Prevalence Estimates Revisited

A month ago today I looked at covid testing data from the 50 states plus DC. I wondered whether the rate of testing correlated positively or negatively with the percentage of positive test results. US guidelines for reopening are predicated on the correlation turning negative — states that have lowered their rates of contagion would be able to widen the net, catching infected people who are experiencing few or no symptoms. When I ran the numbers the correlation coefficient (r) between those two variables was +0.65 — a strong positive relationship. States experiencing a more severe outbreak of the epidemic were doing more testing; i.e., increased testing was a poor indicator of readiness to reopen.

Another month has passed. Since then the US has experienced a modest downward trend in confirmed diagnoses, while the testing rate per 100K people has nearly doubled. Consequently the percentage of tests yielding positive results has dropped substantially, from 13% to 5.5%.

Given more widespread testing of a broader swath of the populace, I expected that the relationship between testing frequency and the percentage of tests showing positive would have weakened. Not so: using updated data, the r is still +0.66. Implication: testing isn’t being increased willy-nilly, nor is it reaching people whose symptoms or social contacts imply lower likelihood of infection. Instead, testing is still focused on people who seem clinically likely to have been infected. However, the tests are now being administered to patients whose symptoms are less severe than they were a month ago. That’s sound clinical practice, though still not an indicator of readiness to reopen.

I ran a couple of other correlations:

r (testing frequency by death rate) = 0.57 — this strong positive statistical relationship reinforces the presumption that diagnostic tests are being administered to relatively severe cases.

r (test-positive percentage by death rate) = 0.93 — this correlation is so strong as almost to be an identity function. It doesn’t mean that everybody who tests positive for corona dies from the disease: nationally, 6% of test-positive cases have died. What the strong correlation does mean is that, averaged across individual cases, a state’s test-positive rate serves as a very accurate leading indicator for its death rate.

Now I’m just noodling around with the data… In North Carolina 3.3% of test-positive cases have died. Based on well-designed immunity studies, the covid mortality rate is around 1%; i.e., 1% of those who are infected eventually die. By inference, the NC population has had .033/.01 = 3.3 times as many covid infections as the number of people who’ve tested positive. NC has had 236 test-positive cases per 100K population; 236 x 3.3 = 779 infected people per 100K, or 0.8% of the state population total.

That number seems awfully low on the face of it. Let’s take New York for validation: a well-designed immunity study conducted a month ago estimated that 14% of the NY population had been infected; by now it’s probably around 16%. Using the testing data for NY:  8.1% deaths per test-positives, 1862 test-positives per 100K. .01862 x 8.1 = 15% of the population has been infected — that’s pretty close.

National data show that there have been 523 test-positives per 100K and a deaths per positive test result rate of 5.8%: .00523 x 5.8 = 3% of the US population has been infected — a number that converges on other prevalence estimates.

Take 3% of 330 million = 10 million infected, divide it by 1.7 million test-positives, and you get 6: that’s the national multiplier for transforming US test-positives to population prevalence of covid. The multiplier will continue to shrink as testing rates increase.

 

 

Covid versus Flu

In a recent post I acknowledged that younger people face less risk from coronavirus than older people do from the flu. That was cavalier of me. It’s true enough as of now. The question is whether the epidemic is approaching the end of its first wave, or if it’s still just beginning.

The basic statistical comparisons:

Mortality rate: flu = 0.1%, covid = 1%. For those who get infected, covid is ten times as lethal as flu.

Prevalence: Flu infects around 30 million Americans per year, or 9% of the population. So far covid has infected maybe 10 million Americans, or 3% of the population. As of now you’re 3 times as likely to get flu as covid.

Death rate = mortality x prevalence. For flu = .001 x .09 = 90 per million population. For covid, .1 x .03 = 300 per million. As of now covid has been killing people at more than 3 times the rate as the flu.

Body count: flu = 330M x 90 = 30 thousand; covid = 330M x 300 = 100 thousand.

So, if the corona epidemic were to stop dead in its tracks today, it would have killed more than 3 times as many people as does the flu during a typical season. The flu does in fact stop dead every year. The flu is half as contagious as covid, and a substantial percentage of people take the flu vaccine, reducing the number of potential hosts. After a few months the flu epidemic plays itself out. Covid is different. Instead of a biological vaccine, societies rely on behavioral means of slowing contagion — social distancing, quarantine, etc. If stringent levels of containment were to persist, the current wave of the epidemic might play itself out after having infected 5 percent of the US populace and having killed around 150 thousand:  5 times as deadly as the typical flu season.

