Publications

2025

1. Evaluation of the 2022 West Nile virus forecasting challenge, USA

   Ryan D. Harp, Karen M. Holcomb, Renata Retkute, and 34 more authors

   Parasites & Vectors, Apr 2025

   Abs DOI

   West Nile virus (WNV) is the most common cause of mosquito-borne disease in the continental USA, with an average of ~1200 severe, neuroinvasive cases reported annually from 2005 to 2021 (range 386–2873). Despite this burden, efforts to forecast WNV disease to inform public health measures to reduce disease incidence have had limited success. Here, we analyze forecasts submitted to the 2022 WNV Forecasting Challenge, a follow-up to the 2020 WNV Forecasting Challenge.

2. Examining the Longitudinal Impact of Within- and Between-Day Fluctuations in Food Parenting Practices on Child Dietary Intake: Protocol for a Longitudinal Cohort Study Within a Sample of Preschooler-Parent Dyads

   Katie A. Loth, Julian Wolfson, Martha Barnard, and 4 more authors

   JMIR Research Protocols, May 2025

   Abs DOI

   Background: A healthful diet in early childhood is essential for healthy growth and disease prevention. Parents influence children’s diets through supportive (eg, structure and autonomy support) and unsupportive (eg, coercive control and indulgence) food parenting practices. Historically, much of this work has focused on parents’ “usual” feeding behaviors using survey methods. However, recent studies using ecological momentary assessment methods have allowed assessment of food parenting behaviors in “real time.” This work has revealed that the practices used by parents to feed children vary across contexts and are influenced by factors such as stress or time constraints. Research is needed to understand the dynamic nature of food parenting and its impact on children’s diets. Objective: This study aimed to describe the methods and procedures used in the Preschool Plates cohort study, which aimed to (1) describe within- and between-day fluctuations in food parenting practices across time and context, (2) examine the longitudinal impact of within- and between-day fluctuations in food parenting practices on child dietary intake, and (3) identify momentary predictors of within- and between-day fluctuations in food parenting practices across time and context. Methods: Preschool Plates is a longitudinal cohort study examining the impact of food parenting practices on the dietary intake of 3- to 5-year-old children. A total of 273 parent-preschooler dyads consented and enrolled, and 254 (93%) dyads completed baseline data collection. Dyads will be followed for 2 years using state-of-the-art measures, including an 8-day ecological momentary assessment protocol to assess food parenting, contemporary measures of food parenting, and 3 interview-led 24-hour dietary recalls, collected at baseline, 6 months, 12 months, and 24 months. Child height and weight will be measured at 3 time points. Results: Recruitment for our baseline sample (N=254) occurred between October 2023 and September 2024. Participants will complete follow-up data collection after 6 months, 12 months, and 24 months. A racially and ethnically diverse cohort was enrolled, with 28.3% (72/254) of enrolled participants identifying as White and 71.7% (182/254) identifying as non-White. Conclusions: Findings from the proposed study will inform the development of anticipatory guidance for feeding young children and randomized controlled trials designed to intervene on parents’ responses to momentary factors to encourage interactions with children around feeding that promote optimal diet quality. For example, findings could inform the development of an ecological momentary (ie, real time) intervention that delivers content to participants’ mobile devices in response to real-time assessments of context and circumstance.

3. Adjacency Matrix Decomposition Clustering for Human Activity Data

   Martha Barnard, Yingling Fan, and Julian Wolfson

   Journal of the American Statistical Association, Jul 2025

   In Press

   DOI
4. A Unified Framework for Causal Estimand Selection

   Martha Barnard, Jared D. Huling, and Julian Wolfson

   Mar 2025

   arXiv:2410.12093 [stat]. In Revision.

