Our COVID Mortality Rate Is Down 85%?

That’s a statistic in the news, but what does it mean? CNN reported this on Friday:

Friday’s case count of at least 80,005 surpasses the country’s previous one-day high of 77,362, reported July 16, according to Johns Hopkins University.
US Surgeon General Dr. Jerome Adams cautioned earlier Friday that hospitalizations are starting to go up in 75% of the jurisdictions across the country, and officials are concerned that in a few weeks, deaths will also start to increase.
The good news, Adams said, is that the country’s Covid-19 mortality rate has decreased by about 85% thanks to multiple factors, including the use of remdesivir, steroids and better management of patients.
According to CNN, the Surgeon General  made these remarks during an online discussion of global health policy at Meridian Global Leadership Summit. Meridian is a “non-profit, non-partisan diplomacy center” in Washington. I couldn’t find exactly what he said, either from Meridian’s site, the Surgeon General’s site or the Surgeon General’s Twitter account. The Center for Disease Control doesn’t seem to report a mortality rate.
Looking at statistics from The New York Times, however, indicates what the Surgeon General was talking about. Back in mid-April, the US was reporting around 2,200 deaths for every 32,000 confirmed cases. Now 800 deaths are being reported for around 68,000 cases. That translates into 6.9% of cases ending in death in April vs. 1.2% now, a decline of 83%. So it’s true that the mortality rate has dropped quite a lot.
This is confirmed by two studies reported by National Public Radio:
Two new peer-reviewed studies are showing a sharp drop in mortality among hospitalized COVID-19 patients. The drop is seen in all groups, including older patients and those with underlying conditions, suggesting that physicians are getting better at helping patients survive their illness.
The article mentions other reasons the mortality rate may be dropping:
[Researchers] say that factors outside of doctors’ control are also playing a role in driving down mortality. . . . Mask-wearing may be helping by reducing the initial dose of virus a person receives, thereby lessening the overall severity of illness for many patients. . . . Keeping hospitals below their maximum capacity also helps to increase survival rates. When cases surge and hospitals fill up, “staff are stretched, mistakes are made, it’s no one’s fault — it’s that the system isn’t built to operate near 100%”. . . 
This hardly means we’ve turned the corner on COVID-19, as one of the presidential candidates claims. A mortality rate of 7% is still high relative to other diseases. Serious illness is never a joy and even patients who survive COVID-19 sometimes suffer long-term effects.
In addition, two other numbers recently reported aren’t encouraging. The pandemic is causing significantly more deaths, either directly or indirectly, than are being reported:
In the most updated count to date, researchers at the Centers for Disease Control and Prevention have found that nearly 300,000 more people in the United States died from late January to early October this year compared to the average number of people who died in recent years. Just two-thirds of those deaths were counted as Covid-19 fatalities, highlighting how the official U.S. death count — now standing at about 220,000 [or 225,000] — is not fully inclusive [Stat].
One model predicts that the next four months will be especially bad in the US:
More than 511,000 lives could be lost by 28 February next year, modeling led by scientists from the University of Washington found.

This means that with cases surging in many states, particularly the upper Midwest, what appears to be a third major peak of coronavirus infections in the US could lead to nearly 300,000 people dying in just the next four months.

In fact the University of Washington warned that the situation will be even more disastrous if states continue to ease off on measures designed to restrict the spread of the virus, such as the shuttering of certain businesses and social distancing edicts. If states wind down such protections, the death toll could top 1 million people in America by 28 February, the UW study found [The Guardian].

Finally, the presidential candidate who doesn’t think we’ve turned the corner offered this timely reminder:

President Txxxx’s plan to beat COVID-19

Nine days.

It Was Reckless Endangerment or Worse

At the Rose Garden event last Saturday when they introduced the Republican nominee to the Supreme Court:

Guests mingled, hugged and kissed on the cheek, most without wearing masks. An indoor reception followed the outdoor ceremony.

Seven days later, at least eight people who attended the ceremony have tested positive for the coronavirus, including the president. Several more of the president’s closest aides and advisers have also tested positive.

Then, our president got to work, spreading the joy:


Of course, we’re only heard about some of the famous people who got the virus in the Rose Garden, at the White House, on Air Force One, at the rally or the fundraisers, not the common folk who rubbed shoulders with the rich and famous, or provided security, or served the drinks.

We knew he was a heartless prick who only cares about himself, but I mean, wow.

