The Bizarre Refusal to Apply Cost-Benefit Analysis to COVID Debates

Are those who oppose a ban on cars or a radical reduction in speed limits sociopaths, given the huge number of people they are knowingly consigning to death or maiming?

Authors: Glenn Greenwald 5 hr ago 285536

In virtually every realm of public policy, Americans embrace policies which they know will kill people, sometimes large numbers of people. They do so not because they are psychopaths but because they are rational: they assess that those deaths that will inevitably result from the policies they support are worth it in exchange for the benefits those policies provide. This rational cost-benefit analysis, even when not expressed in such explicit or crude terms, is foundational to public policy debates — except when it comes to COVID, where it has been bizarrely declared off-limits.

The quickest and most guaranteed way to save hundreds of thousands of lives with policy changes would be to ban the use of automobiles, or severely restrict their usage to those authorized by the state on the ground of essential need (e.g., ambulances or food-delivery vehicles), or at least lower the nationwide speed limit to 25 mph. Any of those policies would immediately prevent huge numbers of human beings from dying. Each year, according to the Center for Disease Control (CDC), “1.35 million people are killed on roadways around the world,” while “crashes are a leading cause of death in the United States for people aged 1–54.” Even with seat belts and airbags, a tragic number of life-years are lost given how many young people die or are left permanently and severely disabled by car accidents. Studies over the course of decades have demonstrated that even small reductions in speed limits save many lives, while radical reductions — supported by almost nobody — would eliminate most if not all deaths from car crashes.

Center for Disease Control, 2020

Given how many deaths and serious injuries would be prevented, why is nobody clamoring for a ban on cars, or at least severe restrictions on who can drive (essential purposes only) or how fast (25 mph)? Is it because most people are just sociopaths who do not care about the huge number of lives lost by the driving policies they support, and are perfectly happy to watch people die or be permanently maimed as long as their convenience is not impeded? Is it because they do not assign value to the lives of other people, and therefore knowingly support policies — allowing anyone above 15 years old to drive, at high speeds — that will kill many children along with adults?

That may explain the motivation scheme for a few people, but in general, the reason is much simpler and less sinister. It is because we employ a rational framework of cost-benefit analysis, whereby, when making public policy choices, we do not examine only one side of the ledger (number of people who will die if cars are permitted) but also consider the immense costs generated by policies that would prevent those deaths (massive limits on our ability to travel, vastly increased times to get from one place to another, restrictions on what we can experience in our lives, enormous financial costs from returning to the pre-automobile days). So foundational is the use of this cost-benefit analysis that it is embraced and touted by everyone from right-wing economists to the left-wing European environmental policy group CIVITAS, which defines it this way:

Social Cost Benefit Analysis [is] a decision support tool that measures and weighs various impacts of a project or policy. It compares project costs (capital and operating expenses) with a broad range of (social) impacts, e.g. travel time savings, travel costs, impacts on other modes, climate, safety, and the environment.

This framework, above all else, precludes an absolutist approach to rational policy-making. We never opt for a society-altering policy on the ground that “any lives saved make it imperative to embrace” precisely because such a primitive mindset ignores all the countervailing costs which this life-saving policy would generate (including, oftentimes, loss of life as well: banning planes, for instance, would save lives by preventing deaths from airplane crashes, but would also create its own new deaths by causing more people to drive cars).

While arguments are common about how this framework should be applied and which specific policies are ideal, the use of cost-benefit analysis as the primary formula we use is uncontroversial — at least it was until the COVID pandemic began. It is now extremely common in Western democracies for large factions of citizens to demand that any measures undertaken to prevent COVID deaths are vital, regardless of the costs imposed by those policies. Thus, this mentality insists, we must keep schools closed to avoid the contracting by children of COVID regardless of the horrific costs which eighteen months or two years of school closures impose on all children.

It is impossible to overstate the costs imposed on children of all ages from the sustained, enduring and severe disruptions to their lives justified in the name of COVID. Entire books could be written, and almost certainly will be, on the multiple levels of damage children are sustaining, some of which — particularly the longer-term ones — are unknowable (long-term harms from virtually every aspect of COVID policies — including COVID itself, the vaccines, and isolation measures, are, by definition, unknown). But what we know for certain is that the harms to children from anti-COVID measures are severe and multi-pronged. One of the best mainstream news accounts documenting those costs was a January, 2021 BBC article headlined “Covid: The devastating toll of the pandemic on children.”

