About

Lauren Gardner is the Alton and Sandra Cleveland Professor in the Department of Civil and Systems Engineering at Johns Hopkins University, where she also holds a joint appointment in the Bloomberg School of Public Health. She is a member of the Data Science and AI Institute. Gardner specializes in modeling infectious disease risk, focusing on virus diffusion as a function of climate, land use, human behavior, mobility, and other contributing risk factors. She leads COVID-19 modeling efforts in partnership with U.S. cities to develop customized models for estimating local COVID-19 risk and optimizing resource allocation for surveillance and targeted testing. Her group contributes weekly COVID-19 case and death predictions to the CDC’s ensemble forecast through the COVID-19 Forecast Hub.

Research topics

• Computer Science
• Medicine
• Political Science
• Sociology
• Artificial Intelligence
• Business
• Engineering
• Econometrics
• Geography
• Economics
• Operations research
• Data science
• Mathematics
• Demography
• Machine Learning
• Data Mining
• Actuarial science
• Statistics
• Management science
• Meteorology
• Telecommunications
• Demographic economics
• Virology
• Environmental health

Selected publications

• The Pandemic’s Echo: A Population-Based Assessment of MMR Rates and Associated Factors in the Wake of COVID-19

  medRxiv · 2025-02-14 · 3 citations

  preprintOpen accessSenior authorCorresponding

  Objectives We generate a high-resolution data set on MMR vaccination coverage in the U.S. before and after the COVID-19 pandemic. We use this data to conduct a spatiotemporal analysis of vaccination patterns and uncover the associations between MMR vaccination rates and key factors such as socioeconomic conditions, COVID-19 impact, vaccine policy and other health-related variables. Methods We collect county-level 2-dose MMR vaccination rates for kindergarten children from 2017 – 2024 for 2,266 U.S. counties, compare coverage patterns and trends before and after the COVID-19 pandemic, and implement multivariable weighted logistic regression models to identify factors associated with low and declining MMR vaccination rates in U.S. counties. Results We reveal a nationwide decline in MMR rates following the COVID-19 pandemic, with declines observed in 1,688 of the counties evaluated. We find state-level non-medical exemption (NME) policies to be the strongest associated factor with low and declining MMR rates at the county level post-pandemic. We also identify a positive association between county-level MMR rates and uptake of other vaccine types, the minority proportion in a county and Republican-aligned counties. In contrast, county-level MMR rates are negatively associated with post-secondary education rates. In addition to NMEs, MMR rate declines are positively associated with the proportion of rural population and a higher religious diversity at the county level, and negatively associated with household income and the proportion of Latinx people. Conclusions The significant association between NMEs and low and declining MMR rates suggest these state-level policies are harmful and lead to reduced vaccination coverage post-pandemic. The positive associations among vaccine types suggest spillover effects in vaccine seeking behavior. The association between MMR rates and Republican-aligned counties contrast COVID-19 vaccination patterns, highlighting the complex nature of political polarization on vaccine-related behavior.

  PublisherOA PDFDOI

• A Multi-pathogen Hospitalization Forecasting Model for the United States: An Optimized Geo-Hierarchical Ensemble Framework

  medRxiv · 2025-01-08

  preprintOpen accessSenior author

  Abstract Accurate forecasting of infectious diseases is crucial for timely public health response. Ensemble frameworks have shown promising outcomes in short-term forecasting of COVID-19, among other respiratory viruses, however, there is a need to further improve these frameworks. Here, we propose the Multi-Pathogen Optimized Geo-Hierarchical Ensemble Framework (MPOG-Ensemble), a novel forecasting machine learning framework to forecast state-level hospitalizations of influenza, COVID-19, and RSV in the U.S. This framework is multi-resolution: it integrates state, regionally-trained, and nationally-trained models through an ensemble layer and applies various optimization methods to parameterize the model weights and enhance overall predictive accuracy. This proposed framework builds on existing forecasting literature by 1) employing an ensemble of three spatially hierarchical models with state-level forecasts as the output; 2) incorporating four distinct weight optimization methods to generate the ensemble; 3) utilizing clustering methods to dynamically identify multi-state regions as a function of short-term and long-term hospitalization trends for the regionally-trained model; and 4) providing a generalized multi-pathogen framework to forecast the expected near-term hospitalizations from Influenza, RSV and COVID-19. Results demonstrate MPOG-Ensemble is a robust framework with relatively high performance. Extensive experimentation using historical multi-pathogen data highlights the predictive power of our framework compared to existing ensemble approaches. Its robust performance underscores the framework’s effectiveness and potential for improving and broadening infectious disease forecasting.