   Abs DOI

   Estimating the causal effect of a treatment or health policy with observational data can be challenging due to an imbalance of and a lack of overlap between treated and control covariate distributions. In the presence of limited overlap, researchers choose between 1) methods (e.g., inverse probability weighting) that imply traditional estimands but whose estimators are at risk of considerable bias and variance; and 2) methods (e.g., overlap weighting) which imply a different estimand, thereby modifying the target population to reduce variance. We propose a framework for navigating the tradeoffs between variance and bias due to imbalance and lack of overlap and the targeting of the estimand of scientific interest. We introduce a bias decomposition that encapsulates bias due to 1) the statistical bias of the estimator; and 2) estimand mismatch, i.e., deviation from the population of interest. We propose two design-based metrics and an estimand selection procedure that help illustrate the tradeoffs between these sources of bias and variance of the resulting estimators. Our procedure allows analysts to incorporate their domain-specific preference for preservation of the original research population versus reduction of statistical bias. We demonstrate how to select an estimand based on these preferences with an application to right heart catheterization data.

2024

1. Exploring Climate-Disease Connections in Geopolitical Versus Ecological Regions: The Case of West Nile Virus in the United States

   S. Kane Moser, Julie A. Spencer, M. Barnard, and 3 more authors

   GeoHealth, Mar 2024

   Abs DOI

   Many infectious disease forecasting models in the United States (US) are built with data partitioned into geopolitical regions centered on human activity as opposed to regions defined by natural ecosystems; although useful for data collection and intervention, this has the potential to mask biological relationships between the environment and disease. We explored this concept by analyzing the correlations between climate and West Nile virus (WNV) case data aggregated to geopolitical and ecological regions. We compared correlations between minimum, maximum, and mean annual temperature; precipitation; and annual WNV neuroinvasive disease (WNND) case data from 2005 to 2019 when partitioned into (a) climate regions defined by the National Oceanic and Atmospheric Administration (NOAA) and (b) Level I ecoregions defined by the Environmental Protection Agency (EPA). We found that correlations between climate and WNND in NOAA climate regions and EPA ecoregions were often contradictory in both direction and magnitude, with EPA ecoregions more often supporting previously established biological hypotheses and environmental dynamics underlying vector-borne disease transmission. Using ecological regions to examine the relationships between climate and disease cases can enhance the predictive power of forecasts at various scales, motivating a conceptual shift in large-scale analyses from geopolitical frameworks to more ecologically meaningful regions.

2023

1. Scoping review of Culex mosquito life history trait heterogeneity in response to temperature

   S. Kane Moser, Martha Barnard, Rachel M. Frantz, and 6 more authors

   Parasites & Vectors, Jun 2023

   Abs DOI

   Mosquitoes in the genus Culex are primary vectors in the US for West Nile virus (WNV) and other arboviruses. Climatic drivers such as temperature have differential effects on species-specific changes in mosquito range, distribution, and abundance, posing challenges for population modeling, disease forecasting, and subsequent public health decisions. Understanding these differences in underlying biological dynamics is crucial in the face of climate change.

2. Changing temperature profiles and the risk of dengue outbreaks

   Imelda Trejo, Martha Barnard, Julie A. Spencer, and 6 more authors

   PLOS Climate, Feb 2023

   Abs DOI

   As temperatures change worldwide, the pattern and competency of disease vectors will change, altering the global distribution of both the burden of infectious disease and the risk of the emergence of those diseases into new regions. To evaluate the risk of potential summer dengue outbreaks triggered by infected travelers under various climate scenarios, we develop an SEIR-type model, run numerical simulations, and conduct sensitivity analyses under a range of temperature profiles. Our model extends existing theoretical frameworks for studying dengue dynamics by introducing temperature dependence of two key parameters: the mosquito extrinsic incubation period and the lifespan of mosquitoes, which empirical data suggests are both highly temperature dependent. We find that changing temperature significantly alters dengue risk in an inverted U-shape, with temperatures in the range 27-31°C producing the highest risk. As temperatures increase beyond 31°C, the determinants of dengue risk begin to shift from mosquito biting rate and carrying capacity to the duration of the human infectious period, suggesting that changing temperatures not only alter dengue risk but also the potential efficacy of control measures. To illustrate the role of spatial and temporal temperature heterogeneity, we select five US cities where the primary dengue vector, the mosquito Aedes aegypti, has been observed, and which have had dengue cases in the past: Los Angeles, Houston, Miami, Brownsville, and Phoenix. Our analysis suggests that an increase of 3°C leads to an approximate doubling of the risk of dengue in Los Angeles and Houston, but a reduction of risk in Miami, Brownsville, and Phoenix due to extreme heat.