The “devastating toll” referenced by the article is not the death count from COVID for children, which, even in the world of the Delta variant, remains vanishingly small. The latest CDC data reveals that the grand total of children under 18 who have died in the U.S. from COVID since the start of the pandemic sixteen months ago is 361 — in a country of 330 million people, including 74.2 million people under 18. Instead, the “devastating toll” refers to multi-layered harm to children from the various lockdowns, isolation measures, stay-at-home orders, school closures, economic suffering and various other harms that have come from policies enacted to prevent the spread of the virus:

From increasing rates of mental health problems to concerns about rising levels of abuse and neglect and the potential harm being done to the development of babies, the pandemic is threatening to have a devastating legacy on the nation’s young. . . .

The closure of schools is, of course, damaging to children’s education. But schools are not just a place for learning. They are places where kids socialize, develop emotionally and, for some, a refuge from troubled family life.

Prof Russell Viner, president of the Royal College of Pediatrics and Child Health, perhaps put it most clearly when he told MPs on the Education Select Committee earlier this month: “When we close schools we close their lives.”

For More Information: https://greenwald.substack.com/p/the-bizarre-refusal-to-apply-cost

It could take three years for the US economy to recover from COVID-19

Authors: Laura Oliver 30 Mar 2020

The US and Eurozone’s economies could take until 2023 to recover from the impact of the COVID-19 coronavirus crisis, according to a new report from consultancy McKinsey & Company.

If the public health response, including social distancing and lockdown measures, is initially successful but fails to prevent a resurgence in the virus, the world will experience a “muted” economic recovery, says McKinsey. In this scenario, while the global economy would recover to pre-crisis levels by the third quarter of 2022, the US economy would need until the first quarter of 2023 and Europe until the third quarter of the same year.

If the public health response is stronger and more successful – controlling the spread of the virus in each country within two-to-three months – the outlook could be more positive, with economic recovery by the third quarter of 2020 for the US, the fourth quarter of 2020 for China and the first quarter of 2021 for the Eurozone.

In these scenarios involving partially effective interventions, policy responses could partially offset economic damage and help to avoid a banking crisis, says McKinsey. The firm has modelled nine scenarios, ranging from rapid and effective control of the virus with highly effective policy interventions to a broad failure of public health measures and ineffective policy and economic interventions.

The economic impact in the US, however, could exceed anything experienced since the end of World War II.

For More Information: https://www.weforum.org/agenda/2020/03/economic-impact-covid-19/

Economic Impact of COVID-19

Analysis of the national and regional economic impacts of the novel coronavirus and steps taken to contain the COVID-19 outbreak

Economic Brief

June 2021, No. 21-18

Was There a Better Way to Contain COVID-19?

Article by: Andreas Hornstein

Many countries resorted to social distancing as a first response to contain the spread of COVID-19. Arguably, social distancing measures caused major disruptions of the economy. Targeted interventions — such as more effective quarantine, contact tracing and random testing — may have had less harmful economic outcomes and more effective containment of COVID-19.


From March to April 2020, after many states introduced stay-at-home orders in response to the emerging COVID-19 pandemic, U.S. employment declined by about 14 percent. Upon relaxing the stay-at-home orders, employment recovered to within 6 percent of pre-pandemic levels by late summer. Following the introduction and eventual loosening of stay-at-home orders, deaths due to COVID-19 declined from more than 2,000 per day in April to about 500 per day in the summer of 2020, before increasing to about 3,000 per day by the end of the year. Total 2020 deaths due to COVID-19 were near 430,000, or about 0.1 percent of the U.S. population.

The decline in work-related contacts and social contacts associated with the stay-at-home orders arguably helped contain the pandemic and the number of disease-related deaths but came at the cost of lost output. Could a more targeted approach — such as improved quarantine, contact tracing and random testing — have attained similar or better disease-related outcomes while avoiding the sharp decline in output and employment?

A simulation study in a standard infectious disease model modified to account for known features of the spread of COVID-19 suggests that modest improvements in quarantine measures — augmented by contact tracing and random testing — would have resulted in far fewer deaths while not being too costly to implement.

Furthermore, evaluating the reduction in COVID-19-related deaths at values used by U.S. federal agencies in cost-benefit studies of regulations suggests that the benefits from reduced deaths far exceed the benefits from increased output.1

Examining an Alternative Method for Combating COVID-19

Indiscriminate social distancing limits the spread of COVID-19 because it reduces contact rates for all individuals, whether or not they are infectious. Quarantine is less disruptive because it only removes known infectious individuals.

