COVID-19 Modeling Team at Forefront of Pandemic Projections and Planning
Just weeks after the World Health Organization declared the coronavirus a global pandemic in March 2020, a team of Stanford Health Policy faculty and researchers scrambled to launch a modeling framework to investigate the epidemiology of COVID-19 and to evaluate policy responses.
A year later, the Stanford-CIDE Coronavirus Simulation Model (SC-COSMO) remains at the forefront of dozens of projection models in the United States and Mexico, while helping the state of California and its prison system, hospitals, and health care providers plan for and mitigate the impact of the pandemic. As of May 2021, the SC-COSMO team’s work has resulted in a half dozen studies published in medical journals and open data sites.
“The pandemic has continued to evolve, as have the policy questions and available interventions,” says Jeremy Goldhaber-Fiebert, PhD, associate professor of medicine at Stanford Health Policy (SHP). “Basic questions about how quickly the virus would spread in diverse populations were followed by urgent planning for hospital capacity during the surges and then nonpharmaceutical interventions and social distancing questions.”
Jeremy Goldhaber-Fiebert, PhD
Goldhaber-Fiebert is one of the principal investigators of the SC-COSMO project, along with Fernando Alarid-Escudero, assistant professor at the Center for Research and Teaching in Economics in Mexico, and Jason Andrews, MD, associate professor of infectious diseases at Stanford Medicine. Other SHP faculty, among two dozen investigators on the team, are Joshua Salomon, PhD, and David Studdert, LLB, ScD, MPH, both Stanford Health Policy professors of medicine. Studdert is also a professor of law at Stanford Law School.
“We have had to consider the timing and magnitude of subsequent epidemic waves, what fraction of the population may have acquired natural immunity, and what waning immunity might mean. The team has risen to the challenge time after time,” Goldhaber-Fiebert says.
In the summer and fall of 2020, the team focused on school re-openings and how to prevent and control outbreaks in state prisons. Since then, they have been looking at questions regarding vaccination rollout and scale-up, especially in the context of the viral variants that may be threatening yet another surge. Other key analyses have focused on the geographic, socioeconomic, and race and ethnic disparities in COVID-19 risk, access, and outcomes.
“The vaccination rollout effort suggests that prioritizing interventions based on both individual characteristics and geographic concentration of risk might help to achieve better outcomes in terms of reducing illness and mortality overall, and reducing disparities,” says Salomon, a senior fellow at the Freeman Spogli Institute for International Studies who heads up the Stanford Prevention Policy Modeling Lab.
The Prisons and Jails Project
One of the team’s first projects was working with county jails and the California Department of Corrections and Rehabilitation to reduce the spread of COVID-19 among the incarcerated.
“Incarcerated people are a particularly vulnerable group: They reside in close proximity, making it difficult or impossible to employ the same disease control measures that are being used in the general population,” says Goldhaber-Fiebert, who co-leads the prison project with Andrews and Studdert.
Andrews describes the work in jails and some of its important milestones.
“We partnered locally with county jails in San Mateo and Santa Clara and with California’s prison system,” he says, noting that Stanford students Yiran Liu, who is pursuing her PhD in cancer biology, and Chris LeBoa, an undergraduate human biology major, led a study of infection rates and prevention measures in the jails.
Jason Andrews, MD
“Incarcerated individuals are heavily impacted by certain measures being taken to prevent spread,” Andrews says. “For example, many of them have had their court dates delayed, they haven’t been able to see their family members in person, and their classes have been suspended. These measures are all taken to protect health, but their impact on mental health and well-being may be underappreciated.”
Goldhaber-Fiebert says that the work in the jails allowed the team to address COVID-19 challenges on a larger scale, using data on more than 100,000 men and women incarcerated in California state prisons. Two other Stanford students, Tess Ryckman, a PhD candidate in health policy, and Elizabeth Chin, a PhD candidate in bioinformatics, led the work to analyze these data, create high-resolution models of transmission, and simulate the effects of prevention interventions, including vaccination.
Shortly after the prison project was launched, some 44,000 people in U.S. prisons had tested positive for COVID-19, according to the Marshall Project. That figured skyrocketed to 396,265 by May of 2021, with 2,886 deaths.
Through a $1 million gift from the Horowitz Family Foundation, Stanford Medicine established a COVID-19 Emergency Response Fund to support research and prevention strategies to slow and eventually stop the spread of COVID-19 infection in California prisons and jails.
