The COVID-19 pandemic has killed over 300,000 people worldwide to date and has led to large reductions in economic activity as people take steps to protect themselves and as governments implement policies designed to control the virus’ spread. In the United States, these intense public and private social distancing efforts have indeed helped to control spread of infection, but have come with mounting economic costs and potential health risks of their own. Across the country, state and local governments are grappling with decisions about when and how to reopen workplaces, schools, and social venues and how to create an environment in which their citizens feel safe enough to resume these activities.

Decision-makers must balance the imperatives for reopening to restore economic activity, education, and social life against the epidemiological risks of renewed transmission. Reopening too soon, or without the right epidemic control measures in place, is likely to produce additional waves of infection. Experience from other epidemics suggests these waves could produce surges of infection as high or worse than what the country has experienced so far. The science of epidemics tells us clearly that until a large fraction of the U.S. population has immunity—whether via a widespread vaccine or recovery from previous infection—the risk of resurgent infection will not go away. Given that a successful vaccination effort is likely many months into the future, and that we are currently far from widespread immunity, can the risks of reopening be mitigated or managed? Is there a “middle path” between indefinite shutdown and a freely spreading virus with inevitable high tolls of disease? In this essay, I argue that testing and modeling can help us navigate the uncertain terrain ahead.

INVESTING IN TESTING

The answer may lie in testing, as investment in a strong capacity to test Americans (for presence of or immunity to COVID-19) and policies based on testing have the potential to substantially mitigate the spread of infection, facilitating reopening some or all of the shuttered parts of American life while managing risks. Testing could be used in at least three different ways. First, testing to detect active infections could be combined with contact tracing (identifying those who may have been exposed) and quarantine to help contain emerging clusters of disease. Given sufficient test-and-trace capacity, this type of policy could replace some or even potentially all of the mass social distancing measures currently being used to contain epidemic spread. Other countries (including New Zealand, South Korea, Singapore, and Germany) are using versions of this approach already.

Second, even in the absence of contact tracing, sufficiently widespread and accurate testing could be used to adjust social distancing adaptively— turning shutdown measures on or off to respond to resurgent epidemics, exempting those who might be immune from distancing, or emphasizing protective measures for those at highest risk. Some states in the U.S. are already working toward this goal.

Decision-makers must balance the imperatives for reopening to restore economic activity, education, and social life against the epidemiological risks of renewed transmission. Reopening too soon, or without the right epidemic control measures in place, is likely to produce additional waves of infection.

Third, testing can give scientists much more accurate (and much needed) data to inform our understanding of who is at highest risk from COVID-19, how much spread is occurring among people with no symptoms (especially in children), and how much immunity the U.S. population is developing. These data can, in turn, lead to better projections and better planning.

THE CHALLENGE OF TESTING

Testing comes with its own challenges, as to be successful in mitigating an epidemic, any policy involving testing must be carefully crafted. Policymakers designing a testing approach must consider a number of factors. First, at least two different types of tests are available that give different information: whether a person is currently infected or whether a person has antibodies indicating they have had the disease in the past. For either kind of test, a testing regime must consider how many tests can be administered per day, how accurate the tests are, and how quickly the results become available.

In addition, a policy must define who is given the test—for example, to anyone with symptoms, to those employed in essential parts of the economy, to a random sample of the population, or to highrisk groups.

Finally, a policy must define specific containment actions and how the information from the tests will be used as part of these. Actions that may be part of such a policy include quarantining those with active infections, tracing the contacts of those who are sick, releasing those with immunity from workplace closure precautions, and so on. Many of these come with their own considerations such as how much capacity is there to trace contacts quickly, and what fraction of people will adhere to social distancing or quarantine rules.

The success of any particular configuration of testing-based policy in containing disease outbreaks will depend in part on the choices above (features of the policy) and in part on factors outside policymakers’ control (features of how the disease spreads). These factors include what fraction of cases of COVID-19 are asymptomatic, how contagious those with active infections are and for how long, and the length and degree of any protection conferred by antibodies.