3. Multi-dimensional resilience: A quantitative exploration of disease outcomes and economic, political, and social resilience to the COVID-19 pandemic in six countries

   Lauren J. Beesley, Paolo Patelli, Kimberly Kaufeld, and 6 more authors

   PLOS ONE, Jan 2023

   Abs DOI

   The COVID-19 pandemic has highlighted a need for better understanding of countries’ vulnerability and resilience to not only pandemics but also disasters, climate change, and other systemic shocks. A comprehensive characterization of vulnerability can inform efforts to improve infrastructure and guide disaster response in the future. In this paper, we propose a data-driven framework for studying countries’ vulnerability and resilience to incident disasters across multiple dimensions of society. To illustrate this methodology, we leverage the rich data landscape surrounding the COVID-19 pandemic to characterize observed resilience for several countries (USA, Brazil, India, Sweden, New Zealand, and Israel) as measured by pandemic impacts across a variety of social, economic, and political domains. We also assess how observed responses and outcomes (i.e., resilience) of the COVID-19 pandemic are associated with pre-pandemic characteristics or vulnerabilities, including (1) prior risk for adverse pandemic outcomes due to population density and age and (2) the systems in place prior to the pandemic that may impact the ability to respond to the crisis, including health infrastructure and economic capacity. Our work demonstrates the importance of viewing vulnerability and resilience in a multi-dimensional way, where a country’s resources and outcomes related to vulnerability and resilience can differ dramatically across economic, political, and social domains. This work also highlights key gaps in our current understanding about vulnerability and resilience and a need for data-driven, context-specific assessments of disaster vulnerability in the future.

4. Fusing time-varying mosquito data and continuous mosquito population dynamics models

   Marina Mancuso, Kaitlyn M. Martinez, Carrie A. Manore, and 3 more authors

   Frontiers in Applied Mathematics and Statistics, Jan 2023

2022

1. Freshwater floodplain habitats buffer native food webs from negative effects of nonnative centrarchids and bullfrogs

   Meredith A. Holgerson, Martha Barnard, Byunghyun Ahn, and 2 more authors

   Freshwater Science, Jun 2022

   Abs DOI

   Species introductions are common in freshwater environments and have the potential to transform community and ecosystem structure. Predatory centrarchid fishes and American Bullfrogs (Lithobates catesbeianus Shaw, 1802 previously Rana catesbeiana) are both widespread aquatic invaders implicated in native amphibian declines. In lowland ecosystems, co-occurrence between native and nonnative amphibian and fish taxa is common; however, the mechanisms that facilitate their co-occurrence are poorly studied. Stable isotope analysis offers a tool to examine trophic interactions among native and nonnative taxa, including predation, competition, and shifting food resource availability, which may provide mechanistic insight into the drivers of co-occurrence. In this study, we used stable isotopes (δ13C and δ15N) to determine how the trophic structure of native fishes and amphibians differs between waterbodies with and without nonnative centrarchid fishes and bullfrogs across a floodplain in southwestern Washington, USA. We hypothesized that native species alter their feeding strategies to reduce niche overlap with nonnative taxa. In the presence of nonnative taxa, Three-spine Stickleback (Gasterosteus aculeatus Linnaeus, 1758), all native larval salamander species (Ambystoma gracile Baird, 1859 and Ambystoma macrodactylum Baird, 1850), and 1 of 2 native larval frog species (Rana aurora Baird and Girard, 1852) exhibited shifts in food resources or trophic position. Despite trophic differences, only 1 species (A. macrodactylum) had a smaller niche size in the presence of nonnatives. The observed trophic shifts reflect changes in habitat or food resources, which may reduce competition or predation and promote co-occurrence between nonnative and native taxa. Our results suggest that the co-occurrence of native and nonnative amphibians and fishes in lowland floodplain habitats may be facilitated by a broad range of food resources and complex habitat structure.

