Developing policies for effective COVID-19 containment: The TRACE model

By Ross A. Hammond

States across the U.S. are considering paths to re-opening following months of stay-at-home orders and a widespread shuttering of the economy in response to the threat of COVID-19. Policymakers now face the task of crafting strategies that will allow resumption of activity without producing additional waves of infection that could do even more damage to health and economies. Looking to successful examples from around the world, many of these strategies involve widespread testing, coupled with contact tracing and selective quarantine. Yet many questions that are important in designing such policies remain difficult to answer. Getting the answers “right” may be the difference between successful containment or a damaging resurgence of infection. Some of these questions include:

• How effective can a test-and-trace policy really be in containing future waves of infection? Is such an approach feasible in the United States?
• How much testing capacity is needed for effective containment? How much capacity to trace contacts will be needed?
• How accurate must tests be?
• What is the most efficient way to use limited testing capacity?
• How might success depend on still-uncertain assumptions about the spread of the disease itself?
• What social distancing measures might still be needed to enhance containment?

To help answer these questions and provide specific guidance to decision-makers, we offer new analysis based on a model entitled TRACE (Testing Responses through Agent-based Computational Epidemiology), developed collaboratively by researchers at Brookings and Washington University in St Louis. Unlike many other COVID-19 models, TRACE is not a forecasting model. It is intended instead as a policy laboratory to assist in the design of effective containment policies using testing and contact tracing. By considering a very wide array of possible policy variations, capturing scenarios that encompass the extensive uncertainty still surrounding COVID-19, and providing specific quantitative inputs and outcomes, TRACE aims to be a practical tool to help decisionmakers manage many of the implementation decisions they face in crafting a re-opening strategy. TRACE is an agent-based computational model, allowing it to include variations in age, activity pattern, infectivity, and contact networks—all features that evidence so far suggests are important determinants of how COVID-19 spreads.

Our analysis based on the TRACE model identifies promising intervention strategies to successfully suppress the spread of COVID-19 while allowing relaxation of many or all of the mass social-distancing measures that have been in place across the country. Suppression means not just “flattening the curve” (by spreading out infections over time) but ongoing containment that curtails sustained spread and prevents large numbers of new cases. A primary goal of TRACE is to identify policies that yield true suppression of the epidemic while gradually relaxing social distancing.

Our results also suggest that while the ability of policies based on testing and contract tracing to effectively suppress epidemic spread can depend on the setting and timing—a single policy does not necessarily suit all circumstances—some policies are highly robust to the uncertainty facing policymakers about the underlying biology of COVID-19. All of the policies we simulated also underscore the importance of sufficient adherence by individual citizens to quarantine, self-isolation, or limited social distancing measures. This indicates that an important goal for policy may be to encourage adherence through consistent, widespread messaging, and to make self-isolation financially and logistically feasible.

The goal of re-opening large parts of the country while suppressing COVID-19 and preventing a large second wave of infection may well be possible, and not that far out of reach from our current capabilities. But to do this, we will likely need to refocus and re-orient our current approach to make best use of limited resources, and we will need to tailor features of any specific policy implementation to local conditions to maximize chances of success. TRACE was designed to help facilitate this process, and we hope it will prove a valuable resource for decision-makers working to bring our country through this crisis.

The TRACE team includes Ross A. Hammond, Matt Kasman, Joseph T. Ornstein, and Rob Purcell. You can find more information about the model here.