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Comment by dan-robertson | Hacker News Reader

If you look at test results you probably won’t learn much. There will be far fewer tests than cases and most cases will never be tested. It seems more likely that you’ll just be measuring some other effect of how testing is done, for example if the number of tests per day ramps up slowly (and non exponentially) then it seems likely that you will observe Rt to be decreasing (as the number of confirmed cases isn’t shooting up), especially if people are mostly only tested when they turn up in hospital and are expected to be positive. I don’t know how much the author’s model accounts for that but I find the confidence intervals very unconvincing. I would expect them to be much larger.

This study tries to estimate Rt in several European countries from the death rates though there are issues with those statistics too: https://imperialcollegelondon.github.io/covid19estimates/

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I saw an interesting alternate statistics where you measure the overall mortality rate and compare to previous years. EU only, but something like this might exist elsewhere as well:

http://euromomo.eu/

The data lags behind a bit, but since it measures total number of deaths, it doesn't matter if different countries measure coronoa-related deaths differently. Everyone measures actual deaths. The numbers are hard to fudge for politicians who wants to boost their results.

As a bonus, this also measures how your society is handling the pandemic overall. We know that people will die of the lockdowns as a second-order or third-order effect, and this way we can measure that as well.

The lockdowns will also reduce deaths from other contagious diseases and accidents, particularly car crashes.

Yes.. although excess mortality is the thing that we'll look at in retrospect to gauge the overall human cost of this disease, I don't think it is all that useful for measuring "how well is containment working" in real time. Too many confounding factors (both positive and negative) as you mention.

Personally I think the best metric is one of the simplest -- the raw percentage of positive tests. This is because it simultaneously captures two important factors: if the percentage is high it means that either you are finding the virus all over the place OR it means your testing capacity is so low that you only are testing highly probably cases. If either of these are true, you should not think about reopening.

There is also category of people who did not died of corona, but would not died had there been no corona epidemic. Hospital being overflown means that many people are not getting same care as they would had there been ventilators, drugs and doctors available.

Curiously, hospitals have seen a stark drop in heart attacks, strokes, and other common issues, so this may not actually be a very large number.

https://www.nytimes.com/2020/04/06/well/live/coronavirus-doc...

Best data is excess mortality.

Many deaths won't be attributed to covid19 but still caused by it.

Here an explanation:

https://twitter.com/firefoxx66/status/1249996541424816128

Excess mortality strikes me as very poor data. It seems very unlikely that, during a once-in-a-century pandemic with completely unprecedented severe lockdowns, we should expect all-cause mortality to be close to the baseline.

That is not what we see. Mortality for 65+ is way above the baseline because of covid19.

Sorry, what isn't what we see? I don't think there's a reasonable baseline at all; I think we have no idea what mortality "should" look like, in a world where everyone's under house arrest and afraid of hospitals. You can argue it's lower because people are taking less risks, higher because they're under more stress, lower because they're eating out less, higher because they don't get hospital treatment they need.

Belgium has this specifically for our country [0]. It has a 4 week lag, but they've started to include interim data in the weekly updates by the national bureau for epidemics. The last weekly update included the "excess deaths" compared to previous years: [1]

   Week      Monday        Observed  Expected    %         Deaths
                           Deaths    Deaths      Excess    Per 100K
   
   2020-W11  09/03/2020    2,220     2,302       -3.6      19.4
   2020-W12  16/03/2020    2,531     2,275       11.3      22.1
   2020-W13  23/03/2020    3,116     2,249       38.5      27.3

In other words: in the last week of March, we had 38.5% more deaths than usual. Or 7 extra deaths per 100K inhabitants. That's terrifying.

Side note: Belgium is one of the only countries that includes deaths in carehomes and at home. The deaths in carehomes is almost half of all covid-19 deaths here.

[0] https://epistat.wiv-isp.be/momo/

[1] https://epidemio.wiv-isp.be/ID/Documents/Covid19/COVID-19_Da... (NL or FR only, sorry)

How do you interpret a zero negative excess death rate? Sweden has over 1000 Covid attributed deaths, but almost no difference in weekly deaths compared to the same weeks in 2019. Death numbers are extremely noisy and especially in the flu season with the flu one year perhaps twice as deadly as the previous year.

