So Where’s the Vaccine?

Perhaps you’ve been wondering where the vaccine is and how much is on the way. We’d know more if the previous administration hadn’t displayed an extraordinary combination of malevolence and incompetence. The good news is that we’ll know more soon. From The Guardian

The Biden administration has spent its first week in office attempting to manually track down 20m vaccine doses in the pipeline between federal distribution and administration at clinic sites, when a dose finally reaches a patient’s arm.

The Trump administration’s strategy pushed the response to the coronavirus pandemic to individual states and omitted pipeline tracking information between distribution and when the shot is actually administered, Biden administration officials told Politico.

The lack of data has now forced federal health department officials to spend hours on the phone tracking down vaccine shipments, the news website reported.

Nobody had a complete picture,” Dr Julie Morita, a member of the Biden transition team and executive vice-president of the Robert Wood Johnson Foundation, told Politico. “The plans that were being made were being made with the assumption that more information would be available and be revealed once they got into the White House.”

As of Saturday, 49 million doses of vaccine have been distributed by the federal government, but only 27 million administered by states, according to the US Centers for Disease Control and Prevention (CDC).

About two million of those doses are believed to be accounted for by a 72-hour lag in reported administration, Politico reported. That still leaves millions in the pipeline between delivery and patient. At least 16 states have used less than half the vaccine doses distributed to them, USA Today reported this week.

Much of our work over the next week is going to make sure that we can tighten up the timelines to understand where in the pipeline the vaccine actually is and when exactly it is administered,” Dr Rochelle Walensky, [the new] director of the CDC, told USA Today.

. . . The CDC’s first report on early vaccine rollout is expected in February.

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:

EjavL0-WoAAdOxl

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. . . .