1. Allison Schrager writes,
Among the unknowns about the virus: the true hospitalization and death rates; how infectious it is; how many asymptomatic patients are walking around; how it affects young people; how risk factors vary among different countries with different populations, pollution levels and urban densities. It seems certain the virus will overwhelm hospitals in some places, as it has in China and Italy. We also don’t know how long these extreme economic and social disruptions will last. Without reliable information, predictions are based on incomplete data and heroic assumptions.
…The way forward is testing as many people as possible—not only people with symptoms. Some carriers are asymptomatic. California is starting to test asymptomatic young people to learn more about transmission and infection rates. Testing everyone may not be feasible, but regularly testing a random sample of the population would be informative.
This is the analytical mindset, which is sorely needed. What I called the “suits vs. geeks divide” in 2008 is haunting us again. Ten days ago, the challenge was to get the suits to understand exponential growth. Hence, they were two weeks behind. Now, the challenge is to get the suits to make decisions based on rational calculations as opposed to fears or whoever shouts the loudest in their ears.
But much needs to change. Think about the “analytics revolution” in baseball. In the 1980s, the revolution started*, with Bill James and others questioning the value of the routinely-calculated statistics. Just as one example, data geeks discovered that a batter’s value was better measured by on-base percentage than batting average, even though the latter was prominently featured in the newspapers and the former was not. Soon, the geeks started longing for statistics that weren’t even being kept, and they started efforts to track and record the desired metrics.
(*In 1964, Earnshaw Cook wrote an analytical book, but he drew no followers, probably because personal computers had not yet been invented.)
Based on what we are seeing now, I think that epidemiology is ripe for an analytics revolution. To me as an outsider, the field relies too much on simulations using hypothetical parameters and not enough on identifying the data that would be useful in real time and making sure that the such data gets collected.
A key coronavirus unknown is the asymptomatic rate, the fraction of those infected who have either no symptoms or symptoms mild enough to be confused with a common cold and not reported. A high asymptomatic rate is decidedly good news: it would mean that the death rate is lower, that the hospital system is less likely to be overrun, and that we are closer to achieving herd immunity. From an economic point of view, a high asymptomatic rate means it is safe to relax restrictions relatively soon, and that hospitalizations can be kept within limits as economic activity resumes.
Conversely, a low asymptomatic rate would require trading off losing many lives against punishing
economic losses.Neither the asymptomatic rate nor the prevalence of the coronavirus can be estimated if tests are prioritized to the symptomatic or if the included asymptomatic are unrepresentative (think NBA players).
Instead, we need widespread randomized testing of the population.
It may seem counterintuitive that we should be rooting for a high number of people running around with the virus without symptoms. But that would mean, among other things, that their presence is not creating huge risks for the rest of the population. You want the ratio of mild cases to emergency-room cases to be high.
We should be doing a stochastic process random probability sample of the country to find out where the hell the virus really is.
Note that he has a lot of anger against President Trump. I won’t push back at Mr. Brilliant (I’m not being sarcastic, that is his name), but I think his rhetoric is stronger than his case. See my post on anger.
4. Dan Yamin says,
But there is one country we can learn from: South Korea. South Korea has been coping with corona for a long time, more than most Western countries, and they lead in the number of tests per capita. Therefore, the official mortality rate there is 0.9 percent. But even in South Korea, not all the infected were tested – most have very mild symptoms.
The actual number of people who are sick with the virus in South Korea is at least double what’s being reported, so the chance of dying is at least twice as low, standing at about 0.45 percent – very far from the World Health Organization’s [global mortality] figure of 3.4 percent.
He is at least taking care not to take statistics at face value. But don’t be satisfied with trying to guess based on data that don’t measure what you want. Try to get the authorities to provide you with the numbers you need.
There are a lot of variables interacting in highly nonlinear ways. The number of viruses transferred, the method of exposure (this is a respiratory virus, so inhalation is likely to be worse than ingestion), the people’s ages and other ailments, rapidly varying trends in behavior (that change the number and types of exposures) … This is the domain of chaos theory. Very small changes lead to radically divergent behavior, which means the data will never be nearly good enough for a reliable prediction.
“ this is a respiratory virus, so inhalation is likely to be worse than ingestion”
Is this true?
