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Conspiracy Theories Can Kill

There have always been conspiracy theories, with greater or lesser degrees of plausibility, that are believed by at least a substantial minority of the population. These are generally harmless except when someone with perceived authority gives sanction to such a theory, emboldening people to act upon them. This is exactly what happened on January 6.

The mob that stormed the Capitol consisted of diverse groups, including survivalists, QAnon conspiracy theorists, militias, white supremacists, and anti-Semites. Some of the better prepared groups had murderous or otherwise terroristic intentions, while others were violent only in their unlawful entry and destruction of property. Many more, unaffiliated with any group, were simple thrill-seekers caught up in crowd euphoria, entering the Capitol because everyone else was doing it. (See Elle Reeve’s immersion reporting, CNN, January 7, 2021, 1:00 ET) What they all had in common was anger at a perceived injustice, motivated by a strong belief that the 2020 presidential election had been stolen.

It is not unprecedented for so many to believe a presidential election was stolen. Many still believe this was the case for the 2000 election, though Gore would have still lost under the recount rules its legal team accepted, if these had been implemented. (Chicago Tribune) Some historians hold that the 1960 election was swung to Kennedy by mob votes in Chicago and Texas, though this is unprovable. Nixon, believing this, nonetheless thought it prudent not to contest the election openly, both for his own political future and the good of the country, though he allowed his surrogates to pursue legal challenges that corrected the vote count slightly. There is nothing untoward about aggressive legal challenges to perceived electoral fraud or error. What is unprecedented is for the losing candidate to proclaim publicly that he was defrauded even after all legal challenges have failed.

Trump did not stop even there. He actually advocated blocking constitutional processes by extra-legal means. First, he encouraged state legislatures to refuse to certify the electors’ votes, or even to replace the electors outright. When that failed, he waged a weeks-long pressure campaign on Congress and his own vice president to contest or even reject the electoral votes received, remanding them to the states, that they might choose new electors. (Reportedly, he even considered invoking martial law, and he seems to still be getting advice on this as of yesterday.) Moreover, he urged his followers to rally at the Capitol that day to demand that this be carried out. When the vice president failed to “do the right thing,” many in the crowd naturally believed they had no recourse but storming the Capitol in order to reverse the “stolen” election.

Some of the better organized groups had come to this conclusion weeks earlier, as evidenced by their planning. This is why the role of Trump as an inciter was much more obvious in the preceding weeks than in the content of his January 6 speech. It was in the preceding weeks that he asserted unequivocally that the election was fraudulent, and that he would never concede. On January 4 in Georgia, he gave the most incendiary speech yet, calling the opposition Communists and Marxists who hate this country. By doing this, he was putting them beyond the pale, outside of the polity. This is the rhetoric that lays the groundwork for civil war. Between this and what was discussed on fora such as thedonald.win, it was obvious to me then that a civil war would be attempted. I believed it would fail immediately, based on the usual levels of security at the Capitol for such events.

What was shocking is not that the crowd attempted to storm the Capitol, but that they succeeded. Many past protesters would have loved to do the same if it were possible. Exactly how this security failure occurred remains to be fully investigated. Trump was unfortunate in this momentary success by the crowd, of which he was an avid spectator. By invading the Capitol and threatening the lives of the entire Congress and vice president, the riot rose to the level of insurrection. Trump did not denounce it until it was clear that it would end. Only in the aftermath did he acknowledge there would be a peaceful transition of power. The leader of a failed coup should not complain if the worst penalty he faces is being debarred from office.

If Trump did not exactly “lie” about the election being stolen, since he sincerely believes the falsehood, he is nonetheless guilty of a consistent disregard for truth, clinging to falsified claims that he wishes were true. An example is his repetition of the claim that 139% of people voted in Detroit, which is easily refuted by publicly available data. To believe the election fraudulent, we should have to believe that the notoriously mendacious Trump and his allies are the only truth-tellers, against a conspiracy including:

  • Judges in state and federal courts, including some Trump appointees
  • Election officials of both parties in various states
  • Most major news organizations
  • Numerous poll workers paid near minimum wage
  • Volunteer poll watchers
  • Federal intelligence agencies
  • The U.S. Postal Service

Anyone so conspiracy-minded is impervious to facts, since one can always add the source of any unfavorable facts to the list of conspirators. Such vast conspiracies are credible only to those lacking familiarity with how the election and result canvassing processes work.

All of Trump’s fraud claims are without factual foundation, which is why his lawyers had the good sense not to present most of them in court. There were some state practices that may have been erroneous in law, but misapplication of the law is not generally grounds for disenfranchising voters who followed the published rules. More importantly, no individual or state has standing to sue for such errors unless they can show actual, not speculative, harm.

