5 ms·

The reason why people are obsessed with case numbers and not deaths is that case numbers control the case numbers in the insufficiently immunized which predicts

The reason why people are obsessed with case numbers and not deaths is that case numbers control the case numbers in the insufficiently immunized which predicts death toll 3 weeks ahead of time. Why would you want to control a system with 2-3 weeks of additional lag in your input data when case numbers (a thing that is somewhat easy to measure) correlates so well future deaths? Ideally politician would have some family of models the equivalent of the CDC runs 3 weeks into the future which accounts for seasonality, social events so we could model predictive control on the death numbers but my impression is that is not done (transparently), due to mistrust in experts.

TechBro8615 5y ago
That’s not necessarily true. If you suddenly start testing low risk people you weren’t testing before, then you can get an increase in cases without necessarily an increase in death. For example, if you implement a travel rule that every traveler needs to get tested a day before they leave and a day after they arrive, then you’re going to see an increase in cases (both real and false positives).
- arrrg 5y ago
  Yeah, but in practice that hasn’t been relevant. It’s important to be cautious, but it’s also important to not be a know-it-all who just because they can think of a limitation of a data collection / statistical method they can suddenly cast doubt on the whole thing, even though people who know much more about it already probably did exactly the same thing. Finden a limitation of some data is extremely easy. Extremely easy. It will literally always be possible whenever people are working with data about people because this shit is hard. That doesn’t mean you get to name that limitation, cast doubt on something and piss off. That’s unproductive and irresponsible. These are the facts: incidence is a great leading indicator if some care is taken with it and it’s regularly put into the larger context (i.e. given more vaccinations a higher incidence is possible until things turn to shit). And you can shout data limitations at that central fact until you are blue in the face but it’s still gonna be there and true.
  - freemint 5y ago
    For a successful technology(/medical intervention), reality must take precedence over public relations, for nature cannot be fooled
ethbr0 5y ago
All of that plus "What kills people from COVID now?" To which the answer is "Lack of access to ICU-capable health facilities." And what causes lack of access? Total number of people hospitalized. And how does that number grow? Exponentially. So every public health department in the world is optimizing for "How do we prevent our limited number of ICU-capable health facilities from being overwhelmed." And that comes down to predicting the future hospitalization rate, which can be predicted by the current case count as you said.
- toyg 5y ago
  I agree completely, with the addition that this has been the case pretty much from the start. Evolution of therapies has been minimal, the killer factor has always been the level of pressure on health services - which doesn't just mean ventilators as initially thought, but the entire care process over several days. And this is why vaccines are a game changer: they remove the need for hospitalization.
  - ethbr0 5y ago
    Also, obligatory: staff. Which is to say the health care system is sized for employing enough highly-trained trauma / ICU staff to support normal operations, with some small (very small) amount of surge capacity. And people under-estimate the specialization & experience required for high-level ICU and respiratory care. You can't just take a floor nurse and transfer them over to do the new work. And the amount of complex hospitalizations due to COVID can very quickly become a couple orders of magnitude above "normal operations."
- landemva 5y ago
  ' "What kills people from COVID now?" To which the answer is ' Matching with observed reality a better answer may be: 18 months of enhancing the comorbidities of being overweight and in poor physical condition. Sitting on couch while snacking on carbohydrates and fear has been detrimental to many people.
freeflight 5y ago
> Why would you want to control a system with 2-3 weeks of additional lag in your input data when case numbers (a thing that is somewhat easy to measure) correlates so well future deaths? Because case numbers are actually not easy to measure. I'm basing this of my personal experience working in a German healthcare company, we took up testing during the pandemic; All the tests we do are on unvaccinated people. That's because unvaccinated people are the only ones who need mandatory, and up to date, tests for pretty much everything from the barber to eating at a restaurant. Recently, it's become so strict that unvaccinated people even need a test to use public transport. Vaccinated people do not need these mandatory tests, I have no clue who tests them and at what intervals, it certainly ain't the public testing stations, as those are now literally swarmed by the unvaccinated and nobody would go there if they didn't have to. Which means that in practice we have a massive sampling bias going on; Somebody who's unvaccinated will be very likely to get flagged as positive very quickly, while somebody who's vaccinated would need to decide on their own to do a test.
- makomk 5y ago
  The obvious solution to this problem is to do actual random sampling: pick members of the public in as close to a random fashion as possible, test them for Covid regardless of vaccination status or symptoms, and you should be able to get a reasonably unbiased estimate of actual infection levels in the community. The UK has been doing this since about spring 2020, but weirdly no-one else seems to even though it's obviously useful.
  - freemint 5y ago
    Just doing random sampling on a small group to estimate the error ratio should also work. Also as long as everyone who has symptoms can get a test the error ratio is bounded by the ratio of people with symptoms making it irrelevant for this control problem.
- freemint 5y ago
  Better is the worst enemy of good. Please suggest a different metric then. One that can be measured easily and will do about as well as the biased infected numbers or one which will do better than the biased infected numbers at a similar effort. There is little control theoretic relevance of the Dunkelziffer smoothly varying between 2 and 6 as long as that doesn't make the controller overlook a wave (it doesn't). The sign of the policy should still be the same and if measures don't work, harder measures are imposed means we basically got a PI(D) controller and can achieve stationary solutions even if the Dunkelziffer changes smoothly.

