The SC-COSMO team is a diverse group of faculty and graduate students.
We are committed to providing useful, high-quality modeling to address a range of pressing questions regarding the response to COVID-19: resource planning, forecasting, and policy/intervention evaluation. We are a multi-disciplinary, multi-institutional team including expertise and experience in infectious disease, epidemiology, mathematical modeling and simulation, statistics, decision science, health policy, health law, and health economics. The team also collaborates with broader communities of researchers and decision makers focused on COVID-19, enabling us to leverage additional expertise, data, and insights as needed.
Our SC-COSMO projects
Framework and Methods
Expanding Stanford-CIDE COSMO modeling framework and methodologies for COVID-19 epidemic modeling across diverse geographies and population.
As COVID-19 transmission leads to its spread throughout the world’s diverse populations, it is critical to efficiently model and forecast its future spread between and within these populations. Doing so supports timely and optimal resource planning and decisions between potentially appropriate and effective interventions. Efficiency in modeling means that we must not invent new models de novo for each population but rather build a set of generally applicable common methodologies and a flexible and scalable framework for model calibration, forecasting, and application to decision analyses and other purposes. Such methodologies and frameworks can then be quickly adapted and applied for particular populations. The goal of this project is to extend, expand, and accelerate the current SC-COSMO framework and methods and to implement these improvements.
State of California
COVID-19 county-level modeling for the state of California.
To inform the response to the COVID-19 epidemic, this project provides the state of California with county-level COVID-19 estimates including quantities like of the current number of infections and detected cases and projections of future needs for hospital and ICU beds, personal protective equipment (PPE), and ventilators. These estimates and projections are made at the county level and updated on a frequent basis. Projections are made based on various assumptions about the epidemic and for various scenarios (e.g., effectiveness of non-pharmaceutical interventions). The project also focuses on developing intuitive and usable tools for those who are not themselves modeling experts.
States of India
COVID-19 modeling for the states of India.
The project develops forecast models of the COVID-19 epidemic in India with Wadhwani AI and its Indian governmental partners, providing a rapid response to urgent needs for planning and resource allocation. The project also refines these rapid subnational models to synthesize additional data sources and to provide more detailed evaluations of various intervention and policy scenarios. The project develops tools that enable Wadhwani AI and government partners to examine and explore alternative assumptions and parameters on key projected outcomes. The project also focuses on working with Wadhwani AI to refine estimates of intervention effects using advanced data analyses, for example using mobile phone location data.
States of Mexico
COVID-19 modeling for the states of Mexico.
This project generates access in real-time to information on the COVID-19 pandemic as well as projections of the effects of potential strategies to mitigate such pandemic in Mexico. To achieve this goal, the project focuses on three specific objectives: 1) collecting, synthesizing, and openly sharing the most relevant and useful data about the COVID-19 pandemic; 2) accelerating the development of the SC-COSMO model and its adaptation to the Mexican situation to incorporate new information on the evolution of the epidemic and refining models specific to each state in Mexico as well as producing projections from different mitigation strategies; 3) identifying a set of feasible mitigation strategies, comparing the health and economic consequences in the population in the medium and long term to make these results useful in supporting decision makers selecting the best interventions, and disseminating these results in a clear and understandable manner to the general population.
California Department of Corrections
and Rehabilitation (CDCR)
COVID-19 modeling among California Prison Populations.
Prisoners are particularly vulnerable to COVID-19 as they reside in close proximity, making standard disease control practices (e.g., home stay, social distancing) difficult or impossible to observe. Inmates also have high rates of comorbid illnesses and other characteristics that increase their risk of dying from COVID-19. Consequently, the pandemic threatens to have a devastating impact on the 2.3 million inmates in the U.S., including 131,000 in state prisons and 82,000 in local jails in California. This project will provide timely and accurate COVID-19 forecasts to inform the efforts of prison health systems to plan proactively, select optimal mitigation strategies, and provide care for incarcerated populations.
Resources and Publications
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