The Boston Globe: What's the best case scenario for the coronavirus pandemic?

16:06 into the video:

ML: The best case scenario would be that we had been missing way more cases than we thought, and that there are lots of people out there who have been infected and have developed an immune response that we just didn’t know about.

BV: And how likely do you think that is? I  mean know I we need the serological test to look for antibodies among the  population  who are not coming into emergency rooms and IC units to know this for sure, but what’s your sense of that, the fatality rate, are we overestimating that right now?

ML: I think it is clear that, I mean everybody in my business agrees that in the US we are not detecting all cases because of lack of testing, and that maybe the true number is 5 to 10 times the number we think it is, but that’s still a tiny fraction of the US population, and even a small fraction of the populations in the hotspots. So, that could be right, it could be in that range or there could be surprises in both directions.  The surprise in the beneficial direction is maybe there are just a lot of very mild cases that we haven’t seen or that preceded when there was any testing to be had, or that we just somehow missed. And I think that is possible, and there are tiny anecdotal bits of evidence that we are missing maybe more cases than we thought.

On the other side, we are assuming that infection leads to immunity, at least for a while, and while that may well be true there was a paper that came out on a preprint server this morning suggesting that in China maybe a third of people who had documented infections did not have visible immune responses. So if that were true, then that means that for each infection that we get there is less immunity in the population than we thought.

So how those two kind of surprises will play out I think we’ll only know…  we will know the first part of how many people have immune responses at all, I’d  say in a couple of weeks we will begin to have a picture, but the second question of whether those  immune responses are protective, I think it’s gonna   take longer because it is a much harder study to do. 

Dr. Osterholm predicted a pandemic like coronavirus and he outlines his battle plan

Dr. Michael Osterholm: This is not a sprint, this is a marathon

USA Today interview with Dr. Marc Lipsitch, April 8, 2020

3:54 min into the video: We may have some surprises, it may be there are a lot more people who have been infected than we know, and on the downside, which would be good news in the sense that having a high level in the population , and there may be that some of these people don’t develop protective immune responses, and that is the negative surprise we could have.

Navigating the Covid-19 pandemic: We’re just clambering into a life raft. Dry land is far away

STAT

By MARC LIPSITCH and YONATAN GRAD /  APRIL 1, 2020

Imagine you are in a small boat far, far from shore. A surprise storm capsizes the boat and tosses you into the sea. You try to tame your panic, somehow find the boat’s flimsy but still floating life raft, and struggle into it. You catch your breath, look around, and try to think what to do next. Thinking clearly is hard to do after a near-drowning experience.

You do, though, realize two important things: First, the raft is saving your life for the moment and you need to stay in it until you have a better plan. Second, the raft is not a viable long-term option and you need to get to land.

In April 2020, the storm is the Covid-19 pandemic, the life raft is the combination of intense measures we are using to slow the spread of the virus, and dry land is the end to the pandemic.

The U.S. is still in the clambering-into-the-life-raft phase of responding to Covid-19, and thinking clearly about what to do is still difficult. This confusion has made it hard to appreciate two facts: One is that social distancing combined with scaling up testing, production of medical equipment, and other countermeasures are essential and must be replicated across the country, intensified, and continued. The other is that if these measures have the desired effect of reducing the number of new cases accumulating each day, they provide only a temporary solution.

We still need to find a way to bring the pandemic to a permanent conclusion.

Several countries in Asia controlled their epidemics before a majority of the population was infected. Some, like Taiwan and Singapore, did so by containing infections from the start. Others, like China and Korea, did so only after large outbreaks. The control they have reached is only a life raft, not dry land, because unless there have been extraordinarily high levels of infection that were so mild as to go unnoticed, most people in these countries remain susceptible to infection.