2021

1. Mining and Validating Social Media Data for COVID-19–Related Human Behaviors Between January and July 2020: Infodemiology Study

   Ashlynn R. Daughton, Courtney D. Shelley, Martha Barnard, and 9 more authors

   Journal of Medical Internet Research, May 2021

   Abs DOI

   Background: Health authorities can minimize the impact of an emergent infectious disease outbreak through effective and timely risk communication, which can build trust and adherence to subsequent behavioral messaging. Monitoring the psychological impacts of an outbreak, as well as public adherence to such messaging, is also important for minimizing long-term effects of an outbreak. Objective: We used social media data from Twitter to identify human behaviors relevant to COVID-19 transmission, as well as the perceived impacts of COVID-19 on individuals, as a first step toward real-time monitoring of public perceptions to inform public health communications. Methods: We developed a coding schema for 6 categories and 11 subcategories, which included both a wide number of behaviors as well codes focused on the impacts of the pandemic (eg, economic and mental health impacts). We used this to develop training data and develop supervised learning classifiers for classes with sufficient labels. Classifiers that performed adequately were applied to our remaining corpus, and temporal and geospatial trends were assessed. We compared the classified patterns to ground truth mobility data and actual COVID-19 confirmed cases to assess the signal achieved here. Results: We applied our labeling schema to approximately 7200 tweets. The worst-performing classifiers had F1 scores of only 0.18 to 0.28 when trying to identify tweets about monitoring symptoms and testing. Classifiers about social distancing, however, were much stronger, with F1 scores of 0.64 to 0.66. We applied the social distancing classifiers to over 228 million tweets. We showed temporal patterns consistent with real-world events, and we showed correlations of up to –0.5 between social distancing signals on Twitter and ground truth mobility throughout the United States. Conclusions: Behaviors discussed on Twitter are exceptionally varied. Twitter can provide useful information for parameterizing models that incorporate human behavior, as well as for informing public health communication strategies by describing awareness of and compliance with suggested behaviors.

2019

1. Estimating influenza incidence using search query deceptiveness and generalized ridge regression

   Reid Priedhorsky, Ashlynn R. Daughton, *Martha Barnard, and 2 more authors

   PLOS Computational Biology, Oct 2019

   Abs DOI

   Seasonal influenza is a sometimes surprisingly impactful disease, causing thousands of deaths per year along with much additional morbidity. Timely knowledge of the outbreak state is valuable for managing an effective response. The current state of the art is to gather this knowledge using in-person patient contact. While accurate, this is time-consuming and expensive. This has motivated inquiry into new approaches using internet activity traces, based on the theory that lay observations of health status lead to informative features in internet data. These approaches risk being deceived by activity traces having a coincidental, rather than informative, relationship to disease incidence; to our knowledge, this risk has not yet been quantitatively explored. We evaluated both simulated and real activity traces of varying deceptiveness for influenza incidence estimation using linear regression. We found that deceptiveness knowledge does reduce error in such estimates, that it may help automatically-selected features perform as well or better than features that require human curation, and that a semantic distance measure derived from the Wikipedia article category tree serves as a useful proxy for deceptiveness. This suggests that disease incidence estimation models should incorporate not only data about how internet features map to incidence but also additional data to estimate feature deceptiveness. By doing so, we may gain one more step along the path to accurate, reliable disease incidence estimation using internet data. This capability would improve public health by decreasing the cost and increasing the timeliness of such estimates.

2. Computationally efficient, exact, covariate-adjusted genetic principal component analysis by leveraging individual marker summary statistics from large biobanks

   ^*Wolf, J.M., *Martha Barnard, Xueting Xia, and 3 more authors

   In Pacific Symposium in Biocomputing 2020, Dec 2019

   DOI