Clearly, infectious individuals who display symptoms can be quarantined, but a feature of COVID-19 is that even individuals who do not display symptoms can be infectious. In the model, I distinguish between asymptomatic and symptomatic infectious individuals to study the potential effects of not only quarantine, but also contact tracing and random testing as alternative policy tools to generalized social distancing measures. Contact tracing can work backwards from newly identified symptomatic individuals to identify individuals who may have been infected but do not yet display any symptoms. Random testing of the population provides another means to identify and subsequently quarantine asymptomatic individuals.

For this study, I use a modified SIR model. An SIR-model is a standard epidemiological model, in which susceptible individuals (S) become infected (I) after meeting already infectious individuals. Over time, infected individuals either recover or die (R).

I extend the basic SIR model of a pandemic to explore the relative merits of various policy interventions — in this case, social distancing, quarantine, contact tracing and random testing — in a unified framework. Social distancing is modeled as a uniform reduction in the contact rates at which individuals meet each other, independent of their health status.

The modified SIR model is calibrated to the known characteristics of COVID-19 and is then used to infer the transmission rate of COVID-19 from data on daily deaths for the year 2020. The transmission rate can in turn be used to calculate the model-implied effective reproduction rate for COVID-19. Information on employment and social mobility indexes is then used to separate out the impact of work contacts on the transmission rate.

The simulation study also proposes an alternative employment path that smoothly transitions to the fall employment values — thus avoiding the sharp decline (and subsequent rebound) of employment and the output losses associated with the employment contraction. This path then generates a counterfactual path for transmission rates. In particular, increased work contacts through higher employment increases the transmission rate and the number of infected individuals and cumulative deaths.

The Potential Benefits of Targeted Interventions

The output gains of this alternative employment path are estimated to be nearly $0.5 trillion, about 2.5 percent of 2019 GDP. But this path without targeted interventions — that is, quarantine, contact tracing and random testing — implies a higher overall transmission rate and about twice the fatalities by the end of 2020.

But with targeted interventions, fatalities decline. The study’s baseline simulations assume that 50 percent of symptomatic individuals self-quarantine, consistent with other estimates of quarantine effectiveness.2 Further simulations suggest that a permanent increase of the quarantine rate to 70 percent would have contained cumulative deaths over the second half of 2020. This increase in the quarantine rate initially is not as effective as the steep but temporary actual employment reductions in the first part of the year. But it prevents the surge of infections and deaths at the end of the year, and thus reduces cumulative deaths in 2020 to about 0.063 percent of the U.S. population, a third less than the actual 2020 outcome.

Adding a contact-tracing program that captures 50 percent of asymptomatic individuals infected by a newly symptomatic individual reduces cumulative deaths to about 0.058 percent. Finally, adding a program that randomly tests 0.1 percent of the population every day reduces cumulative deaths to about 0.042 percent.

And the benefits do not include the values from reduced loss of life. The value of a statistical life (VSL) represents a person’s willingness to pay for a reduced probability of death and is a concept used by U.S. federal agencies in the cost-benefit analysis of regulations.3

For example, the Environmental Protection Agency uses an inflation-adjusted 2020 VSL for an adult U.S. citizen of $11.5 million. Rescaling the VSL for the long-term negative health effects of survivors who suffered severe infections, a death is valued at $24 million.

Using this VSL measure, the benefits from reduced mortality on the alternative employment path with increased quarantine, contact tracing and random testing are up to $5 trillion, a multiple of the economic benefits from higher employment and output.

How Much Would an Alternative Path Have Cost?

The simulations suggest that the proposed policies yield better economic and health outcomes, but obviously these policies are not costless. Regarding the potential costs of such policies, we assume:

  • Each infected individual going into quarantine is paid $4,400.
  • A working contact-tracing program can be established for $25 billion.
  • The cost of a random test is $100.

These rough estimates suggest a cost of about $0.1 trillion, about one fifth of the economic benefits from the reduced employment and output losses.

Conclusion

The proposed alternative policies affect the transmission of the virus by reducing the pool of infected individuals that interact with susceptible individuals. In turn, this reduces the number of total transmissions, infected individuals and cumulative deaths.

