https://www.nationalreview.com/corner/shut-up-the-experts-explained/
People whose expertise has been questioned often respond in ways that further alienate the skeptics. A good illustration comes from Vanity Fair’s profile of Alex Berenson, a leading advocate of the view that lockdowns are too strict. Berenson was one of the first journalists to point out that the IHME model, on which so many states rely, drastically overestimated hospitalizations — even after multiple revisions, and even after taking the effect of lockdowns into account. Here is the response from Gregg Gonsalves, assistant professor of epidemiology at Yale School of Medicine, as quoted by Vanity Fair:
"Models are not crystal balls. A modeler is giving you a range of potential outcomes. What he [Berenson] is doing is what a lot of people who don’t understand science do, they take the uncertainty built into a model and say, “Oh, well, it shows these people don’t know what they are talking about.” He is playing with scientific uncertainty in order to say, “See, I know what is right here.” He is somebody with a messianic complex. And to be clear, all of the models say this is going to be one of the worst epidemics we have ever faced."
Note the arrogant tone, the name-calling, and the argument from authority. This is how Professor Gonsalves intends to win over the skeptics? It’s not even clear what his point is. Yes, all predictive models have uncertainty, but it follows that the more uncertain the model, the less useful its predictions are. That should not be controversial.
Furthermore, IHME has underestimated its own uncertainty. Although I am a mere policy analyst, I do know that when IHME offers 95 percent prediction intervals, then the actual values are supposed to fall outside those intervals only 5 percent of the time. Applying that standard, critics investigated how the IHME model has performed on what should be one of its easiest tasks — predicting the number of deaths that will occur the very next day. They found that over a four-day period, the actual number of next-day deaths in each state fell outside the model’s 95 percent interval about two-thirds of the time. The failure is self-evident. One need not have a “messianic complex” to reject this model’s predictions.
Finally, even if “all of the models say this is going to be one of the worst epidemics,” that is hardly the end of the policy debate. It can be simultaneously true that we face a terrible epidemic and that full lockdowns are an overreaction. The real issue is how far our mitigation attempts can go before they are no longer worth the economic and social costs. Even people who “understand science” might conclude that those attempts have already gone too far
People whose expertise has been questioned often respond in ways that further alienate the skeptics. A good illustration comes from Vanity Fair’s profile of Alex Berenson, a leading advocate of the view that lockdowns are too strict. Berenson was one of the first journalists to point out that the IHME model, on which so many states rely, drastically overestimated hospitalizations — even after multiple revisions, and even after taking the effect of lockdowns into account. Here is the response from Gregg Gonsalves, assistant professor of epidemiology at Yale School of Medicine, as quoted by Vanity Fair:
"Models are not crystal balls. A modeler is giving you a range of potential outcomes. What he [Berenson] is doing is what a lot of people who don’t understand science do, they take the uncertainty built into a model and say, “Oh, well, it shows these people don’t know what they are talking about.” He is playing with scientific uncertainty in order to say, “See, I know what is right here.” He is somebody with a messianic complex. And to be clear, all of the models say this is going to be one of the worst epidemics we have ever faced."
Note the arrogant tone, the name-calling, and the argument from authority. This is how Professor Gonsalves intends to win over the skeptics? It’s not even clear what his point is. Yes, all predictive models have uncertainty, but it follows that the more uncertain the model, the less useful its predictions are. That should not be controversial.
Furthermore, IHME has underestimated its own uncertainty. Although I am a mere policy analyst, I do know that when IHME offers 95 percent prediction intervals, then the actual values are supposed to fall outside those intervals only 5 percent of the time. Applying that standard, critics investigated how the IHME model has performed on what should be one of its easiest tasks — predicting the number of deaths that will occur the very next day. They found that over a four-day period, the actual number of next-day deaths in each state fell outside the model’s 95 percent interval about two-thirds of the time. The failure is self-evident. One need not have a “messianic complex” to reject this model’s predictions.
Finally, even if “all of the models say this is going to be one of the worst epidemics,” that is hardly the end of the policy debate. It can be simultaneously true that we face a terrible epidemic and that full lockdowns are an overreaction. The real issue is how far our mitigation attempts can go before they are no longer worth the economic and social costs. Even people who “understand science” might conclude that those attempts have already gone too far
National Review
Shut Up, the Experts Explained | National Review
People whose expertise has been questioned often respond in ways that further alienate the skeptics.
studies so far tracking the exposure of covid19 and how the mortality rate is far lower then the scaremongers want us to believe.
