Never before has the public had access to so much data on a virus and its effects. For two years, data festooned the daily papers. Dozens of websites assembled it. We were all invited to follow the data, follow the science, and observe as scientists became our new overlords, instructing us how to feel, think, and behave in order to “flatten the curve,” “drive down cases,” “preserve capacity,” “stay safe,” and otherwise deploy all the powers of human will to respond to and manipulate disease outcomes.
We could watch it all in real time. How beautiful were the waves, the curves, the bar charts, the sheer power of the technology. We can look at all the variations and the trajectories, assemble them by country, click here and click there to compare, see new cases, total cases, unvaccinated and vaccinations, infections and hospitalizations, deaths in total or death per capita, and we could even make a game out of it: which country is doing better at the great task, which group is better at complying, which region has the best outcomes.
It was all quite dazzling, the power of the personal computer combined with data collection techniques, universal testing, instant transmission, and the democratization of science. We were all invited to participate from our laptops to bone up on statistics, download and look, assemble and draw, manipulate and observe, and be in awe of the masters of the numbers and their capacity for responding to every trend as it was captured and chronicled in real time.
Then one day, writing at the New York Times, reporter Apoorva Mandavilli revealed the following:
For more than a year, the Centers for Disease Control and Prevention has collected data on hospitalizations for Covid-19 in the United States and broken it down by age, race and vaccination status. But it has not made most of the information public…. Two full years into the pandemic, the agency leading the country’s response to the public health emergency has published only a tiny fraction of the data it has collected, several people familiar with the data said.
Kristen Nordlund, a spokeswoman for the C.D.C., said the agency has been slow to release the different streams of data “because basically, at the end of the day, it’s not yet ready for prime time.” She said the agency’s “priority when gathering any data is to ensure that it’s accurate and actionable.”
Another reason is fear that the information might be misinterpreted, Ms. Nordlund said.
At the appearance of this story, my data science friends who have been digging through the databases for nearly two years all let a collective: argh! They knew something was very wrong and had been complaining about it for more than a year. These are sophisticated people at Rational Ground who keep their own charts and host data programs of their own. They have been curious all along about the exaggerations, the poor communication regarding the gradients of risk, the lags and holes in the demographic data on hospitalization and death, to say nothing of the strange way in which the CDC has been manipulating presentations on everything from masking to vaccination status and much more.
It’s been a strange experience for them, especially since other countries in the world have been absolutely scrupulous about collecting and distributing data, even when the results do not comport with policy priorities. There can be little doubt, for example, that the missing data bears on the issue of vaccine effectiveness and very likely demonstrates that the claim that this was a “pandemic of the unvaccinated” is completely unsustainable, even from the time when it was first made.
In the New York Times story, many top epidemiologists were quoted expressing everything from frustration to outrage.
“We have been begging for that sort of granularity of data for two years,” said Jessica Malaty Rivera, an epidemiologist and part of the team that ran Covid Tracking Project, an independent effort that compiled data on the pandemic till March 2021. A detailed analysis, she said, “builds public trust, and it paints a much clearer picture of what’s actually going on.”
Well, if public trust is the goal, it’s not going so well. In addition to the failings revealed here, there are many other questions concerning cases and whether and to what extent the PCR testing can really tell us what we need to know, to what degree did the misclassification problem affect death attribution, and so much more. It seems that with each month that has gone by, what seemed to be these beautiful pictures of reality have faded into a murky data quagmire in which we don’t know what is real and what is not. And ever more, the CDC itself has urged us to ignore what we do see (VAERS data, for example).
Dr. Robert Malone makes an interesting point. If a scientist at a university or a lab is found to have deliberately buried relevant data because they contradict a preset conclusion, the results are professional ruin. The CDC, however, has legal privileges that allows it to get away with actions that would otherwise be considered fraud in academia.
There are many analogies between economics and epidemiology, as many have noticed over the last two years. The attempt to plan the economy in the past has suffered from many of the same failures as the attempt to plan a pandemic. There are collection problems, unintended consequences, knowledge problems, issues of mission creep, uncertainties over causal inference, a presumption that all agents obey the plan when in fact they do not, and a wild pretense that planners have the necessary knowledge, skill, and coordination required to presume to replace the decentralized and dispersed knowledge base that makes society work.
Murray Rothbard called statistics the Achilles heel of economic planning. Without the data, economists and bureaucrats couldn’t even begin to believe they could achieve their far-flung dreams, much less put them into practice. For this reason, he favored leaving all economic data collection to the private sector so that it is actually useful for enterprise rather than abused by government. In addition, there is simply no way that data alone can provide a genuine full picture of reality. There will always be holes. It will always be late. There will always be mistakes. There will always be uncertainties over causality. Moreover, all data represents a snapshot in time and can prove extremely misleading with changes over time. And these can be fatal for decision making.
We are seeing this play itself out in epidemiological planning too. The endless streams of data over two years have created what Sunetra Gupta calls “the illusion of control” when in fact the world of pathogens and its interaction with the human experience is infinitely complex. That illusion also creates dangerous habits on the part of planners, which we’ve seen.
There was never a reason to close schools, lock people in their homes, block travel, shut businesses, mask kids, mandate vaccines, and so on. It’s almost as if they wanted human beings to behave in ways that better fit their own modeling techniques rather than allow their knowledge base to defer to the complexity of the human experience.
And now we know that we’ve been denied information that the CDC has kept in hiding for the better part of a year, undoubtedly to serve the purpose of forcing the appearance of reality to more closely conform to a political narrative. We only have a fraction of what has been accumulated. What we thought we knew was only a glimpse of what was actually known on the inside.
There is no shortage of scandals associated with pandemic policy over two years. For those who are interested in finding out precisely what caused the lights to be dimmed or even turned out on modern civilization, we can add another scandal to the list.