If prevention lapses and the viral contagion regains momentum, it could continue spreading until 80 percent of the population has been infected and 2.6 million have died. Under the “herd immunity” scenario, covid is nearly a hundred times as deadly as the flu.

About 20% of covid deaths occur among people younger than 65: that’s 20K of the 100K deaths to date. If the epidemic persists until herd immunity is achieved, then 20% of the two and a half million Americans who die will be under 65.

That’s 500 thousand people under age 65 dead of corona over the next year as the epidemic plays itself out in this country. For all of 2018, 700 thousand people under age 65 died from all causes combined.

 

 

Morituri Te Salutant

This morning, on Memorial Day, Trump is tweeting his plans to turn the Charlotte NC convention center into a Circus Maximus:

I love the Great State of North Carolina, so much so that I insisted on having the Republican National Convention in Charlotte at the end of August. Unfortunately, Democrat Governor Roy Cooper is still in Shutdown mood & unable to guarantee that by August we will be allowed full attendance in the Arena. In other words, we would be spending millions of dollars building the Arena to a very high standard without even knowing if the Democrat Governor would allow the Republican Party to fully occupy the space. Plans are being made by many thousands of enthusiastic Republicans, and others, to head to beautiful North Carolina in August. They must be immediately given an answer by the Governor as to whether or not the space will be allowed to be fully occupied. If not, we will be reluctantly forced to find, with all of the jobs and economic development it brings, another Republican National Convention site. This is not something I want to do. Thank you, and I LOVE the people of North Carolina!

Ave, Magister Ludi!

 

I’m Over 65, and It’s Not My Fault

Okay, so I get it. Those over 65, the oldest 15 percent of the US population, account for 80 percent of corona deaths. To the under-65 crowd the covid risk is still considerable: a mortality rate of around 0.2%, potentially killing half a million of them before herd immunity is achieved. Still, the younger demographics face less risk from corona that the flu poses to their elders. I’m 68, and before covid I didn’t go out of my way to avoid catching the flu. Why shouldn’t the country reopen? Go ahead, youngsters: go back to work and school and play, ditch the masks and suck in each other’s breath and spit. I’m prepared to keep my distances until the vaccine rolls out or the herd gets immune, whichever comes first.

The shame, and the outrage, is that the lockdown will have gone to waste.

If the authorities had taken the epidemic seriously a couple of weeks sooner. If everyone had been more diligent during the lockdown. If businesses had followed the guidelines before reopening. Hundreds of thousands fewer people would have been infected; tens of thousands fewer would have died.

Regret and resentment can’t change the past. Why can’t they change the present and the future? Why, two months from now, will we be saying what-if about how the reopening has gone wrong, how we’re right back where we started in mid-March, before the lockdown, when the virus was running rampant through the populace?

Now that we’re here, don’t blame us, the old and the comorbid, for treading on the People’s freedoms and disrupting their livelihoods and destabilizing their mental health. We’re as much the victims of this public health fiasco as anyone else. Don’t gussy up the coming slaughter with bad Darwinism. Don’t invoke ROI or worker solidarity to justify the collateral damage. Don’t dismiss the pandemic as a temporary distraction from the important issues.

Call it what it is: a massive and tragic failure.

A month ago there was hope that the covid plateau would take a decidedly downward tilt, slowing the rate of new infections and deaths until the first wave was extinguished. It might have happened. It has happened elsewhere. France, a month ago averaging nearly a thousand deaths per day, has over the past ten days seen its daily covid death count drop to around 120. France has reason to hope that, in systematically and prudently reopening the country, new cases and deaths will continue to decline even as the economy recovers. Not here. In more than half the states the death tolls are plateaued or climbing. Nonetheless, the country is reopening; hundreds of thousands, maybe millions more will die.

For many, perhaps most, the benefits of going back to ordinary life outweigh the risks. But in exposing themselves to infection they too become vectors of contagion. We who are older, those of us who live with chronic health conditions, are expected to protect ourselves, to self-quarantine, as the virus sweeps through the population. Fair enough. What sorts of resources might help us maintain our self-quarantine for the next year or two as we try to survive the coming onslaught? ICU beds, adequately equipped and staffed by medical professionals whose ranks aren’t decimated by infection? Widespread testing and contact tracing? Yes, certainly, but also the more quotidian issues: home delivery of goods and services, psychosocial support, safe strategies for living together with family and friends while living apart.