Viruses do not remember they were previously under control and will resurge when restrictions are lifted. Just look at what happened in 1918, when cities that had cracked down on the transmission of influenza lifted their restrictions and flu transmission rose again. Mathematical models of Covid-19 by our group and others that incorporate these lessons show that, in the short term, social distancing and other interventions can reduce the impact of the virus. But the same models show that when these interventions are eased, the problem returns.

Let’s be clear. With something like Covid-19 there is the first peak, and there’s the whole epidemic. For the first peak, the evidence so far points to a worrisome possibility of overwhelming our intensive care units — even with the degree of social distancing we’ve achieved — as we’re seeing in New York City. But every bit we slow and flatten the curve will make that less likely and less dramatic, if and when it happens.

It is very possible that after this first wave subsides, we will still have a largely susceptible population, though that depends on how well the social distancing works. Effective treatments and increased ICU capacity could reduce the demand for critical care, lightning the load on the health system, but again, these measures only delay things.

If the SARS-CoV-2 virus has a contagiousness of three, meaning every case infects three other people, then we won’t get to the end of the epidemic until two-thirds of the population has become immune by infection or by vaccination. Successful control of the first peak of infections could leave a majority (perhaps a large majority) of the U.S. population still susceptible to the virus.

There are several broad ideas for how to get to dry land, which is widespread immunity in the population. But each has enormous problems.

One way is to let up on social distancing soon and let the epidemic run its course. That would lead to many deaths and completely overwhelm health care systems around the country. Another way is to maintain intense social distancing until there is a vaccine — but the arrival of a vaccine is uncertain and, absent a miracle, will likely take more than a year. Meanwhile, society and the economy would suffer.

If the first wave really is controlled, another option would be to try multiple rounds of social distancing: instituting it to bring the epidemic under control then letting up, perhaps only in certain areas, to allow cases to occur and immunity to accumulate gradually in the population, and then again introducing another round of social distancing. Our model of this process shows that it would take multiple rounds and would be challenging to accomplish without errors that lead to ICU overload. It would also be difficult to maintain the political and social will to implement this.

The most ambitious approach would be to intensify social distancing and scale up testing until we have the ability to know about nearly every case of Covid-19, trace his or her contacts, and control the spread of the disease one case at a time. This, though, is hard to envision. Even though Singapore detected the infection early, Covid-19 has stretched the island’s public health system to the limits, and our public health system has not had the practice and the resources devoted to stopping a pandemic that Singapore has invested since it faced down severe acute respiratory syndrome (SARS) in 2003. And continued risk of imported cases of Covid-19 from elsewhere in the world — or even from other parts of the country — would lead us in this best-case scenario to restrict and intensively screen travelers for an extended period.

As epidemics and responses to them are local, the scenario in one part of the U.S. could differ from that in another. A report from the Institute for Disease Modeling suggests that even Seattle’s relatively prompt response may have only slowed the spread of the infection and it may see a single-peaked epidemic with much of the population infected, despite social distancing efforts. If accurate, recently reported fever data from a networked thermometer company that illness rates may be coming down, not just growing more slowly, then we may see a second peak once social distancing efforts are lifted.

Clearly, we need more testing to understand each region’s epidemic trajectory.

A vaccine is ultimately our best hope, but that is in the future — many months away, if not a year or more, in the rosiest scenarios.

Whatever path we choose — and it may be a mix of paths in different parts of the country, as the local epidemics and responses are so varying — we should be working overtime to make use of the time we buy with social distancing. That means:

·        Building capacity to control transmission by continued social distancing, massive testing, and meeting the needs of healthcare workers for personal protective equipment.

·        Investing in efforts to mitigate the impact of Covid-19 by rapidly finding treatments, increasing health care capacity, and further accelerating work on a vaccine.

Despite the near-drowning of hospitals and intensive care units we’ve observed in many countries, and may soon witness in the U.S., we must think clearly and understand that getting through the first phase of this pandemic only gets us into the life raft, not to dry land.