The Golden State
Another major SC-COSMO project is providing the state of California with county-level COVID-19 estimates for such things as infection counts, detected cases, and projections of future needs for hospital beds. SC-COSMO modeling is featured in the California COVID Assessment Tool, or CalCAT, which provides assessments of the short-term forecasts of COVID-19 trends and presents scenarios of the course of the disease across the 58 counties in the Golden State.
Instead of relying on one or two projection models—as some countries and U.S. states did when the pandemic first hit—the CalCAT tool incorporates COVID-19 estimates from a number of respected organizations, including Stanford, UCLA, MIT, Johns Hopkins University, and Imperial College London. The Stanford team provided more than 10 rounds of projections for the state from June through December 2020.
“It’s like using the wisdom of the crowd,” says Goldhaber-Fiebert. “Instead of hanging your hat on one model, you’re looking at a range of predictions to help you plan and forecast—and leveraging the whole community of researchers and analysts who are working on this problem.”
Latino populations throughout California have higher average levels of exposure risk due to occupation and housing characteristics. Areas with high exposure risk tend to have higher case rates but below average testing rates
The team looked at more than 1,900 California county and state-level public health orders related to the virus from January 2020 through February 2021 and made the data publicly available as well as MedRxiv, an open-source medical research website for pre-peer-reviewed studies and public comment. They also developed a data visualization tool that allows users to easily visualize and compare information within and across counties.
“Stanford’s new health order data set helps California officials understand the course of COVID and plan the ongoing response,” says Ryan McCorvie, a statistician working for the California Department of Public Health’s COVID-19 modeling group. “Analysis of the detailed local response in each county can help policy makers across the state judge outcomes effectively.”
Partners in Mexico
The Stanford members of the SC-COSMO team also collaborate with their partners in Mexico, working on strategies to mitigate the pandemic by collecting, synthesizing, and openly sharing the most relevant and useful data, while adapting the SC-COSMO model to the Mexican context.
“Having real-world impact requires conducting high-quality analytic work as well as engagement with policy makers and communicating findings in understandable ways to the media and the public,” says Alarid-Escudero. The team in Mexico has helped inform COVID-19 policy making in several states, including Hidalgo and Aguascalientes, providing analyses of data on cases, hospitalizations, and deaths, as well as projections.
"We are motivated because timely and rigorous science can be used to protect people’s health and well-being, especially those who are often neglected or are at greatest risk"
“We also communicated our findings from modeling analyses focused on end-of-year holiday social gatherings, distancing, and implications for school reopening for the 20 million people living in the Mexico City Metropolitan area,” Alarid-Escudero says. “Shortly, we will be launching an interactive tool with model projections for all of the states of Mexico.”
Team members who led this work include Andrea Luviano, Valeria Gracia, and Yadira Peralta.
“For the team, this past year’s focus on COVID-19 has been very productive but also extremely intense,” says Goldhaber-Fiebert. “We are motivated because timely and rigorous science can be used to protect people’s health and well-being, especially those who are often neglected or are at greatest risk. While we hope the pandemic will soon recede and with it the pace of COVID-19-specific work, we have developed long-term collaborations, tools, and research programs around infectious disease modeling, health in incarcerated populations, and disparities in health equity that will carry on for years to come.”
Stanford Student Collaborations
The SC-COSMO project has allowed Stanford students to use the modeling and data analytic tools to shed light on important questions about the pandemic.
Marissa Reitsma, a PhD candidate in health policy, for example, used five years of the American Community Survey of the Census Bureau to map out areas with a high proportion of people at increased risk of being exposed to COVID-19 due to their occupation and housing characteristics. She and her colleagues published their findings in the Journal of General Internal Medicine. Their study found that communities of color may be most susceptible to low vaccine coverage due to long-standing disparities in health care, mistrust fueled by a history of exploitation in clinical trials, and other structural risk factors.
“This study provides hard numbers to what has been acknowledged in public discourse,” Reitsma says. “We hope our study motivates equity-focused policies like support for safe self-isolation, cash assistance, and paid sick leave for low-income individuals that need to quarantine.”
Reitsma also worked with Anneke Claypool, a PhD candidate in management science and engineering, focusing on the fact that Black and Hispanic populations are being hit harder than most by the pandemic due to a variety of socioeconomic and economic reasons. The two students won an early-career grant from the Stanford Center for Population Health Sciences to analyze multiple streams of data, which they are using to evaluate the effects of different interventions and policies in order to identify the most important drivers of racial disparities. They believe their results will help decision makers prioritize effective interventions. Their work has been focused on approaches to vaccine access and acceptance to improve population health.