All sorts of statistics are important in formulating public policy (and for informing individuals). At the end of February, the New England Journal of Medicine published the following statistics from the China Medical Treatment Expert Group for Covid-19 (derived from 1099 patients with laboratory-confirmed cases of Covid-19 from 552 hospitals in 30 provinces, autonomous regions, and municipalities in mainland China through January 29, 2020) [2]:
| Age | 47 years median 45-58 interquartile range |
| Female | 41.9% |
| Admitted to ICU | 5% |
| Invasive mechanical ventilation | 2.3% |
| Died | 1.4% |
| Had direct contact with wildlife | 1.9% |
| Incubation period | 4 days median 2-7 interquartile range |
To supply perspective, the journal offered an essay [3] that observedWe found a lower case fatality rate (1.4%) than the rate that was recently reportedly, probably because of the difference in sample sizes and case inclusion criteria. Our findings were more similar to the national official statistics, which showed a rate of death of 3.2% among 51,857 cases of Covid-19 as of February 16, 2020. Since patients who were mildly ill and who did not seek medical attention were not included in our study, the case fatality rate in a real-world scenario might be even lower. Early isolation, early diagnosis, and early management might have collectively contributed to the reduction in mortality in Guangdong.
Of course, these statistics are a small part of all the available data, and far more alarming conclusions about exponential growth (it's present), containment (it's too late), and mitigation ("flatten the curve" to give countries more time to prepare to handle the fraction of cases requiring hospitalization -- around 5%?) can be drawn from modeling the true rate [4]. But even the very high fatality rate in Italy is not easy to interpret. [5]History suggests that we are actually at much greater risk of exaggerated fears and misplaced priorities. There are many historical examples of panic about epidemics that never materialized (e.g., H1N1 influenza in 1976, 2006, and 2009). There are countless other examples of societies worrying about a small threat (e.g., the risk of Ebola spreading in the United States in 2014) while ignoring much larger ones hidden in plain sight. SARS-CoV-2 had killed roughly 5000 people by March 12. That is a fraction of influenza’s annual toll. While the Covid-19 epidemic has unfolded, China has probably lost 5000 people each day to ischemic heart disease. So why do so many Americans refuse influenza vaccines? Why did China shut down its economy to contain Covid-19 while doing little to curb cigarette use? Societies and their citizens misunderstand the relative importance of the health risks they face. The future course of Covid-19 remains unclear (and I may rue these words by year’s end). Nonetheless, citizens and their leaders need to think carefully, weigh risks in context, and pursue policies commensurate with the magnitude of the threat. [2]
The lack of reliable data makes it difficult to formulate a credible policy. A provocative essay by John Ioannidis [6] argued that
Would it make sense to isolate the older part of population and allow others to return to school and work? Adjusting "universal quarantines" along these lines has its advocates. [1,7]The data collected so far on how many people are infected and how the epidemic is evolving are utterly unreliable. Given the limited testing to date, some deaths and probably the vast majority of infections due to SARS-CoV-2 are being missed. We don’t know if we are failing to capture infections by a factor of three or 300. Three months after the outbreak emerged, most countries, including the U.S., lack the ability to test a large number of people and no countries have reliable data on the prevalence of the virus in a representative random sample of the general population.
This evidence fiasco creates tremendous uncertainty about the risk of dying from Covid-19. Reported case fatality rates, like the official 3.4% rate from the World Health Organization, cause horror — and are meaningless. Patients who have been tested for SARS-CoV-2 are disproportionately those with severe symptoms and bad outcomes. As most health systems have limited testing capacity, selection bias may even worsen in the near future.
The one situation where an entire, closed population was tested was the Diamond Princess cruise ship and its quarantine passengers. The case fatality rate there was 1.0%, but this was a largely elderly population, in which the death rate from Covid-19 is much higher.
Projecting the Diamond Princess mortality rate onto the age structure of the U.S. population, the death rate among people infected with Covid-19 would be 0.125%. But since this estimate is based on extremely thin data — there were just seven deaths among the 700 infected passengers and crew — the real death rate could stretch from five times lower (0.025%) to five times higher (0.625%). It is also possible that some of the passengers who were infected might die later, and that tourists may have different frequencies of chronic diseases — a risk factor for worse outcomes with SARS-CoV-2 infection — than the general population. Adding these extra sources of uncertainty, reasonable estimates for the case fatality ratio in the general U.S. population vary from 0.05% to 1%.
That huge range markedly affects how severe the pandemic is and what should be done. A population-wide case fatality rate of 0.05% is lower than seasonal influenza. If that is the true rate, locking down the world with potentially tremendous social and financial consequences may be totally irrational. It’s like an elephant being attacked by a house cat. Frustrated and trying to avoid the cat, the elephant accidentally jumps off a cliff and dies.
[The above was last updated on March 28, 2020.]
Note of March 26, 2020: The single best document for perspective on the virus, the disease, the response to it, and the available precaution for individuals that I have read is an informal report on "How to fight the coronavirus SARS-CoV-2 and its disease, COVID-19" from Michael Lin.
REFERENCES
- Sharon Begley, When Can We Let Up? Health Experts Craft Strategies to Safely Relax Coronavirus Lockdowns, STAT. Mar. 25, 2012, https://www.statnews.com/2020/03/25/coronavirus-experts-craft-strategies-to-relax-lockdowns/
- Wei-jie Guan et al., Clinical Characteristics of Coronavirus Disease 2019 in China, New Engl. J. Med., Feb. 28, 2020, DOI: 10.1056/NEJMoa2002032
- David S. Jones, History in a Crisis — Lessons for Covid-19, New Engl. J. Med., Mar. 12, 2020, DOI: 10.1056/NEJMp2004361
- Tomas Pueyo, Coronavirus: Why You Must Act Now, Medium, Mar. 10, 2020, https://medium.com/@tomaspueyo/coronavirus-act-today-or-people-will-die-f4d3d9cd99ca
- Graziano Onder, Giovanni Rezza & Silvio Brusaferro, Case-Fatality Rate and Characteristics of Patients Dying in Relation to COVID-19 in Italy, JAMA, Mar. 23, 2020, doi:10.1001/jama.2020.4683
- John P.A. Ioannidis, A Fiasco in the Making? As the Coronavirus Pandemic Takes Hold, We Are Making Decisions Without Reliable Data, Stat, Mar. 17, 2020, https://www.statnews.com/2020/03/17/a-fiasco-in-the-making-as-the-coronavirus-pandemic-takes-hold-we-are-making-decisions-without-reliable-data/
- Eran Bendavid and Jay Bhattacharya, Is the Coronavirus as Deadly as They Say? Current Estimates About the Covid-19 Fatality Rate May Be Too High by Orders of Magnitude, Wall St. J., Mar. 24, 2020, https://www.wsj.com/articles/is-the-coronavirus-as-deadly-as-they-say-11585088464?mod=trending_now_3
