COVID-19 And What the Data Tells Us

The Punchline: We got it wrong and now we’re largely tracking it wrong…

Did you know the “new cases” in daily reports are actually “old infections newly reported”??? Most of what is reported is old news and not indicative of what is happening or how we are trending.

We must look at “Date of Onset” to do ACCURATE trend analysis, but the CDC stopped reporting onset data in April.

Using our own Data from the CDC, we can see that while 45,000 cases were reported on March 23, in reality we already had 140,000 positive tests that had yet to be reported (date of onset).

And now that we know testing only captured 5–10% of actual infections, based on multiple studies, it appears at least several million (yes Million) were already infected by March 23rd, which were never tested.

The CDC is also now combining PCR & Antibody tests, so the “new cases” could be some of the millions of cases from 4 months ago. NPR called them out on it.

Per Nate Silver, a world-renowned Statistician, the average person might as well ignore the cases being reported, it’s apples and oranges.

We are suffering from data illiteracy in this country — from the Institutions capturing it (like the CDC) to the Politicians making decisions from it, to the Media reporting on it, all the way down to the Public consuming it.

Think of it this way, the CDC now believes (as do I) that ~10% of the U.S. has already been infected (i.e. 32M). So if we tested everyone with antibody tests, we’d have ~30 million new cases added to the report. Clearly we wouldn’t have truly had 30,000,000 new infections in 1 day… but our main sources of news are reporting it that way. If your state/city is using this data, rather than the date of onset or hospitalization data, then they don’t understand how to use data.

As someone who modeled Covid19 in March and accurately predicted it (so far), this is how I believe our reporting should look. *The CDC actually took a similar approach in reporting the 2009 H1N1 Pandemic*