Postscript:  The White House doctor (i.e. public relations representative) has now “clarified” the timeline, explaining that when he said “72 hours” he meant “day three”, not “three days ago” (?). So it was actually Thursday night, after all the traveling about, when the president knew he had the virus, not Wednesday morning. Of course, everyone who speaks for the president can be trusted to deliver the unvarnished truth, so there’s nothing to see here. Obviously.

An Overlooked Variable May Be the Key to the Pandemic

A writer for The Atlantic argues that there’s “a potential, overlooked way of understanding this pandemic that would help answer [questions about it], reshuffle many of the current heated arguments, and, crucially, help us get the spread of COVID-19 under control”:

By now many people have heard about R0—the basic reproductive number of a pathogen, a measure of its contagiousness on average. But unless you’ve been reading scientific journals, you’re less likely to have encountered k, the measure of its dispersion. The definition of k is a mouthful, but it’s simply a way of asking whether a virus spreads in a steady manner or in big bursts, whereby one person infects many, all at once. After nine months of collecting epidemiological data, we know that this is an overdispersed pathogen, meaning that it tends to spread in clusters, but this knowledge has not yet fully entered our way of thinking about the pandemic—or our preventive practices.

The now-famed R0 (pronounced as “r-naught”) is an average measure of a pathogen’s contagiousness, or the mean number of susceptible people expected to become infected after being exposed to a person with the disease. If one ill person infects three others on average, the R0 is three. This parameter has been widely touted as a key factor in understanding how the pandemic operates. News media have produced multiple explainers and visualizations for it. . . . . Dashboards track its real-time evolution, often referred to as R or Rt, in response to our interventions. . .

Unfortunately, averages aren’t always useful for understanding the distribution of a phenomenon, especially if it has widely varying behavior. If Amazon’s CEO, Jeff Bezos, walks into a bar with 100 regular people in it, the average wealth in that bar suddenly exceeds $1 billion. . . .Clearly, the average is not that useful a number to understand the distribution of wealth in that bar, or how to change it. . . . Meanwhile, if the bar has a person infected with COVID-19, and if it is also poorly ventilated and loud, causing people to speak loudly at close range, almost everyone in the room could potentially be infected—a pattern that’s been observed many times since the pandemic begin, and that is similarly not captured by R. That’s where the dispersion comes in.

There are COVID-19 incidents in which a single person likely infected 80 percent or more of the people in the room in just a few hours. But, at other times, COVID-19 can be surprisingly much less contagious. Overdispersion and super-spreading of this virus are found in research across the globe. A growing number of studies estimate that a majority of infected people may not infect a single other person. A recent paper found that in Hong Kong, which had extensive testing and contact tracing, about 19 percent of cases were responsible for 80 percent of transmission, while 69 percent of cases did not infect another person.

This finding is not rare: Multiple studies from the beginning have suggested that as few as 10 to 20 percent of infected people may be responsible for as much as 80 to 90 percent of transmission, and that many people barely transmit it.

This highly skewed, imbalanced distribution means that an early run of bad luck with a few super-spreading events, or clusters, can produce dramatically different outcomes even for otherwise similar countries. Scientists looked globally at known early-introduction events, in which an infected person comes into a country, and found that in some places, such imported cases led to no deaths or known infections, while in others, they sparked sizable outbreaks. . . . In Daegu, South Korea, just one woman, dubbed Patient 31, generated more than 5,000 known cases in a megachurch cluster.

Unsurprisingly, SARS-CoV, the previous incarnation of SARS-CoV-2 that caused the 2003 SARS outbreak, was also overdispersed in this way: The majority of infected people did not transmit it, but a few super-spreading events caused most of the outbreaks. MERS, another coronavirus cousin of SARS, also appears overdispersed, but luckily, it does not—yet—transmit well among humans.

This kind of behavior, alternating between being super infectious and fairly noninfectious, is exactly what k captures, and what focusing solely on R hides. . . .

Nature and society are replete with such imbalanced phenomena, some of which are said to work according to the Pareto principle, named after the sociologist Vilfredo Pareto. Pareto’s insight is sometimes called the 80/20 principle—80 percent of outcomes of interest are caused by 20 percent of inputs—though the numbers don’t have to be that strict. Rather, the Pareto principle means that a small number of events or people are responsible for the majority of consequences. This will come as no surprise to anyone who has worked in the service sector, for example, where a small group of problem customers can create almost all the extra work. . . .

