About

Adrian Dobra is a professor at the University of Washington in the Department of Statistics. He is an elected member of the International Statistical Institute (2024) and a fellow of the American Statistical Association (2023). His professional contact information includes an email address at adobra@uw.edu and a phone number +1 206 543-8460. Further details about his research focus, background, and key contributions are not provided on the page.

Research topics

• Medicine
• Gerontology
• Artificial Intelligence
• Computer Science
• Environmental health
• Family medicine
• Mathematics
• Psychology
• Physical medicine and rehabilitation
• Econometrics
• Geography
• Statistics
• Demography
• Nursing
• Internal medicine

Selected publications

• An Object-Oriented Spatial Statistics Approach for Human Activity Space Estimation

  arXiv (Cornell University) · 2026-05-08

  preprintOpen accessSenior author

  Human activity spaces are shaped by individual mobility and the built environment, motivating statistical methods that integrate GPS observations with GIS representations of places and routes. We propose a novel methodology to estimate activity spaces in built environments from GPS data within the Object Oriented Spatial Statistics framework. We characterize daily mobility through the distribution of time across spatial polygons and road segments, aiming to capture entity-specific time-use fractions and level-$γ$ activity spaces. We develop a time-weighted estimator to handle irregularly sampled GPS observations. We derive an error bound that quantifies the effects of measurement error, nearest-entity misclassification, temporal gaps, boundary crossings, and day-to-day variability. We also develop a map-augmented representation of daily activity patterns, a dwell-time-weighted distance for clustering daily trajectories, and polygon- and road-based stability summaries. Simulation studies and a real-data application demonstrate that the proposed framework recovers concentrated stationary anchors, interpretable travel corridors, and distinct stabilization behavior for dwelling and movement components, supporting the benefits of weighting under irregular sampling. KEYWORDS: GPS data, GIS, human mobility, space-time geography.

  PublisherDOI

• An Object-Oriented Spatial Statistics Approach for Human Activity Space Estimation

  ArXiv.org · 2026-05-08

  articleOpen accessSenior author

  Human activity spaces are shaped by individual mobility and the built environment, motivating statistical methods that integrate GPS observations with GIS representations of places and routes. We propose a novel methodology to estimate activity spaces in built environments from GPS data within the Object Oriented Spatial Statistics framework. We characterize daily mobility through the distribution of time across spatial polygons and road segments, aiming to capture entity-specific time-use fractions and level-$γ$ activity spaces. We develop a time-weighted estimator to handle irregularly sampled GPS observations. We derive an error bound that quantifies the effects of measurement error, nearest-entity misclassification, temporal gaps, boundary crossings, and day-to-day variability. We also develop a map-augmented representation of daily activity patterns, a dwell-time-weighted distance for clustering daily trajectories, and polygon- and road-based stability summaries. Simulation studies and a real-data application demonstrate that the proposed framework recovers concentrated stationary anchors, interpretable travel corridors, and distinct stabilization behavior for dwelling and movement components, supporting the benefits of weighting under irregular sampling. KEYWORDS: GPS data, GIS, human mobility, space-time geography.

  PublisherOA PDF

• AHRI:Sesikhona! (we are here) wheel of fortune

  Africa Health Research Institute · 2026-03-18

  datasetOpen access
  PublisherDOI

• Estimating Population Viral Load Contextual Exposure Using GPS-Derived Activity Spaces in Rural South Africa

  arXiv (Cornell University) · 2026-04-30

  preprintOpen accessSenior author

  This article introduces novel methodologies for estimating contextual exposure to HIV population viral load using GPS data. We propose a comprehensive analytical framework comprising (i) local (grid-cell level) estimation of HIV population viral load, (ii) derivation of individual activity spaces from GPS trajectories, and (iii) quantification of contextual exposure to HIV within these activity spaces. We integrate HIV surveillance and sociodemographic survey data with GPS-based mobility data collected in rural KwaZulu-Natal, South Africa, to characterize mobility patterns among young adults aged 20-30 years. Using derived measures of mobility and contextual exposure, we assess whether participants' sex and age systematically influence the magnitude, configuration, and heterogeneity of their mobility patterns. Furthermore, we describe analytical approaches to examine how contextual exposure to HIV evolves as activity spaces extend beyond static residential locations, outlining procedures to identify GPS-tracked participants at elevated risk of HIV acquisition. KEYWORDS: Population viral load exposure; GPS-based mobility analysis; Activity space

  PublisherDOI

• Estimating Population Viral Load Contextual Exposure Using GPS-Derived Activity Spaces in Rural South Africa

  ArXiv.org · 2026-04-30

  articleOpen accessSenior author

  This article introduces novel methodologies for estimating contextual exposure to HIV population viral load using GPS data. We propose a comprehensive analytical framework comprising (i) local (grid-cell level) estimation of HIV population viral load, (ii) derivation of individual activity spaces from GPS trajectories, and (iii) quantification of contextual exposure to HIV within these activity spaces. We integrate HIV surveillance and sociodemographic survey data with GPS-based mobility data collected in rural KwaZulu-Natal, South Africa, to characterize mobility patterns among young adults aged 20-30 years. Using derived measures of mobility and contextual exposure, we assess whether participants' sex and age systematically influence the magnitude, configuration, and heterogeneity of their mobility patterns. Furthermore, we describe analytical approaches to examine how contextual exposure to HIV evolves as activity spaces extend beyond static residential locations, outlining procedures to identify GPS-tracked participants at elevated risk of HIV acquisition. KEYWORDS: Population viral load exposure; GPS-based mobility analysis; Activity space

  PublisherOA PDF

• Trends in population HIV viral suppression: a longitudinal analysis

  AIDS · 2025-05-29 · 2 citations

  articleOpen access

  This study examines trends in HIV viral suppression and the impact of age and sex on suppression rates in a rural KwaZulu-Natal (KZN) population-based cohort from 2011 to 2023. Population viral suppression improved over time, peaking at 71% for men and 79% for women in 2022, but declined in 2023 possibly due to COVID-19 disruptions. Viral suppression rates were substantially lower than reported regional estimates for KZN. Disparities among younger males highlight the need for interventions to achieve UNAIDS 95-95-95 targets.

