About

Katherine B. Ensor is the Noah G. Harding Professor of Statistics at Rice University and serves as the Director of the Center for Computational Finance and Economic Systems (CoFES). She is a leading expert in applying computational and statistical analysis to help build resilient and adaptive communities, with efforts spanning public health, community analytics, environmental statistics, and computational finance. Since May 2020, she has been instrumental in establishing and implementing statistical systems for assessing health information from wastewater samples for SARS-CoV-2 and its variants, expanding this scope to include up to 29 communicable illnesses through Houston Wastewater Epidemiology. Ensor led the development of the Urban Data Platform (UDP) for the Greater Houston Area, facilitating cross-disciplinary research on urban issues such as evictions, COVID-19, and flooding. With over 35 years of faculty experience at Rice, she has contributed significantly to the department, including serving as chair from 1999 to 2013, and has been involved in numerous initiatives such as joint Ph.D. programs, professional master's programs, and undergraduate minors. Her research focuses on data science, high-dimensional time series, spatial processes, machine learning, Bayesian methods, and stochastic processes. Ensor has held prominent service roles, including chairing the NASEM Committee on Frontiers of Statistics, serving on the Board of Trustees for NSF's IPAM, and being the president of the American Statistical Association from 2021 to 2023. She has received numerous awards and honors recognizing her leadership, scholarship, and mentoring, including the Florence Nightingale David Award, the William Sealy Gossett Lecture and Award, and the ASA Founders Award.

Research topics

• Computer Science
• Medicine
• Environmental science
• Virology
• Environmental health
• Data Mining
• Environmental engineering
• Engineering
• Machine Learning
• Sociology
• Risk analysis (engineering)
• Pathology
• Management science
• Biology
• Internal medicine
• Mathematics
• Genetics
• Data science
• Software engineering
• Business
• Computer network
• Operations management
• Demography
• Telecommunications

Selected publications

• The Weight of the Storm: Using Weighting Methods to Determine the Effect of Hurricane Harvey on Mental Health Outcomes in Harris County, Texas

  Public Health Reports · 2026-01-17

  articleOpen access

  OBJECTIVES: Nonprobability sampling, commonly used in disaster research, can lead to incorrect estimates or limit the generalizability of results. We collected data through the Texas Flood Registry (TFR) and used raking and propensity score weighting to provide insight into the effect of Hurricane Harvey (hereinafter, Harvey) on Harris County, Texas. METHODS: From April 2018 through October 2020, residents of areas affected by Harvey enrolled in the TFR completed a survey on their storm-related experiences (n = 20 653). Using logistic regression, we assessed the relationship between Harvey-related exposures and distress among Harris County residents (n = 12 279). We used raking to adjust the sample distribution to reflect demographic characteristics of Harris County and propensity scores to address confounding. RESULTS: Of respondents, 56% and 43% reported home damage and income loss due to Harvey, respectively. From April 2018 through April 2020, respondents completed the Impact of Event Scale questionnaire (n = 10 631), with 23% reporting symptoms consistent with severe distress related to Harvey. The raking-adjusted odds ratio of greater Harvey-related distress was 6.21 (95% CI, 5.44-7.09) times higher among residents who had home damage than among those who did not and 2.92 (95% CI, 2.59-3.30) times higher among those who had economic loss than among those who did not. CONCLUSIONS: We found consistent associations between adverse storm experiences and Harvey-related distress across unweighted and weighted approaches. We recommend using raking to adjust a nonprobability sample to better reflect population demographic characteristics and obtain general trends of postdisaster exposures and outcomes. We recommend using propensity scores when outcomes may be related to unmeasured confounding.

  PublisherOA PDFDOI

• Understanding transmission and infections of respiratory syncytial virus through wastewater-based epidemiology

  Environmental Science Water Research & Technology · 2026-01-01

  articleOpen access1st authorCorresponding

  RSV RNA measured in wastewater can be used to estimate infection levels in a community, linking sewer surveillance signals to inferred RSV cases through statistical epidemiological models.

  PublisherDOI

• Tracking <i>Candida auris</i> in Communities via Wastewater: Facility-Level Surveillance and Targeted Sequencing

  medRxiv · 2026-02-02

  articleOpen access

  Abstract Candida auris is a multidrug-resistant fungal pathogen that presents substantial challenges for healthcare facilities due to its high mortality rates among vulnerable populations. Six C. auris clades have been identified based on their susceptibility to antifungal treatment and environmental stressors. Identifying the circulating C. auris clade(s) is critical for understanding transmission and selecting a disease control strategy. To inform targeted implementation of community wastewater monitoring for C. auris , samples were collected over 34 weeks from 8 nursing homes and 6 downstream wastewater treatment plants (WWTPs). Detection rates and concentrations of C. auris DNA were significantly higher in samples from nursing homes compared to those from WWTPs. Amplicon sequencing methods were developed and applied to characterize the circulating C. auris clade in a nursing home wastewater sample. This study demonstrates the utility of wastewater monitoring as a resource-efficient approach for detecting and subtyping C. auris in vulnerable communities.

