1.18 overall and 1.18 excluding New York. Stunning one-day slowdowns in several states, including New Jersey, Michigan, Illinois, and Louisiana. We will see how much the reporting catches up the next two days.

1. A study from Italy.

We found no statistically significant difference in the viral load (as measured by genome equivalents inferred from cycle threshold data) of symptomatic versus asymptomatic infections

Pointer from Megan McArdle.

Although the margin that I care about is mild vs. severe rather than symptomatic vs. asymptomatic, this is somewhat discouraging for those of us who would like to believe that getting the virus in low amounts would be ok.

Note the the study measures viral load among people after they have the virus, not viral load at onset. I think the only way to make guesses about viral load at onset is to do case studies that look at how people got the virus.

I think that what this study strongly reinforces is the hypothesis of asymptomatic spreading.

2. A case study that suggests being in the path of airconditioning flow and an asymptomatic spreader gave people the disease.

I now think that these sorts of case studies are going to be the best way to gather useful evidence about the virus. I would especially like to see case studies that can help assess whether there is anything we can do to affect whether one gets a mild or severe case.

Suppose that I could visit a fortune teller and get an answer to two questions.

Will I get the virus sooner, or will I get it later?

Will my case be mild, or will it be severe?

Here is how I would react to each of these possibilities:

I am assuming that if I get a mild case, then this will have no adverse long-term effects and that I will be immune going forward. These assumptions may not be 100 percent correct, but as long as they are most likely true I would stick to these rankings.

If you agree with me so far, then we have a framework for understanding the thinking of Johan Giesecke of Sweden. Many thanks to commenter John Alcorn for the pointer.

1. Lockdown policy was originally sold as a way of moving from left to right, that is from sooner to later. That is what was meant by “flattening the curve.” If I am going to get a severe case, then the lockdown makes me slightly better off. If I am going to get a mild case, it actually makes me slightly worse off, because I would rather get immunity sooner than later.

2. What I most care about is not getting a severe case. If I am going to get a severe case, then I would rather get it later, because I hope that by that point there is better treatment available.

All of the criteria that policy makers are using to decide on “re-opening,” whether they are models or trends in data, say something about sooner or later, which I hardly care about. They say nothing about mild or severe, which is what I most care about. When they use the terms “scientific” or “data-driven” to describe their thought process, I call Baloney Sandwich. Their science and their data don’t address the important issue.

Most of what we know about mild vs. severe concerns demographic categories. For example, very old people are particularly likely to get severe cases. We want to keep the virus out of nursing homes.

For young people, the risk of getting a severe case is not zero. But should they be treating the risk as more significant, say, than the risk of driving on the highway? Of course, everyone is in the dark because of the Unknown Denominator. Even if we know how many young people have died, we have no good estimate of how many young people have had the virus.

The most important question is what we can do to make it more likely to get a mild case than a severe case. If our health experts wanted to actually be useful, they would undertake to give us guidance on that. We could start by undertaking studies designed to pin down the Unknown Denominator. It would be most helpful to pin it down by demographic group, so that we could know something about the risk that each of us faces with respect to getting a severe case. Beyond that, perhaps government could conduct studies of clusters of people who have gotten severe illness to understand how they contracted the disease.

Some of us believe a hypothesis that viral load matters for mild vs. severe. If that is correct, then individuals can make better choices by avoiding spending a lot of time in enclosed spaces near other people. Also, they can help themselves by wearing masks, and they can help others by wearing masks whenever they are in public places. So subsidies for masks and laws requiring face covering in public places could be appropriate.

I wish that as a society we could switch emphasis away from the sooner or later axis. The emphasis on sooner or later gives inordinate power to government officials and the public health “experts” to control out lives, without ever getting at what matters.

Overall down to 1.21 and excluding New York down to 1.23 Good news or weekend reporting lull?

Some states with low 3DDRRs today: New York 1.18, Louisiana 1.15, and Colorado 1.19
Some states with high 3DDRRs today: California 1.31, New Jersey 1.29

California, which had a lockdown order early, had a relatively low early-April 3DDRR, staying well below 2. The other states had higher 3DDRRs in early April. If in a week New Jersey is 10 basis points below California and the other states are 20 basis points below, I could claim some indication for the heavy in, heavy out model.

Balaji Srinivasan gave what I call a micro critique of the Santa Clara study. I am going to provide a macro critique, and in the process I will articulate the significance of the 3DDRR, the ratio of cumulative deaths in a given day to the cumulative deaths as of three days earlier.