Not only are the elderly most vulnerable bodily; they’re also most vulnerable economically. During recovery from the 2007-2008 recession, those over 55 who lost their jobs took more than twice as long on average to find employment as did younger workers. In the corona recovery the hiring age bias will be far more pronounced, with employers committed to maximizing productivity while minimizing sick leave and health insurance premiums. Biden has caught flak from single-payer enthusiasts for expanding Medicare eligibility down only to age 60, but during the epidemic they’re the ones who most need access to low-cost healthcare. Facing the near-impossibility of finding new jobs for the foreseeable future, older workers should continue to qualify for enhanced unemployment benefits until they’re eligible to collect Social Security.

What are the odds? As the country picks up speed, attention will shift from illness and death to jobs and stocks and football. Sequestered, scapegoated by capital and labor alike, all but forgotten, the old and infirm won’t be heard as they’re nudged under the trolley.

Mr. Brightside Changes His Tune

I guess that’s what happens when you read a new, more pessimistic covid model just before going to bed. The realization came to mind at 3:30 am. Eventually I made myself go back to sleep, but I dreamed I was riding in an elevator that kept filling up with more people on every floor. Then I got out of the elevator and onto a bus: at every stop more people got on. Clearly it was time to get up and write this post.

My mistake was in the doubling. It’s not how long it takes for the number of people in the population who have ever been infected to double. It’s how long it takes for those currently infected to double their number.

Assume that a newly infected person is contagious for around ten days. If there were the same number of newly infected people 10 days ago as there are today, then that 10-days-ago cohort doubled its number before immunity or death dropped them from the ranks of the currently contagious. That’s the plateau, when the reproduction rate — the Rt  — is 1. When Rt=1 the virus progresses through the population at a steady rate until it runs out of new bodies to infect. That’s herd immunity.

Rates of newly diagnosed cases are subject to testing bias: as more tests are conducted, the number of virus-positive results will increase even if the rate of infection stays the same or decreases. I’ve been using death rate as a lagging indicator of new infections. The mortality rate for the disease is estimated at 1%; assuming no change in demographics of those who contract the virus, then each person whose death is recorded today stands as a proxy for 100 people who got infected maybe 3 weeks ago. That proxy calc still seems valid enough.

So, total US covid deaths over the past 10 days, from May 12 to May 22 = 13,919. Deaths over the preceding 10 days, May 2 to May 12 = 16,274. Divide the first number by the second to get the Rt for 3 weeks ago = 0.85. That’s pretty good. How about the 10 days before that: Rt = 16,274/19,550 = 0.83. So the reproduction number is stable, based on the level of social distancing that’s been practiced in this country over the past month or so. Using the calcs I outlined in the last post, the viral wave would wash itself out by around the end of March 2021, with maybe 6 percent of the US population having been infected and 170,000 having died from the disease.

That fairly prolonged path to viral extinction is predicated on maintaining present levels of social distancing. But now the country is opening up again, and there’s not much wiggle room in the Rt. People are generally cautious, but they’re going back to work and they’re being encouraged to return to the shops and the churches. Will the Rt stay below 1, or will it shoot back up to the predistancing reproduction rate of around 2.5? Somewhere in between seems likely: let’s say Rt=1.5. Under that scenario new infections, and new deaths, will start going up 50% every 10 days.

At that rate, a year from now 80 percent of Americans will have been infected and 2.5 million will have died. Herd immunity.

Trump says that, even if new viral spikes pop up, the country will not shut down again. He asserts that we’ll be able to put out the fires. That’s a whole hell of a lot of fires. Herd immunity.

The new predictive model I read last night was published as a collaboration between Imperial College London and the World Health Organization. No wonder Trump  cut off WHO funding. So far 100,000 Americans have died from covid; the CDC’s updated model shows the virus killing 500,000 Americans, and that’s assuming that current levels of social distancing are maintained — which they won’t be. No wonder Trump and his minions are throwing shade at the CDC. A number of prominent epidemiologists regard the CDC estimates as too optimistic.

 

Mr. Brightside Peeks Over the Plateau

Something different seems to be happening.

First, some background. Corona’s R0 — the virus’s basic reproduction rate in the wild when unrestrained by mitigation — is around 2.5: each infected person spreads the disease to an average of 2.5 other people. Assume that an infected person remains contagious for around 10 days. Divide the contagion interval by the R0: 10/2.5 = 4 days: that’s the basic doubling rate — the number of days it takes for the total number of people in the population who have been infected to double. Doubling the total number of corona diagnoses every 4 days: that’s what the US upswing looked like in early April, before sheltering in place and social distancing were widely enacted.