Simulations of a stylized pandemic model suggest that alternative COVID-19 containment policies such as improved quarantine, contact tracing and random testing could have led to improved economic outcomes and reduced cumulative deaths. The net economic benefit from a policy that avoided the sharp economic slowdown is calculated to be nearly $0.4 trillion, about 2 percent of 2019 GDP. In addition, using the VSL concept the benefit from reduced deaths is estimated to be up to $5 trillion, about 24 percent of 2019 GDP.


For More Information: https://www.richmondfed.org/publications/research/coronavirus#fcf95461f4e84b8085eef4d39128e9a0

Explaining the economic impact of COVID-19: Core industries and the Hispanic workforce

Authors: Aaron Klein and Ember SmithFriday, February 5, 2021

ABSTRACT 

As the United States prepares for a COVID-19 recovery, policymakers need to understand why some cities and communities were more vulnerable to the pandemic’s economic consequences than others. In this paper, we consider the association between a city’s core industry, its economic susceptibility to the pandemic, and the recession’s racially disparate impact across six select metropolitan areas. We find that areas with economies that rely on the movement of people—like Las Vegas with tourism—faced substantially higher unemployment at the end of 2020 than cities with core industries based on the movement of information. Further, we find the hardest-hit areas have larger Hispanic or Latino communities, reflecting the demographic composition of workers in heavily impacted industries and susceptible areas. We conclude by recommending targeted federal policy to address the regions and communities most impacted by the COVID-19 recession.

INTRODUCTION

More so than any prior economic downturn, the COVID-19 recession has crushed certain industries—those that depend on the movement of people—while leaving others relatively unscathed—those that depend on the movement of information. City economies are concentrated in different industries: Las Vegas and Orlando in travel and tourism, Seattle and San Francisco in technology, and Washington D.C. in government. Thus, the COVID-19 recession’s economic geography is uniquely impacted by the pandemic’s effect on a city’s primary industry. Overlaying geography with race reveals another under-appreciated impact of this recession: an increase in the economic hardship faced by Hispanic or Latino communities.

This piece explores the economic implications of the COVID-19 recession using select metropolitan areas (often referred to by the name of the metro’s primary city), identifying problems and offering policy responses. We examine six metropolitan areas: three with heavy concentration in industries negatively impacted by COVID-19 (Las Vegas, Orlando, and Reno) and three with economies heavily concentrated in industries less negatively, or even positively, impacted by COVID-19 (Seattle, San Francisco, and Washington, D.C.). We find that the cities with industries more acutely impacted have a higher concentration of Hispanic or Latino residents.

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CITY AND METRO ECONOMIES BEFORE COVID-19

Cities and metropolitan areas often specialize in select industries, creating agglomeration economies. Put simply, there is an economic benefit when firms producing similar goods are located near each other. For example, the auto industry is headquartered in Detroit, finance in New York, entertainment in Los Angeles, information technology in Seattle, and so on. The performance of core industries spills over to supporting industries and affects the entire regional economy; restaurants and retail stores do better when the core industry is booming and struggle when it is not. In this section, we discuss the primary industries in each metropolitan area of interest prior to COVID-19.

Before COVID-19, Orlando had the largest tourism industry in the nation, producing $26 billion per year, while Las Vegas came in second at over $19 billion.[1] However, Las Vegas’ total GDP is smaller than Orlando’s, so the impact of tourism is relatively larger—hospitality and leisure employed more than a quarter of Las Vegas workers in 2019.[2] There are a larger share of leisure and hospitality workers in Las Vegas than government workers in D.C. Orlando and Reno have similarly high employment concentrations in hospitality and leisure, although production as a portion of their economy is sizably smaller than in Las Vegas. Figure 2 shows that roughly one in five workers in Orlando (21%) worked directly in hospitality and leisure in 2019, as did 16% (roughly one in seven) of Reno’s workforce.[3] In these cities, many secondary industries—like the professional or business sector—are driven by their primary economic engines.

Select metropolitan area industry employment - portion of total employment

Seattle and San Francisco, on the other hand, specialize in technology, an industry that may have benefitted from COVID-19. Seattle is the well-known birthplace of Microsoft and the home of Amazon. San Francisco is the modern-day home of enormous tech conglomerates like Salesforce and Adobe and features major corporate offices for many of the Silicon Valley giants located nearby. Anchor industries employ different types of workers; employment in Seattle and San Francisco are both over two times (2.36 and 2.14 respectively) more concentrated in their largest occupational group, computer and mathematical occupations, than the national average.[4] Orlando, by contrast, has slightly less than the national rate of employment in computer and mathematical occupations, while that figure plummets in Las Vegas (50%) and Reno (54%).[5] Put another way, San Francisco and Seattle have more than four times as many employees in computers and math than Las Vegas and Reno, proportionate to the total number of workers in each metro.