The virus kills by damage to the lungs. It will eventually get from anywhere in the body to the lungs, but inhaling it directly into the lungs gives it a head start in its race against your immune system.
Your desire for lots of asymptomatic cases appears to assume that they aren’t spreading the virus around. I would not like it at all if millions of Typhoid Marys are running around undetected.
I would, because my odds of being a Typhoid Mary would be much larger than my odds of being a casualty. If all cases were asymptomatic, we could all get it and not even care.
“If you’re sick, stay home. If you’re sick, stay home.”
In Canada, the Alberta and B.C. message and protocol changed today. They are still very consistent about only symptomatic people transmitting the virus but they are saying extremely mild symptoms, including a sore throat and runny nose, are common COVID-19 symptoms.
I don’t know how to change my decentralized Cough Isolation message. Maybe Social Distancing is a simpler message that mostly works if strictly followed along with good hand hygiene. This geek is confused over why the main message isn’t Self-Isolation with the onset of ANY mild respiratory symptoms, with Social Distancing as a good precaution.
Protocol and testing change: 14 days of asymptomatic Self-Isolation after any travel BUT 10 full days of Self-Isolation after symptom onset (a more complex formula). They are also stopping testing of those Self-Isolating and are turning their focus on testing all health care workers and using Case Tracing to extend out into the community.
B.C. announced 43 new cases today bringing the total to 472 but 100 are officially recovered now. They are in the downward far right of the Bell Curve. March 12 was the scary turning point for the epidemiologists when they realized the virus genome pattern they were seeing matched Washington State’s published genome.
I’m confused why this couldn’t have been achieved without the shutdown.
“the chance of dying is at least twice as low, standing at about 0.45 percent”
To what extent is “the” fatality rate a characteristic of COVID-19 vs. a characteristic of the distribution within a population of various risk factors that make one vulnerable to COVID-19 (age, pre-existing respiratory condition, etc.)? People hear that the fatality rate is 0.45% and think something along the lines of, “If I catch the disease, then nature draws a random number uniformly distributed between 0 and 100. If the number is less than 0.45, then I die.” Hence, the notion that the “chance of dying is 0.45%”. But, is reality something more like 99.55% of people don’t have the pre-existing conditions and, hence, are very unlikely to die while 0.45% of the population does have some of those conditions and is very likely to die? If so, then “the” fatality rate is really determined by population characteristics that are known even before infection. So, why don’t we look at pre-existing population characteristics (age, respiratory conditions, etc.) to estimate fatality rates rather than waiting to see realized death rates with all the attendant problems of untested, asymptomatic, etc.? Understood, that we might need some data at early stages to learn what the pre-existing conditions are and, of course, it’s always good to confirm with observation of realized death rates. But, the conditional death rates (by age, pre-existing condition, etc.) seem to be much more important than unconditional rates. To translate data from one population, say S Korea, to make predictions about another, say the US, one must use conditional death rates, not unconditional, because the populations are different. Also, conditional death rates are what allow us to target measures where they will have the most effect.
China isn’t reporting any new confirmed cases outside of Wuhan now. Assuming that they aren’t actively hiding cases (which I don’t believe they are), I think that would serve as evidence that there were not that many asymptomatic cases that China missed outside of Hubei. Because if there were millions of asymptomatic cases, you would think that you would be seeing flare ups of local transmission now that quarantine is being lifted, especially if there are some people in whom the virus lingers without causing severe symptoms.
Burgos,
If quarantines were effective and people we locked down sufficiently long to get to a point where no one was contagious any longer… it seems that your logic would not be valid.
To Arnold’s main point tho.. it doesn’t seem like it would be that hard to get a reliable random sample. Why aren’t we doing that now. Like starting tomorrow morning?
What I don’t understand is why we haven’t conducted a random sample of tests. This seems like the most obvious thing to do right now. How can we know anything about that to do about this without knowing the prevalence of what’s out there in the population.
Why not get Census and the CDC working together tomorrow? Find out what’s out there and then we would know what to do and where.
I find it astounding that this hasn’t been done yet.
People need to remember that the Covid-19 test is a snap shot. Just because someone if negative today does not mean the will be negative tomorrow or that they will never get infected.
Also, the idea of tracing fails if the jet set starts getting back on aircraft and travelling around the world. Tracing is done by local governments and world travels are covered by multiple jurisdictions.