Yue Chu

Yue Chu

Population health scientist & demographer

Population Health Scientist & Demographer

Measurement under imperfect information

I study how incomplete and uneven data shape what we know about population health, and develop demographic, statistical, computational, and AI methods to improve measurement and decision-making.

AI, statistical methods, and data systems for population health measurement

Research agenda Publications Academic CV
23peer-reviewed articles WHOglobal research infrastructure Demographic + statistical + AImeasurement methods R · Python · Julia · SQLresearch software and data systems

Across mortality, fertility, verbal autopsy, survey data quality, and research infrastructure, the work follows a common path from measurement problem to methodological innovation, operational system, and practical use.

  • Reliable population health estimates Developing interpretable estimates of mortality, fertility, disease burden, and health disparities when observations are sparse, incomplete, or uneven.
  • Stronger data systems Improving survey and surveillance data quality, curation, and usability while examining how reporting processes and measurement choices shape observed patterns.
  • Research translated into practice Building validation studies, reusable software, and research infrastructure that move new methods into collaborative population-health systems and decision-making.

Background

I am a Postdoctoral Fellow at the Institute for Population Research at The Ohio State University. My training spans clinical medicine, population health, demography, sociology, epidemiology, and applied statistics. I hold a PhD in Sociology with a minor in Statistics and Demography from Ohio State and an MSPH in Population, Family and Reproductive Health from the Johns Hopkins Bloomberg School of Public Health. Collaborative work with WHO, the Africa Health Research Institute, and other international partners connects methodological research with population-health measurement, research infrastructure, and implementation.

Selected Research Areas

Measuring population health when data are incomplete

I develop demographic and statistical approaches for estimating mortality, fertility, and disease burden from sparse, incomplete, or heterogeneous survey and surveillance data, with uncertainty made explicit.

Demographic estimation Bayesian methods Mortality and fertility

Understanding how measurement processes shape observed health patterns

I study how reporting, classification, survey design, and data quality influence the health patterns and inequalities researchers observe, including validation work that connects reported events with surveillance records.

Measurement error Survey methods Data quality and disparities

Building next-generation measurement systems

I design AI and multimodal methods, reusable research software, and data infrastructure that connect methodological advances to verbal autopsy, adaptive data collection, and operational population-health systems.

AI and multimodal learning Research software Data infrastructure

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Selected Publications

2024

Lancet Global Health

Temporal changes in cause of death among adolescents and adults in six countries in Eastern and Southern Africa: a multi-country cohort study using verbal autopsy data.

Yue Chu, M. Marston, A. Dube, C. Festo, E. Geubbels, S. Gregson, K. Herbst, et al.

Lancet Global Health. 2024;12(8):e1278–e1287.

Publication DOI PDF

2022

The Annals of Applied Statistics

A flexible Bayesian framework to estimate age- and cause-specific child mortality over time from sample registration data

A. E. Schumacher, T. H. McCormick, J. Wakefield, Yue Chu, J. Perin, F. Villavicencio, N. Simon, L. Liu.

The Annals of Applied Statistics. 2022;16(1):124–143.

DOI

2021

PNAS

Estimating seroprevalence of SARS-CoV-2 in Ohio.

D. Kline, Z. Li, Yue Chu, J. Wakefield, W. C. Miller, A. N. Turner, S. J. Clark.

Proceedings of the National Academy of Sciences of the United States of America. 2021;118(26):e2023947118.

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