Underdispersion in the Covid19 case/death numbers may suggest data manipulations | Hacker News Reader

Underdispersion in the Covid19 case/death numbers may suggest data manipulations

medrxiv.org

58 pointsr72133 comments

The NYTimes has also been comparing death statistics with Covid deaths. They've noticed that many places have a higher incidence of deaths, but not a corresponding incidents of Covid cases. (In other words, Covid deaths are getting underreported.)

NYTimes site tracking this: https://www.nytimes.com/interactive/2020/04/21/world/coronav...

> In other words, Covid deaths are getting underreported.

This may be true, but excess deaths aren't necessarily covid deaths.

We turned our society upside down for more than a year. Lots of other things might have been affected by that. You need to account for increases in suicides, pedestrian fatalities, untreated medical conditions and many other possible causes before you can confidently show underreporting.

(Not that I know that any one of those stats did go up, but I don't know that they didn't either.)

Occam's razor suggests the 900k excess deaths are likely caused by the virus with the high mortality rate, not the lockdowns - particularly when economic slowdown is typically correlated with a decrease in all-cause mortality.

Given that we have 900k or so excess deaths, and we have identified 700-900k people who have died from covid, seems difficult to say "we can't really know"

I think some countries are absolutely underreporting (either intentionally or due to poor infrastructure), but in the US in the last 10 months of 2020, the New York Times only finds 68,700 excess deaths (roughly 3% of all deaths) not attributed to COVID. A 3% increase could easily come from marginal increases in other causes.

Absolutely agree - I thought you were making a stronger claim than you were.

> (Not that I know that any one of those stats did go up, but I don't know that they didn't either.)

In 2020, the US had a spike in deaths by:

* heart disease

* unintentional injuries

* Alzheimer's disease

* diabetes

Roughly 1/3 of the excess deaths that year were not caused by covid: https://jamanetwork.com/journals/jama/fullarticle/2778234#24...

I don't know if we have 2021's numbers yet, but it looks like a lot of people are expecting deaths by cancer to spike in 2021 or 2022.

1. this is a preprint

2. they're comparing Russia (and other countries) to the USA, noting their covid mortality data has less variation than expected, therefore underdispersion, therefore undercounting and manipulation. (lmk if i got that right)

> We additionally tested all 85 Russian federal regions as well as all 60 public health jurisdictions in the USA. In Russia, 82 regions out of 85 were flagged for underdispersion ... In contrast, for USA jurisdictions, not a single one showed underdispersion

edit- too close to battle lines, apologies and peace be with you today

Your comment makes no sense but I take it you are trying to make some analogy between covid lockdowns and the killing of 11+ million people by the Nazis?

e: and now I get downvoted by people who didn't even see the original comment :)

your words are an escalation, I did not say what you just said, period.

The evidence of manipulation is pretty evident: look at the death statistics [0] for the periods around March - June 2021 and June - September 2021. It's flat, a tad under some round number.

It's a shame, really.

[0] https://www.worldometers.info/coronavirus/country/russia/

Before people start upvoting this because they think conspiracies are at play and the title is salacious, read the abstract: they're proposing a statistical test and validating against countries with known undercounting. This is a methods paper:

> We suggest a statistical test for underdispersion in the reported Covid-19 case and death numbers, compared to the variance expected under the Poisson distribution. Screening all countries in the World Health Organization (WHO) dataset for evidence of underdispersion yields 21 country with statistically significant underdispersion. Most of the countries in this list are known, based on the excess mortality data, to strongly undercount Covid deaths. We argue that Poisson underdispersion provides a simple and useful test to detect reporting anomalies and highlight unreliable data.

A lot of countries aren't counting Covid deaths because they're poor and don't have good health systems. The title should really be changed to something less click-baitey, like "Statistical method for detecting undercounting of Covid-19 deaths"

From the Author:

>I cannot think of any honest data collection issue that would result in such underdispersion. I believe this does automatically mean some amount of data tampering (which may not necessarily have malicious intent though; but for most countries on the list I suspect it does).