Marc Lipsitch is professor of epidemiology and director of the Center for Communicable Disease Dynamics at the Harvard T.H. Chan School of Public Health, where Yonatan Grad is an assistant professor of immunology and infectious diseases

U of M infectious disease expert says easing of COVID-19 'months away'

How likely are you to die of coronavirus?

We may have far more infections than previously thought, and constructing models to estimate numbers is extremely hard

BY TOM CHIVERS

April 1, 2020

The national conversation is dominated by coronavirus statistical models at the moment. The Imperial College model, the Oxford model, the other Imperial model. 

I want to talk about the models, and what they tell us, because the outputs of these models drive the government’s response — and thousands of lives could turn on them. It’s important, therefore, that we understand them, and why the numbers they give us are so different. These figures led Peter Hitchens, the Mail on Sunday columnist, to complain that the number of deaths has jumped around from 500,000 to 20,000, to 5,000. I can see why people are confused, if they just think “the models” are taking the same numbers and spitting out these weirdly different results.

But first, I want to talk about something much simpler. It’s the question that many of us, I’d say, most want to know, when we’re anxiously thinking about Covid-19 and ourselves and our loved ones. That is: if someone gets the disease, how likely are they to die?

The splendid Our World in Data (OWID) — who, full disclosure, I’ve been doing some editing for during this crisis — have been working on answering that question. So has Oxford’s equally splendid Centre for Evidence-Based Medicine (CEBM).

It is probably the most basic question you can ask about a disease, and yet it’s bloody hard to answer. It changes between places and over time, and between different groups; it depends on a variety of hard-to-measure factors, and the answer you get itself directly affects our understanding of several other numbers, all of which are vital to any model of the disease.

So. You may have heard a term being used: the “case fatality rate”, or CFR. That is the number of deaths divided by the number of confirmed cases. When journalists talk about the “death rate”, that’s often what they are referring to. If a country has 10,000 confirmed cases and 100 deaths, then the CFR in that country is (100/10,000), or 1%.

That is not what we are looking for, and it is probably not even very close to what we are looking for.

Instead what we want is the “infection fatality rate”, or IFR. That is the number of deaths divided by the number of people who actually have the disease. The number of people who have tested positive for the disease is probably only a fraction of the total number who had it, because only a fraction of the population has actually been tested. 

Obviously, the IFR is much harder to determine accurately. The only people getting tested will be the people who are most ill, so your IFR is probably much lower than your CFR, because your denominator — the number you’re dividing by — is probably much bigger. 

So if your country has tested absolutely everyone and found all cases of the disease, then your IFR is the same as your CFR, or 1%. But if it has only found 10% of the people with the disease, then your 10,000 confirmed cases are just the tip of a 100,000-person iceberg. With those 100 deaths, your IFR would be (100/100,000) or 0.1%.

Sadly, all we know is the CFR, and it changes hugely from country to country. Professor Jason Oke, a statistician at Oxford University and one of the people behind the CEBM analysis, points out that the CFR in Italy is many times higher than that in Germany; 11% of confirmed cases have died in the former, compared to 0.79% in the latter. “That can’t be down to healthcare differences,” he says: “Italy isn’t a third-world country. And it can’t be demographics — Italy has an old population [and older people are at greater risk], but Germany isn’t far behind it.” 

The likely explanation is that the difference is down to testing. Italy has largely tested people with symptoms, in hospitals; Germany has tested many thousands of people who have no symptoms. That probably helps prevent some deaths, but the most direct impact will be that it hugely increases the denominator; again, if you’re dividing your 100 deaths by 100,000 instead of 10,000, your death rate will be much smaller. Germany’s will be closer to (but not the same as) the IFR, the number we really want.

So, a country’s CFR will vary depending on how many tests it’s done, because that changes the denominator. But at least the numerator — the number being divided — is probably pretty straightforward, right? A death is a death.

