An international comparison of the second derivative of Covid-19 deaths [pdf]

thanks. there are a couple other typos I noticed this morning as well. shouldn't HN with Bourbon.

Define rigor.

In context I suspect you mean something like "treat data as objective." I can go to France and pickup and hold the literal kilogram. U can't do that, yet, with people's brains to the level needed for psych measurement.

Personally, I prefer social sciences and epi methods (get economics out of here...) Because they are more transparent about the role of the researcher and the limitations of their data. They don't bluntly trust it...the engineers I work with largely do. They assume data represents truth and is largely without meaningful error.

I am not...however i am married to one. The ranting recently is kind of fun to listen too. My partner is working on covid. I learn a lot, I'm no expert in epidemiology...but...I also do/teach statistics in a biology field. My degrees are is in mechanical engineering and education statistics. (And that's probably enough info to out me to any real life friends in HN ::waves::)

Basically the problem is the authors don't know enough about the data they are getting to run analysis on it. They are not clear, and absolutely need to be clear, on all things data for this paper to work.

Specific examples:

Are the websites they gather from reporting presumptive positives or confirmed positives?

Are they Getting information on when specific deaths occurred? Or are they using the latest update time, and the total number of deaths to date and then assuming a connection?

Are countries accurately reporting? Are they even capable of accurately reporting?

Are they testing enough (all) of the dead or are they relying on presumptive positives?

It's a data thing. Epidemiology is really really good at high quality data and analysis...but that takes time, more time than we have had to do good science during a crisis. It's why you don't do brain surgery in an ER. They are also really good about not over interpreting data...because data can be hinky.

One of the biggest things people in my undergraduate stats course cover with is specificity and sensitivity...false positives and negatives exist. That gets more complex when you consider that when talking about true positives versus false positives you have to have a gold standard test to reference against. So largely you are looking at one test versus another, even if one of those tests is really accurate, or pathology based, then things take time. That test doesn't typically produce a truly binary result but some chemical threshold that we treat as a binary. Decisions at each of those changes in data representation affect your outcomes. Then we talk about how it's impacted by prevalence and I try and teach them ROC curves and someone tells at me about how if you test positive for a disease you have that disease. (Seriously this happens about once a semester)

Where the students are really struggling isn't math, it's philosophical. The idea that a test result isn't objective truth is kinda bonkers the first time you encounter it. Especially for my engineers. I jokingly talk in class about how it's not a math class it's an estimation and bs detection class. I've seen this so much with covid...test results are not perfectly accurate. You can, and often should, bias them towards certain clinical goals. The first tests from cdc were problematic because they were overly sensitive (or contaminated...don't know yet, probably just too sensitive).

The reality more than anything is that there is so much miscommunication about covid, unavoidable communication, that I'm not sure anything is fully trust worthy yet. Weeks ago one of the first rapid studies published in JAMA ( or was it NEJM?) By a German group was submitted, reviewed, published, and withdrawn in about a week. People use words like 'positive test result' and can mean fundamentally different things when taking to each other and don't realize it.

And apologies I'm not following my normal anal hn comment style and citing this a bunch. Responding from my phone.

"No decision is the worst decision"

No, it only requires that one of the methods is slightly effective.

Not taking any of these measures is highly effective at failing to control the virus. We're currently seeing it play out across Europe and the US.

Or that they have significant additional effect over social distancing alone, which we won’t know for some time yet.

I've seen very little coverage that really uses the term exponential in a way that suggests those reporting know the implications of that. I've seen one plot with a logarithmic y-axis, and that was in the Guardian some time ago. From it, it appeared a crude heuristic could be drawn that from the point that lockdown is implemented, the cumulative deaths appear to grow between two and three orders of magnitude.

I've been grabbing the data put out by Johns Hopkins [0] for the last few days to update some plots in a very hacky Jupyter notebook.

Crucially, the plots are normalized to (estimated) populations and the y-scale is logarithmic. I've put them on github in case anyone else is interested [1].

[0] https://github.com/CSSEGISandData/COVID-19

[1] https://github.com/dpwm/covid19-analysis/blob/master/Coronav...

edit: Clarified that though exponential growth is widely used, the implications of that are not followed up. It's past my bedtime!

yep. My first thought was why the hell they didn't they use the word acceleration for better readability.

It's not an exponential. It's a shifted sigmoid.

A constant 2nd-derivative means quadratic growth, not exponential.

I'm bit confused by the graphs, because they show the 2nd-derivative going to 0. But to stop this thing, we actually need to decelerate, eg going negative on the 2nd-derivative.

You can’t compute the second derivative of data from May 2020 when it is currently March 2020. Where did the data come from?