  • Covid19 is serious, but it’s significantly less than we thought
  • Fatality: According to data from the best-studied countries and regions, the lethality of Covid19 is on average about 0.3%, which is about ten times lower than originally assumed by the WHO. The CDC is now estimating ~0.26% in the U.S.
  • Fatality in Context (i.e. Risk): The risk of death for the general population of school and working-age is typically in the range of a daily car ride to work. The risk was initially overestimated because many people with only mild or no symptoms were not taken into account. i.e. we’re capturing most of the deaths but only 5–10% of the infections. Risk varies by age significantly. For kids (particularly toddlers), they are ~20X’s more likely to die from the Flu or Pneumonia than COVID. For those over age 70, the risk of fatality is ~2.5% — this is much higher for those in poor health.
  • Risk & Symptoms: For every 100 people infected ~20-50 of them will show no symptoms (i.e. asymptomatic). Of the symptomatic, ~80% have mild symptoms. Even among 70–79-year-olds, about 60% remain symptom-free. So when accounting for symptomatic + asymptomatic, ~90% have mild or no symptoms. While most won’t know it, for a few, it’s vicious and deadly. *I had it, and it was brutal*
  • Immunity: For the ~30M Americans who already had Covid19, we don’t yet know how effective or how long the antibodies will last. But based on other Coronaviruses, it’s reasonable to assume it’s protective for 6 to 34 months. We also don’t know what % of our population has a strong enough innate immune system to prevent infection in the first place. But up to 60% of the population may already have some level of cross-reactive protection at a cellular level due to contact with previous coronaviruses. So it is possible up to 70% of the U.S. has varying levels of resistance.
  • Comorbidity: is very high. i.e. The median or the average age of the deceased in most countries (including Italy) is over 80 years and only about 1% of the deceased had no serious preconditions. In NYC almost everyone had underlying conditions.
  • Highest Risk: In many states, up to two-thirds of all extra deaths occurred in nursing homes — we clearly failed our most vulnerable 😢
  • The Costs: (Lives vs. Lives) not Economy vs. Lives. We may never know this fully, but up to 50% of all additional deaths may have been caused not by Covid19, but by the effects of the response, i.e. policies & panic. For example, the treatment of heart attacks and strokes decreased by up to 60% because many patients stopped visiting hospitals.
  • The Costs: The number of people suffering from unemployment, psychological problems, suicides, delayed treatment, and domestic violence has skyrocketed. Several experts believe that these may claim more lives than the virus itself. According to the UN, millions of people around the world may fall into absolute poverty and famine, which causes more disease/death. One estimate from professors at Stanford & Duke has calculated we’ve now lost more years of life due to our response than the virus.
  • Data Accuracy: We have gaps everywhere, but “hospitalizations” are probably the cleanest and earliest indicator we have. Everyone wants to talk about Fatalities, and we have work to do there. We’re missing some, but we also have clear instances of over-counting, i.e. gunshot victims counted because they had tested positive. It’s not clear whether we have more instances of over-counting or undercounting — or whether they died from or simply with Official figures usually do not reflect this distinction. We’ll need a strong post-mortem study that looks at these.
  • Data Accuracy: For now the best method we have to cut through the noise is to look at mortality in total, “excess deaths.” And even this depends on what you believe. i.e. if we assume 50% of the excess deaths are from our measures, then we’ve over-counted by 70%. If we assume Covid19 is the only variable at play, then we’ve undercounted by 15%. In any case, our counts are likely “in the ballpark” — i.e. tens of thousands.
  • Baselines & Context: The normal overall mortality per day is about 8,000 people in the US. Influenza mortality per season is up to 80,000. Most of our leading forecasts suggest Covid19 will account for ~5% (150,000) of our typical annual deaths (~3,000,000).
  • Capacity: Many clinics in Europe and the US remained strongly underutilized or almost empty during the Covid19 peak and in some cases had to send staff home. In the U.S. we lost 4M healthcare jobs so far and numerous operations/therapies were canceled, including some organ transplants and cancer screenings.
  • Capacity: At peak, New York City had around 1 in 6 hospital beds open and around 1 in 10 ICU beds open. Hospitals had capacity. Nationally, the CDC reports that “COVID Like Illness” at most represented ~7% of hospitalizations … it’s currently under 2%.
  • Forecasts: Most current forecasts assume we’ll go from ~130K fatalities to 150–200K over the coming months. And using the CDC IFR estimate, this means infections would go from ~30M to ~50M. There is debate about how much higher it will go and the size of the second wave. Some believe we’ll top out between 15–20% of the population infected, whereas others say 70–90%. I’d point out we have never had a pandemic over 20–30%.
  • Sources & Scientific Debate: We’ve  used a long list of great resources to put this all together: WorldoMeter, OurWorldinData, and the CDC. We’ve also found to be one of the better sources on “context” and a balanced discussion for Covid19. If you only follow MSM and Social Media, chances are you may be surprised to learn there is a fair number of seasoned and credentialed dissenters, including Nobel Prize-winning scientists who completely disagree on the approach most of the World’s governments have taken. In fact, one of the Top 100 most cited scholars, a BioMedical Statistician, and Sr. Epidemiologist from Stanford, John Loannidis, wrote a scathing paper and then conducted one of the first seroprevalence studies. He was berated for it (and to be fair, it did have issues). Only now the CDC and almost every serology study are showing he was more right than wrong (on a rate basis), and more accurate than those who criticized him. The question has to be asked, are we listening to the right scientists?
  • Source Accuracy: Most of the predictions have been grossly wrong For example, many models assumed a 20% hospitalization rate, whereas we’re seeing ~1%. The models that influenced UK & US policy predicted 90K deaths by now in Sweden for not locking down, they are closer to 5K. At the center of many of our policies/strategies was Imperial College & Neil Ferguson. He is the same modeler who said 200M people could die from the bird flu (vs. 282 actual) and H1N1 had a fatality rate of 0.4% (actual was 0.026% or 15X’s lower). Plus, whoever Cuomo and DeBlasio were listening to in NYC when they said they needed 30,000 more ventilators within a week or tens of thousands more would die — should lose credibility. We need to start listening to those who are right and stop listening to those who are wrong.
  • Media Accuracy— Unfortunately, many media outlets have failed to report accurately and with context, and yes, in some cases have totally misrepresented the data and/or used images incorrectly. Some used emotional headlines like “cases surging” while cases were actually decelerating from 30% daily growth rates down to 2–4% … and they would quote “more people dead than 9/11,” but don’t mention that more people die EVERY DAY from normal causes than 9/11. I make no conclusions as to whether it’s incompetence or mal-intent. But it’s definitely wrong, misleading, and fear-mongering.


Just remember this while digesting these numbers: these findings are the results of thousands of charts, “facts” given by various government and private healthcare entities, and reams of medical data. Assumptions MUST be made in compiling all this. But we’ve tried to abandon any use of opinion in the preparation of these numbers. True numbers with actual experienced data points are mandatory if we expect any accuracy in results computation.

What we WILL be able to do is build on these numbers as new data and numbers on all fronts are experienced going forward. Projections shown above are estimates, but these estimates are based on past facts and projected going forward using several assumptions drawn for past experiences.

Just know this: COVID19 is no doubt a serious disease. Any medical condition that kills is a horror in itself, and one that kills thousands is hundred times worse. Fortunately, we can all breathe a little easier knowing that it looks certain we will in no way see 1900s Spanish flu numbers with COVID! At least that’s one really good thing we can draw from all these numbers. And there’s no opinion there!

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