  PublisherOA PDFDOI

• Improving policy design and epidemic response using integrated models of economic choice and disease dynamics with behavioral feedback

  PLoS Computational Biology · 2025-10-03 · 4 citations

  articleOpen access

  Human behavior plays a crucial role in infectious disease transmission, yet traditional models often overlook or oversimplify this factor, limiting predictions of disease spread and the associated socioeconomic impacts. Here we introduce a feedback-informed epidemiological model that integrates human behavior with disease dynamics in a credible, tractable, and extendable manner. From economics, we incorporate a dynamic decision-making model where individuals assess the trade-off between disease risks and economic consequences, and then link this to a risk-stratified compartmental model of disease spread taken from epidemiology. In the unified framework, heterogeneous individuals make choices based on current and future payoffs, influencing their risk of infection and shaping population-level disease dynamics. As an example, we model disease-decision feedback during the early months of the COVID-19 pandemic, when the decision to participate in paid, in-person work was a major determinant of disease risk. Comparing the impacts of stylized policy options representing mandatory, incentivized/compensated, and voluntary work abstention, we find that accounting for disease-behavior feedback has a significant impact on the relative health and economic impacts of policies. Including two crucial dimensions of heterogeneity-health and economic vulnerability-the results highlight how inequities between risk groups can be exacerbated or alleviated by disease control measures. Importantly, we show that a policy of more stringent workplace testing can potentially slow virus spread and, surprisingly, increase labor supply since individuals otherwise inclined to remain at home to avoid infection perceive a safer workplace. In short, our framework permits the exploration of avenues whereby health and wealth need not always be at odds. This flexible and extendable modeling framework offers a powerful tool for understanding the interplay between human behavior and disease spread.

  PublisherDOI

• Characterizing the impact of incorporating spatially aggregated human mobility data into infectious disease models

  medRxiv · 2025-11-28

  preprintOpen access

  Abstract Models of infectious disease dynamics should align the spatial scale of mobility data to the scale of travel relevant to infer disease introduction events and subsequent local transmission. Despite this, the biases of spatially aggregating mobility data, which are more commonly available, on model inferences are rarely explored. Here, we examine the sensitivity of infectious disease modeling results to different spatial scales of human mobility by integrating multiscale mobility data from Sri Lanka into SEIR metapopulation models. Aggregated mobility data were obtained from mobile phone records at three increasingly coarser spatial scales to simulate epidemic spread. We found that travel was not evenly distributed amongst nested spatial units when data were disaggregated, with rural subunits exhibiting more external travel than urban subunits. In simulations of non-specific disease transmission, these different scales of mobility aggregation yielded wide variations in estimates of epidemic size and spatial invasion timing. However, modeled differences depended on disease characteristics such as transmissibility and exogenous factors like seeding location urbanicity. Our results carry implications for infectious disease modeling best practices and public health response, particularly the policy decisions made from model inferences that were or were not informed by the relevant spatial scale of mobility data such as intervention timing, risk communication, and resource allocation. Significance Statement Infectious disease models serve an important role in disease forecasting and outbreak response. As data on human mobility have become increasingly detailed and widely used, the question of spatial scale is paramount to effectively approximating disease dynamics. Our work finds significant discrepancies in estimations of spatial invasion timing and epidemic magnitude simply by changing the scale of mobility data integrated into a transmission model. This could have consequences for estimation of key parameters obtained from such models, the interpretation and communication of infection risk, and ultimately the public health response to a disease threat. We also highlight situations where large model discrepancies are unlikely, such as for highly infectious pathogens and where disease is initially seeded into an urban location.

  PublisherOA PDFDOI

• Mechanistic Modeling of <i>Aedes aegypti</i> Mosquito Habitats for Climate‐Informed Dengue Forecasting

  GeoHealth · 2025-09-01

  articleOpen access

  Abstract The mosquito‐borne disease dengue is sensitive to climate, in part because of the influence climate has on breeding habitats of dengue's Aedes mosquito vectors. Dengue risk assessment models currently leverage climate‐dengue statistical associations, yet what remain understudied are the mechanistic pathways that yield different statistical relationships in different locations. We hypothesize that elucidating the mechanisms by which spatiotemporal variability in climate influences dengue incidence will improve dengue dynamics predictions across climatically distinct locations and beyond dengue's well‐known seasonal cycles. We test this hypothesis by investigating a key pathway in the climate‐dengue process chain: climate impacts on Aedes breeding habitats. We have implemented a mechanistic modeling pipeline that simulates climatic influence on habitat water dynamics and thereby on relative population size of the vector. We use this modeling pipeline, driven by meteorological data, to simulate monthly Aedes populations for three climatically distinct cities in Sri Lanka. We find that simulated vector abundance is plausibly associated with climate conditions and that climate drivers of vector abundance vary among locations. Moreover, tercile‐tercile comparisons of dengue incidence against model variables indicate that risk assessments based on predicted vector abundance perform similarly to those based on meteorology alone—the signal of weather variability and its relationship to dengue propagates through the modeling pipeline. These results justify future testing of this modeling pipeline within a dengue risk assessment framework, where its process‐based structure may be leveraged to guide proactive dengue control efforts in high‐risk years and to simulate impacts of future climate conditions on dengue dynamics.

