Stanford-CIDE Coronavirus
Simulation Model

SC-COSMO

Our Model
About us

About our model


The SC-COSMO modeling framework enables modeling of the epidemiology of COVID-19 for diverse populations and geographies. At its core, it is an age-structured, multi-compartment susceptible-exposed-infected-recovered (AS-MC-SEIR) model implemented in the R programming language. The model incorporates realistic demography and patterns of contacts sufficient for transmission. The model also incorporates non-pharmaceutical interventions (NPIs) (e.g., “social distancing”) timing and effects on reductions in contacts which may differ by demography. The model framework also allows for the comparison of many, future what-if scenarios and how they might impact outcomes over time and cumulatively (e.g., infections, cases, deaths, etc.). The framework can be implemented rapidly for specific populations and geographies based on generally available data from these places along with methods we have implemented to infer data elements that are unavailable (e.g., imputation and calibration).

Dynamic real-time decision-making system

Full technical details of the SC-COSMO modeling framework are posted in the Resources section; here, we provide a brief technical overview of its capabilities. Because the framework is intended to be used for modeling many different populations and geographies, it also includes the ability to easily load/parametrize population- and geography-specific inputs that are generally publicly available (e.g., the age-structure of a given county), methods to infer setting-specific inputs that may not be available (e.g., realistic contact matrices) based on demographic features and a database of existing empirically-derived contact matrices, and to calibrate to setting-specific COVID-19 data (e.g., detected cases time-series, testing time-series, etc.) accounting for incomplete detection and uncertainty. Benchmarking even using a single modern computer to date shows that the calibration and projection/forecasting features of the model under multiple scenarios are fast (e.g., on the order of 10s of seconds to 50+ states or counties), and the framework is designed with open source tools and in such a way that it can be scaled using symmetric parallel processing to further speed its simulations especially useful when propagating uncertainty to its outcome projections.

About us


The SC-COSMO team is a diverse group of faculty and graduate students 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.

Jeremy Goldhaber-Fiebert

Associate Professor of Medicine | Stanford Health Policy

Fernando Alarid-Escudero 

Assistant Professor | CIDE

Jason Andrews

Assistant Professor of Medicine | Stanford Medicine

Joshua Salomon

Professor of Medicine | Stanford Health Policy 

David Studdert

Professor of Medicine | Stanford Health Policy 

Yadira Peralta

Assistant Professor | CIDE 

Natalia Kunst

Research Fellow | Yale Schools of Public Health an Medicine & PhD Candidate | University of Oslo

Andrea Luviano

Research Assistant | CIDE

Marissa Reitsma

PhD Student | Stanford Health Policy 

Tess Ryckman

PhD Student | Stanford Health Policy 

Neil Rens

MD Student | Stanford Medicine

Ally Daniels

JD Student | Stanford Law School

Liz Chin

PhD Student | Stanford Medicine 

Regina Isabel Medina Rosales

Research Assistant | CIDE

Hirvin Azael Diaz Zepeda

Research Assistant | CIDE

Anneke Claypool

PhD Student | Stanford Medicine 

Mariana Fernandez

Software Engineer | CIDE 

Radhika Jain

Postdoctoral Fellow | FSI Asia-Pacific Research Center

Jose Manuel Cardona Arias

Data Analyst | Innovations for Poverty Actions

Valeria Gracia

Senior Data Scientist and Modeler | CIDE

Lea Prince

Research Data Analyst | Stanford Health Policy

Neesha Joseph

Program Manager | Stanford Health Policy

Zulema Garibo

Administrative Affiliate | Stanford Health Policy

Marcela Pomar Ojeda

Project Manager | CIDE

Our work

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.

Read more

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.

Read more

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.

Read more

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.

Read more

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.

Read more

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


1. Model Technical Description

PDF

2. SC-COSMO State of California county-level modeling results

Link

3. Comunicado de prensa al 3 de mayo de 2020 
PDF

More coming soon

Media

Stanford Health Policy Experts on COVID-19 and its Consequences 

FSI Stanford Virtual Research Seminar

Stanford Works With California Prisons to Test and Prevent COVID-19
FSI | Stanford Health Policy
CIDE Expert on modeling COVID-19 and its Consequences 

Milenio Television | Interview with Dr. Fernando Alarid-Escudero

Ciudad de México se ha salvado del colapso, reabrir sin pruebas puede cambiarlo todo

The Washington Post, May 25, 2020

See more

Contact us


Please let us know if you have any questions

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