Sadly, that’s not the case either. Dr Hannah Ritchie, one of the data scientists at OWID, points out that the death statistics are complex too, for two reasons. One is prosaic: a lot of people who have the disease and will die of it have not yet died. “The period from onset to death is about a month,” she says. So your simple “divide the numerator by the denominator” rule — the number of deaths by the number of patients — doesn’t work, because your number of deaths is a product of how many people had the disease a month ago, not how many people have it now.

That’s bad enough. But there’s a more profound problem, which is that deaths themselves can be recorded very differently in different places. Professor Sir David Spiegelhalter, a statistician at the University of Cambridge, says that the UK simply counts people who have tested positive and then died. But in some other countries, people are recorded as having died of Covid-19 if they had the symptoms, even if they weren’t tested (“suspected” as opposed to “confirmed”); in others, people outside hospitals are not tested and so are not recorded.

“Even the number of deaths is not a perfect statistic at all,” Spiegelhalter says. El Pais did an interesting look at some of the international differences here; Britain’s Office for National Statistics explains why its numbers look different from the official Government ones here.

To some extent it doesn’t matter, says Spiegelhalter: as long as each individual country maintains the same regime, ”the number of deaths is still a good monitor for the shape of the epidemic”. 

But it’s not clear that they are fixed; countries may have good reasons to change the way they collect data as circumstances change, but it apparently happens often enough that the World Health Organisation feels that they have to ask countries to notify them when they do it. Famously, China did so earlier in the epidemic, but others do too: in complying with the WHO’s request, Australia has noted that it has changed its definition of a Covid-19 “case” (and therefore a Covid-19 “death”) at least 12 times since 23 January. 

So, in essence, to work out the IFR — the number we want — we need to know two other factors: the number of people infected with Covid-19, and the number of people who died of it; the denominator, and the numerator. And, sadly, both numbers are uncertain.

How much does any of this matter? Well: now, I’m going to try and build my very own, rather stupid model. I’m not going to try to predict the future; I’m just going to try to use some very simple numbers to “predict” the number of cases there are in the UK right now.

First, the number of reported Covid-19 deaths in the UK is 1,408. Second, the lag between infection and death is about three or four weeks; let’s say three. Third, the number of (confirmed) cases in the UK has been doubling about every three to five days; let’s say five (and assume it’s staying constant; forget social isolation for now).

With those numbers, we can plug in our guess at the IFR (the infection fatality rate, remember: the real number we want to know, the “if I get it, how likely am I to die” number) and use it to work out roughly how many cases we’d expect now.

So what number should we use as our IFR? I’m going to use three: one from Imperial College London’s MRC team, the one behind the famous model; and two from CEBM. Imperial’s latest work assumes an IFR of 1%; CEBM estimates it to be between 0.1% and 0.26%.

If we take the Imperial 1%, then that means that we can multiply the 1,408 deaths now by 100 to get the number of people who’d had the disease three weeks ago, because we think about one in 100 of them died. So about 140,000 people.

Then we can take our doubling time — we said five days — to get how many cases we’d see now. In three weeks, 21 days, you’d see four doublings; two to the power four is 16. So 16 times 140,000, which is 2,240,000. We could imagine that we’ve probably got about two million cases now.

But let’s use the CEBM numbers. First the highest one, 0.26%. If we take that, we can multiply the 1,408 deaths by 400, instead of 100, to give us the number of infections three weeks ago. That is 560,000. Then we can multiply that by 16 to give us an estimate of how many there are now: about 9,000,000.

And how about if we use their lowest estimate, 0.1%? Same routine: 1,408 multiplied by 1,000, multiplied by 16: more than 20,000,000.

So by changing a single number, the IFR, to one of three plausible values, in a very simple model, we get outputs that range from “3% of people have already had it” to “30% of people have already had it”. And that’s before we start messing around with the other assumptions; is doubling time three days, not five? Is infection to death four weeks, not three? Is “1,408 deaths” even correct?

