Random Testing and the Coronavirus: Evidential Limits by Professor John Ioannidis By Brian Simpson

     Here is evidence that the coronavirus is not another black death, dangerous to certain sectors of the population, but still so widespread that many people have antibodies to it and did not know until randomly tested:
  https://www.nytimes.com/2020/04/21/health/coronavirus-antibodies-california.html

“Two new studies using antibody tests to assess how many people have been infected with the coronavirus have turned up numbers higher than some experts had expected. Both studies were performed in California: one among residents of Santa Clara County, south of San Francisco, and the other among residents of Los Angeles County. In both cases, the estimates of the number of people infected in those counties were far higher than the number of confirmed cases. The studies, conducted by public health officials and scientists at Stanford University and the University of Southern California, have earned the ire of critics who questioned both the recruitment methods and the analyses. In the Santa Clara County study, researchers tested 3,330 volunteers for antibodies indicating exposure to the virus. Roughly 1.5 percent were positive. After adjustments intended to account for differences between the sample and the population of the county as a whole, the researchers estimated that the prevalence of antibodies was between 2.5 percent and a bit more than 4 percent. The county’s population is 1.9 million. That means that 48,000 to 81,000 people were infected with the coronavirus in Santa Clara County by early April, the investigators concluded. In Los Angeles County, researchers conducted tests at drive-through sites and at participants’ homes and estimated that 2.8 percent to 5.6 percent of the county’s adult population carried antibodies to the coronavirus. There are 10.4 million people in Los Angeles County. If accurate, that would mean that 220,000 to 442,000 residents had been exposed. By comparison, only 8,000 cases had been confirmed in the county by early April, when the testing was done. While finding volunteers was not difficult, it’s not clear how representative they were of the populations at large.

     Of course, there is always the problem of establishing representativeness of the larger, general population, but the study does raise numerous questions.  For example, the case fatality rate of the flu is calculated based on a random testing of the population, but with the coronavirus, the fatality rate has been calculated based upon hospital attendance of people seeking treatment. The hospitals, at least in the US, had been recording any deaths that could plausibility be from Covid-19, as being from the coronavirus, which as many academics have pointed out, will  inflate the statistics.

     As pointed out by Professor John Ioannidis of Stanford University, someone who has a strong research background in criticising the bad use of statistics in the sciences, including the biomedical field, there are methodological problems with the reliability of Covid-19 data:
  https://www.statnews.com/2020/03/17/a-fiasco-in-the-making-as-the-coronavirus-pandemic-takes-hold-we-are-making-decisions-without-reliable-data/

“The data collected so far on how many people are infected and how the epidemic is evolving are utterly unreliable. Given the limited testing to date, some deaths and probably the vast majority of infections due to SARS-CoV-2 are being missed. We don’t know if we are failing to capture infections by a factor of three or 300. Three months after the outbreak emerged, most countries, including the U.S., lack the ability to test a large number of people and no countries have reliable data on the prevalence of the virus in a representative random sample of the general population. This evidence fiasco creates tremendous uncertainty about the risk of dying from Covid-19. Reported case fatality rates, like the official 3.4% rate from the World Health Organization, cause horror — and are meaningless. Patients who have been tested for SARS-CoV-2 are disproportionately those with severe symptoms and bad outcomes. As most health systems have limited testing capacity, selection bias may even worsen in the near future. The one situation where an entire, closed population was tested was the Diamond Princess cruise ship and its quarantine passengers. The case fatality rate there was 1.0%, but this was a largely elderly population, in which the death rate from Covid-19 is much higher. Projecting the Diamond Princess mortality rate onto the age structure of the U.S. population, the death rate among people infected with Covid-19 would be 0.125%. But since this estimate is based on extremely thin data — there were just seven deaths among the 700 infected passengers and crew — the real death rate could stretch from five times lower (0.025%) to five times higher (0.625%). It is also possible that some of the passengers who were infected might die later, and that tourists may have different frequencies of chronic diseases — a risk factor for worse outcomes with SARS-CoV-2 infection — than the general population. Adding these extra sources of uncertainty, reasonable estimates for the case fatality ratio in the general U.S. population vary from 0.05% to 1%.

Yet if the health system does become overwhelmed, the majority of the extra deaths may not be due to coronavirus but to other common diseases and conditions such as heart attacks, strokes, trauma, bleeding, and the like that are not adequately treated. If the level of the epidemic does overwhelm the health system and extreme measures have only modest effectiveness, then flattening the curve may make things worse: Instead of being overwhelmed during a short, acute phase, the health system will remain overwhelmed for a more protracted period. That’s another reason we need data about the exact level of the epidemic activity. One of the bottom lines is that we don’t know how long social distancing measures and lockdowns can be maintained without major consequences to the economy, society, and mental health. Unpredictable evolutions may ensue, including financial crisis, unrest, civil strife, war, and a meltdown of the social fabric. At a minimum, we need unbiased prevalence and incidence data for the evolving infectious load to guide decision-making. In the most pessimistic scenario, which I do not espouse, if the new coronavirus infects 60% of the global population and 1% of the infected people die, that will translate into more than 40 million deaths globally, matching the 1918 influenza pandemic. The vast majority of this hecatomb would be people with limited life expectancies. That’s in contrast to 1918, when many young people died. One can only hope that, much like in 1918, life will continue. Conversely, with lockdowns of months, if not years, life largely stops, short-term and long-term consequences are entirely unknown, and billions, not just millions, of lives may be eventually at stake. If we decide to jump off the cliff, we need some data to inform us about the rationale of such an action and the chances of landing somewhere safe.”

     That wise advice seems to have been ignored until recently. Clearly, we need to know how widespread Covid-19 is in the general population by random testing of representative samples of people, before any more major world-breaking decisions are made. This crisis has shown the severe limitations of many so-called experts, who seemed to have panicked like rabbits instead of being the clinically detached objective scientists like Professor Ioannidis.