The one Trump claim that had legal merit was the challenge of the Pennsylvania rule allowing late-arriving ballots, contrary to the legislative will. There were only 10,000 such ballots, not nearly enough to change the electoral outcome, nor did the late ballots break more favorably for Biden than the timely mail-in ballots. The conservative-leaning 3rd Circuit Court of Appeals correctly found the plaintiffs had no standing, and even if they did, they would not be granted relief, due to the lateness of the petition so close to the election. The late ballots were excluded anyway.

Since Trump’s strategy has simply been to conjecture that every conceivable mode of electoral fraud or error actually did occur, space does not permit the refutation of every single claim. That which is freely asserted may be freely denied with equal force. As for those claims which have at least some equivocal factual support, here are some fact checks:

In every election, there are minor instances of error and fraud, but quantity matters. There are no error-free elections, but the errors are generally much too small to affect the outcome with rare exceptions (e.g. Florida in 2000). Sometimes larger errors occur (in the thousands) on election night counts, but those are just first drafts for the purpose of notifying the public, not the official certified count. The initial count is followed by a thorough results canvassing process, during which the more substantive omissions can be identified. This happens in every election, and there was nothing unusual about this one. More importantly, the original records (ballots and machines) are kept available for inspection, so the data isn’t lost.

Enhanced voter ID and better signature verification would only guard against voter impersonation, which is known to be a statistically rare form of fraud. For fraud to occur on the scale Trump claims (hundreds of thousands of votes in a single state), there would need to be the complicity of election officials. Yet even states with Republican election officials have contradicted his claims.

Notably, Trump still repeated crackpot Dominion theories about removal or replacement of machines in his infamous call to the Georgia secretary of the state, along with other factless claims that had been already debunked publicly. In this call, Trump demonstrates himself to be an uninformed, uncritical thinker who only accepts “facts” in accordance with his preconceived conclusion that he could not have lost the election. Yet we are to accept his highly partial testimony over that of the election officials with access to primary data (all of which is monitored by observers of both parties).

More broadly, an examination of the county-by-county results in key areas is consistent with statewide and nationwide trends, adjusted for the known political demographics of each county. There are no stunning outliers in any major city. In fact, Trump did slightly better than expected in some cities, and fared better than expected with higher turnout, but had slightly weaker support than in 2016 in the suburbs, consistent with trends in recent years. That a president with approval ratings consistently below 50% should lose a tight election is hardly an indicator of fraud. Much less should we expect any Democratic conspirators to be kind enough to permit down-ballot Republicans to win their elections even in areas where Trump lost the presidential race.

Anyone still unpersuaded is likely impervious to argument, because they make the cognitive error of starting with a desired conclusion, and then accepting or rejecting data based on conformity with that conclusion. Such errors are common and generally harmless, except when they are actively and avidly reinforced by someone of the President’s stature and public influence. If a John Bircher or Lyndon LaRouche became President, the result would be comparably noxious to public discourse. The problem is more acute when the conspiracy in question is the purported crime of fraudulent government takeover. Thus we have the crazed spectacle of rioters engaging in an actual insurrection while believing themselves to be resisting an imagined one.

Our uncritical, deluded president undoubtedly provided much amusement at times, but foolishness stops being funny when people die, even if that consequence was not directly intended by the fool. There is something to be said for boring, policy wonky, slick-talking politicians. Perhaps the older, more genteel form of mendacity will have to suffice for now, if truth is too optimistic an ideal for democratic discourse. Ironically, the Trumpistas’ “attack on democracy” may itself have been symptomatic of a deep flaw in secular democracy itself, insofar as it makes the will of the people the arbiter of truth.

Methodological Problems in Epidemiology

As much of the world looks to slowly ramp down COVID-19 isolation measures, it remains unclear whether this global social experiment should be considered wise or foolish. The prevalence of infections is < 1% in every country in the world except the microstate San Marino. This is better than projected by most models, and could be interpreted as a success for isolation, an overestimation of the virus's infectiousness, or a natural seasonal effect. This question is not resolvable insofar as it depends on the counterfactual of what would have happened if isolation was not imposed. As mentioned in the last post, spread to 60% of the population with millions of deaths was never realistic. That alarmist scenario relied on a naive application of epidemiological models that have poor predictive ability. Using an SEIR model with the estimated parameters for COVID-19, one indeed gets a grim picture. Yet if one were to insert the parameters for seasonal influenza (R0 = 1.3, avg. incubation period = 2 days, avg. duration of infectiousness = 5 days, mortality rate = 0.1%) into the same model, you would have over 40% infected and 150,000 fatalities in the first year, far more than what occurs in reality. The reproduction rate of a disease depends not only on the duration of contagiousness, but also the likelihood of infection per contact (secondary attack rate) and contact rate. These last two are highly variable by region, social structure, and perhaps even individual physical susceptibility.