  PublisherDOI

• From Occurrence to Consequence: A Comprehensive Data-driven Analysis of Building Fire Risk

  ArXiv.org · 2025-03-11 · 1 citations

  preprintOpen access

  Building fires pose a persistent threat to life, property, and infrastructure, emphasizing the need for advanced risk mitigation strategies. This study presents a data-driven framework analyzing U.S. fire risks by integrating over one million fire incident reports with diverse fire-relevant datasets, including social determinants, building inventories, weather conditions, and incident-specific factors. By adapting machine learning models, we identify key risk factors influencing fire occurrence and consequences. Our findings show that vulnerable communities, characterized by socioeconomic disparities or the prevalence of outdated or vacant buildings, face higher fire risks. Incident-specific factors, such as fire origins and safety features, strongly influence fire consequences. Buildings equipped with fire detectors and automatic extinguishing systems experience significantly lower fire spread and injury risks. By pinpointing high-risk areas and populations, this research supports targeted interventions, including mandating fire safety systems and providing subsidies for disadvantaged communities. These measures can enhance fire prevention, protect vulnerable groups, and promote safer, more equitable communities.

  PublisherOA PDFDOI

• An optimized geo-hierarchical ensemble model to forecast hospitalizations from respiratory viruses in the United States

  Epidemics · 2025-11-17 · 1 citations

  articleOpen accessSenior author

  Accurate forecasting of infectious diseases is crucial for timely public health response. Ensemble frameworks have shown promising outcomes in short-term forecasting of COVID-19, among other respiratory viruses, however, there is a need to further improve these frameworks. Here, we propose the generalized Optimized Geo-Hierarchical Ensemble Model (OGEM), a novel forecasting machine learning framework to forecast state-level hospitalizations of influenza, COVID-19, and RSV in the U.S. independently. This framework is multi-resolution: it integrates state, regionally-trained, and nationally-trained models through an ensemble layer and applies various optimization methods to parameterize the model weights and enhance overall predictive accuracy. This proposed framework builds on existing forecasting literature by 1) employing an ensemble of three spatially hierarchical models with state-level forecasts as the output; 2) incorporating four distinct weight optimization methods to generate the ensemble; 3) utilizing clustering methods to dynamically identify multi-state regions as a function of short-term and long-term hospitalization trends for the regionally-trained model; and 4) providing a generalized framework to forecast the expected near-term hospitalizations from Influenza, RSV and COVID-19. Results demonstrate OGEM is a robust framework with relatively high performance. Extensive experimentation using historical data highlights the predictive power of our framework compared to existing ensemble approaches. Its robust performance underscores the framework's effectiveness and potential for improving and broadening infectious disease forecasting. • Employing an ensemble of three spatially hierarchical models to generate state-level forecasts. • Implementing four distinct weight optimization methods to refine ensemble performance. • Dynamically identifying multi-state regions using clustering methods that capture hospitalization trends over varying time horizons for the regionally-trained model. • Developing a generalized forecasting framework for near-term hospitalizations due to Influenza, RSV, and COVID-19.

  PublisherDOI

• Trends in County-Level MMR Vaccination Coverage in Children in the United States

  JAMA · 2025-06-02 · 29 citations

  articleOpen accessSenior authorCorresponding

  This study examines county-level vaccination rates for children from 2017 to 2024 for all US states.

  PublisherOA PDFDOI

• A multi-city COVID-19 forecasting model utilizing wastewater-based epidemiology

