How to measure COVID risk: Lives or life-years?

The implicit aim of government and state policies regarding COVID and the explicit focus of news media coverage of the pandemic is on lost lives. This is understandable. Every life is precious, and every lost life is a tragedy. However, everyone would agree that a life cut short is more tragic than a life well-lived to old age. One measure of the tragedy is in terms of lost life-years; a life cut short is a greater loss of life-years. It is the current focus on lives rather than life-years that leads to policies designed to protect the elderly from COVID more than younger people (e.g., special shopping hours), since the lives of the elderly are more likely to be lost. Paradoxically, however, many young people are unduly fearful of COVID and many older people are somewhat cavalier regarding COVID. But maybe both attitudes are rational when considering the potential risk of lost life-years rather than lost lives.

A remarkable feature of COVID deaths is their age dependency, with nearly a 1,000-fold difference between the probability of death from COVID for the elderly as compared to children. In fact, it has been pointed out that the age distribution of COVID deaths is similar to the age distribution of all deaths (see graph below, noting the huge spike for 85+, which is nearly identical for COVID versus death by any other cause). In other words, regardless of one’s age, the probability of dying in the next year is directly proportional to the probability of dying from COVID, once infected. As a direct result of this, the average age of COVID deaths is nearly the same as life expectancy (life expectancy is the mathematical average of all lifespans in the population).

US COVID-19 Reported Deaths by Age 2/1 – 11/28

Roughly speaking, around 1% of people in the U.S. die each year from any cause (i.e., around 3 million). Early in the pandemic, it was believed that the infection fatality rate for COVID was approximately 1% (note that infected is not the same as confirmed COVID cases considering that many infections are never confirmed). Because the infection fatality rate and death by any cause are both 1%, and in light of the age distribution of COVID deaths, this implies that the risk of dying from COVID, if one were infected, is equivalent to the probability that one would die in the next year from any cause. A 20-year old does not expect to die in the next year whereas an 80-year old knows there is a non-negligible chance that their next year might be their last. Putting this differently, one might say that the risk of death from living a normal year of life is about the same, for any given age group, as the risk of dying from a COVID infection.

Subsequent work established that the infection fatality rate is lower than 1%, and is approximately .5% in the United States (i.e. 5 deaths for every 1,000 persons infected). This number is heavily dependent on the age distribution in the population, with values ranging from .1% (Kenya) to 1% (Japan) across different countries (CDC report release November 24, 2020). So, if 1% of the population dies from any cause each year in the U.S., and if .5% of infected individuals die from COVID in the U.S., this implies that the risk of dying from COVID, if infected, is approximately equivalent to the risk of dying from living for 6 months, regardless of one’s age.

This life-years perspective on COVID risk can be used to determine which COVID mitigation strategies are sensible, weighing the costs against the benefits, and which are too extreme. For instance, an extreme form of lock-down in which no one was allowed to leave their home (i.e., shelter in place) for an entire year might be sensible to protect society from the bubonic plague, given its 50% infection fatality rate. However, such a policy would not be sensible for COVID – it would not make sense for everyone to suffer through 12 months of utter misery simply to save themselves from 6 months worth of life-risk from COVID. During 12 months of shelter in place, the probability that one would die from something other than COVID would be greater than the probability of dying from COVID, if infected.

If society is not in extreme lock-down, this ‘6-month benchmark’ can be applied to guide recommended activities. Wearing a mask does not entail much misery and it doesn’t have any lasting negative consequences. The same is true for avoiding hugs with people outside your home. But how about losing one’s job? By how much does that shorten one’s life expectancy? If it does so by more than 6 months, then it is better to risk COVID than to lose your job. How about missed education, which is known to shorten life expectancy? Gaining weight because of lack of access to a gym? Letting a small business collapse? Many of the COVID restrictions could result in the loss of personal endowments (e.g., weight gain, education loss) and the loss of brick-and-mortar institutions (e.g., businesses, universities, libraries, athletics clubs) that will cause the loss of more than 6 months’ worth of life expectancy for many of the people affected, or, if not lost life expectancy, then a severe reduction in the quality of life for a time period substantially greater than 6 months.

This life-years risk assessment highlights why there is an urgent need for government relief. Only with payroll relief and other grants (e.g., to help schools reopen) can we keep people employed, keep children in school, and keep our businesses and institutions in existence. This relief can help keep the life-years cost of COVID mitigation measures under the 6-month threshold.

David Huber (

Constructive comments welcome. Thanks to Rosie Cowell, Carlo Dallapiccola, and Adrian Staub for very helpful discussion.

One thought on “How to measure COVID risk: Lives or life-years?

  1. I’m a bit confused. Are you suggesting that the disproportionate-to-life-years lost mitigation strategies are appropriate as long as there are government programs to mitigate the effects of those mitigation strategies?

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