Moving beyond the technology versus tourism binary, we add the nation’s capital and government hotspot, Washington, D.C., where one in five workers are employed directly by the government. The corresponding army of lawyers is a good indicator of how the primary industry of a city drives secondary workforces; D.C. has almost three times (2.76) as many legal service workers per capita as the national average. With governing also comes a demand for research (military and civilian) and, as a result, D.C. has an even greater share of employees in computer and mathematics than Seattle or San Francisco (2.46 times the national average), approaching five times as many as Las Vegas and Reno, as a proportion of each metro’s workers.[6]

For More Information: https://www.brookings.edu/research/explaining-the-economic-impact-of-covid-19-core-industries-and-the-hispanic-workforce/

Social and economic impact of COVID-19

Authors: Eduardo Levy Yeyati and Federico Filippini Tuesday, June 8, 2021

INTRODUCTION

The impact of the pandemic on world GDP growth is massive. The COVID-19 global recession is the deepest since the end of World War II (Figure 1). The global economy contracted by 3.5 percent in 2020 according to the April 2021 World Economic Outlook Report published by the IMF, a 7 percent loss relative to the 3.4 percent growth forecast back in October 2019. While virtually every country covered by the IMF posted negative growth in 2020 (IMF 2020b), the downturn was more pronounced in the poorest parts of the world (Noy et al. 2020) (Figure 2).

Figure 1. Global GDP growth in a historical perspective

The impact of the shock is likely to be long-lasting. While the global economy is expected to recover this year, the level of GDP at the end of 2021 in both advanced and emerging market and developing economies (EMDE) is projected to remain below the pre-virus baseline (Figure 3). As with the immediate impact, the magnitude of the medium-term cost also varies significantly across countries, with EMDE suffering the greatest loss. The IMF (2021) projects that in 2024 the World GDP will be 3 percent (6 percent for low-income countries (LICs)) below the no-COVID scenario. Along the same lines, Djiofack et al. (2020) estimate that African GDP would be permanently 1 percent to 4 percent lower than in the pre-COVID outlook, depending on the duration of the crisis.  

Figure 2. Global GDP growth 2020
Figure 3. Quarterly World GDP (GDP forecast in Jan-2020 vs. Jan-2021, 2019 Q1 = 100)

The pandemic triggered a health and fiscal response unprecedented in terms of speed and magnitude. 

At a global scale, the fiscal support reached nearly $16 trillion (around 15 percent of global GDP) in 2020. However, the capacity of countries to implement such measures varied significantly. In this note, we identify three important preexisting conditions that amplified the impact of the shock:

  • Fiscal space: The capacity to support household and firms largely depends on access to international financial markets,
  • State capacity: Fast and efficient implementation of policies to support household and firms requires a substantial state capacity and well-developed tax and transfer infrastructure; and
  • Labor market structure: A large share of informal workers facing significant frictions to adopt remote working, and high levels of poverty and inequality, deepen the deleterious impact of the crisis.

Additionally, the speed and the strength of the recovery will be crucially dependent on the capacity of the governments to acquire and roll out the COVID-19 vaccines.

This paper presents a succinct summary of the existing economic literature on the economic and fiscal impact of the pandemic, and a preliminary estimate of the associated economic cost. It documents the incidence of initial conditions (with a particular focus on the role of the labor market channel) on the transmission of the shock and the speed and extent of the expected recovery, summarizes how countries attempted to attenuate the economic consequences and the international financial institutions assisted countries, reports preliminary accounts of medium-term COVID-related losses, and concludes with some forward-looking considerations based on the lessons learned in 2020.

For More Information: https://www.brookings.edu/wp-content/uploads/2021/06/Social-and-economic-impact-COVID.pdf

Pandemic Impact on Mortality and Economy Varies Across Age Groups and Geographies

Authors: VICTORIA UDALOVA  |  MARCH 08, 2021

The initial impact of the COVID-19 pandemic on the U.S. economy was widespread and affected people across all age groups and all states while the initial mortality impact targeted mostly older people in just a few states according to independent research by the U.S. Census Bureau.

