We Need a Rational Approach to Reopening

We Need a Rational Approach to Reopening

by Patrick Viguerie and Alex Viguerie - May 27, 2020

After unprecedented lockdowns and restrictions to flatten the curve of Covid-19 infection, business and government leaders are wrestling with the question of when and how to ease them. Various approaches are being tried, and there are wide differences of opinion. Much of the broader debate is polarized, as if only two conditions exist - “closed” or “open.” The stakes couldn’t be higher. If there were ever a time for rational decision making, it is now. Leaders should apply three principles to what may be the most consequential decision of their careers.

Be clear about the objective.

Charles Kettering, who invented the electric starter for automobiles in the early 20th century, famously said, “A problem well-stated is a problem half-solved.” The starting point for any reopening decision - for a country, state, community, or business - must begin with its objective. The objective must be reasonable and achievable, and actions must focus on achieving it. The U.S. lockdown was initially framed as “15 days to slow the spread” and prevent overwhelming the limited capacity of health systems.

Policymakers judged that objective to be sensible at the time, and worth its costs - even if it took longer than planned. Having achieved that goal, what should be next? Extreme objectives don’t appear reasonable at this point; just as there is no way to eliminate all risk of Covid-19 transmission, there is no way to quickly and fully restore economic activity. Likewise, continuing the current lockdowns for 18 months while we wait for a vaccine as some have called for is not practical either. And removing all restrictions could create a second wave. And so on.

Objectives for reopening must balance multiple and competing considerations. Clear thinking here will go a long way toward helping frame sensible action choices. Decision scientists call these “saddle point” problems, which typically involve minimizing one quantity and maximizing another. For example, an objective could be “to minimize Covid-19 impact while maximizing non-Covid-19 health outcomes” (e.g., ensuring people receive treatment for other conditions). Or it could be “to minimize deaths from Covid-19 while maximizing preservation of employment.” Framing the question precisely, as Kettering reminds us, is the first step toward a clear solution.

Apply granularity.

In comparing U.S. military efforts in Iraq and Afghanistan, General David Petraeus said in 2010: “We have never had the granular understanding of local circumstances in Afghanistan that we achieved over time in Iraq. One of the key elements in our ability to be agile in Iraq during the surge was a pretty good understanding of who the power brokers were in local areas, how the systems were supposed to work, how they really worked.” The initial Covid-19 responses have involved the broadest, bluntest instrument possible: mass shelter-in-place restrictions. Yet the impact of Covid-19 has been anything but uniform.

There are striking granular differences across geographies and highly compartmentalized impacts within population/demographic groups and professional/occupational cohorts.

As of May 3, the top 5 U.S. states by case count made up 54% of total cases and 62% of deaths. The top 10 U.S. counties alone had nearly 30% of cases and 32% of deaths. The bottom 10 states taken together had less than 1% of total cases. As widely reported, Covid-19 is most fierce when it strikes older people and those with chronic health conditions, while it largely spares the young. The death rate for a 70-year old is estimated to be 40 times that of a 35-year old.

Granular problems usually call for granular solutions - solutions that focus resources and interventions specifically and in a highly targeted way. There are many solution choices (between very open and very closed) that can be applied in different ways in different settings (e.g., different geographies, different cohorts, different circumstances). Sweden’s approach to isolating old populations and largely avoiding lockdowns is one example; Italy’s approach to quarantining Lombardy early is another, as was China’s decision to isolate the city of Wuhan.

So far, the most significant Covid-19 outbreaks have occurred in places with high density and high contact rates between people. These are places where “community spread” has occurred - think New York City and Milan. Although geography is most often thought of at a country or state/province level, granularity applies to cities, communities, neighborhoods, campuses, and even individual buildings or parts of buildings (think elevators vs. individual offices): all can exhibit different densities and contact rates. A full commercial flight has a high density and contact rate as does a crowded Apple store.

Epidemiologists believe that Covid-19 spreads primarily through close contact with an infected person (who may be asymptomatic). Spread occurs in an interaction involving two or more people. A ride in an Uber, a visit to the grocery store, or a visit to the hair salon are all examples of interactions. A granular approach to reopening must consider the most common interactions and the risk associated with each - which is related to the intensity of the interaction and the number of people involved. High-intensity interactions increase the risk that an infected person transmits the virus; a larger number of people increases the likelihood that someone in the group is infected and increases the risk of transmission to more than one person in the group. Increase the number of people and the possible routes of transmission multiply: with 2 people, there are only 2 possible transmissions; with 3 there are 6; with 4 there are 12. This fact alone means that a tattoo studio or pet groomer, following appropriate guidelines, creates far less risk than a crowded flight does.

The power of the granular approach is in helping systematically understand the various interactions that a geography, community, or business faces, assessing their risk, and identifying ways to either reduce intensity (e.g., through better masks), or reducing the number of people involved in the interaction (e.g., increasing distance, creating split teams in the workplace).

Build the capacity to learn and adapt quickly.

There is still much we don’t know about Covid-19, but we are learning more every day. When faced with profound uncertainty, decision makers often fall into the trap of inaction, waiting for more information to emerge, or the trap of abandoning analytical rigor altogether - basing decisions on gut instinct. Neither is helpful. Inaction is a form of action. While no analysis can provide perfect certainty at this point, decision makers need a clear-headed assessment of the current facts on Covid-19, and an understanding of the critical uncertainties and their importance. Both are needed to frame the objective well and develop good solutions.

And given the uncertainty, equally important is building the capacity to learn and adapt quickly: to monitor progress against the objective, assimilate new information and learning as it emerges, and refine and evolve decisions accordingly. Importantly, this is a capability to put in place, not a task to complete.

The situation now is highly fluid, and the coming weeks will be marked by much experimentation and learning around which reopening strategies work well and which don’t: better tools, better practices, better solutions will all ultimately emerge. There are 200 country-wide experiments going on now, and in the U.S. alone, more than 100 experiments are taking place across 50 states and in multiple cities. One initiative worth tracking is the collaboration between New Zealand, Austria, and five other countries that are banding together in re-opening as a way to jointly build business ties and tourism among themselves. Learning from these experiments, and from the granular outliers - both good and bad - is essential. Leaders must be able to cut through the noise of daily statistics and headlines to understand these experiments and decide how to apply learnings to their own settings. Well-designed experiments - with specific hypotheses to test, data gathered, and results monitored - will maximize what we can learn. A fundamental challenge is the lack of a shared set of assumptions and a common language. Establishing these early will accelerate progress.

General Petraeus described how granular insight and learning enabled agility during the surge in Iraq. Agility matters here, too: an effective response must also be an agile response.

Patrick Viguerie is the Managing Partner of the strategy consulting firm Innosight, and was previously a senior partner at McKinsey & Company.

Alex Viguerie is a post-doctoral fellow at the University of Pavia in Italy. He is currently leading a global effort to model Covid-19 spread.