THE VALUE OF MODELING

Given the complexity of these choices, and the uncertainty about many of the factors above, decision-makers considering a testing-based policy will benefit from the use of modeling. Quantitative dynamic models have been used effectively as policy tools in many previous epidemics, both to forecast the potential course of spread and as “policy laboratory” to understand the potential consequences of interventions. When confronting such a complex challenge, this type of model offers important advantages for policy design over reliance on either “mental models” (intuition) or the use of data alone. They allow counterfactuals and projections across diverse settings to be considered, past experience and extensive theory from combatting past epidemics to be incorporated, and experiments with many different policy options to be conducted within the model in ways that would not be feasible or ethical in the real world. For some types of model, the kind of diversity (demographic, geographic, social, and medical) that characterizes countries such as the U.S. can be taken into account to yield insights that are not “one size fits all.”

The use of models is not without drawbacks, and it is essential that modeling be used effectively and responsibly, following best practices on the part of both model designers and model consumers. But such models offer enormous potential to help answer key questions in the current situation, such as: How much testing and tracing capacity would it take to relax some or all social distancing without creating a large second wave of infection? What is the best way to use testing across a wide range of scenarios and uncertainty? Quantitatively, how well might various testing policies do—how many new cases would still occur, how quickly, and for whom? Models can also help tune policies to be as efficient as possible—maximizing the degree to which key economic and educational activities can be resumed while minimizing epidemiological risks. An example of the insights that such models can offer for COVID-19 containment policies based on testing can be found in the TRACE project, which evaluates thousands of potential policy configurations to find options that are robust across the enormous uncertainty facing decision-makers.

In the search for a “middle path” forward—a path that avoids the harms to health and the economy of either indefinite lockdown or unmitigated spread of the virus until widespread immunity offers a more lasting solution—testing can play a starring role, and models can help policymakers design approaches that are as effective, as efficient, and as robust to uncertainty as possible.

       

The Brookings Institution hosted a virtual event to complement the launch of a new publication on how to reopen America. The event opened with keynote remarks from Dr. Jennifer Nuzzo of Johns Hopkins Bloomberg School of Public Health. An expert panel of Brookings experts discussed the roadmap for reopening America. Brookings President John R. Allen moderated the discussion.

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The Brookings Cafeteria is part of the Brookings Podcast Network.

       

Bringing in experts from across the Institution, Brookings is launching a comprehensive effort to answer such questions. The first volume of essays, “Reopening America: How to Save Lives and Livelihoods,” provides key considerations implicit to conversations regarding reopening, recovery, and renewal across the United States.

On June 2, the Brookings Institution hosted a virtual event to complement the launch of this publication. The event began with keynote remarks from Dr. Jennifer Nuzzo of Johns Hopkins Bloomberg School of Public Health. The expert panel followed, featuring several authors who have contributed essays and analysis as part of Brookings’ larger roadmap for reopening America. Brookings President John R. Allen moderated the discussion.

Questions from the audience followed the discussion. Viewers can submit questions for speakers by emailing events@brookings.edu or via Twitter to @BrookingsInst or by using the hashtag #CovidReopening.

       

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.

       

The White House perspective 

An economic shock of this size brings many uncertainties, said Kevin Hassett, White House senior economic advisor and former chair of the Council of Economic Advisers. He emphasized one: When states reopen, will their economies restart?  

“We’re taking an active role in informing people, both about the science and about what’s going on on the ground,” he said, “and I think with that, then people in the towns and states are going to make the judgments about exactly what they can do.” 

Hassett described the U.S. as in “the build–a–bridge–to–the–other–side phase,” assisting households and businesses while the shutdown persists. As economic activity revives, focus will turn to longer-run policies to nurture the post-COVID recovery—but, he said, “if you were to go into … call it maybe the final phase … this week, then you might be putting the cart ahead of the horse.”  

Weighing benefits and risks  

James Stock, a Harvard economist, nonresident senior fellow at Brookings, and author of a Hutchins Center background paper assessing strategies for reopening the economy, emphasized that the purpose of reopening is to get people back to work. To do so while managing the risks to public health means selective relaxation of restrictions.  

