Welcome to
Dr. Gardner's Lab

Lauren Gardner

Alton and Sandra Cleveland Professor

Department of Civil and Systems Engineering, WSE

Department of Epidemiology, Bloomberg School of Public Health (Joint Appointment)

Director, Center for Systems Science and Engineering

Lauren Gardner is the Alton and Sandra Cleveland Professor in the Department of Civil and Systems Engineering at Johns Hopkins Whiting School of Engineering and holds a joint appointment In the Bloomberg School of Public Health. She is the creator of the interactive web-based dashboard being used by public health authorities, researchers, and the general public around the globe to track the outbreak of the novel coronavirus that spread worldwide beginning in January 2020.

Gardner is a specialist in modeling infectious disease risk, including COVID-19, measles, dengue, Zika, Avian influenza, and other emerging infectious diseases. Her work focuses holistically on virus diffusion as a function of climate, land use, human behavior, mobility and other contributing risk factors. Gardner leads COVID-19 modeling efforts in partnership with U.S. cities to develop customized models to estimate COVID-19 risk at the local level, and to optimize resource allocation for surveillance and targeted testing. Her group also contributes weekly COVID-19 case and death predictions to the CDC’s ensemble forecast through the COVID-19 Forecast Hub. The COVID-19 dashboard, which debuted on January 22, 2020 continues to be cited every day by multiple major media outlets, and has served as a resource for a number of federal agencies, including U.S. Vice President Mike Pence’s coronavirus task force. Since its launch, the dashboard has recorded over 200 billion feature requests, which are the number of interactions visitors have with the underlying data available on the site.

Gardner has received research funding from U.S. governmental organizations and philanthropies including NIH, NSF, NASA, the CDC, and Bloomberg Philanthropies. She has published over 100 scholarly articles, letters, communications, and conference proceedings, and supervises a research group of PhD students and post docs. Gardner is an invited member of multiple international professional committees, and reviewer for top-tier journals and grant-funding organizations. She is an invited participant of various scientific advisory committees, including the U.S. Transportation Research Board committees on Network Modeling, Transportation and Health, and Aviation Security and Emergency Management. She has supervised more than 30 students and post-docs, and teaches undergraduate- and graduate-level courses on network modeling and transport systems at Johns Hopkins.

In addition to winning the 2022 Lasker Bloomberg Public Service Award, America’s top medical research prize, for creating the COVID-19 dashboard that became the world’s most trusted source for reliable, real-time data about the pandemic, she was named one of TIME’s 100 Most Influential People of 2020; was included on BBC’s 100 Women List 2020: Women who led change; was named one of Fast Company’s Most Creative People in Business for 2020; was on the Baltimore Sun’s 25 Women to Watch list, 2020; and was among the Baltimore Business Journal’s Best in Tech 2020. She was also a winner of the 2020 Route Fifty Navigator Award, which honors individuals and teams who, while working with or in state, county, or municipal governments, demonstrate their ability to implement a great idea that improves public sector services and the communities they serve. She was also one of six Johns Hopkins experts who briefed congressional staff about the novel coronavirus outbreak during a Capitol Hill event in early March 2020,

Prior to joining JHU in 2019, Gardner was a senior lecturer in civil engineering at the University of New South Wales (UNSW) Sydney, in Australia. She received her BSArchE in architectural engineering, her MSE in civil engineering, and her PhD in transportation engineering at the University of Texas at Austin.

Research

My research focuses on advancing the state-of-art in data-driven epidemic planning and decision making in order to provide outbreak assessment and control recommendations based on best available evidence. Most notably, I lead the efforts behind the interactive web-based dashboard being used by public health authorities, researchers, and the general public around the globe to track the outbreak of the novel coronavirus in 2020.

For a comprehensive list of my publications, please refer to my Google Scholar.

SELECT RESEARCH PROJECTS

 

National Science Foundation (NSF) “RAISE: IHBEM: Modeling Dynamic Disease-Behavior Feedbacks for Improved Epidemic Prediction and Response”. 10/01/2022 – 09/30/2025. Award ID: 2229996

Bloomberg Philanthropies, City Data Analytics Initiative. Aug 2021 – Aug 2024.

Centers for Disease Control and Prevention (CDC) SHEPheRD Project: Center for Accelerating Modeling Uptake. Contract number: 200-2016-91781. 2021-2024.

National Science Foundation (NSF) RAPID “Real-time Forecasting of COVID-19 risk in the USA”. 2021-2022. Award ID: 2108526

Centers for Civic Impact (CCI). “A Multi-Scale Covid-19 Risk Model for Orleans Parish” May-Dec 2020.

CDC BAA – Virus Genomics and Human Mobility Reveal the Patterns SARS-CoV-2 Spread.  Collaboration with Yale School of Public Health. Project Period: 9/1/2020 – 8/31/2022.