To fight a super-spreading disease effectively, policy makers need to figure out why super-spreading happens, and they need to understand how it affects everything, including our contact-tracing methods and our testing regimes.

There may be many different reasons a pathogen super-spreads. Yellow fever spreads mainly via the mosquito Aedes aegypti, but until the insect’s role was discovered, its transmission pattern bedeviled many scientists. . . . Much is still unknown about the super-spreading of SARS-CoV-2. It might be that some people are super-emitters of the virus, in that they spread it a lot more than other people. . . .

In study after study, we see that super-spreading clusters of COVID-19 almost overwhelmingly occur in poorly ventilated, indoor environments where many people congregate over time—weddings, churches, choirs, gyms, funerals, restaurants, and such—especially when there is loud talking or singing without masks. For super-spreading events to occur, multiple things have to be happening at the same time, and the risk is not equal in every setting and activity. . . .

[Muge Cevik of the University of St. Andrews] identifies “prolonged contact, poor ventilation, [a] highly infectious person, [and] crowding” as the key elements for a super-spreader event. Super-spreading can also occur indoors beyond the six-feet guideline, because SARS-CoV-2, the pathogen causing COVID-19, can travel through the air and accumulate, especially if ventilation is poor. Given that some people infect others before they show symptoms, or when they have very mild or even no symptoms, it’s not always possible to know if we are highly infectious ourselves. We don’t even know if there are more factors yet to be discovered that influence super-spreading.

But we don’t need to know all the sufficient factors that go into a super-spreading event to avoid what seems to be a necessary condition most of the time: many people, especially in a poorly ventilated indoor setting, and especially not wearing masks. As Natalie Dean, a biostatistician at the University of Florida, told me, given the huge numbers associated with these clusters, targeting them would be very effective in getting our transmission numbers down.

Overdispersion should also inform our contact-tracing efforts. In fact, we may need to turn them upside down. Right now, many states and nations engage in what is called forward or prospective contact tracing. Once an infected person is identified, we try to find out with whom they interacted afterward so that we can warn, test, isolate, and quarantine these potential exposures. But that’s not the only way to trace contacts. And, because of overdispersion, it’s not necessarily where the most bang for the buck lies. Instead, in many cases, we should try to work backwards to see who first infected the subject.

Because of overdispersion, most people will have been infected by someone who also infected other people, because only a small percentage of people infect many at a time, whereas most infect zero or maybe one person. As Adam Kucharski, an epidemiologist, . . . explained to me, if we can use retrospective contact tracing to find the person who infected our patient, and then trace the forward contacts of the infecting person, we are generally going to find a lot more cases compared with forward-tracing contacts of the infected patient. [Those] will merely identify potential exposures, many of which will not happen anyway, because most transmission chains die out on their own. . . .

Even in an overdispersed pandemic, it’s not pointless to do forward tracing to be able to warn and test people, if there are extra resources and testing capacity. But it doesn’t make sense to do forward tracing while not devoting enough resources to backward tracing and finding clusters, which cause so much damage. . . .

Perhaps one of the most interesting cases has been Japan, a country with middling luck that got hit early on and followed what appeared to be an unconventional model, not deploying mass testing and never fully shutting down. By the end of March, influential economists were publishing reports with dire warnings, predicting overloads in the hospital system and huge spikes in deaths. The predicted catastrophe never came to be, however, and although the country faced some future waves, there was never a large spike in deaths despite its aging population, uninterrupted use of mass transportation, dense cities, and lack of a formal lockdown.

[Hitoshi Oshitani of Japan’s COVID-19 Cluster Taskforce] told me that in Japan, they had noticed the overdispersion characteristics of COVID-19 as early as February, and thus created a strategy focusing mostly on cluster-busting, which tries to prevent one cluster from igniting another. Oshitani said he believes that “the chain of transmission cannot be sustained without a chain of clusters or a megacluster.” Japan thus carried out a cluster-busting approach, including undertaking aggressive backward tracing to uncover clusters. Japan also focused on ventilation, counseling its population to avoid places where the three C’s come together—crowds in closed spaces in close contact, especially if there’s talking or singing . . .

Oshitani contrasts the Japanese strategy, nailing almost every important feature of the pandemic early on, with the Western response, trying to eliminate the disease “one by one” when that’s not necessarily the main way it spreads. Indeed, Japan got its cases down, but kept up its vigilance: When the government started noticing an uptick in community cases, it initiated a state of emergency in April and tried hard to incentivize the kinds of businesses that could lead to super-spreading events, such as theaters, music venues, and sports stadiums, to close down temporarily. Now schools are back in session in person, and even stadiums are open—but without chanting.

