Sarah Turner
· University Professor of economics, education and public policyUniversity of Virginia · Public Policy
Active 1978–2024
About
Sarah Turner is a University Professor at the University of Virginia, with appointments in economics, education, and public policy. She holds the Souder Family Endowed Chair in economics and is a leading expert in labor economics and the economics of education, focusing on higher education and high-skill labor markets. Professor Turner investigates how higher educational opportunities and policies influence the labor market, analyzing factors affecting college completion, access, and attendance, as well as the behavioral impact of financial aid policies, including student aid, social insurance, and welfare. Her research also explores academic labor markets, the role of international students and skilled workers in the U.S. economy, and the effects of economic disruptions on educational and labor market transitions. She has examined the impact of student loan policies, international student flows, skilled migration, and minimum wage changes on educational and labor outcomes. Professor Turner publishes extensively in leading academic journals and has served on editorial boards, including the American Economic Review. She is a faculty research associate at the National Bureau of Economic Research and a research affiliate at the University of Michigan’s Population Studies Center. With over 25 years at UVA, she teaches courses in labor economics and the economics of education, having chaired the department of economics from 2013 to 2016. Recognized as one of the most influential education scholars in the United States in 2024, she was awarded the Jefferson Scholars Foundation Faculty Prize in 2023 for her leadership, scholarship, and citizenship.
Research topics
- Computer Science
- Political Science
- Artificial Intelligence
- Business
- Economics
- Econometrics
- Medicine
- Geography
- Machine Learning
- Actuarial science
- Operations research
- Engineering
- World Wide Web
- Meteorology
- Mathematics
- Statistics
- Environmental health
- Data science
Selected publications
Proceedings of the National Academy of Sciences · 2022 · 311 citations
- Computer Science
- Political Science
- Artificial Intelligence
Short-term probabilistic forecasts of the trajectory of the COVID-19 pandemic in the United States have served as a visible and important communication channel between the scientific modeling community and both the general public and decision-makers. Forecasting models provide specific, quantitative, and evaluable predictions that inform short-term decisions such as healthcare staffing needs, school closures, and allocation of medical supplies. Starting in April 2020, the US COVID-19 Forecast Hub (https://covid19forecasthub.org/) collected, disseminated, and synthesized tens of millions of specific predictions from more than 90 different academic, industry, and independent research groups. A multimodel ensemble forecast that combined predictions from dozens of groups every week provided the most consistently accurate probabilistic forecasts of incident deaths due to COVID-19 at the state and national level from April 2020 through October 2021. The performance of 27 individual models that submitted complete forecasts of COVID-19 deaths consistently throughout this year showed high variability in forecast skill across time, geospatial units, and forecast horizons. Two-thirds of the models evaluated showed better accuracy than a naïve baseline model. Forecast accuracy degraded as models made predictions further into the future, with probabilistic error at a 20-wk horizon three to five times larger than when predicting at a 1-wk horizon. This project underscores the role that collaboration and active coordination between governmental public-health agencies, academic modeling teams, and industry partners can play in developing modern modeling capabilities to support local, state, and federal response to outbreaks.
The United States COVID-19 Forecast Hub dataset
Scientific Data · 2022 · 126 citations
- Computer Science
- Machine Learning
- Computer Science
Academic researchers, government agencies, industry groups, and individuals have produced forecasts at an unprecedented scale during the COVID-19 pandemic. To leverage these forecasts, the United States Centers for Disease Control and Prevention (CDC) partnered with an academic research lab at the University of Massachusetts Amherst to create the US COVID-19 Forecast Hub. Launched in April 2020, the Forecast Hub is a dataset with point and probabilistic forecasts of incident cases, incident hospitalizations, incident deaths, and cumulative deaths due to COVID-19 at county, state, and national, levels in the United States. Included forecasts represent a variety of modeling approaches, data sources, and assumptions regarding the spread of COVID-19. The goal of this dataset is to establish a standardized and comparable set of short-term forecasts from modeling teams. These data can be used to develop ensemble models, communicate forecasts to the public, create visualizations, compare models, and inform policies regarding COVID-19 mitigation. These open-source data are available via download from GitHub, through an online API, and through R packages.
Evaluation of individual and ensemble probabilistic forecasts of COVID-19 mortality in the US
medRxiv (Cold Spring Harbor Laboratory) · 2021 · 77 citations
- Computer Science
- Political Science
- Artificial Intelligence
Abstract Short-term probabilistic forecasts of the trajectory of the COVID-19 pandemic in the United States have served as a visible and important communication channel between the scientific modeling community and both the general public and decision-makers. Forecasting models provide specific, quantitative, and evaluable predictions that inform short-term decisions such as healthcare staffing needs, school closures, and allocation of medical supplies. Starting in April 2020, the US COVID-19 Forecast Hub ( https://covid19forecasthub.org/ ) collected, disseminated, and synthesized tens of millions of specific predictions from more than 90 different academic, industry, and independent research groups. A multi-model ensemble forecast that combined predictions from dozens of different research groups every week provided the most consistently accurate probabilistic forecasts of incident deaths due to COVID-19 at the state and national level from April 2020 through October 2021. The performance of 27 individual models that submitted complete forecasts of COVID-19 deaths consistently throughout this year showed high variability in forecast skill across time, geospatial units, and forecast horizons. Two-thirds of the models evaluated showed better accuracy than a naïve baseline model. Forecast accuracy degraded as models made predictions further into the future, with probabilistic error at a 20-week horizon 3-5 times larger than when predicting at a 1-week horizon. This project underscores the role that collaboration and active coordination between governmental public health agencies, academic modeling teams, and industry partners can play in developing modern modeling capabilities to support local, state, and federal response to outbreaks. Significance Statement This paper compares the probabilistic accuracy of short-term forecasts of reported deaths due to COVID-19 during the first year and a half of the pandemic in the US. Results show high variation in accuracy between and within stand-alone models, and more consistent accuracy from an ensemble model that combined forecasts from all eligible models. This demonstrates that an ensemble model provided a reliable and comparatively accurate means of forecasting deaths during the COVID-19 pandemic that exceeded the performance of all of the models that contributed to it. This work strengthens the evidence base for synthesizing multiple models to support public health action.
Recent grants
NIH · $47k · 2011
NIH · $7.2M · 2010
Frequent coauthors
- 60 shared
Marylyn D. Ritchie
University of Pennsylvania
- 45 shared
Alexander F. Koeppel
Signature Research (United States)
- 30 shared
VP Nagraj
Signature Research (United States)
- 22 shared
Bruce Budowle
- 22 shared
Scott Dudek
- 20 shared
Shakeel Jessa
Signature Research (United States)
- 20 shared
August E. Woerner
University of North Texas
- 20 shared
Jianye Ge
University of North Texas
Awards & honors
- Souder Family Endowed Chair in economics
- Jefferson Scholars Foundation Faculty Prize (2023)
- Education Week RHSU Edu-Scholar Rankings most influential ed…
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