I want to reiterate: this is a very simple, stupid model, put together by a journalist, not an epidemiologist. The actual models will be far more complex, and will take into account other things — the number of cases in hospital and so on — to try to ground them in objective fact. Don’t mistake this for some plausible estimate of infection numbers. And there are loads of other things to worry about: people suffering long-term health consequences, even if they live; people dying of other things because the healthcare system is overwhelmed.

But the problem that I’m illustrating is real. Small, plausible adjustments to your inputs make your model spit out very different things. The assumptions you make are vital. We haven’t even started to think about other crucial things — for instance, government interventions, and how effective they are. 

“The different scenarios — isolation, closing schools, quarantines — they come with massive assumptions about how adherent people are,” says Ritchie. “You get massively varying outputs, depending on what you put in.” Plus, of course, it’s all circular: your model influences how you respond; your response changes what numbers get put back into the model.

What you need is better numbers, to plug into the models. And that’s what people are trying to get. The CEBM paper uses, among other things, numbers from Iceland, which — being tiny — managed to test a huge proportion of its population, nearly 3%. It found 963 cases and just two deaths; an CFR of 0.2%, from which the CEBM extrapolates an IFR of 0.05%. But that’s likely an underestimate, because quarantining has protected the elderly, the most at-risk group. Others have done something similar with passengers on the cruise ship Diamond Princess, finding a CFR of around 1% — likely an overestimate, since the passengers tended to be older.

Another way has been to screen small groups who are in quarantine, such as the Diamond Princess passengers or passengers on aircraft, to see how many people 1) test positive and 2) show symptoms. If you know how many people have the disease but are asymptomatic, then you can extrapolate to the wider population — if you’re testing all the people who are symptomatic, and you know that 50% of infected people don’t show symptoms, and you find 20,000 cases, you can estimate that the real number is more like 40,000.

But there’s a problem here, too, which is that “asymptomatic” is not a simple thing, according to George Davey Smith, an epidemiologist at the University of Bristol. If you’re sitting in quarantine and you cough, you might be recorded as symptomatic; but out in the real world, you’re not going to take yourself to hospital for a gentle cough, so you still won’t get tested.

Instead of there being a neat “symptomatic/asymptomatic” division, you have a third group: people with some symptoms but who think it’s just a cold in the chest. (As I write, I’m coughing a little; but I don’t think that’s Covid-19, I think it’s just because I went for a run this morning and the cold irritated my lungs. But I’d probably be recorded as “symptomatic” if I were in quarantine and being screened.)

And yet another way is to look at how many people die every year, and how many people are dying now, and seeing whether more people are dying than usual. That’s how we attribute deaths to flu each year, says Spiegelhalter; the European Monitoring of Excess Mortality group (EuroMOMO) uses this data to say that in an average year in the UK, 17,000 deaths are “associated” with influenza. But so far there is no excess death at all, except in Italy: the EuroMOMO charts are all around the seasonal average. That will likely change, but we forget that in a population of millions, you’d expect thousands of deaths every day anyway: even the Covid-19 pandemic is still being lost in the noise.

In the end, we need testing. And not just the sort of testing we have now — PCR testing, which shows who has the virus right now; we need serological testing, which shows who has had it in the past. That will come along relatively soon, and hopefully can be quite quickly used to test randomly selected people, like an opinion poll sampling a population; then we can see how many people have had it, and from there work out the IFR. But for the moment we don’t have that. 

I wanted to write this to give an impression of how appallingly difficult the modeller’s job is. I write, sometimes, pieces about statistics — I suggested that claims about the loneliness epidemic, teen suicides, and the media’s influence on Brexit were overstated, for instance. They involved very basic maths, done for very low stakes: if I messed up, if I failed to carry a 2 or whatever, I would look very stupid and would be very embarrassed, but no one would die. 

Whereas, if the Imperial College modellers get it wrong, with their far more complex maths and their far more uncertain inputs, they could sway government policy enormously. Whether we lock down society or carry on as normal depends heavily on the outputs of models like these. And it’s not that there’s an easy “better safe than sorry” option; if we crash the economy, it will (eventually) cause real health problems.