Weighing activities’ economic benefits against their risks will be important, Stock explained. He suggested that low-value, high-risk activities like professional sporting events might be suspended indefinitely. For many low-contact industries, Stock argued that there isn’t much benefit in waiting to reopen—and that includes brick-and-mortar shopping with protections to thwart the spread of the virus. He noted, though, that consumers and workers need to be confident that measures will work.  

An optimistic outcome is possible, but the conditions for optimism are dark, Stock warned. “If people go back to even a near normal social life, the virus can rekindle, and deaths will rise, no matter what protections there are at the workplace … so it’s super important in my view that we prioritize the economic activities and keep a laser-like focus on what’s going to allow people to get back to work while restraining non-work social activities. It doesn’t sound like a lot of fun, but I think it’s better than the alternative.”  

COVID-19 and inequalities 

Sir Angus Deaton, a Nobel laureate and senior scholar at Princeton University’s Woodrow Wilson School of Public and International Affairs, highlighted inequalities of the pandemic, including the much-publicized racial and ethnic disparities. 

Many Americans with college degrees can go on working at home, but those without college degrees are likely to face dire alternatives: “The essential workers risk their lives, and the nonessential workers risk their livelihoods.” The pandemic will heighten the earnings differential between these groups, as well as the difference in mortality rates that Deaton and co-author Anne Case highlighted in a new book, “Deaths of Despair.” 

The political inequalities of the pandemic will soon shift, Deaton predicted. Because the virus has caused more deaths in blue states, he said, “A red state governor is losing his business in exchange for blue state lives, so for him, opening up is a no-brainer, which is sort of why it’s happening.” But that balance will likely change: “[I]t’s clear that as deaths move into red states, there will be less political difficulty about helping states in trouble, and there will be a greater consensus for a smart opening.” 

Epidemiological perspectives 

Ross Hammond, senior fellow and director of the Center on Social Dynamics and Policy at Brookings, said models from epidemiology are useful both in forecasting and in evaluating different policy interventions. “For models to be useful and to do more good than harm, it’s essential that they are built and applied appropriately,” Hammond said. Models need to capture three features of the coronavirus: heterogeneity, spatial structure, and uncertainty.  

Caroline Buckee, associate professor of epidemiology at Harvard University’s T. H. Chan School of Public Health, emphasized differences in how the virus is playing out across the country. “It’s not one big epidemic; it’s multiple small epidemics,” she said. “We have hyperlocal epidemics that we think result from the distribution of populations of varying socioeconomic status who can or cannot afford to do physical distancing interventions.” Buckee called for hyperlocal decisions that account for inequalities and the relationships between neighborhoods.  

Buckee also outlined epidemiological considerations for reopening: How will we monitor the epidemic while reopening? What will the decision rule be for adapting policies? Because the epidemic is hyperlocal and staggered, what spatial scale is appropriate for policymaking? What are we willing to tolerate? 

This webinar was a prelude to a forthcoming Brookings project on “Reopening America and the World.” 

       

Tobacco use remains the single largest preventable cause of death and disease in the United States, killing more than 480,000 Americans each year and incurring over $300 billion per year in costs for direct medical care and lost productivity.

In addition, of all cigarettes sold in the U.S. in 2016, 35% were menthol cigarettes, which are particularly popular among African American adult, adolescent, and young adult smokers.

Tobacco Town Minnesota: The Study

New research from Brookings’ Center on Social Dynamics and Policy, in partnership with scholars from Washington University in St. Louis, the Mitchell Hamline School of Law, and the University of Colorado at Denver builds upon the Tobacco Town Minnesota project to explore the impact of tobacco control policies in the retail environment.

The new study uses an agent-based model (ABM) to explore the impact of two types of policy interventions: restricting sales of menthol cigarettes and reducing the density of tobacco retailers in a given community. ABMs are a class of computational simulation model that can provide useful insight into complex, adaptive systems, including those that arise from dynamic interactions between people and their environment. In this case, each “agent” in the model represents a smoker who makes purchase decisions over time—whether, where and which product type to purchase—based on a combination of their own attributes (e.g. where they reside and work, product preferences) and the simulated retail environment.