Conventional compartmentalized models have poor predictive ability for seasonal influenza, as they do not account for other factors besides herd immunity and isolation that could slow the spread of disease. A Los Alamos study was able to create a model with parameters that fit to past seasonal data and should hopefully have predictive power for future seasons. Such an approach, however, is useless for novel pandemics. As the authors note, these models are all highly sensitive to choice of prior parameters, but we cannot know these until after the epidemic has run its course.

The problem of predictive modeling is exacerbated by the poor quality of public health data, which is often woefully incomplete or inconsistent, with categorizations often driven by policies or other unscientific criteria. Public health systems do a better job of recording the number of infected than they do for those exposed or recovered. Even here they are limited to those who seek medical treatment, and often diagnoses are made by symptoms rather than definitive tests. Cause of death on death certificates is driven by bureaucratically imposed standards. Even in scientific studies, researchers classify subjects according to one or another cause of death, and treat comorbidities as risk factors increasing the chance of death by the primary cause. It would be more rigorous to acknowledge that there is not always a single cause of death, and instead to treat comorbidities as contributing causes by factor analysis. This would let us know the mortality contribution of each disease to the population, but it would remain generally impossible to give a single “cause of death” for each individual.

Some parameters of COVID-19 are fairly well known at this point. The infected are contagious from 48 hours before showing symptoms to 3 days afterward. The secondary attack rate is surprisingly low, only 0.45% (compared to 5%-15% for seasonal flu). Thus the relatively high R0 is attributable not so much to high contagiousness, but to the longer duration of contagiousness, especially while presymptomatic, so that infected people have more contacts while contagious than seasonal flu victims would. The 2009-10 H1N1 pandemic, by contrast, had a secondary attack rate of 14.5%, yet it infected 61 million out of 307 million in the US, just under 20%. It is implausible that COVID-19, with its much lower attack rate, could ever attain a comparable prevalence level.

Why, then, are the death statistics so much higher than would be suggested by the low infectiousness and low prevalence? On the one hand, many jurisdictions, notably New York, have decided to include so-called “probable” COVID-19 related deaths, and most public health data includes no serious attempt to account for comorbidities as causal factors, though they occur in well over 90% of fatal cases. On the other hand, the increase in deaths versus last year in many areas greatly exceeds even this high count, so it could be argued we are undercounting COVID-19 fatalities. The problem here is that many of the excess deaths could be caused not by COVID-19 per se, but by the overloading of medical facilities, resulting in less than immediate critical care. Some of these excess deaths might even be caused by the quarantine measures, as diagnostic and non-emergency medical visits have been cancelled.

It would not be uncommon for the number of deaths to be revised upward or downward by a large factor retrospectively. A year after the H1N1 pandemic, a study suggested that the deaths attributed to H1N1 ought to be revised 15 times higher. Whether H1N1 deaths were undercounted or COVID-19 deaths are overcounted remains to be seen, and is unlikely to be resolved, given the problems of data and methodology we have touched upon.

The truly frightening thing is that major public health policy decisions are made on woefully inadequate data and modeling, which will likely be radically revised after each pandemic passes, and the moment for decision-making is past. Public health officials will always err on the side of caution, but as we have noted in the previous post, this is not practicable for an indefinite period of time. At some point we must be willing to poke our heads out of our caves and assume the risk of living.

After all, as recently as the early twentieth century, people went about their business even while living under the threats of smallpox, polio, and measles, any one of which had higher infectiousness and fatality rates than the current pandemic. By objective criteria, there is nothing exceptional about COVID-19 as an infectious disease. What is exceptional is the post-WWII belief that life should be free from deadly risk, enabled by technological means to perform many service economy jobs remotely.

Overreaction vs. Sober Risk Assessment of COVID-19

COVID-19 was at first believed to be a public health threat on par with SARS, with a mortality rate around 10%. Since then, better data has shown that it has much lower case mortality, comparable to the case mortality of ordinary pneumonia (which is about 1.4%, see here and here). It is a threat only to the elderly and those with pre-existing health problems, again like ordinary pneumonia. Bizarrely, the world’s politicians, public health officials, journalists, and other opinion leaders have instead decided to escalate their reaction, as though unaware of the change in factual reality, or unwilling to admit error.

The most striking thing about the cycle of one-upmanship in overreaction is that the solution is always to curtail freedom. If people are willing to renounce their freedoms over small risks, how easily will governments be able to curtail freedom when there is a more serious threat. As with the exploitation of 9/11, this objective is attained by promoting excessive fear, which short-circuits reasoning even among the educated.

