stuart Gilmour, Hiroshi Nishiura, and Hiroaki Miyata. An assessment of self-reported covid-19 related symptoms of 227,898 users of a social networking service in Japan: Has the regional risk changed after the declaration of the state of emergency? The Lancet Regional Health-Western Pacific, 1:100011, 2020.
  5. Daisuke Yoneoka (co-first), Takayuki Kawashima, Yuta Tanoue, Shuhei Nomura, Keisuke Ejima, Shoi Shi, Akifumi Eguchi, Toshibumi Taniguchi, Haruka Sakamoto, Hiroyuki Kunishima, Stuart Gilmour, Hiroshi Nishiura, and Hiroaki Miyata. Early SNS-based monitoring system for the COVID-19 outbreak in Japan: A population-level observational study. Journal of Epidemiology, page JE20200150, 2020.
  6. Daisuke Yoneoka, Cindy Im, and Yutaka Yasui. Parallel repulsive logicre gression with biological adjacency. Biostatistics, 21(4):825–844, 2020.
  7. Daisuke Yoneoka (Co-first), Yuta Tanoue, Takayuki Kawashima, Shuhei Nomura, Shoi Shi, Akifumi Eguchi, Keisuke Ejima, Toshibumi Taniguchi, Haruka Sakamoto, Hiroyuki Kunishima, Stuart Gilmour, Hiroshi Nishiura, and Hiroaki Miyata. Large-scale epidemiological monitoring of the covid-19 epidemic in Tokyo. The Lancet Regional Health-Western Pacific, 3:100016, 2020.
  8. Daisuke Yoneoka and Masayuki Henmi. Clinical heterogeneity in random-effect meta-analysis: Between-study boundary estimate problem. Statisticsin Medicine, 20;38(21):4131-4145 2019.
  9. Daisuke Yoneoka and Masayuki Henmi. Meta-analytical synthesis of regression coefficients under different categorization scheme of continuouscovariates. Statistics in medicine, 36(27):4336–4352, 2017.
  10. Daisuke Yoneoka and Masayuki Henmi. Synthesis of linear regression co-efficients by recovering the within-study covariance matrix from summarystatistics. Research synthesis methods, 8(2):212–219, 2017.
  11. Daisuke Yoneoka, Eiko Saito, and Shinji Nakaoka. New algorithm for con-structing area-based index with geographical heterogeneities and variableselection: An application to gastric cancer screening. Scientific reports, 6(1):1–7, 2016.
  12. Daisuke Yoneoka and Eiko Saito. A statistical note on analyzing and interpreting individual-level epidemiological data. Journal of Epidemiology, 25(4):337–338, 2015.
  13. Daisuke Yoneoka, Masayuki Henmi, Norie Sawada, and Manami Inoue. Synthesis of clinical prediction models under different sets of covariates withone individual patient data. BMC Medical Research Methodology, 15(1):1–11, 2015.