During April 2020, the first full month of the pandemic, the United States experienced an additional 2.4 deaths per 10,000 individuals beyond predictions based on historical mortality trends. This was a 33% increase in all-cause national mortality — deaths caused directly or indirectly by the coronavirus.

There was a weak correlation between increased mortality rates and negative economic impact across states. There were states that experienced significant employment displacement but no additional mortality, for example. On the other hand, there were states that experienced large mortality impacts but modest economic impacts.

These additional deaths during the early days of the pandemic were highly concentrated in older age groups and in a few states.

Recent research examined the relationship between the pandemic’s mortality and economic impacts across different age groups and geography.

Economic Impact of COVID-19 Pandemic

The COVID-19 pandemic has caused a devastating loss of life but it has also devastated the nation’s economy.

Similar to the excess mortality concept, the pandemic’s economic impact is calculated by taking the difference between what is expected (based on historical trends) and what actually happens during a given period.

The ratio of employment to population is one measure of economic activity that shows the share of population 16 years and older working full- or part-time.

This measure closely tracks other possible measures of economic activity such as unemployment rate, percent of population with unemployment insurance claims, consumer spending, and small business employment.

Declines in the employment-to-population ratio that exceeded predictions indicate there was additional employment loss in the country due to the pandemic.

The decline in the employment-to-population ratio in the United States in April 2020 was significant. Historical trends predicted a 61.3% ratio but it turned out to be 51.5%. This additional national decline was 9.9 per 100 individuals in April 2020 (Figure 1). That means there were fewer people employed than was expected before the pandemic.

Impacts Varied by Geography

Deaths caused directly or indirectly by COVID during the first full month of the pandemic were highly geographically concentrated.

About half of all national excess deaths were in just two states: New York and New Jersey.

But the economic impact pattern was completely different because it was more geographically widespread.

Every state, except for Wyoming, experienced a statistically significant decline in the employment-to-population ratio during that time.

The two states with the largest initial declines in employment — Nevada and Michigan — only accounted for about 7% of the national employment displacement.

There was a weak correlation between increased mortality rates and negative economic impact across states. There were states that experienced significant employment displacement but no additional mortality, for example. On the other hand, there were states that experienced large mortality impacts but modest economic impacts.

For More Information: https://www.census.gov/library/stories/2021/03/initial-impact-covid-19-on-united-states-economy-more-widespread-than-on-mortality.html

COVID-19 false dichotomies and a comprehensive review of the evidence regarding public health, COVID-19 symptomatology, SARS-CoV-2 transmission, mask wearing, and reinfection

Authors: Kevin EscandónAngela L. RasmussenIsaac I. BogochEleanor J. MurrayKarina EscandónSaskia V. Popescu & Jason Kindrachuk BMC Infectious Diseases volume 21, Article number: 710 (2021) 

Abstract

Scientists across disciplines, policymakers, and journalists have voiced frustration at the unprecedented polarization and misinformation around coronavirus disease 2019 (COVID-19) pandemic. Several false dichotomies have been used to polarize debates while oversimplifying complex issues. In this comprehensive narrative review, we deconstruct six common COVID-19 false dichotomies, address the evidence on these topics, identify insights relevant to effective pandemic responses, and highlight knowledge gaps and uncertainties. The topics of this review are: 1) Health and lives vs. economy and livelihoods, 2) Indefinite lockdown vs. unlimited reopening, 3) Symptomatic vs. asymptomatic severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection, 4) Droplet vs. aerosol transmission of SARS-CoV-2, 5) Masks for all vs. no masking, and 6) SARS-CoV-2 reinfection vs. no reinfection. We discuss the importance of multidisciplinary integration (health, social, and physical sciences), multilayered approaches to reducing risk (“Emmentaler cheese model”), harm reduction, smart masking, relaxation of interventions, and context-sensitive policymaking for COVID-19 response plans. We also address the challenges in understanding the broad clinical presentation of COVID-19, SARS-CoV-2 transmission, and SARS-CoV-2 reinfection. These key issues of science and public health policy have been presented as false dichotomies during the pandemic. However, they are hardly binary, simple, or uniform, and therefore should not be framed as polar extremes. We urge a nuanced understanding of the science and caution against black-or-white messaging, all-or-nothing guidance, and one-size-fits-all approaches. There is a need for meaningful public health communication and science-informed policies that recognize shades of gray, uncertainties, local context, and social determinants of health.

For More Information: https://bmcinfectdis.biomedcentral.com/articles/10.1186/s12879-021-06357-4