There are two types of factual distortions when making these faulty risk assessments. First, the risk of the new threat is overestimated. Second, already existing risks are underestimated or ignored altogether. These errors combined to create a gross overestimate of marginal risk.

According to a study of 1099 Chinese patients, published in the New England Journal of Medicine, the mortality of COVID-19 is 1.4% of those who test positive. Since at least as many others are asymptomatic and never tested, true mortality is likely 0.5% to 0.8%.

The increased risk of death is mortality times prevalence. In China, prevalence is 1 in 15,000. In Italy it’s 1 in 5000. In the USA it’s 1 in 200,000. In all these nations, the risk of death is less than or equal to dying in a car accident. So driving a car instead of taking public transit to avoid COVID-19 may actually increase your risk of death. In any event the marginal risk, positive or negative, is miniscule. Someone genuinely worried about this level of risk should avoid driving or riding in an automobile.

Suppose that containment fails, as seems likely, and further that this new strain becomes as prevalent as other forms of flu, so that COVID-19 should have about 2% prevalence, i.e. 1/5 of flu cases (10% prevalence). The increased risk of death, compared with average flu mortality of 0.1%, would be 1/50 * 1/200 = 1/10,000. Here I assume mortality of 0.6% for COVID-19 vs 0.1% for average flu. This is to compare apples to apples, since the flu figures include (estimated) unreported cases. Most sites get this wrong, and compare the flu figures for all cases against the COVID-19 figures for positively-tested patients only.

This figure of 1 in 10,000 is likely overstated, since it excludes consideration that many of the “excess deaths” are in people with preexisting conditions who would have died of something else shortly. This pessimist scenario, in a nation of 300 million, would result in 30,000 excess deaths.

Preventing such a scenario is certainly worthy of strenuous measures, but not without limit. One must also consider the effectiveness of such measures, and the cost in terms of public health. Sinking the economy and depriving people of months of income may cause comparable excess deaths, especially if people are prevented from getting cancer screenings as some health systems are recommending. 30,000 excess deaths represents a 50%-60% increase in annual flu deaths, but there are other bigger killers, both those existing, and those we may create by excessive reaction to this new public health risk. A simplistic attitude that “no measure is too big” fails to be a rational form of risk management.

At some point, we may have to grapple with the possibility that containment does not work. The USA may not have the same legal means at its disposal to compel quarantine that may exist in the more centralized authority of Italy or China. Also working against containment is the low mortality rate, the possibility of carrying the virus in mildly symptomatic and asymptomatic individuals, and the unusually long incubation period. Indeed, once the virus proliferates beyond a certain threshold, containment of COVID-19 would seem to be as impracticable as containing the common cold or the flu. While we may not have reached that point yet, we must recognize the possibility that at some point continued efforts at containment are not worth the cost, simply because of their low probability of success.

The reactions have been so rapid, and so outpace the actual facts on the ground, even when the number of cases is statistically negligible, that we cannot consider them to represent the result of careful deliberation. Rather, as in the closure of multiple universities on the same day, it is more like the imitative behavior of a panicked and stampeding herd. In such a climate, it may take more courage to do less than to do more. It is very easy to say that money is no object and leave the private sector to pay for government largesse. Those of us who have to make budgets and do not have the power to print money may have a different perspective. This is not a mere economic problem, for it can swiftly transform into a humanitarian catastrophe at least as great as the one ostensibly being prevented.

Update: 28 March 2020

Misinformation continues to spread. First, there is the oft-repeated claim that, absent our draconian containment measures, the virus would spread to 60% of the population, resulting in millions of deaths in the US. This is a cumulative figure over two or three seasons, ignoring the near-certain fact that pharmaceutical measures and natural antibodies will reduce the virulence of the disease by next season. It is effectively an impossible scenario, and again is not comparing apples to apples, as the seasonal flu death figure is annual.

Second, the mortality rate continues to be overstated. As testing becomes limited only to those who are hospitalized, the “mortality rate” of tested positives will increase, since you are actually measuring only the most severely affected subset of cases. Worse, in Italy, anyone who dies with coronavirus is counted as a death due to COVID-19, although 99% of fatal cases had comorbid conditions. The best data from South Korea, which has far more aggressive testing, currently points to a mortality rate of 0.7%. Using this figure as an upper bound and applying the more exact population of 327 million for the US yields a “pessimist” scenario of 39,000 excess deaths this season. We may get there anyway as outright containment has proven ineffective, and we are now hoping only for mitigation, i.e., slowing the spread.