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Day 104: COVID-19 It’s Over, But How Do You Convince People That It’s Over?
The chart above is from the website Rt Covid-19 created by Instagram co-founder Kevin Systrom. The website purports to be tracking the effective reproduction number of the virus that causes COVID-19 by localities:
Most people are more familiar with R0. R0 is the basic reproduction number of an epidemic. It’s defined as the number of secondary infections produced by a single infection. If R0 is greater than one, the epidemic spreads quickly **. If R0 is less than one, the epidemic spreads, but limps along and disappears before everyone becomes infected. The flu has an R0 between one and two while measles sits in the high teens. While R0 is a useful measure, it is flawed in an important way: it’s static.
We’ve all witnessed that humans are adaptable. Our behavior changes, whether mandated or self-prescribed, and that changes the effective R value at any point in time. As we socially distance and isolate, R plummets. Because the value changes so rapidly, Epidemiologists have argued that the only true way to combat COVID19 is to understand and manage by Rt.
I agree, and I’d go further: we not only need to know Rt, we need to know local Rt. New York’s epidemic is vastly different than California’s and using a single number to describe them both is not useful. Knowing the local Rt allows us to manage the pandemic effectively.
States have had a variety of lockdown strategies, but there’s very little understanding of which have worked and which need to go further. Some states like California have been locked down for weeks, while others like Iowa and Nebraska continue to balk at taking action as cases rise. Being able to compare local Rts between different areas and/or watch how Rt changes in one place can help us measure how effective local policies are at slowing the spread of the virus.
Tracking Rt also lets us know when we might loosen restrictions. Any suggestion that we loosen restrictions when Rt > 1.0 is an explicit decision to let the virus proliferate. At the same time, if we are able to reduce Rt to below 1.0, and we can reduce the number of cases overall, the virus becomes manageable. Life can begin to return to ‘normal.’ But without knowing Rt we are simply flying blind.
“[I]f we are able to reduce Rt to below 1.0, and we can reduce the number of cases overall, the virus becomes manageable. Life can begin to return to ‘normal.’ ” The chart above suggests we are in a good place. Particularly when you see it has part of a trend line. The following graphs show the progress using the same calculation methodology from 4 weeks ago, to last week:
They last tweaked their model on 4/26 but re-ran the updated model against their complete dataset, so they are comparing “apples to apples.” Their model may be good or not, but it does seem to confirm observational data: the epidemic has definitely slowed in this country.
But do people really see that? Yes, a lot of people are anxious to get back to life. But are they feeling confident that doing so is the right decision? How do you persuade those that remain fearful that the “quarantine breakers” are actually common-sense individuals not just recklessly selfish?
This is going to be tough. The public has been fed a lot of data where the numbers rise steeply. As the body count mounts, and it will continue to do so, how do you get people to realize that things are winding down? Observational data is always the most powerful: “Who are you going to believe? Me, or your lying eyes?” the old joke goes.
The President has shifted his focus away from the health crisis to how we get our economy back. That is a start. The people going to the beach and surrounding their capitols and city halls also contribute to the pressure to get back to work. But media and the progressives are highly invested in the narrative of fear. They will continue to amplify “confirmed cases” and obscure the fact that the rising case count is actually good news so long as the rate of serious illness and death continues to fall. Testing confirms the prevalence but not the severity of this illness.
Keep an eye on hospitalizations in your area. If hospitalizations continue to fall, it is really over. Ask your social and church groups when they think it will be ok to meet in real life? Prompt their questions about how to interpret what their eyes are seeing.
One by one, we emerge into the light.
[Note: Links to all my COVID-19 posts can be found here.]
Published in General
That’s easy. We actually get the virus under control. As opposed to saying it’s under control when it’s not. People are not fools.
We will know we can reopen when there are clorox wipes, purell and toilet paper on the shelves at your local grocery.
Perhaps there is a lesson here that mothers can contribute to the international dialogue on the pandemic and how to handle it.