Suppose that we have 10 deaths in a population of 20,000. How deadly is the virus, and how widely has it spread? We face the problem of the Unknown Denominator. If 100 people have had the virus,then the death rate is 10 percent, and it has not spread widely (yet). If 1000 people have it, then the death rate is 1 percent, and it has spread modestly. If 10,000 people have had the virus, then the death rate is 0.1 percent, and it has already spread so far that it will not spread much farther.

The Santa Clara study suggests a high spread rate and a low death rate. The authors report their results as indicating that at least 50 times as many people have had the virus as have been reported positive in tests conducted by County medical officials. This suggests that in calculating the true fatality rate for the virus, we should divide the reported case fatality rate by 50, giving a result of something like 1 in a thousand, or 0.1 percent. With 35,000 deaths in the United States, that would imply that 35 million people have had the virus.

Commenters on this blog have pointed to studies in other countries that seem to give similar results. But there are other studies in various parts of the United States and in other countries that suggest that far less than 10 percent of the population has had the virus.

Srinivasan argues that the Santa Clara results are likely distorted by a test that can produce a high number of false positive results when applied to a population that is mostly negative for the virus, that the study sample probably includes a high proportion of positive individuals relative to the population, and that the implied high spread rate exceeds that of similar past epidemics.

My own skepticism comes from the dynamic of the disease as we have observed so far. My intuition comes from my family’s annual vacations on the Delaware seashore.

I am one of those people who is mesmerized by ocean waves coming ashore. I can stand for long periods at a spot where some of the waves wash over my feet and others stop just short of reaching me. I like to guess which waves will get to me, and which waves won’t.

One phenomenon I noticed I call heavy in, heavy out. When a heavy wave comes in over my ankles, the next wave gets diminished. This is because the heavy wave recedes quickly, and in the process it pushes back against the subsequent wave.

My intuition is that the spread of the virus would operate the same way. If it spreads really rapidly, it will also recede rapidly. Because the virus will have a hard time finding new targets, we will see heavy in, heavy out.

It was to spot a heavy in, heavy out pattern that I chose to track the 3DDRR. I decided that reported cases were too much affected by ever-changing testing criteria to be useful in identifying trends in the wave. Although deaths are a lagging indicator, I decided that they would work better for providing a more reliable picture of how quickly the wave was receding.

So far, the wave is receding slowly. Because it is receding slowly, I infer that we are not experiencing heavy in, heavy out. Therefore, I doubt that the virus has a miniscule death rate and a spectacularly high spread rate.

Of course, my thinking could be wrong. As more studies come in, if they are consistent with the Santa Clara study, my estimates of the death rate and the spread rate will move in the direction of the Santa Clara study. But from the macro perspective of the 3DDRR, I am skeptical.

A meta-analysis says,

Clusters of cases have also been reported following family, work, or social gatherings where close, personal contact can occur [40,41]. As an example, epidemiologic analysis of a cluster of cases in the state of Illinois showed probable transmission through two family gatherings at which communal food was consumed, embraces were shared, and extended face-to-face conversations were exchanged with symptomatic individuals who were later confirmed to have COVID-19 [40].

The risk of transmission with more indirect contact (eg, passing someone with infection on the street, handling items that were previously handled by someone with infection) is not well established and is likely low.

Pointer from a commenter.*

This is not the final word, but suppose that it is confirmed by solid experimental evidence. In that case, maybe the science will say that for most of us, all the social distancing we need is to adopt the custom of bowing when we greet relatives, friends, and associates, rather than shaking hands or hugging or cheek-kissing. The science may say that it also is safer not to live in a nursing home, ride a subway, or spend a lot of time in an enclosed room with a person who might be sick, especially if they are coughing or singing. But the “6 foot rule” and frequent hand-washing and staying at home except for essential outings might turn out to be Bubbameise, a Yiddish expression that roughly translates as your grandmother’s superstition. Masks also could be Bubbameise, but without causing much disruption.

*I have been complemented on the high signal/noise ratio in the comments section on this blog. .

The overall rate is 1.26, and outside of New York it is 1.32

Look at Aaron Lindsey’s spreadsheet. The 3DDRR starts to turn down around March 27. The steepest drop is during the first week of April. Since then, the decline has been agonizingly slow.

If you assume that the trend in deaths lags the trend in infections by about three weeks, this says that we started to turn the corner on infections at the end of the first week in March, which I think is before most people were changing behavior (am I right about that?). Then the steepest drop in infections took place between about March 10 and March 17, when people were changing behavior but no lockdowns were in place. Since then, the infection rate as declined further, but more slowly.

What is the role of the state in the virus crisis? Continue reading →