National Institute of Health (NIH), “Consortium for Viral Systems Biology (CViSB)”. Collaboration with The Scripps Research Institute and UCLA. Grant Number: 3U19AI135995-03S1. 2020 –2021.

NASA COVID-19 Supplement to “Environmental Determinants of Enteric Infectious Disease: a GEO platform for analysis and risk assessment”. 2020

National Science Foundation (NSF) RAPID “Development of an interactive web-based dashboard to track COVID-19 in real-time”. 2020. Award ID: 2028604

TEACHING

  • “Introduction to Network Modeling” (EN560.453, EN560.653), Department of Civil and Systems Engineering, Fall

Current Team

  • Ensheng Dong, PhD candidate
  • Hongru Du, PhD candidate
  • Maximilian Marshall, PhD candidate
  • Sonia Jindal, PhD candidate
  • Kristen Nixon, PhD candidate
  • Naomi Rankin, PhD Candidate
  • Samee Saiyed, Research Assistant
  • Andreas Nearchou, Research Assistant

Contact


Select Awards

    1. “Lessons from the COVID data wizards”, Nature, March 23, 2022, url: https://www.nature.com/articles/d41586-022-00792-2
    2. “What Happens When the World’s Most Popular COVID-19 Dashboard Can’t Get Data?”, TIME (online), September 29, 2021, url: https://time.com/6101967/covid-19-data-gaps/
    3. “15 Minutes with Dr. Lauren Gardner,” Healthywomen, April 15, 2021, url: https://www.healthywomen.org/your-health/lauren-gardner
    4. “2021 Groundbreaking Women List,” Worth, February 18, 2021, url: https://www.worth.com/groundbreakers-2021/
    5. “How Science Beat the Virus and What It Lost in the Process”, The Atlantic – online December 14, 2020 and print January/February 2021 issue, url: https://www.theatlantic.com/magazine/archive/2021/01/science-covid-19-manhattan-project/617262/
    6. “BBC 100 Women 2020”, BBC (online) November 24, 2020 url: https://www.bbc.com/news/world-55042935
    7. “The Best Inventions of 2020: 2020’s Go-To Data Source – Johns Hopkins Corona­virus Resource Center”, TIME (online), November 19, 2020, url: https://time.com/collection/best-inventions-2020/5911434/johns-hopkins-coronavirus-resource-center/
    8. 2020 Route Fifty Navigator Award, November 2020 url: https://www.route-fifty.com/management/2020/09/winners-2020-navigator-awards/168408/
    9. “The ReaLIST 2020: Meet the 20 most influential technologists in Baltimore”, Technical.ly Baltimore, November 16, 2020, url: https://technical.ly/baltimore/2020/11/16/reallist-engineers-20200-influential-technologists/
    10. 2020 Citation for Leadership and Achievement -awarded by the Board of the Council of Scientific Society Presidents (CSSP), November 2020, url: https://www.sciencepresidents.org/leadership-citation
    11. Most Innovative Work by a Healthcare Organization – awarded by Healthcare Internet Hall of Fame, November 2020, url: https://hihof.com/2020/11/2020-healthcare-internet-hall-of-fame-inductees-announced/
    12. “25 Women to Watch 2020”, The Baltimore Sun, October 21, 2020 url: https://www.baltimoresun.com/features/women-to-watch/bs-fe-women-to-watch-2020-web-20201019-dwopgd3uffcirgk44pcvwieoqq-story.html
    13. “The Most Influential People of 2020”, TIME, September 2020 (online and magazine), url: https://time.com/collection/100-most-influential-people-2020/5888182/lauren-gardner/
    14. “Johns Hopkins’s COVID-19 dashboard alerted us to trouble ahead. Meet the woman who made it”, Fast Company, August 2020 (online)/September 2020 (magazine), url: https://www.fastcompany.com/90525437/most-creative-people-2020-lauren-gardner
    15. “Johns Hopkins’ dashboard: The people behind the pandemic’s most visited site”, CNN, July 2020 url: https://amp.cnn.com/cnn/2020/07/11/health/johns-hopkins-covid-19-map-team-wellness-trnd/index.html
    16. “Best in Tech 2020 – Lauren Gardner, Johns Hopkins University”, Baltimore Business Journal, June 2020 url: https://www.bizjournals.com/baltimore/news/2020/06/26/bbj-best-in-tech-2020-lauren-gardner-john-hopkins.html
    17. “How a Johns Hopkins Professor and Her Chinese Students Tracked Coronavirus”, The Wall Street Journal, May 2020 url: https://www.wsj.com/articles/how-a-johns-hopkins-professor-and-her-chinese-students-tracked-coronavirus-11589016603?mod=mhp
    18. “Behind the Johns Hopkins University coronavirus dashboard”, Nature Index, April 2020 url: https://www.natureindex.com/news-blog/behind-johns-hopkins-university-coronavirus-dashboard
    19. Johns Hopkins University Coronavirus Briefing on Capitol Hill, March 2020, C-SPAN url: https://www.c-span.org/video/?470092-1/johns-hopkins-university-coronavirus-briefing
    20. “Coronavirus: How U.S. hospitals are preparing for COVID-19, and what leading health officials say about the virus”, 60 Minutes (CBS News), March 2020 url: https://www.cbsnews.com/news/coronavirus-containment-dr-jon-lapook-60-minutes-2020-03-08/
    21. “Here’s How Computer Models Simulate the Future Spread of New Coronavirus”, Scientific American, February 2020 url: https://www.scientificamerican.com/article/heres-how-computer-models-simulate-the-future-spread-of-new-coronavirus/
    22. 200+ Local, national and international news articles referencing U.S. Measles Risk Model published in Lancet Inf Dis. Please see subset: https://systems.jhu.edu/portfolio/measles/
    23. “Mapping the contagion”, feature article in Uniken (UNSW’s flagship magazine), Spring 2012, Issue 66. http://newsroom.unsw.edu.au/news/science-technology/mapping-contagion
    24. “Scientists wrestle with possibility of second Zika-spreading mosquito” by Susan Milius, May 16, 2016. url: https://www.sciencenews.org/article/scientists-wrestle-possibility-second-zika-spreading-mosquito