When it was discovered that painkillers were harmful to newborns, Lamaze came along. Dr. Lamaze developed a way for women in painful labor to get through it without the painkillers that were readily available to them. Millions and millions of women have turned down the temptation to use painkillers because they knew they could get through it with a presence of a coach by their side telling them how far along they were to the end of the labor.
People can get through anything if they know there is an end in sight and how close they are to that end. If our present leaders would embrace that knowledge and use it as part of their containment strategy–and now that we know this virus reacts to changes in temperature and humidity just in the way the flu does–we could manage the next virus outbreak in November.
What we are seeing all around us now confirms that this virus is behaving like the flu. As the weather becomes more warm and humid, the virus stops making people sick.
The herd immunity thing does not seem to work protectively for the flu, and it doesn’t seem to work for this virus either from what I’ve seen so far. Perhaps it will. We will learn that next November. I’m not sure why the 1918 flu lasted 18 months. It has always fascinated researchers that it just went away after 18 months. A flu vaccine was not invented until the 1940s. But it did. That may be the case with this virus too.
But at least we know we have a good six months to play outside and go to the beach and get an ice cream cone. If only we could get the press and the politicians to say this to people, we could have a few months to live normally before it returns. By then we’ll have some mitigating strategies too like cranking up the humidifiers in winter homes. :-)
The tough part, @marcin, is that there really is not an end in sight. Not really. But I agree that if there were a way to re-frame the situation to put people more at ease, that would excellent. My sense is that once people get outside and enjoy the sunshine and fresh air, they will start to experience less fear.
But there is an end in sight. That’s what I’m trying to say. Look at the faces of those triumphant nurses in France!
There is an end in sight now. At least for a while.
We got through it. Let’s clean up the mess and enjoy our lives for six months.
Those nurses in France should be an inspiration to us all. Just as the stories and pictures of the Wuhan healthcare workers scared the heck out of us all last fall, these nurses should inspire us too. :-)
I need to introduce you to a magician. People can be fooled — just not all of them all of the time.
Truer than you know.
I was just thinking about the humidity part of the equation. This is actually not newfound knowledge. When my kids were little, my pediatrician would tell me at the start of the colds season to put a kettle on in the house every day. He said not to bother with the humidifiers because they never get hot enough to kill the viruses and bacteria and can end up spreading them. He wanted truly boiling water to create some steam in the house to compensate for the drying effect of the heating system in the home. It always worked. The steam would keep everyone’s upper respiratory tract functioning to keep us healthy.
That’s probably the big part of the covid-19 virus, why people who were exposed to it got so sick from it. That’s the inside-weather part of the equation. It won’t keep the asymptomatic carriers from not exposing everyone, but it will keep people from become deathly ill from the exposure.
We need to wake some old doctors. :-) Okay, guys, you can’t retire yet. You have some old knowledge about life that we need to relearn. :-) The new doctors don’t know this stuff. :-)
I don’t think that this article actually makes a projection, but rather reports several other projections. I do not find them very helpful, because they are very short-term. The 538 article shows predictions for the next 3 weeks, which isn’t very long. It’s pretty easy to be accurate in a period this short.
I have some minor disagreements with the facts here, along with my major disagreement with the conclusion.
I don’t think that NYC is representative, probably because R0 (and Rt) varies significantly by location. I think that your general estimate of the range, 2.0-3.5, is pretty reasonable.
I doubt that Rt is below 1, and I do not think that it is feasible to get it below 1. I do think that it is lower than it would be without the various protective measures, but I don’t think that anyone knows how much, and I don’t think that anyone knows how effective the various components of the response are.
There does not seem to be a decline in new cases. If you look at the daily reported cases graph at Worldometer (here), it looks quite flat, around 30,000/day for the entire month of April. Daily deaths are pretty flat too, about 2,000/day for the past 3 weeks or so. The reported cases is for “confirmed” cases that have tested positive, so it is a small fraction of total infections. If Rt was less than 1, I would expect a significant decline, which we do not see. [Continued]
I think that you are correct about a second wave. What is your alternative? Lockdown forever? Are you balancing the costs of the lockdown against the hypothetical benefits?
