4 ms·

I think the researchers behind the Santa Clara study should be applauded for going out and gathering data. While the methodology may have been flawed, I think t

I think the researchers behind the Santa Clara study should be applauded for going out and gathering data. While the methodology may have been flawed, I think that having a small amount of flawed data is better than having no data. My hope is that the public controversy of this study spurs more and better serological studies in the future. I don't agree with the premise that scientists should be silenced or suffer personal attacks for putting out controversial or even flat out "bad" science. I think we have seen that approach backfire, and lead to distrust of expert opinions and traditional news sources. Instead I think that we should be focusing on improving scientific literacy in the general population, and electing leaders who surround themselves with and take advice from competent scientists. And if people in certain countries, districts, etc. are not interested in doing so... well... they are certainly free to vote however they see fit and live with the results of that decision.

not2b 7y ago
The main criticism is that they came to a conclusion that was completely unsupported by the evidence. They made an error you'd flunk a first-year statistics student for: ignoring the effect of false positives. They then politicized their bad conclusion by doing a media blitz. This is especially dangerous when the conclusion you are prematurely coming to is something that a lot of people really want to believe. When their report first came out, it gave me hope. I live in Santa Clara County; what they were saying would lead to a very low death rate -- unless you allow for as few as 1% false positives, and then you realize that the numbers they report could also arise if no one had any antibodies. They shouldn't be silenced, but they should be criticized, and criticized intensely, for a botch a first-year grad student shouldn't make.
- rurban 7y ago
  So you are denying the evidence also? Look at the global absolute data, which does support this study. The IFR is around 0.3, in some countries much lower 0.03, in some countries at around 1%. The absolute number of deaths are much lower than in a normal flu season. 200.000 this year, with a normal year being at the 250.000-500.000 range. Without vaccination and without proper treatment plans (which turned out flat wrong) So some newer studies try to find out why and how this disease is so weak. Affected countries, like Belgium, Spain, NL, England, US like to know why their rates are higher. It's not the measures, it's not the weak ICU infrastructure. it's something else. Like age group, seniors living seperately of with the families, diabetes, obesity, air pollution, or other co-morbidities. Currently the numbers are not too troubling, but still would make an interesting study.
- freetime2 7y ago
  > They made an error you'd flunk a first-year statistics student for: ignoring the effect of false positives. I don't think it's fair to say they "ignored" the effect of false positives. The paper very clearly states: > We consider our estimate to represent the best available current evidence, but recognize that new information, especially about the test kit performance, could result in updated estimates.For example, if new estimates indicate test specificity to be less than 97.9%, our SARS-CoV-2 prevalence estimate would change from 2.8% to less than 1%, and the lower uncertainty bound of our estimate would include zero. Some statisticians have accused them of being sloppy with their estimate of their lower uncertainty bound (among other things). The authors have stated that they disagree, and are working to put out additional data to show that their findings are "robust". I honestly don't know (I don't have enough of a background in stats to have an opinion either way), but I look forward to seeing what the authors put out next and how the scientific community responds to it. And more importantly than the results of this single study, I’m looking forward to more antibody testing in the future.

not2b 7y ago

The main criticism is that they came to a conclusion that was completely unsupported by the evidence. They made an error you'd flunk a first-year statistics student for: ignoring the effect of false positives. They then politicized their bad conclusion by doing a media blitz. This is especially dangerous when the conclusion you are prematurely coming to is something that a lot of people really want to believe. When their report first came out, it gave me hope. I live in Santa Clara County; what they were saying would lead to a very low death rate -- unless you allow for as few as 1% false positives, and then you realize that the numbers they report could also arise if no one had any antibodies. They shouldn't be silenced, but they should be criticized, and criticized intensely, for a botch a first-year grad student shouldn't make.

rurban 7y ago
So you are denying the evidence also? Look at the global absolute data, which does support this study. The IFR is around 0.3, in some countries much lower 0.03, in some countries at around 1%. The absolute number of deaths are much lower than in a normal flu season. 200.000 this year, with a normal year being at the 250.000-500.000 range. Without vaccination and without proper treatment plans (which turned out flat wrong) So some newer studies try to find out why and how this disease is so weak. Affected countries, like Belgium, Spain, NL, England, US like to know why their rates are higher. It's not the measures, it's not the weak ICU infrastructure. it's something else. Like age group, seniors living seperately of with the families, diabetes, obesity, air pollution, or other co-morbidities. Currently the numbers are not too troubling, but still would make an interesting study.
freetime2 7y ago
> They made an error you'd flunk a first-year statistics student for: ignoring the effect of false positives. I don't think it's fair to say they "ignored" the effect of false positives. The paper very clearly states: > We consider our estimate to represent the best available current evidence, but recognize that new information, especially about the test kit performance, could result in updated estimates.For example, if new estimates indicate test specificity to be less than 97.9%, our SARS-CoV-2 prevalence estimate would change from 2.8% to less than 1%, and the lower uncertainty bound of our estimate would include zero. Some statisticians have accused them of being sloppy with their estimate of their lower uncertainty bound (among other things). The authors have stated that they disagree, and are working to put out additional data to show that their findings are "robust". I honestly don't know (I don't have enough of a background in stats to have an opinion either way), but I look forward to seeing what the authors put out next and how the scientific community responds to it. And more importantly than the results of this single study, I’m looking forward to more antibody testing in the future.