The goal of mitigation is to reduce the effective reproduction rate, or Rt. When it’s at 1, then each infected person infects one other person before recovering or dying. When Rt=1, the doubling rate is 10/1 or 10 days — just equal to the 10-day average interval of contagion. So as newly infected people are added to the total, the same number of previously infected people stop being contagious. Viral spread through the population becomes a zero-sum game: that’s the plateau. US mortality hit the 10-day doubling plateau around the middle of April. And there it stayed as Trump and the Republican governors agitated for liberation and the citizenry started getting antsy. This lapse of discipline seemed to portend disaster, the epidemic jumping from steady state to accelerated contagion, illness, hospitalization, death.

Here’s the paradox: even as the country has begun opening up again, the numbers have begun looking better.

Rates of confirmed diagnoses can be misleading, because they’re contingent on how many tests are being administered. Deaths are harder to manipulate. While there is a lot of variation, the average daily death toll appears to have decreased, from around 2,000 through most of April to around 1,500 during the first half of May. Over the past week deaths have increased less than 2% per day. That rate of increase, compounded daily, projects to a doubling rate of 35 days (102% x 1035 days = 200%) and an Rt of 0.3 (R0/35 days). This is the way it’s supposed to work once the Rt drops below 1: infections and deaths come down the other side of the plateau until eventually the epidemic extinguishes itself.

At this rate, how long would it take for the wave to wash itself out, with new infections and deaths approaching zero? Here’s a quick-and-dirty Simulation A:

  • Assumptions at baseline: contagion interval = 10 days, Rt = 0.3, daily new infection rate = 160,000.
  • In 10 days, those 160,000 cases will themselves have infected 160K x 0.3 = 48K people. But by then those original 160K cases will no longer be contagious, resulting in 160K – 48K = 112K fewer active cases in the population.
  • 20 days from now, those 48K newly infected people will in turn have passed the virus on to 48 x 0.3 = 14.4K people.
  • By 30 days new cases will have dropped to 14.4K x 0.3 = 4.3K daily.
  • 40 days from now — around the beginning of July — daily new cases will be running at 4.3K x 0.3 = 1.3K. Given a mortality rate of 1%, the death toll would have dropped to 13 per day nationwide.

Under Scenario A, maybe 20,000 people would die of corona in the US over the next six weeks, but that’s a lot better than the 80,000 who have died over the past six weeks. And that would be the end of it — the first wave would have washed itself out. These are the sorts of projections that the IHME has been generating, though they repeatedly have had to adjust their numbers upward as the plateau persisted. Stagewise reopening guidelines are predicated on new infections and deaths decreasing, making it possible to relax incrementally and safely the rigorous lockdown levels of mitigation.

So what happens now, with Trump and company undermining their own epidemiologists’ recommendations and with so many parts of the country jumping the gun? It depends on the extent to which mitigation efforts are relaxed. Relatively strict enforcement has dropped the virus’s effective reproduction rate from 2.5 to around 0.3. If Wild West anything-goes laxity prevails and the reproduction rate jumps above 1, then infection rates and deaths will climb again and we’ll be back where we started. Suppose we split the difference, and the Rt settles in at around 0.8. Simulation B:

  • Over the next 10 days, today’s 160,000 newly infected cases will have infected 160K x 0.8 = 128K people.
  • By the beginning of July (40 days from now): 160K x 0 .84 = 65.5K new cases daily.
  • By around the end of the year (210 days from now) there would be 160K x 0.821 = 1.4K new cases and 14 new deaths daily. The first wave would be extinguished.

In Scenario B, relaxing remediation from strict to moderate compliance would subject our population to an additional  8 months of illness, but at steadily decreasing rates. Over those 8 months maybe another 7 million will be infected and another 70 thousand people will die before the wave is extinguished. Scenario B’s toll in human suffering and death would be considerably worse than Scenario A, but it’s still a lot better than the unbridled contagion that preceded lockdown.

By the time the wave of contagion dissipates in Scenario B, around 5 percent of the national population will have been infected and 160,000 will have died — that turns out to be pretty darned close to the current IHME projection of around 150,000 covid deaths. It’s tragic to be sure, but nothing like the casualties that would mount up if contagion were to spread unchecked until herd immunity is reached.  The results modeled in Scenario B could conceivably be achieved even without deploying the more targeted armamentarium of extensive diagnostic testing and case isolation and social contact tracing. Any success in implementing those methods of abatement could further reduce the effective reproduction rate and accelerate the wave’s dissipation.