Finnish: 20-50x undercount
Scottish: 27-55x
Stanford: 50-85x
Italian: 30x
Mass: 17x
Germany: 0.37% CFR
Denmark: 0.21%
https://www.helsinkitimes.fi/finland/finland-news/domestic/17561-thl-coronavirus-may-have-infected-dozens-of-times-more-in-finland.html
https://www.medrxiv.org/content/10.1101/2020.04.13.20060467v1.full.pdf
https://www.medrxiv.org/content/10.1101/2020.04.14.20062463v1
https://www.technologyreview.com/2020/04/09/999015/blood-tests-show-15-of-people-are-now-immune-to-covid-19-in-one-town-in-germany/
https://www.bostonglobe.com/2020/04/17/business/nearly-third-200-blood-samples-taken-chelsea-show-exposure-coronavirus/
https://covmodel.org/2020/04/17/sars-cov-2-preliminary-serology-test-reports-from-scotland-denmark-and-finland-give-contradictory-results-on-infection-fatality-ratio/
http://www.publichealth.lacounty.gov/phcommon/public/media/mediapubhpdetail.cfm?prid=2328
New French antibody data from a high school in Oise (north of Paris): 25 percent of 651 students, teachers, and others were infected. Yes, 1 in 4 - even more than today's NYC data!
No deaths in the group of 171 infected (median age 37). Nine hospitalized.
https://www.medrxiv.org/content/10.1101/2020.04.18.20071134v1
The odds that a primary case transmitted COVID-19 in a closed environment was 18.7 times greater compared to an open-air environment (95% confidence interval [CI]: 6.0, 57.9). Conclusions: It is plausible that closed environments contribute to secondary transmission of COVID-19 and promote superspreading events.
https://www.medrxiv.org/content/10.1101/2020.02.28.20029272v2
Finnish: 20-50x undercount
Scottish: 27-55x
Stanford: 50-85x
Italian: 30x
Mass: 17x
Germany: 0.37% CFR
Denmark: 0.21%
https://www.helsinkitimes.fi/finland/finland-news/domestic/17561-thl-coronavirus-may-have-infected-dozens-of-times-more-in-finland.html
https://www.medrxiv.org/content/10.1101/2020.04.13.20060467v1.full.pdf
https://www.medrxiv.org/content/10.1101/2020.04.14.20062463v1
https://www.technologyreview.com/2020/04/09/999015/blood-tests-show-15-of-people-are-now-immune-to-covid-19-in-one-town-in-germany/
https://www.bostonglobe.com/2020/04/17/business/nearly-third-200-blood-samples-taken-chelsea-show-exposure-coronavirus/
https://covmodel.org/2020/04/17/sars-cov-2-preliminary-serology-test-reports-from-scotland-denmark-and-finland-give-contradictory-results-on-infection-fatality-ratio/
http://www.publichealth.lacounty.gov/phcommon/public/media/mediapubhpdetail.cfm?prid=2328
New French antibody data from a high school in Oise (north of Paris): 25 percent of 651 students, teachers, and others were infected. Yes, 1 in 4 - even more than today's NYC data!
No deaths in the group of 171 infected (median age 37). Nine hospitalized.
https://www.medrxiv.org/content/10.1101/2020.04.18.20071134v1
The odds that a primary case transmitted COVID-19 in a closed environment was 18.7 times greater compared to an open-air environment (95% confidence interval [CI]: 6.0, 57.9). Conclusions: It is plausible that closed environments contribute to secondary transmission of COVID-19 and promote superspreading events.
https://www.medrxiv.org/content/10.1101/2020.02.28.20029272v2
www.helsinkitimes.fi
THL: Coronavirus may have infected dozens of times more than confirmed in Finland
The Finnish Institute for Health and Welfare (THL) on Wednesday published the results of its preliminary study of the prevalence of coronavirus antibodies in the population of Uusimaa.
New French antibody data from a high school in Oise (north of Paris): 25 percent of 651 students, teachers, and others were infected. Yes, 1 in 4 - even more than today's NYC data!
No deaths in the group of 171 infected (median age 37). Nine hospitalized.
https://www.medrxiv.org/content/10.1101/2020.04.18.20071134v1
No deaths in the group of 171 infected (median age 37). Nine hospitalized.
https://www.medrxiv.org/content/10.1101/2020.04.18.20071134v1
medRxiv
Cluster of COVID-19 in northern France: A retrospective closed cohort study
Background The Oise department in France has been heavily affected by COVID-19 in early 2020.
Methods Between 30 March and 4 April 2020, we conducted a retrospective closed cohort study among pupils, their parents and siblings, as well as teachers and non…
Methods Between 30 March and 4 April 2020, we conducted a retrospective closed cohort study among pupils, their parents and siblings, as well as teachers and non…