Results of the agent-based models

Experimentation using the model showed that restricting all cigarette sales or menthol cigarette sales to tobacco specialty shops may have the largest effect on the total costs of purchasing cigarettes. Coupling one of these policies with one that establishes a minimum distance between tobacco retailers may enhance the impact. Combining these policies also has the potential to reduce disparities in tobacco use across populations.

These simulations revealed the importance of context in designing tobacco control policies. For example, low-income communities in Minnesota and beyond, especially urban ones, experience disproportionately high tobacco retailer density. As a result, the impact of policies may be smaller in these communities than in higher income ones. Therefore, policymakers should carefully consider the “match” between tobacco retailer policies and the setting in which they will be applied (sometimes referred to as ‘precision’ prevention). Doing this is essential in order to avoid ineffective (or even counterproductive) efforts.

Beyond Tobacco Town Minnesota

ABMs allow researchers to create “virtual policy laboratories” that can be used to guide the adoption of effective policies and practice. Not only is the Tobacco Town model a tool for policymakers and community stakeholders to use as described in this study, but it is also highly extensible: it can be the basis for future simulation models that explore a wider variety of potential policies in a broader array of settings. In addition, this research is a demonstration of how a collaboration between an interdisciplinary group of researchers and policy experts can address challenging, long-standing public health problems.

Read the full paper here.

       

Why childhood obesity matters

According to the latest data, childhood obesity affects nearly 1 in 5 children in the United States, a number which has more than tripled since the early 1970s. Children who have obesity are at a higher risk of many immediate health risks such as high blood pressure and high cholesterol, type 2 diabetes, and asthma. They are also much more likely to be obese as adults. Despite extensive public health efforts to combat childhood obesity, there is no sign of a decline in its incidence, and the issue remains a significant concern both in the U.S. and abroad.

Romp & Chomp: An intervention that works

There is evidence, however, that a whole-of-community obesity prevention intervention approach can effectively reduce childhood obesity. These interventions are community-based initiatives that bring together community stakeholders and leverage existing resources and networks to implement obesity prevention efforts.

One example of a successful intervention of this kind was the Romp & Chomp project that took place in Geelong, Australia between 2004 and 2008. The intervention took a broad, inclusive approach to creating healthier environments for young children in the region by promoting healthy eating and active play in education and childcare settings.

A core component of this intervention was a central steering committee composed of representatives from different community stakeholder groups. This committee met periodically to plan intervention activities across the community, including professional development for early childhood care and education staff members, social marketing, and policy development and implementation in early childhood settings.

The Romp & Chomp project resulted in a significant decline in the number of children with overweight and obesity in the intervention community, in addition to significant improvements in behavioral indicators such as reduced consumption of packaged snacks and sugar-sweetened beverages.

Learning from the Romp & Chomp intervention

In order to understand why the Romp & Chomp intervention was successful and make actionable recommendations for other communities, we need to understand the process that drove success in the Romp & Chomp community.

We hypothesized that a process that we call stakeholder-driven community diffusion (SDCD) was central to the success of the Romp & Chomp program. This process involves the steering committee of community stakeholders spreading knowledge about and engagement with obesity prevention efforts in the community through their existing social network connections. These increases in knowledge and engagement are expected to drive changes in policy and practice in settings such as childcare, health care, and local government. These midstream changes are, in turn, expected to have downstream impacts on behaviors, and subsequently on rates of childhood obesity and overweight.

In order to test our hypothesis, we employed a computational simulation that captures social interactions between members of a community over time and produces estimates of community-level change emerging from those interactions. The results of these simulations provide evidence supporting our hypothesis that knowledge about and engagement with childhood obesity prevention was increased as a result of steering committee activity.

Why these results matter

We combined two robust bodies of literature: whole-of-community interventions and computational modeling of social phenomena through networks. The tool that we develop here not only provides important insight about why one specific intervention worked, but also can guide future childhood obesity prevention interventions.

Becca Portman provided editorial and graphics assistance for this report summary.