  PublisherOA PDFDOI

• Uncovering Dynamic Relationships Between SARS-CoV-2 Wastewater Concentrations and Community Infections via Bayesian Spatial Functional Concurrent Regression

  Data Science in Science · 2025-06-24 · 1 citations

  articleOpen accessSenior author

  Monitoring wastewater concentrations of SARS-CoV-2 yields a low-cost, noninvasive method for tracking disease prevalence and provides early warning signs of upcoming outbreaks in the serviced communities. There is tremendous clinical and public health interest in understanding the complex relationships between wastewater viral loads and infection rates in the population. As both data sources may contain substantial noise and missingness, in addition to spatial and temporal dependencies, properly modeling this relationship must address these numerous complexities simultaneously while providing interpretable and clear insights. We propose a novel Bayesian functional concurrent regression model that accounts for both spatial and temporal correlations while estimating the time-dependent effects between wastewater concentrations and positivity rates over time. We explicitly model the time lag between the two series and provide full posterior inference on the possible delay between spikes in wastewater concentrations and subsequent outbreaks. We estimate a time lag likely between 5 to 11 days between spikes in wastewater levels and reported clinical positivity rates. Additionally, we find a dynamic relationship between wastewater concentration levels and the strength of its association with positivity rates that fluctuates between outbreaks and non-outbreaks.

  PublisherDOI

• Sensitivity, throughput, and cost analysis of concentration methods for multi-target pathogen wastewater monitoring

  medRxiv · 2025-05-13

  preprintOpen access

  Abstract Wastewater-based epidemiology is an efficient method for monitoring the transmission of diverse pathogens in communities. Standard wastewater surveillance workflows typically involve wastewater concentration, nucleic acid extraction, and pathogen quantification. While various concentration methods are used, most comparisons of concentration methods have focused primarily on SARS-CoV-2, highlighting the need for further research to guide method selection for monitoring a suite of diverse pathogens. In this study, a head-to-head comparison of six different concentration methods was performed, including direct extraction (with and without bead beating), electronegative (HA) filtration, solids concentration, and magnetic bead-based concentration (using Nanotrap® particles; with and without bead beating). Methods were assessed for sensitivity, inhibitor removal, and recovery rates of fourteen microorganisms, including viruses, bacteria, and fungal pathogens. The cost of each method was also estimated. Results showed that the concentration method selection significantly impacts the sensitivity and economic costs of the wastewater monitoring workflow. Based on the results, a concentration approach that combines HA filtration and solids concentration is recommended to optimize detection across various pathogens. This study provides data-driven insights to enhance the reliability and cost-effectiveness of wastewater surveillance systems that can support public health responses for a broad range of diseases. Synopsis Six concentration methods were compared in terms of sensitivity and cost for the detection of 14 diverse pathogens in wastewater.

  PublisherOA PDFDOI

• Sensitivity, Throughput, and Cost Analysis of Concentration Methods for Multitarget Pathogen Wastewater Monitoring

  ACS ES&T Water · 2025-09-04 · 3 citations

  article

  Wastewater-based epidemiology is an efficient method for monitoring the transmission of diverse pathogens in communities. While various concentration methods are used, most were selected to detect SARS-CoV-2 and other respiratory viruses. Research is needed to guide the method selection for monitoring diverse pathogens in wastewater. In this study, a head-to-head comparison of six different concentration methods was performed, including direct extraction (with and without bead beating), electronegative (HA) filtration, solid concentration, and magnetic bead-based concentration (using Nanotrap particles; with and without bead beating). Methods were assessed for sensitivity, inhibitor removal, recovery rates, and cost, targeting 14 microorganisms including viruses, bacteria, and fungal pathogens. Results showed that the concentration method selection significantly impacts the sensitivity and economic costs of the wastewater monitoring workflow. While no single method was optimal for all targets, combining HA filtration and solid methods in parallel for the same sample is recommended to sensitively detect viruses, bacteria, and fungal pathogens. The magnetic bead-based method can be automated but costs more per sample and is less sensitive for some targets. This study provides data-driven insights to enhance the reliability and cost-effectiveness of wastewater surveillance systems that can support public health responses for a broad range of diseases.