It’s not always the restrictiveness of the rules, but whether they target the right dangers. As [one scientist] put it, “Japan’s commitment to ‘cluster-busting’ allowed it to achieve impressive mitigation with judiciously chosen restrictions. Countries that have ignored super-spreading have risked getting the worst of both worlds: burdensome restrictions that fail to achieve substantial mitigation. The U.K.’s recent decision to limit outdoor gatherings to six people while allowing pubs and bars to remain open is just one of many such examples.”

Could we get back to a much more normal life by focusing on limiting the conditions for super-spreading events, aggressively engaging in cluster-busting, and deploying cheap, rapid mass tests—that is, once we get our case numbers down to low enough numbers to carry out such a strategy? Many places with low community transmission could start immediately. . . .

Seven Months Later, What We Know About Covid-19 (and Don’t)

Our president announced that New Zealand suffered a major surge of Covid-19 on Monday (“big surge in New Zealand, you know it’s terrible, we don’t want that”). They had nine new cases. The U.S. had 42,000. 

For somewhat more reliable information, see this informative summary from StatNews (the article has more about each item):

. . . In the time since Chinese scientists confirmed the rapidly spreading disease in Wuhan . . . an extraordinary amount has been learned about the virus, SARS-CoV-2, the disease it causes, Covid-19, and how they affect us.

Here are some of the things we have learned, and some of the pressing questions we still need answered.

What we know

Covid and kids: It’s complicated 

. . . Everything Covid is complex, and kids are no exception. While deaths among children and teens remain low, they are not invulnerable. And they probably contribute to transmission of SARS-CoV-2, though how much remains unclear. . . 

There are safer settings, and more dangerous settings

Research has coalesced on a few key points about what types of setting increase the risk that an infectious person will pass the virus to others. . . . 

People can test positive for a long time after they recover. It doesn’t matter 

There was a lot of angst a few months ago about some people who had seemingly recovered from Covid-19 infections continuing to test positive for the virus for weeks. Were they infectious? Should recommendations be changed for how long infected people should be isolated? It turns out it is an issue of testing. . . .

After the storm, there are often lingering effects 

Name a body part or system and Covid-19 has left its fingerprints there. . . . There are growing worries that these and other health effects will be long-lasting. . . .

‘Long-haulers’ don’t feel like they’ve recovered

We know they’re out there, but we don’t know how many, why their symptoms persist, and what happens next. . . . 

Vaccine development can be accelerated. A lot

An extraordinary amount of progress toward Covid-19 vaccines has been made, in record time. . . . 

People without symptoms can spread the virus

Whatever group you’re talking about, there are some key implications for the pandemic, and trying to rein it in. . . .

Mutations to the virus haven’t been consequential 

Coronaviruses in general do not mutate very quickly compared to other viral families. This is a good thing . . .  .

Viruses on surfaces probably aren’t the major transmission route

The general consensus now is that “fomites” — germs on surfaces — aren’t the major transmission route for Covid-19. . . .But it’s clear from lots of studies that surfaces around infected people can be contaminated with viruses and the viruses can linger. . . . 

What we don’t know

People seem to be protected from reinfection, but for how long? 

The thinking is that a case of Covid-19, like other infections, will confer some immunity against reinfection for some amount of time. But researchers won’t know exactly how long that protection lasts until people start getting Covid-19 again. So far, despite some anecdotal reports, scientists have not confirmed any repeat Covid-19 cases. . . .

What happens if or when people start having subsequent infections? 

Given that most respiratory viruses are not “one-and-done” infections — they don’t induce life-long immunity in the way a virus like measles does — there is a reasonable chance that people could have more than one infection with Covid-19. . . .

How much virus does it take to get infected? 

Whether you become infected or not when you encounter a pathogen isn’t just a question of whether you’re susceptible or immune. It depends on how much of the virus (or bacterium) you encounter. . . .

How many people have been infected?

There have been 21 million confirmed cases of Covid-19 around the world, and 5.3 million in the United States. Far more people than that have actually had the virus. . . .

It’s not clear why some people get really sick, and some don’t 

The sheer range of outcomes for people who get Covid-19 — from a truly asymptomatic case, to mild symptoms, to moderate disease leading to months-long complications, to death — has befuddled infectious disease researchers. . . .