Covid restrictions have also reduced (a lot) some other seasonal diseases like flu and noro/caliciviruses, so you get an effect in the other direction too. When those that die from Covid are in the highest age groups it's not unthinkable that a part of them would have been killed by the flu too, only the flu didn't come this year because of Covid. Perhaps that's why we have a lot Covid deaths but no excess deaths? Who knows. It's too noisy and has too many dependencies to be very useful I think.

Less people driving?

Didn't think about that. Looking at figures it's less than 6/week killed in traffic, while all deaths is 1700/week, so it's unlikely to make a dent. Easter is probably one of the worst traffic weeks, but even then it's probably not enough for a difference.

Also perhaps fewer elective medical operations, which result in fewer iatrogenic deaths (3rd leading cause of death, at least in the US).

This is a good candidate. Although some of this effect might be countered by people dying because they didn't get elective treatment.

Regardless: when you change pretty much every parameter of society (risk behavior, driving, crime, health care, ...) I don't understand how one can use the overall death statistics and try to attribute them to Covid.

I think the elective treatment theory is a better point than my driving guess. If you have a disease and your options are to get surgery or take medicine, you are probably being pushed towards non-surgical options, even if that would have overall worse results on average. This might ultimately raise the mortality rate, but would temporarily depress it.

Anecdotally, I definitely find myself trying to be safer right now. I have avoided ladder work on my house and been super careful when using a knife. I just do not want to go to a hospital right now.

I think looking at the overall death statistics are super interesting though. If traffic deaths fall, that is in some ways a byproduct of Covid. If more cancer patients die, that is also a byproduct. All of this goes into factoring how much damage and protection both the disease and the quarantine orders have caused.

The idea is that if your country/region doesn't have excess total deaths, then whatever pandemic restrictions and policy you've implemented is good enough.

To make an absurd example: Duterte in the Philippines has allegedly ordered the military to shoot at people who violate curfew. That helps bring corona-related deaths down drastically, but if your population is dying because you're shooting them or they're starving to death because they can't leave their homes, that's obviously not a good whole-society policy.

And that's why staring at only the corona-related numbers is wrong.

I'm also following the debate in Sweden, which has less restrictions de jure than its neighbours, but people are de facto self-isolating and keeping their distance to almost the same degree. There are lots of people in Sweden screaming that government absolutely must impose a stricter lockdown because the corona-related numbers are worse than in Denmark and Norway, who have stricter lockdowns, therefore Sweden should also follow suit.

But if Sweden doesn't have excess mortality, if Sweden's total mortality rate is the same as Denmark and Norway, while Sweden's economy is less restricted, then I would argue that Sweden is doing the right thing. Or at least not doing the wrong thing. :-)

> The idea is that if your country/region doesn't have excess total deaths, then whatever pandemic restrictions and policy you've implemented is good enough

I disagree with this inference. Different countries have different demography, different weather, different cultural habits, different infrastructures, etc. On top of that, we still don't know how many people would only have benign symptoms when they are infected.

I would refrain anyone to compare pandemic restrictions with a correlation in total death of a country. There are simply too many unknowns, both politically and scientifically

A GP in Hong Kong is attempting to study this across 1000 people, which is only marginally fewer than the number of cases in Hong Kong as of today (1013 confirmed cases): https://www.otandp.com/blog/estimating-the-longitudinal-sero...

Hong Kong government is also measuring the real time effective reproduction number: https://chp-dashboard.geodata.gov.hk/covid-19/en.html (click on the arrows at the bottom to scroll left and right through various dashboards)

That HK government website is amazing.

You're spot on. Kevin's idea and graphs are nice but they rely on a flawed hypothesis, namely that all states test the same way, taste the same amount per capita etc. They don't. So our graphs can only be as good as the numbers we put in and testing is still very insufficient.

> This study tries to estimate Rt in several European countries from the death rates though there are issues with those statistics too: https://imperialcollegelondon.github.io/covid19estimates/

Most obviously that report used the reporting date of deaths as the death date, even though actual death date data was available. The results are dramatically different because of it.

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