As you probably know by now, I’m skeptical of the benefits. I do not dispute that there is some benefit in the short term, but I don’t see any evidence that the plague is actually subsiding. It seems to be continuing at a lower level. This just delays, but does not prevent, the infections and deaths that would otherwise occur. The response to the virus is causing enormous economic and other harm, in return for a brief delay in the inevitable infections and deaths.
It is difficult to quantify any of this.
For Ro to vary by location probably would mean that the virus DNA varies significantly by location. Depends on exactly what you mean by location, I suppose.
I think that R0 or Rt can vary by location based on the physical characteristics of the location, and the behavior of people in the location. The most obvious example is NYC. I suspect that R0 (or Rt) is higher in NYC than in a rural area, because of much greater population density, transportation systems like subways that crowd large number of people into small locations, even more crowded streets.
I wouldn’t be surprised if R0 (or Rt) also varies by culture. For example, a more reserved Asian or German culture might well have a lower R0/Rt than a culture like Italy, where people are constantly touching, hugging, and kissing each other.
Or Sweden in May.
New York is an obvious outlier which should have been obvious to its officials at the time. Over a million people on the freaking subway every day. But no, the virus is not passed between humans. No, it’s transmission is not prevented with masks.
If the ChiComs and the American Left (but I repeat myself) had been honest about this, if the nation had been awake instead of impeaching, if we had any common sense, the losses would have been reduced by 2/3
I would like to see some evidence of the effectiveness of the lockdown. NYC left the subways open and ordered poor people to spend more time together in crowded public housing. If the continued spread could be traced to these obvious gaping holes in the social distancing strategy but other locked down segments in town saw declines, then the lockdowns had an effect. If the bug spread indiscriminately anyway, then it did not.
Crowded places (South Dakota meat processing plants, Mardi Gras in NO) with lots of poorer people (Detroit) get hammered. Two prison studies found 90%+ infection rates but very high incidence of asymptomatic cases.
But grocery store employees (true front line exception to the shutdown) are nor reportedly hard hit.
Geographically the bug is now everywhere. The incidence of infection is more a function of population density and pre-existing general health than the nature of or starting date for various prophylactic decrees.
The “second wave” is not going to hit virgin territory either immunologically or behaviorally and with a reduced availability of highly susceptible hosts, especially if early treatment regimens are deployed thus greatly reducing mortality.
The masks people wear in the stores are mostly a joke. There has never been a serious lockdown. We might as well have done the Swedish approach—no large crowds but otherwise cautious normal. We would be immunologically ready for the “second wave” and even if we did not postpone as many deaths in the short term we likely would have reduced the final total when it’s finally over.
You seem to be using R0 and Rt interchangeably, with both subject to change due to the same variables. I don’t think that’s the way those values work in any useful models or comparisons. In the aquatic organisms I used to study, the thing called R0 (we didn’t call it that, in the literature it was often called the intrinsic rate of increase) could vary greatly due to water temperature, but the actual rate of increase was the R0 modified by other factors, such as mortality rates, food supply, competition with other organisms feeding on the same foods (or on overlapping foods), and predation. I think this CDC article (which I haven’t read all the way to the end) is getting at something analogous for infectious diseases.
But the distinction between what is “intrinsic” (or “0”) and what is not depends a lot on what question you are trying to answer.
As I understand it it, you medical types on here can correct me if I’m wrong, unless/until there’s a vaccine, we’re all eventually going to get infected and either get better or die. The goal should be to protect the most vulnerable to the extent possible, and try to slow the infection rate so as not to overwhelm the medical system that is working to put more of those infected in the “get better” category instead of the “or die” category.
Useful models? There are useful models? (Really, there aren’t any models; they are just curve fitting programs.)
I’m not using them interchangably, or at least I don’t think so, but I think that the argument applies to both R0 and Rt. As I understand it, R0 is a constant in the model, representing something like the number of cases expected to be directly generated by a single case, at time 0 — i.e. at the time when no one has yet been infected and no one is immune. Rt is the same thing, at time t, as the model progresses.
In either case, I would think that R0 or Rt will be affected by behavior, and by crowding, and by other physical and social factors.