  The Science of The Total Environment · 2025-01-01 · 7 citations

  articleOpen accessSenior author

  The COVID-19 pandemic highlighted shortcomings in forecasting models, such as unreliable inputs/outputs and poor performance at critical points. As COVID-19 remains a threat, it is imperative to improve current forecasting approaches by incorporating reliable data and alternative forecasting targets to better inform decision-makers. Wastewater-based epidemiology (WBE) has emerged as a viable method to track COVID-19 transmission, offering a more reliable metric than reported cases for forecasting critical outcomes like hospitalizations. Recognizing the natural alignment of wastewater systems with city structures, ideal for leveraging WBE data, this study introduces a multi-city, wastewater-based forecasting model to categorically predict COVID-19 hospitalizations. Using hospitalization and COVID-19 wastewater data for six US cities, accompanied by other epidemiological variables, we develop a Generalized Additive Model (GAM) to generate two categorization types. The Hospitaization Capacity Risk Categorization (HCR) predicts the burden on the healthcare system based on the number of available hospital beds in a city. The Hospitalization Rate Trend (HRT) Categorization predicts the trajectory of this burden based on the growth rate of COVID-19 hospitalizations. Using these categorical thresholds, we create probabilistic forecasts to retrospectively predict the risk and trend category of six cities over a 20-month period for 1, 2, and 3 week forecasting windows. We also propose a new methodology to measure forecasting model performance at change points, or time periods where sudden changes in outbreak dynamics occurred. We also explore the influence of wastewater as a predictor for hospitalizations, showing its inclusion positively impacts the model's performance. With this categorical forecasting study, we are able to predict hospital capacity risk and disease trends in a novel and useful way, giving city decision-makers a new tool to predict COVID-19 hospitalizations. • SARS-CoV-2 wastewater viral concentrations usefully precede COVID-19 hospitalizations • SARS-CoV-2 wastewater viral loads benefit hospitalization forecasting performance • City-level categorical forecasts clearly predict hospital burden (75–89 % accuracy) and growth rates (46–70 % accuracy) • Analyzing forecasting performance at transition points demonstrates forecast utility • COVID-19 hospitalization growth rates are more difficult to predict than burden

  PublisherDOI

• Simulating Aedes Mosquito Habitats for Climate-informed Dengue Forecasting

  2025-08-13

  preprintOpen access

  The mosquito-borne disease dengue is dependent on climate, in part because of the climate sensitivities of its Aedes mosquito vectors. These climate–dengue associations are leveraged in dengue risk assessment models, which incorporate statistical relationships between meteorological variables (e.g., rainfall, air temperature) and dengue cases. However, the mechanistic pathways that result in different statistical relationships in different locations remains understudied. A mechanistic understanding of how interannual variability in climate translates to interannual variability in dengue incidence may aid in predicting dengue dynamics across climatically distinct locations and beyond dengue’s well-known seasonal cycles. In this work we investigate interannual variability within a key pathway in the climate-disease process chain: climate impacts on the Aedes vectors’ breeding habitats. We have implemented a process-based modeling pipeline that simulates climatic influence on habitat water dynamics (using the energy balance container model WHATCH’EM) and thereby on the relative population size of the vector (using models of environment-dependent vector survival and development rates for eggs, larvae, and pupae). We use this modeling pipeline, driven by meteorological data from 2001 to 2020, to simulate monthly relative population sizes of the vector for three climatically distinct locations in Sri Lanka (Negombo, Jaffna, Nuwara Eliya). We find that modeled vector population sizes are plausibly associated with climate conditions (e.g., in the cold, mountainous city of Nuwara Eliya, years of high 2-m air temperature tend to yield high populations) and that the climate drivers of population vary among locations. Moreover, tercile-tercile comparisons of dengue against model variables support the idea that dengue’s connection to temperature is strongly affected by the effect of temperature on vector population size. These results justify future work on rigorously assessing this modeling pipeline’s utility within a dengue risk assessment framework, where its process-based structure may be leveraged to inform intervention strategies in high-risk years and simulate impacts of future climate conditions.

  PublisherOA PDFDOI

Recent grants

• RAISE: IHBEM: Modeling Dynamic Disease-Behavior Feedbacks for Improved Epidemic Prediction and Response

  NSF · $1000k · 2022–2026

• RAPID: Development of an Interactive Web-based Dashboard to Track COVID-19 in Real-time

  NSF · $200k · 2020–2022

• RAPID: Real-time Forecasting of COVID-19 risk in the USA

  NSF · $200k · 2021–2022

Frequent coauthors

• S. Travis Waller

  44 shared

• Raja Jurdak

  36 shared

• Moritz U. G. Kraemer

  University of Oxford

  31 shared

• Jessica Liebig

  Commonwealth Scientific and Industrial Research Organisation

  24 shared

• Ahmad El Shoghri

  CSIRO Health and Biosecurity

  20 shared

• Hamada S. Badr

  Johns Hopkins University

  20 shared

• Sahotra Sarkar

  Pacific Northwest National Laboratory

  17 shared

• Benjamin F. Zaitchik

  Planetary Science Institute

  17 shared

Awards & honors

• 2024 Merck Future Insight Prize
• 2022 Lasker~Bloomberg Public Service Award
• WITI's Women in Technology Hall of Fame (2024)
• BBC’s 100 Women List 2020: Women who led change
• Fast Company’s Most Creative People in Business (2020)