Latest information on COVID-19


The Stanford Center for Biomedical Informatics Research (BMIR) is leading data science efforts to address the COVID-19 pandemic—to enhance patient care, to measure the scale of the infection, and to model the demand on healthcare resources.

Many scientists within BMIR are involved, including Mark Musen, Manisha Desai, Tina Hernandez-Boussard, and Nigam Shah. In this unprecedented situation, BMIR faculty members are working hard to apply their skills to address the COVID-19 pandemic.

There is a lot of excitement for using data science and modeling to forecast how COVID-19 will spread and affect their communities, and BMIR Biomedical Informatics researchers are at the forefront of that work. 

As the COVID-19 outbreak commenced, public health agencies had a critical need for on the ground data to inform not only the spread of the virus but to understand the clinical diagnostic and treatment implications.

The Centers for Disease Control and Prevention (CDC) respectively reached out to BMIR given our essential access to and expertise with real-world electronical medical data, further bolstered by Stanford’s lead in developing SARS-CoV-2 Testing.

Utilizing the advanced clinical research data infrastructure developed here (STARR-OMOP), our team was able to provide near real-time updates on clinical processes and outcomes surrounding Covid-19 to enable the CDC to drive their ongoing public health efforts.


COVID Research by BMIR Faculty

Predicting best strategies for scarce resource allocation during COVID-19 pandemic
Tina Hernandez-Boussard, PhD, and Manisha Desai, PhD

Hernandez-Boussard and Desai will use data that shows the time of hospital admission up to the time of mechanical ventilation to predict patient recovery, details of hospital discharge and survival at 24 hours, 7 days, and later. This model will include important factors for COVID-19 recovery and will be continuously updated in real-time as emerging data become available. In the event of resource scarcity, this effort has the potential to inform optimal resource allocation at Stanford Health care.

Modeling outcomes of COVID-19 patients
Tina Hernandez-Boussard, PhD, and Manisha Desai, PhD

Hernandez-Boussard and Desai plan to use data from patients who tested positive for COVID-19 at Stanford Health Care to investigate how disease burden varies significantly in patients infected with the virus. Currently, the association of co-infections, symptomatology, comorbidities and other parameters on patient trajectories is unknown.

Building a Bay Area SARS-CoV-2 information commons
Nigam Shah, PhD, and Yvonne Maldonado, MD

Shah and Maldonado are hoping to better anticipate the evolution of the COVID-19 pandemic. Existing predictions of the pandemic are uncertain due to many factors, such as a lack of plentiful and optimal data and the fact that models vary depending on the location. The scientists are creating a platform that combines diverse inputs to quantify the effects of the shelter-in-place order. The goal is to create more accurate, higher-resolution models about COVID-19 pandemic parameters.

Continuous symptom profiling of patients screened for SARS-CoV-2
Nigam Shah, PhD

Shah’s team is analyzing medical notes describing symptoms of patients screened and tested for SARS-CoV-2 at Stanford Medicine. They want to determine if combinations of symptoms are predictive of SARS-CoV-2 test results to assist screening in low-resource settings. They also hope to use information from the medical notes — such as symptoms, duration of disease, travel history and more — to anticipate which patients will require admission or eventual ICU care.

Answering COVID-19 clinical research questions with electronic health record data
Nigam Shah, PhD; Marcello Chang and Jonathan Lu

Chang and Lu and their collaborators are collecting clinical questions related to COVID-19 that can be addressed through observational electronic health record data. The group is investigating connections between the disease and low levels of lymphocytes, lower cholesterol and abnormal chest X-rays. If you would like to submit a question, please do so here. A list of questions that have been submitted can be found here. Student submissions can be found here. For questions, please contact

BMIR Focus

The Stanford Center for Biomedical Informatics Research (BMIR) uses advanced research techniques to discover, apply, translate, and organize data that make a difference for health and healthcare. With its expertise in clinical and translational informatics research and biostatistics, the division works to uncover new ways to advance personalized medicine and to enhance human health and wellness. 

Collaboration is in our DNA. We are excited about the prospect of working with other experts who share our goal to connect data to health and medicine. We encourage you to contact Mark Musen, Director of BMIR ( to learn more about the Stanford Center for Biomedical Informatics Research. 

Also please join us at an upcoming BMIR Center for Biomedical Informatics Research Colloquium.

Explore BMIR

At BMIR, we develop computational methods for biomedical discovery that influence medical decisions.           

Learn more about the cutting-edge ways we are advancing technology and biomedicine to improve human health.

Our state of the art research advances patient care by improving semantic technology, biostatistics, and the modeling of biomedical systems. Read more about our research labs.

Join us for our weekly research talks featuring world-renowned scientists, faculty, staff, and students.                           

BMIR Colloquia and Research in Progress talks occur on Thursdays from 12-1 PM during the academic year in Medical School Office Building room X275, 1265 Welch Road, Stanford, CA. See schedule.

Notable Projects and Services

CEDAR is making data submission smarter and faster, so biomedical researchers and analysts create and use better metadata.


The NCBO manages a repository of all the world’s publicly available biomedical ontologies and terminologies—now more than 390 in number.

Protégé is the most widely used ontology-development system in the world.

Diagnostics - Infectious Diseases

EteRNA, an online puzzle, enlists video gamers to try to design a sensor module that could make diagnosing TB as easy as taking a home pregnancy test. Learn more at  Can An Online Game Help Create A Better Test For Tuberculosis?

Green button: the promise of personalizing medical practice guidelines in real time.

CoINcIDE, is a novel methodological framework for the discovery of patient subtypes across multiple datasets that requires no between dataset transformations.

Learn more at CoINcIDE: All together now