Bayesian spatiotemporal modelling for disease mapping: an application to preeclampsia and gestational diabetes in Florida, United States Article

Sun, Ning, Bursac, Zoran, Dryden, Ian et al. (2023). Bayesian spatiotemporal modelling for disease mapping: an application to preeclampsia and gestational diabetes in Florida, United States . ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH, 30(50), 109283-109298. 10.1007/s11356-023-29953-0

International Collaboration

cited authors

  • Sun, Ning; Bursac, Zoran; Dryden, Ian; Lucchini, Roberto; Dabo-Niang, Sophie; Ibrahimou, Boubakari

sustainable development goals

authors

publication date

  • October 1, 2023

published in

keywords

  • AIR-POLLUTION
  • Bayesian hierarchical model
  • Disease mapping
  • Environmental Sciences
  • Environmental Sciences & Ecology
  • Florida-USA
  • Gestational diabetes
  • IMPACT
  • Life Sciences & Biomedicine
  • MELLITUS
  • PARTICULATE MATTER PM2.5
  • PREGNANCY
  • Preeclampsia
  • RISKS
  • SPACE-TIME VARIATION
  • Science & Technology
  • Spatial inequality
  • Spatiotemporal modelling
  • TRENDS

Digital Object Identifier (DOI)

publisher

  • SPRINGER HEIDELBERG

start page

  • 109283

end page

  • 109298

volume

  • 30

issue

  • 50