A February 2020 review found that 10 years of austerity may have caused the growth in life expectancy to stall, especially among the poorest; I’m sceptical of the “130,000 deaths caused by austerity” stat, but it’s pretty clear that it had a real negative impact. The post-Covid-19 world will almost certainly involve huge austerity to pay for the vast costs incurred during the virus.

Get it wrong one way, and thousands of people die unnecessarily from the virus; get it wrong the other, and you crash our public services and kill people that way. (I’ve only seen one attempt to model the health outcomes of that crash, and I have no way of judging it; for what it’s worth, though, it does say they will be extremely terrible; on the other hand, recessions don’t seem to shorten life expectancy, so who knows.)

So I’m very sympathetic to the modellers. But there are things which would help, and which they can do, but haven’t, so far at least. Ferguson’s team has not released the code his model is based on; he says he is working with software developers to do so, but proponents of open science, like Davey Smith and his colleague Marcus Munafò, say this isn’t happening fast enough.

“These models are so sensitive to their assumptions,” says Munafò. “And they’re black boxes.” The code is 13 years old; it’s vital that other scientists are allowed to look at it, check it for mistakes and stress-test its assumptions. There are other, open-source models available, but the Imperial one is still kept under wraps, and it shouldn’t be.

Because a lot rides on the outputs. If millions have already been infected and the disease is less deadly than we think, then our response should be very different to if millions are still to be infected and tens or hundreds of thousands more will die. 

The latest from Ferguson’s team suggests that between 1% and 5% of the UK’s population has already been infected. Oke of CEBM thinks that that could be an underestimate — he thinks that the disease was circulating in China for a month or so before it was announced. “There were early reports of doctors saying they were seeing unusual respiratory symptoms, which were suppressed,” he says. That could have brought it all here much earlier.

When I mentioned the Oxford “study” — in fact a model showing what plausible inputs could produce what we’ve seen, one of which was a very low IFR and huge number of people already infected — he didn’t endorse it, but said “I think a lot of people have underestimated how far this has already spread, and how early.” Davey Smith also thinks that those Imperial figures could well be an underestimate. I have no idea if they’re right or wrong, but whether they are or not matters a great deal.

For the record, to take us back to the beginning, Peter Hitchens had flatly misunderstood what was going on. The 500,000 number was a worst-case scenario if we did nothing; the 20,000 was Ferguson’s team’s estimate of what we’d see now that social-distancing measures and so on are in place.

The 5,000 wasn’t from Ferguson’s team at all but from an electrical engineering group also at Imperial who just eyeballed the death curve on the China graph, fitted the UK numbers so far onto that, and extrapolated from there. “They explicitly say they’re not doing any epidemiological modelling at all,” says Spiegelhalter, “and they retracted it two days later on Twitter,” after it became obvious that it was wrong.

But we shouldn’t be complacent and assume that, just because people are misunderstanding what the modellers are doing, the models must be correct. Everything that comes out of them is the product of what goes in, and all of that is going to be wrong, to some degree. “The key point is that the numbers we have now are not correct,” says Ritchie. 

If you look back to previous outbreaks, such as the 2009 swine flu epidemic, the numbers people were using while it was still going on were wildly different from the ones scientists settled on afterward: early estimates in 2009 were between 0.1% and 5.1%; the eventual WHO estimate was just 0.02%, similar to seasonal flu. In a fast-moving situation, it is easy to make large mistakes. (In either direction! I am not suggesting that the Covid-19 situation will necessarily be similarly overstated.)

These models are the best information we have at the moment. But they are hugely uncertain, and likely to be wrong. All we can do is try to get better information for them, and make the best decisions we can under conditions of appalling uncertainty — and be forgiving of the modellers who are desperately trying to make life-changing, history-changing decisions at high speed and with bad data.