  PublisherDOI

• A Nonlinear Hierarchical Time Series Approach to Citywide Trend Assessment of Viruses, Hot Spot Signals, and Right-Sizing System

  Data Science in Science · 2025-10-01 · 1 citations

  articleOpen access1st authorCorresponding

  Wastewater surveillance has proven to be a cost-effective cornerstone in public health, offering vital insights into a spectrum of community health issues, particularly during the COVID-19 pandemic. For many municipalities, multiple locations are measured regularly in time, and trend assessment becomes key for health department action. We implement nonlinear hierarchical state-space time series methods that capture the citywide trend and identify deviations in the trend for sewersheds serving different population sizes. This work aims to identify disease dynamics across a region and provide an early warning system for a rise in viral levels in a community. Our fast online algorithm enables real-time statistical evaluations of deviations in virus levels from a citywide trend, aiding in the early detection of potential outbreaks. This actionable information empowers public health authorities to promptly identify and address citywide trends and emerging hotspots within communities. Additionally, we address methods for right-sizing the system by asking whether samples from wastewater treatment plants can be combined before lab analysis, thereby reducing the financial burden of this surveillance aspect. We demonstrate our methodology using weekly measurements of SARS-CoV-2 RNA concentrations in wastewater from July 6, 2020, through October 28, 2024, in Houston, TX, collected from 32 wastewater treatment plants that serve 2.2 million people.

  PublisherDOI

• Characterizing spatiotemporal trends in extreme precipitation in Southeast Texas

  UNC Libraries · 2025-07-04

  articleOpen accessSenior author
  PublisherDOI

• A Novel Framework for Internal Responses to Detection of Pathogens in Wastewater by Public Health Agencies

  Public Health Reports · 2024-06-13 · 3 citations

  articleOpen access

  OBJECTIVES: To build on the success of wastewater surveillance during the COVID-19 pandemic, jurisdictions funded under the Centers for Disease Control and Prevention National Wastewater Surveillance System are looking to expand their wastewater programs to detect more pathogens. However, many public health agencies do not know how to use the collected wastewater data to formulate public health responses, underscoring a need for guidance. To address this knowledge gap, the Houston Health Department (HHD) developed a novel response framework that outlines an internal action plan that is tailored by pathogen type after detection of various pathogens in wastewater. MATERIALS AND METHODS: In July 2023, HHD met with subject matter experts (eg, bureau chiefs, program managers) in internal departments, including epidemiology, immunization, and health education, to discuss the general outline of the response framework and each department's anticipated role after pathogen detection. RESULTS: The internal framework established a flow for notifications and the actions to be taken by departments in HHD, with the goals of (1) ensuring timely and efficient responses to pathogen detections, (2) creating accountability within departments for taking their assigned actions, and (3) making certain that HHD was prepared for intervention implementation when a new pathogen was detected. PRACTICE IMPLICATIONS: As more public health agencies expand their wastewater surveillance programs to target additional pathogens, development of internal action plans tailored to departmental capacity and programs is an important step for public health agencies. The information compiled in this response framework can be a model for other public health agencies to adopt when expanding the scope of their wastewater monitoring systems.

  PublisherOA PDFDOI

• Spatial-Temporal Extreme Modeling for Point-to-Area Random Effects (PARE)

  Journal of Data Science · 2024-01-01 · 2 citations

  articleOpen accessSenior author

  One measurement modality for rainfall is a fixed location rain gauge. However, extreme rainfall, flooding, and other climate extremes often occur at larger spatial scales and affect more than one location in a community. For example, in 2017 Hurricane Harvey impacted all of Houston and the surrounding region causing widespread flooding. Flood risk modeling requires understanding of rainfall for hydrologic regions, which may contain one or more rain gauges. Further, policy changes to address the risks and damages of natural hazards such as severe flooding are usually made at the community/neighborhood level or higher geo-spatial scale. Therefore, spatial-temporal methods which convert results from one spatial scale to another are especially useful in applications for evolving environmental extremes. We develop a point-to-area random effects (PARE) modeling strategy for understanding spatial-temporal extreme values at the areal level, when the core information are time series at point locations distributed over the region.

  PublisherOA PDFDOI

Frequent coauthors

• Loren Hopkins

  University of California, Davis

  109 shared

• Lauren B. Stadler

  Rice University

  66 shared

• David Persse

  New York City Fire Department

  65 shared

• Kaavya Domakonda

  Houston Health and Human Services Department

  41 shared

• Komal Sheth

  Memorial Hermann

  40 shared

• Catherine D. Johnson

  Houston Health and Human Services Department

  40 shared

• Edward Septimus

  Memorial Hermann

  37 shared

• Janeana White

  Houston Health and Human Services Department

  36 shared

Labs

• Katherine B. Ensor LabPI

  Not provided

Education

• Ph.D., Statistics

  Texas A&M University

  1986

• M.S., Mathematics

  Arkansas State University

  1982

• BSE, Mathematics

  Arkansas State University

  1981

Awards & honors

• Florence Nightingale David Award and Lectureship by the Comm…
• William Sealy Gossett Lecture and Award by the ISI World Sta…
• American Statistical Association Founders Award (2024)
• Elected Member of the International Statistical Institute (I…
• Elected Fellow of the Royal Statistical Society (RSS) (2023)