The title is fine as it is. It's not click baity at all, and it accurately reflects the study authors's views. I mean it is exactly the title of the study after all.

"We believe that the most likely explanation for observed underdispersion patterns is deliberate data tampering,"

No, the paper identifies more countries than that. That's what makes the article interesting.

> Most of the countries in this list are known, ...

It literally does not, except in the sense that it ran the method on a list of countries. From section 2.3:

> Overall, 8 out of 10 countries with the highest undercount ratios in the World Mortality Dataset at the moment of writing demonstrated statistically significant underdispersion in our analysis.

Translation: they ran it against a list of countries with known "undercount ratios", and found that 8/10 had high scores. Also:

> The correlation of underdispersion index to undercount ratio was 0.40 (Figure 3).

This is a poor overall correlation, and they do no sensitivity / specificity analysis so you can't jump to the conclusion that a score is meaningful. They make the argument that it works better for the countries with the highest undercounts...but if you know the countries have a high undercount you already know the answer by other methods. And even there, 80% sensitivity isn't that great.

> Most of the countries in this list are known, based on the excess mortality data, to strongly undercount Covid deaths.

I take great issue with the word *known* up above.

Somebody would need to do a deep dive into this data, but I'd say that this study is alarmingly starting with a premise that is not proven and may be completely false.

If there are excess deaths in poor countries, are they likelier to come from Covid or economic devastation imposed by things like lockdowns, cutting off tourism income, and other economic restrictions? In rich countries, the average person was not close to not being able to acquire a meal at the start of Covid. In poor countries, even in good times being able to put food on the table is a real concern for average people, let alone after 2 years of super limited tourism and various lockdowns and economic restrictions that worsened many peoples' already precarious situations.

> I take great issue with the word known up above.

It's a quote from the abstract. I didn't write it.

> Somebody would need to do a deep dive into this data, but I'd say that this study is alarmingly starting with a premise that is not proven and may be completely false.

This paper introduces no new data. It takes an existing data set, and runs a statistical analysis on it.

This is not really discussed (for somewhat obvious reasons), but economic decline is associated with a decline in all-cause mortality, not an increase.

The metrics and data collection around a subject so intensely tied to politics as Covid case/death numbers is bound to experience some measure of data manipulation. Especially when a large amount of funding and national prestige is at-play. To me, this is would be one of those perfectly believable conspiracy theories.

I guess I still think it's interesting as a methods paper? It's also useful to have different sources of evidence about COVID underreporting.

I agree that it seems to assume too much malice as a reason for underreporting in general though.

It's sort of interesting (it actually doesn't seem to be very good, with an R2 of 0.4 and 80% sensitivity at the high end), but I can pretty much guarantee from the comments here that people have no idea what it actually says. Just look at the sibling comments here -- most are about whether or not countries are under-reporting deaths.

This is just more fuel for the eternal debate about Covid being bad.

As discussed in the paper: there are multiple known causes for undercounting, and this analysis, by design, only catches one of them. I.e. the two main causes for undercounting would be insufficient counting resources and deliberate manipulation, and this method is designed to detect only deliberate manipulation. For such an analysis, you would expect neither extremely high correlation nor extremely high sensitivity.

I agree it's not the best paper, and there's lots of unrealistic assumptions being made, but I still liked to read it. If nothing else it's useful to see how these models map or don't map onto other sources of information.

I also agree though that it would be nice if these sorts of things were approached differently, more from the perspective of epidemiological modeling or something.

Hold on. They arbitrarily pick a model and decide that all data that goes against the model is fake. This is pure garbage.

My grandpa's death was declared due to COVID-19. He was vaccinated.

He went to the hospital after falling and breaking bones. He was in his 80s, was obese, had diabetes, and a cocktail of other problems.

I suppose you could say it was COVID because the pneumonia killed him.

One thing is certain, the data has been manipulated.

In fact, covid was very heavily politicized and censored. Virtually any allegation of 'manipulation' is going to fall on both sides.

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