In this talk, I will provide an overview of data science methods, including methods
for Bayesian analysis, causal inference, and machine learning, to inform
environmental policy. This is based on my work analyzing a data platform of
unprecedented size and representativeness. The platform includes more than 500
million observations on the health experience of over 95% of the US population older
than 65 years old linked to air pollution exposure and several confounders. Finally, I
provide an overview of studies on air pollution exposure, environmental racism,
wildfires, and how they also can exacerbate the vulnerability to COVID-19.
Press Coverage
• https://www.nytimes.com/2021/08/13/climate/wildfires-smoke-covid.html
• https://www.nytimes.com/2020/04/07/climate/air-pollution-coronavirus-covid.html
• https://www.nytimes.com/2020/12/07/climate/trump-epa-soot-covid.html?smid=tw-share
• https://science.sciencemag.org/content/360/6388/473
• https://www.npr.org/sections/health-shots/2017/06/28/534594373/u-s-air-pollution-stillkills-thousands-every-year-study-concludes
• https://www.statnews.com/2016/11/14/climate-change-agreements/
• https://news.harvard.edu/gazette/story/2016/08/smoke-waves-will-affect-millions-incoming-decades/
• https://sites.sph.harvard.edu/francesca-dominici/senator-cory-booker-talking-about-nejmstudy/
4 Aprile 2022
Francesca Dominici
Harvard University