  PublisherDOI

• Modeling Human Spatial Mobility Patterns with the Lévy Flight Cluster Model

  ArXiv.org · 2025-08-30

  preprintOpen access

  Despite the extensive collection of individual mobility data over the past decade, fueled by the widespread use of GPS-enabled personal devices, the existing statistical literature on estimating human spatial mobility patterns from temporally irregular location data remains limited. In this paper, we introduce the Lévy Flight Cluster Model (LFCM), a hierarchical Bayesian mixture model designed to analyze an individual's activity distribution. The LFCM can be utilized to determine probabilistic overlaps between individuals' activity patterns and serves as an anonymization tool to generate synthetic location data. We present our methodology using real-world human location data, demonstrating its ability to accurately capture the key characteristics of human movement.

  PublisherOA PDFDOI

• A statistical framework for analyzing activity pattern from GPS data

  ArXiv.org · 2025-04-06

  preprintOpen accessSenior author

  We introduce a novel statistical framework for analyzing the GPS data of a single individual. Our approach models daily GPS observations as noisy measurements of an underlying random trajectory, enabling the definition of meaningful concepts such as the average GPS density function. We propose estimators for this density function and establish their asymptotic properties. To study human activity patterns using GPS data, we develop a simple movement model based on mixture models for generating random trajectories. Building on this framework, we introduce several analytical tools to explore activity spaces and mobility patterns. We demonstrate the effectiveness of our approach through applications to both simulated and real-world GPS data, uncovering insightful mobility trends.

  PublisherOA PDFDOI

• Optimized Random-Combinations of Total Fertility Rates and Life Expectancies at Birth for Probabilistic Population Projections

  Population Research and Policy Review · 2025-01-08

  article
  PublisherDOI

• Latent Class Analysis of Well-Being in Older Women

  Journal of Women s Health · 2025-10-01

  articleOpen access

  Background: Previous efforts to assess well-being and health focused on individual indicators of hedonic, evaluative, or eudaemonic measures or summated scores reflecting all dimensions. The objectives of this study were to develop profiles that preserve distinct dimensions of hedonic, evaluative, and eudemonic well-being while permitting its exploration as a predictor of health endpoints. Methods: A total of 81,148 Women’s Health Initiative (WHI) participants with well-being measures collected in 2011–2012 (mean age = 76.4 years) were included. Women were recruited to the WHI Clinical Trial and Observational Cohort, continued participation in WHI Extensions (2005–2010 and 2010–2015), and completed the 2011–2012 questionnaire. Classes were identified from hedonic (life enjoyment, happiness, life satisfaction, quality of life) and eudaemonic (personal growth, purpose in life, environmental mastery, self-mastery, self-control) measures using latent class analysis. Characteristics were described by classes, and associations with all-cause mortality were examined using logistic regression. Results: Four well-being classes were identified. Class 2 (17.8%) had the lowest (worst) well-being scores, and class 4 (53.9%) had the highest (best) well-being scores in all dimensions. Class 1 (6.4%) had high hedonic and moderate eudaemonic with low life enjoyment. Class 3 (21.9%) had high hedonic and moderate eudaemonic scores with low self-mastery. Women in class 4 were younger, more educated, reported higher annual incomes, least likely to smoke, and most likely to drink alcohol daily. Relative to class 4, odds ratios (95% confidence interval) of all-cause mortality were 1.33 (1.24–1.43), 1.75 (1.67–1.84), and 1.26 (1.20–1.31) for classes 1, 2, and 3, respectively, even after adjustment for demographic and behavioral confounders. Conclusion: Latent class analysis identified groups by levels of hedonic and eudaemonic indicators, preserving information about well-being dimensions while supporting interpretation of relationships with well-being important to older women and health research.

  PublisherDOI

Recent grants

• ATD: Geospatial Graphical Models of Human Response to Emergencies

  NSF · $250k · 2017–2021

• ATD Collaborative Research: Statistical Ensembles for the Identification of Bacterial Genomes

  NSF · $343k · 2011–2015

Frequent coauthors

• Frank Tanser

  University of KwaZulu-Natal

  48 shared

• Till Bärnighausen

  University Hospital Heidelberg

  38 shared

• Jinnan Liu

  Xi'an Jiaotong University

  25 shared

• Mike West

  23 shared

• Beatrix Jones

  Massey University

  20 shared

• Alex Lenkoski

  Norwegian Computing Center

  18 shared

• Maxime Inghels

  Université Paris Cité

  18 shared

• Hae–Young Kim

  New York University

  16 shared

Awards & honors

• Elected Member, International Statistical Institute (ISI) (2…
• Fellow, American Statistical Association (ASA) (2023)