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Emory Rollins School of Public Health
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Emily  Peterson

Asst Professor

Research Assistant Professor

Faculty, Biostatistics and Bioinformatics

My research falls at the intersection of spatial epidemiological mapping, Bayesian hierarchical methods, population dynamics. My primary research focus/expertise is in the development and application of Bayesian hierarchical spatial temporal models for analyzing public health and global health indicators with particular interest in the incorporation of measurement error methods to account for data related errors and uncertainty within an estimation framework. My current projects focus on the following: (1) estimation of trends in maternal outcomes among women who use drugs, (2) maternal mortality misclassification and estimation globally and within local areas, and (3) data fusion methods to estimate true small area populations-at-risk with multiple data sources. I collaborate with academics and public health professionals within Emory, at other academic institutions, at the CDC, and the WHO. 

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Areas of Interest

  • Bayesian Analysis
  • Ecology, Demography and Environmental Sciences
  • Environmental Health
  • Epidemiology
  • Global Health
  • Health Disparities
  • Spatial Analysis/GIS
  • Women’s Health


  • Ph.D. in Biostatistics 2020, University of Massachusetts Amherst
  • M.S. in Biostatistics 2015, Vanderbilt University,
  • M.S. in Public Health Sciences 2013, Penn State University
  • B.S. in Psychology 2006, Davidson College


  • , , A Bayesian hierarchical small area population model accounting for data source specific methodologies from American Community Survey, Population Estimates Program, and Decennial Census data, Annals of Applied Statistics, ,
  • , , A Bayesian approach to estimate maternal mortality globally using national civil registration vital statistics data accounting for reporting errors, Journal of Public Policy, submitted,
  • , , Estimating misclassification errors in the reporting of maternal mortality in national civil registration vital statistics systems: A Bayesian hierarchical bivariate random walk model to estimate sensitivity and specificity for multiple countries and years with missing data, Statistics in Medicine, 41, 2483-2496