Inverse optimization is an area of study where the purpose is to infer the unknown parameters of an optimization problem when a set of observations is available on the previous decisions made in the settings of the problem. We develop a framework to effectively and efficiently infer the cost vector of a linear optimization problem based on multiple observations on the decisions made previously. 

We then test our models in the setting of a diet problem on a data-set obtained from NHANES; The data-set is accessible via the link bellow:

https://github.com/CSSEHealthcare/Dietary-Behavior-Dataset

A set of female individuals with the above criteria were considered. Further demographic and diet considerations (in order to select similar patients) led to selecting 11 different individuals’ one day of intake as the initial dataset for the model. In another setting, we only considered people that have consumed a reasonable amount of sodium and water. We consider these two nutrients as the main constraints in the DASH diet. 



In order to compare different potential data and their performance with the model, we used different data groups from the NHANES database. A group of middle-aged women with certain similar characteristics and a group of people with certain attributes in their diets. In the first group, we did not consider how the individual’s daily diet is reflecting on the constraints that the forward problem had and we relied on their own personal answer to questions regarding hypertension and also how prone they thought they were to type-2 diabetes. The result was a sparse set of variables and an inconclusive optimal solution in regards to the preferences. In the second group, we tried to obtain sub-optimal data. We prioritized the maximum sodium intake constraint and the water intake constraints as our main and most important constraints. 

We introduce a new approach that combines inverse optimization with conventional data analytics to recover the utility function of a human operator. In this approach, a set of final decisions of the operator is observed. For instance, the final treatment plans that a clinician chose for a patient or the dietary choices that a patient made to control their disease while also considering her own personal preferences. Based on these observations, we develop a new framework that uses inverse optimization to infer how the operator prioritized different trade-offs to arrive at her decision. 

We develop a new inverse optimization framework to infer the constraint parameters of a linear (forward) optimization based on multiple observations of the system. The goal is to find a feasible region for the forward problem such that all given observations become feasible and the preferred observations become optimal. We explore the theoretical properties of the model and develop computationally efficient equivalent models. We consider an array of functions to capture various desirable properties of the inferred feasible region. We apply our method to radiation therapy treatment planning—a complex optimization problem in itself—to understand the clinical guidelines that in practice are used by oncologists. These guidelines (constraints) will standardize the practice, increase planning efficiency and automation, and make high-quality personalized treatment plans for cancer patients possible.

Assume that a decision-maker’s uncertain behavior is observed. We develop a an inverse optimization framework to impute an objective function that is robust against misspecifications of the behavior. In our model, instead of considering multiple data points, we consider an uncertainty set that encapsulates all possible realizations of the input data. We adopt this idea from robust optimization, which has been widely used for solving optimization problems with uncertain parameters. By bringing robust and inverse optimization together, we propose a robust inverse linear optimization model for uncertain input observations. We aim to find a cost vector for the underlying forward problem such that the associated error is minimized for the worst-case realization of the uncertainty in the observed solutions. That is, such a cost vector is robust in the sense that it protects against the worst misspecification of a decision-maker’s behavior. 