That is a valid criticism of some models, but certainly not all.
Which models go beyond curve fitting?
So Ro is intrinsic to the virus. It is based on how much viral load you would need to cause an infection. Think of it as number of infectious virus particles per ml. For some viruses you only need 20 viruses per ml (like missals) others might take 10, 100, or 1000 times that load to establish an infection. Ro should not vary for a single strain of virus. The variable is the Rt which is effective rate of infection, which factors in things like environment, behavior etc. Rt can and does vary from location to location, and based on practices.
To answer your question about what I think should be done. I think what we need to do is to replace imperfect indiscriminate distancing with targeted distancing. We need to have a massive testing, contact tracing, and forced isolation of infected individuals for the duration of their infection. The goal needs to be to help find asymptomatic carriers. Right now we can assume anyone displaying symptoms is isolating (assuming they aren’t completely irresponsible) but the asymptomatic will not know to isolate more so than the average current demands, even though they should be isolating completely. Right now we are forced to be indiscriminate because we can not be accurate. We are carpet bombing the problem instead of dropping one guided bomb. Therefore we have much larger collateral damage. I don’t stress testing and contact tracing just because I’m a loon (even though I am a loon, a majestic bird). It is the answer to limiting deaths and opening up the economy. I think making the choice to be hundreds of thousands dead or economic growth to be a politically untenable one. I think shooting for tracing is the solution, and if we can implement it well (big if) it will give us both the least number of deaths and spread and most economic activity.
The phrase “to the extent possible” is the whole ballgame. If nothing else in like mattered other than the end of the bug, then a two-year shutdown could be viewed by some as a prudent choice made possible by government decree. I prefer “feasible” to possible.
If on March 1 we had ordered a government-enforced quarantine of the vulnerable, done weekly testing on nursing home staff, spent money on UV sterilizers for such facilities and prophylactic admin of drugs that look promising when used that way and told the rest of us to wash up and avoid crowds I think the incidence of the bug would be higher but the death incidence much lower, the economic cost magnitudes lower, the short-term future health costs lower and the threat of the “second wave” minuscule. Our approach was wrong.
The researchers I worked with were generally death on models that didn’t do more than fit curves to data and for which the parameters didn’t have any biological explanation. If you tell me which models you looked at in sufficient quantities to determine that they were all curve fitting programs, maybe I can give you a more specific answer that is relevant to your concern.
Agree. In our state the “cases” are increasing by 800-900 per day (Test test test!!), but the number of hospitalizations as a % of “cases” has gone from 15.8% 11 days ago to 13.8% today.
I’ve been saying that on Twitter for a while now. It gets me some nasty replies.
Is New York such an outlier? Lombardy looked worse. Most other states probably strated social distancing sooner than New York compared to the start of COVID19 spread. Not all states got the infection at the same time, and for the first case random fluctuation can be a very big factor in early day.
New York is an outlier by any measure. As for Lombardy, a big Chinese presence in northern Italy apparently gave them a huge starting jump. Still, the variance in mortality and serious cases among jurisdictions is curious. If the mailmen were to deliver the mail with a hug and a kiss at every residence in one high infection-rate town but not in another town with fewer positives, for example, we could point to that as a causal factor but I don’t know what to point to in the present data. Baltimore has similar large pockets of dense low-income areas to NYC but no subway, is that why the incidence there is vastly lower than NYC? But Detroit also has no subway and it is has been much worse than Baltimore. Seems like winning at Name That Vector or Identify That Factor may be as important as testing in planning re-opening success.
Nobody should be nasty. But excluding New York does have the appearance of moving the goalposts for those who started out claiming that this was “just” the flu.
Interestingly, in virtually all of the state data where a “shelter order” is indicated, the Rt trend was already moving downward before the order. In fact there is no discernible effect of the shelter orders apparent in the graphs (or shape of the curves). I get that the underlying data might not be perfect, but I am just taking the data at face value.
And a remarkable number of states seem to be “settling in” at an Rt of about .87 (.79 – .96 range). Unclear if they will drop going forward, but the ‘s’ curve nature implies “not so much”.