TechBro8615 5y ago

That’s not necessarily true. If you suddenly start testing low risk people you weren’t testing before, then you can get an increase in cases without necessarily an increase in death. For example, if you implement a travel rule that every traveler needs to get tested a day before they leave and a day after they arrive, then you’re going to see an increase in cases (both real and false positives).

arrrg 5y ago
Yeah, but in practice that hasn’t been relevant. It’s important to be cautious, but it’s also important to not be a know-it-all who just because they can think of a limitation of a data collection / statistical method they can suddenly cast doubt on the whole thing, even though people who know much more about it already probably did exactly the same thing. Finden a limitation of some data is extremely easy. Extremely easy. It will literally always be possible whenever people are working with data about people because this shit is hard. That doesn’t mean you get to name that limitation, cast doubt on something and piss off. That’s unproductive and irresponsible. These are the facts: incidence is a great leading indicator if some care is taken with it and it’s regularly put into the larger context (i.e. given more vaccinations a higher incidence is possible until things turn to shit). And you can shout data limitations at that central fact until you are blue in the face but it’s still gonna be there and true.
- freemint 5y ago
  For a successful technology(/medical intervention), reality must take precedence over public relations, for nature cannot be fooled

ethbr0 5y ago

All of that plus "What kills people from COVID now?" To which the answer is "Lack of access to ICU-capable health facilities." And what causes lack of access? Total number of people hospitalized. And how does that number grow? Exponentially. So every public health department in the world is optimizing for "How do we prevent our limited number of ICU-capable health facilities from being overwhelmed." And that comes down to predicting the future hospitalization rate, which can be predicted by the current case count as you said.

toyg 5y ago
I agree completely, with the addition that this has been the case pretty much from the start. Evolution of therapies has been minimal, the killer factor has always been the level of pressure on health services - which doesn't just mean ventilators as initially thought, but the entire care process over several days. And this is why vaccines are a game changer: they remove the need for hospitalization.
- ethbr0 5y ago
  Also, obligatory: staff. Which is to say the health care system is sized for employing enough highly-trained trauma / ICU staff to support normal operations, with some small (very small) amount of surge capacity. And people under-estimate the specialization & experience required for high-level ICU and respiratory care. You can't just take a floor nurse and transfer them over to do the new work. And the amount of complex hospitalizations due to COVID can very quickly become a couple orders of magnitude above "normal operations."
landemva 5y ago
' "What kills people from COVID now?" To which the answer is ' Matching with observed reality a better answer may be: 18 months of enhancing the comorbidities of being overweight and in poor physical condition. Sitting on couch while snacking on carbohydrates and fear has been detrimental to many people.

freeflight 5y ago

> Why would you want to control a system with 2-3 weeks of additional lag in your input data when case numbers (a thing that is somewhat easy to measure) correlates so well future deaths? Because case numbers are actually not easy to measure. I'm basing this of my personal experience working in a German healthcare company, we took up testing during the pandemic; All the tests we do are on unvaccinated people. That's because unvaccinated people are the only ones who need mandatory, and up to date, tests for pretty much everything from the barber to eating at a restaurant. Recently, it's become so strict that unvaccinated people even need a test to use public transport. Vaccinated people do not need these mandatory tests, I have no clue who tests them and at what intervals, it certainly ain't the public testing stations, as those are now literally swarmed by the unvaccinated and nobody would go there if they didn't have to. Which means that in practice we have a massive sampling bias going on; Somebody who's unvaccinated will be very likely to get flagged as positive very quickly, while somebody who's vaccinated would need to decide on their own to do a test.

makomk 5y ago
The obvious solution to this problem is to do actual random sampling: pick members of the public in as close to a random fashion as possible, test them for Covid regardless of vaccination status or symptoms, and you should be able to get a reasonably unbiased estimate of actual infection levels in the community. The UK has been doing this since about spring 2020, but weirdly no-one else seems to even though it's obviously useful.
- freemint 5y ago
  Just doing random sampling on a small group to estimate the error ratio should also work. Also as long as everyone who has symptoms can get a test the error ratio is bounded by the ratio of people with symptoms making it irrelevant for this control problem.
freemint 5y ago
Better is the worst enemy of good. Please suggest a different metric then. One that can be measured easily and will do about as well as the biased infected numbers or one which will do better than the biased infected numbers at a similar effort. There is little control theoretic relevance of the Dunkelziffer smoothly varying between 2 and 6 as long as that doesn't make the controller overlook a wave (it doesn't). The sign of the policy should still be the same and if measures don't work, harder measures are imposed means we basically got a PI(D) controller and can achieve stationary solutions even if the Dunkelziffer changes smoothly.