As an example, we consider a diet recommendation problem. Suppose we want to learn the diet patterns and preferences of a specific person and make personalized recommendations in the future. The person’s choice, even if restricted by nutritional and budgetary constraints, may be inconsistent and vary over time. Assuming the person’s behavior can be represented by an uncertainty set, it is important to find a cost vector that renders the worst-case behavior within the uncertainty set as close to optimal as possible. Note that the cost vector can have a general meaning and may be interpreted differently depending on the application (e.g., monetary cost, utility function, or preferences). Under such a cost vector, any non-worst-case diet will thus have a smaller deviation from optimality.  

Radiation therapy is frequently used in diagnosing patients with cancer. Currently, the planning of such treatments is typically done manually which is time-consuming and prone to human error. The new advancements in computational powers and treating units now allow for designing treatment plans automatically.

To design a high-quality treatment, we select the beams sizes, positions, and shapes using optimization models and approximation algorithms. The optimization models are designed to deliver an appropriate amount of dose to the tumor volume while simultaneously avoiding sensitive healthy tissues. In this project, we work on finding the best beam positions for the radiation focal points for Gamma Knife® Perfexion™, using quadratic programming and algorithms such as grassfire and sphere-packing.

In radiation therapy with continuous dose delivery for Gamma Knife® Perfexion™, the dose is delivered while the radiation machine is in movement, as oppose to the conventional step-and-shoot approach which requires the unit to stop before any radiation is delivered. Continuous delivery can increase dose homogeneity and decrease treatment time. To design inverse plans, we first find a path inside the tumor volume, along which the radiation is delivered, and then find the beam durations and shapes using a mixed-integer programming optimization (MIP) model. The MIP model considers various machine-constraints as well as clinical guidelines and constraints.

Perioperative services are one of the vital components of hospitals and any disruption in their operations can leave a downstream effect in the rest of the hospital. A large body of evidence links inefficiencies in perioperative throughput with adverse clinical outcomes. A regular delay in the operating room (OR), may lead to overcrowding in post-surgical units, and consequently, more overnight patients in the hospital. Conversely, an underutilization of OR is not only a waste of an expensive and high-demand resource, but it also means that other services who have a demand are not able to utilize OR. This mismatch in demand and utilization may, in turn, lead to hold-ups in the OR and cause further downstream utilization. We investigate the utilization of operating rooms by each service. The null hypothesis of this work is that the predicted utilization of the OR, i.e., the current block schedule, matches completely with the actual utilization of the service. We test this hypothesis for different utilization definitions, including physical and operational utilization and reject the null hypothesis. We further analyze why a mismatch may exist and how to optimize the schedule to improve patient flow in the hospital.

Primary care is an important piece in the healthcare system that affects the downstream medical care of patients heavily. There are specific challenges in primary care as healthcare shifts from fee-for-service to population health management and medical home, focuses on cost savings and integrates quality measures. We consider the primary care unit at a large academic center that is facing similar challenges. In this work we focus on the imbalance in workload, which is a growing regulatory burden and directly concerns any staff in primary care. It can result in missed opportunities to deliver better patient care or providing a good work-environment for the physicians and the staff. We consider the primary care unit at the large academic center and focus on their challenge in balancing staff time with quality of care through a redesign of their system. We employ optimization models to reschedule providers’ sessions to improve the patient flow, and through that, a more balanced work-level for the support staff. 

This work was performed with the MIT/MGH Collaboration.

In many healthcare services, care is provided continuously, however, the care providers, e.g., doctors and nurses, work in shifts that are discrete. Hence, hand-offs between care providers is inevitable. Hand-offs are generally thought to effect patient care, although it is often hard to quantify the effects due to reverse causal effects between patients’ duration of stay and the number of hand-off events. We use a natural randomized control experiment, induced by physicians’ schedules, in teaching general medicine teams. We employ statistical tools to show that between the two randomly assigned groups of patients, a subset who experiences hand-off experience a different length of stay compared to the other group.

This work was performed with the MIT/MGH Collaboration.

Many outpatient facilities with expensive resources, such as infusion and imaging centers, experience surge in their patient arrival at times and are under-utilization at other times. This pattern results in patient safety concerns, patient and staff dissatisfaction, and limitation in growth, among others. Scheduling practices is found to be one of the main contributors to this problem.

We developed a real-time scheduling framework to address the problem, specifically for infusion clinics. The algorithm assumes no knowledge of future appointments and does not change past appointments. Operational constraints are taken into account, and the algorithm can offer multiple choices to patients.

We generalize this framework to a new scheduling model and analyze its performance through competitive ratio. The resource utilization of the real-time algorithm is compared with an optimal algorithm, which knows the entire future. It can be proved that the competitive ratio of the scheduling algorithm is between 3/2 and 5/3 of an optimal algorithm.

This work was performed with the MIT/MGH Collaboration.

Tracking COVID-19

We are tracking the COVID-19 spread in real-time on our interactive dashboard with data available for download. We are also modeling the spread of the virus. Preliminary study results are discussed on our blog.