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Home | About TRACE-STL | TRACE-STL interactive dashboard


TRACE (Testing Responses through Agent-based Computational Epidemiology) is an agent-based computational model developed by a team from Brookings and Washington University in St. Louis, with the specific goal of providing insights into how policies that use testing and contract tracing might help contain the COVID-19 pandemic. It draws on the extensive body of evidence about both the current and past epidemics, and is also designed to manage a high degree of remaining uncertainty about some of the parameters it uses. The version of the simulation model described here has been developed by the Brookings Institution Center on Social Dynamics and Policy in close consultation with the St Louis Department of Health in order to explore how the COVID-19 pandemic might be effectively contained in St. Louis through summer, 2021.

Setting and population

TRACE-STL has been adapted from the original TRACE model to represent the St Louis metro region (St. Louis city and seven surrounding counties, namely Franklin, Jefferson, Madison,  Monroe, St. Charles, St. Clair, and St. Louis counties). The simulated population of approximately 2.4 million individuals is based upon a well-validated “synthetic population” that draws on multiple high-quality data sources (developed by RTI as part of the National Institutes of Health Models of Infectious Disease program) and situated in a geographically detailed representation of the St Louis area. Simulated individuals (“agents”) in our model meaningfully represent residents in city of St. Louis and surrounding counties. Agents have geospatial associations (e.g. where they live and work), demographic attributes, and infection states. Together, these shape the set of other individuals with whom they have social contact in ways that might transmit the virus. This approach allows us to compare cumulative levels and timing of population infection rates across a number of different combinations of active policies and practices in a simulated but highly realistic setting. We summarize our model here, and describe it in complete detail (including mathematical equations, data sources, and computational implementation) in a forthcoming manuscript.

COVID-19 Infection

TRACE simulates transmission of the COVID-19 virus between individuals and the progression of the infection within individuals. Infection progression in our simulations uses a variant of the classic “Susceptible-Exposed-Infectious-Recovered” epidemiological model that is intended to specifically represent COVID-19 (Figure 1).

A flow chart of Covid-19 “states” and possible “state transmissions” in the model

A flow chart of Covid-19 states and state transmissions in the model

Figure 1. COVID-19 “states” and possible “state transitions” in our model

Simulated individuals who have never experienced a COVID-19 infection (or vaccination) start as “susceptible.” If contact with an infectious individual transmits the virus, then they become “exposed.” After a set incubation period, they become “infectious.” Some individuals have shorter latent periods and are infectious before they display symptoms (i.e. are “pre-symptomatic”), while others never display symptoms (i.e. are asymptomatic). We allow infectivity—that is, one’s ability to transmit the virus to others with whom they interact—to differ across both individuals and infectious type. This allows us to represent the presence of “super-spreaders” who are highly contagious as well as a lower likelihood of non-symptomatic individuals transmitting the disease (e.g. by coughing). Finally, when the infection has run its course, an individual is “recovered.” For the purposes of this model, we assume that anyone in the “recovered” state cannot be re-infected with COVID-19 during the remainder of the simulation (e.g. over the following < 6 months). The degree and duration of immunity conferred by prior infection remain open questions in the scientific literature.

Our simulation incorporates a rollout of vaccinations that reflects current expectations for this endeavor (locally determined rate of rollout, vaccine eligibility criteria, etc) for the time period simulated. Individuals who receive vaccinations are effectively placed in the “recovered” state.

Contact between agents

On any given day, individuals interact with others in ways that might transmit the COVID-19 virus. We simulate multiple settings in which such interactions can occur: in the home, workplace, or school with which each individual is associated as well as contact that might take place outside such settings (e.g. shopping). Contact is based on both a realistic geographic depiction of settings in which each individual spends time as well as an empirically-driven assignment of whom they interact with in those settings (e.g. a school-age child will have a much larger proportion of their daily interactions outside of home and school with other school-age children than with senior citizens, and these interactions are most likely to occur between children who live nearby. Contact in our model also is intended to reflect current conditions: for example, there is county-level variation in the extent of remote learning taking place that influence contacts in school settings. Policy interventions (described below), can alter the contact structure of the population and thus the transmission dynamics.

Model conditions and operation

Each simulation starts with numbers of currently infected and recovered individuals based on recent, zip-code-specific data (adjusted for the possibility of undercounting in these data). The model is allowed to run for six simulated months, effectively representing the course of the pandemic in St. Louis and the surrounding region through early summer, 2021.

Figure 2: Illustrative simulation visualization, St Louis metro region.

Figure 2. Illustrative simulation visualization, St Louis metro region. Agents are color coded by disease state (Blue=Susceptible, Red=Infected, Green=Recovered).

Across model runs, we systematically explore variation in policy and practice to represent different combinations of pandemic containment options. Specifically, we explore different settings for:

  • Testing. How many daily PCR or antigen tests are available, who is given priority for testing, and the accuracy of the test technologies used.
  • Contact tracing. How many contacts of symptomatic or Covid-positive individuals can be traced (and themselves tested and requested to quarantine) each day.
  • Social distancing policies. Restrictions on in-person business activity in St. Louis city or the surrounding region, designed to reduce contact and transmission.
  • Mask usage. How many people wear masks outside of the household, and the effectiveness of mask use (i.e. compliance with current CDC recommendations).

In addition, we explore variation in epidemiological conditions to account for potential variation in the extent of current undercounting of active COVID cases or for an increase in infectivity that might result from the widespread introduction of new COVID variants. The full set of scenarios that we explore over nearly 50,000 simulation runs are summarized in Table 1.


Control Conditions Varied Variations Explored
PCR Test Volume Current daily test volume Increase daily test volume by 50%
PCR Test Priority Symptomatic individuals Identified contacts of symptomatic individuals
Antigent Test Volume Current daily test volume Tenfold increase Hundredfold increase
Antigen Test Priority No priority Tests given twice to each individual
Antigen Test Quality Lower false negative rate Higher false negative rate
Contact Tracing
Tracing Capacity Current daily capacity Tenfold increase
Social Distancing Policies
Business activity restrictions None St. Louis City only Region-wide
Mask Usage
Proportion of people wearing masks outside of home Low estimate High estimate
Mask effectiveness in preventing COVID transmission Low estimate High estimate
Epidemiological Conditions Varied Variations Explored
Initial Active Infection Prevalence Low (based on current case count and a moderate level of undercounting) High (based on current case count and a high level of undercounting)
COVID infectivity Low (current estimate) High (increase by 50%)

Table 1. Policy variations simulated

To see full results from the simulations, see the TRACE-STL Dashboard.

For More Information

For media or collaborative inquiries regarding TRACE-STL, please contact: Shannon Meraw

TRACE-STL was constructed in close collaboration and consultation with the St Louis City Department of Health.

The Brookings Center on Social Dynamics TRACE-STL team includes:

Ross A. Hammond, Ph.D.,  Director, Center on Social Dynamics & Policy, Senior Fellow, Economic Studies, The Brookings Institution; Betty Bofinger Brown Distinguished Associate Professor, Public Health and Social Policy, The Brown School, Washington University in St. Louis; External Professor, The Santa Fe Institute

Matt Kasman, Ph.D., Assistant Research Director, Center on Social Dynamics & Policy, The Brookings Institution

Rob Purcell, Research Programmer, Center on Social Dynamics & Policy, The Brookings Institution

A scientific manuscript with full documentation for the TRACE-STL model is in preparation and we will update this page with a link to this information shortly.

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