Is this moderate-case scenario realistic? Looking at the images on the Internet you’d think that pent-up demand for shopping and haircuts and barhopping and beachgoing will overshoot pre-pandemic baseline levels of in-your-face social proximity. But that’s just clickbait and agitprop. Most people are wary; most expect to maintain social distancing as best they can even as they go back to school and work; most of the old and the health-compromised expect to keep their distance until a vaccine becomes available.

There are regional variations. It’s widely reported that deaths have been going down in the hardest-hit states of NY, NJ, and MA, while for the remaining states the upward trend persists. But the upticks are relatively small, and as a percentage of total deaths they’re declining. In my state of North Carolina daily deaths have been increasing by 3.2%, which projects to a 22-day doubling rate and an Rt of 0.45. That means the wave will recede more slowly, allowing for a smaller margin for error in easing the social distancing protocols. On the other hand, the wave isn’t as tall here — current per-capita rates of new diagnoses and deaths in NC are lower than the national average — so all else equal it wouldn’t take as long to get the NC levels back down to sea level.

The next 3 weeks should tell the tale, as the effects of increased mobility and social proximity and resumed business as usual propagate through the population and the viral contagion cycle runs its course.

 

Am I a Conspiracy Theory Crackpot?

Carl slots conspiracy theorists as lonely losers looking for an affinity group. As a comment I offered my own conspiracy theory: Trump and associates are plotting a herd immunity strategy. After a week of waiting for a reply I’ve tentatively hypothesized from his silence that Carl is in on the fix, not unlike the space aliens who don’t reply to SETI pings as they hatch their insidious plots of invasion. So I figured I had better move my elaborations over here to my own safe space.

Here’s a 2017 article reviewing the psychology of conspiracy theory — I reference it throughout the post.

As well as their purely epistemic purposes, causal explanations serve the need for people to feel safe and secure in their environment and to exert control over the environment as autonomous individuals and as members of collectives. Several early theories of conspiracy belief suggested that people turn to conspiracy theories for compensatory satisfaction when these needs are threatened.

At an abstract level, I’m looking for an explanation — a way of embedding observations in a context of meaning. A meaningful explanation can’t be read directly off the evidence; it must be inferred, operating beneath the surface and behind the curtain. When trying to arrive at a meaningful explanation of observable human behavior, I look for intent, for a purposeful activation of specific cause-effect cascades that achieve the individual’s ends.

Purposeful intent might not be accessible to the agent under observation; it might be operating beneath the surface and behind the curtain of their own self-awareness. Alternatively, hiding their motivations and intentions from observers might be integral to the agent’s purposes. That’s where conspiracy theory comes in.

In general, empirically warranted (vs. speculative), parsimonious (vs. complex), and falsifiable explanations are stronger according to normative standards of causal explanation.

Agreed. But, as the authors acknowledge, conspirators seek to hide the evidence, distracting observers with seemingly simple explanations while also precluding falsification with bucketsful of red herrings strewn along garden paths. And it must be conceded that there are situations in which a conspiracy offers the most concise explanation for the facts on the ground.

Studies have shown that people are likely to turn to conspiracy theories when they are anxious and feel powerless. Other research indicates that conspiracy belief is strongly related to lack of sociopolitical control or lack of psychological empowerment

I acknowledge that with respect to the pandemic I am vulnerable and impotent, potentially amping my motivation for finding someone to blame.

Conspiracy theories may promise to make people feel safer as a form of cheater detection, in which dangerous and untrustworthy individuals are recognized and the threat they posed is reduced or neutralized…

I also acknowledge a longstanding personal mistrust of Trump. He lies, a lot. He’s been accused of other conspiracies, with the accusations supported by what many who’ve investigated the cases regard as a preponderance of the evidence. Would I feel greater control over Trump if I could see through his charades? Would I feel safer upon realizing that a powerful cadre really is out to get me?

…Unfortunately, research conducted thus far does not indicate that conspiracy belief effectively satisfies this motivation. On the contrary, experimental exposure to conspiracy theories appears to immediately suppress people’s sense of autonomy and control…

Like I said.

These same studies have also shown that it makes people less inclined to take actions that, in the long run, might boost their autonomy and control. Specifically, they are less inclined to commit to their organizations and to engage in mainstream political processes such as voting and party politics.

If I believed that Trump wanted me as a casualty of his race-purifying, herd-culling project, would I be less likely to do social distancing and sheltering at home? No, I don’t think so. Would I be less likely to vote against Trump? No, not that either, even though I recognize that voting offers only an illusion of personal autonomy and control.

Conspiracy theories appear to provide broad, internally consistent explanations that allow people to preserve beliefs in the face of uncertainty and contradiction.

The pandemic is fraught with uncertainty. My series of posts have focused on the uncertainties, attempting not to arrive at precise truth but to narrow the confidence intervals. Trump spawns uncertainty and contradiction: his actions and utterances can be subjected to systematic procedures for separating signal from noise.

The epistemic drawbacks of conspiracy theories do not seem to be readily apparent to people who lack the ability or motivation to think critically and rationally. Conspiracy belief is correlated with lower levels of analytic thinking and lower levels of education…

Do I see a drawback in identifying a Trumpian conspiracy for achieving herd immunity? Sure I do. Things would surely go worse for those of us most vulnerable to the virus if we’re specifically targeted for removal in pursuit of national purity and strength of the Fatherland, rather than merely being added to the body count as collateral damage in an attempt to juice the economy and hence Trump’s prospects for reelection.

…It is also associated with the tendency to overestimate the likelihood of co-occurring events and the tendency to perceive agency and intentionality where it does not exist.

I don’t regard myself as susceptible to the conjunction fallacy: I don’t think that Republican crooks are more prevalent than crooks full stop. I am, however, captivated by synchronicities — the improbable conjunction of seemingly independent events. We need to think of ourselves as warriors AND we need to open our country up — when Trump strings together two seemingly contradictory ideas in the same sentence, I’m more likely to find a way to fit them together than to dismiss one or both as empty sloganeering. And I am a sortilege enthusiast, as evidenced in my relying on a random number generator to select the short stories I’ve read and interacted with on this website. Do I interpret these convergences as revelations, or as raw materials for constructing meaning? Hmm… can I say both?

Experimental results suggest that experiences of ostracism cause people to believe in superstitions and conspiracy theories, apparently as part of an effort to make sense of their experience. Members of groups who have objectively low (vs. high) status because of their ethnicity or income are more likely to endorse conspiracy theories. People on the losing (vs. winning) side of political processes also appear more likely to believe conspiracy theories. Conspiracy belief has also been linked to prejudice against powerful groups (Imhoff & Bruder, 2014) and those perceived as enemies. These findings suggest that conspiracy theories may be recruited defensively, to relieve the self or in-group from a sense of culpability for their disadvantaged position.

At last we get to Carl’s loser affinity theory. In coronaworld I’m decidedly of lower status: older, more likely to incur heavy medical expenses without contributing proportionately to the GDP. And I certainly was on the losing side of the 2016 presidential election. Am I looking to justify my societal value by positioning myself as a victim of the powerful ingroup? Sure.

In keeping with this defensive motivation, conspiracy belief is associated with narcissism—an inflated view of oneself that requires external validation and is linked to paranoid ideation.

Well I have been talking a lot about me in this post…

Conspiracy belief is also predicted by collective narcissism—a belief in the in-group’s greatness paired with a belief that other people do not appreciate it enough.

Older is better? Nah. I do believe that the Democrats stand on higher ethical and political ground than the Republicans. Maybe that makes me more prone to attribute evil intent when mere incompetence is to blame.

Experiments show that exposure to conspiracy theories decreases trust in governmental institutions, even if the conspiracy theories are unrelated to those institutions. It also causes disenchantment with politicians and scientists. So far, therefore, empirical research suggests that conspiracy theories serve to erode social capital and may, if anything, frustrate people’s need to see themselves as valuable members of morally decent collectives.

Trump sounds like a prototypical conspiracy theorist. The virus is a conspiracy of the Chinese; high infection rates and body counts and lockdown defenses are a conspiracy of the left. Does he truly believe these crackpot theories? Or are they part of his own conspiracy — acts of verbal legerdemain intended to throw his enemies off the track and under the trolley, techniques for replacing evidence with wild speculation and faith in his authority, ways of neutralizing dissent and consolidating power?

Maybe that’s the real conspiracy here: Trump feigns a conspiracy of herd immunity in order to alienate me from government and to deplete my social capital and self-worth. That unscrupulous bastard!