Epidemiologist and liberal-leaning scientist here. The analysis you’ve provided reveals a profound bias you have against the authors. Ioannidis’ STAT piece and other articles at the time didn’t advocate necessarily for low IFR but noted we are making consequential - and potentially harmful - decisions in the face of uncertainty. In that more truthful portrayal of the authors’ work, their efforts to produce data with honest methods at a time when data was scarce suggests they were acting on their own insights much like all scientists do when they pursue hypotheses in broader theories and paradigms in which they’ve played some role.
The more you write about this, the more I’m concerned that you and many scientists in the field are failing to learn the lesson of COVID-19. The lesson isn’t “GBD bad” but that our health scientific ecosystem is unhealthy due to the ways scientists deviate from good standards of science.
From the Proximal Origin paper claiming a lab origin was “implausible” while the authors privately believed it was “so friggin likely” (and ghostwritten by people with a true conflict of interest: the funders of virology research in Wuhan) to the Imperial College forecasts of early 2020 that created the scientific opposition of many of these scientists, myself, and others by misleading managers with models that truly lacked the consequential sensitivity analyses models typically require (and not ad hoc sensitivity analyses burdensome reviewers like yourself are imposing selectively on the SC serosurvey but not to the Manus hospital analysis, vaccine cost-benefit analyses, and more), it’s becoming clear that the rift from COVID will endure for the rest of our lives. As a young scientist, I look forward to arguing against every point you raised until the day I die, and thus the public will see warring scientists, unsure who to trust, because nobody with platforms seems willing to do the hard work of bridging divides.
Just make note that scientists and science writers like you make liberal epidemiologists like me more supportive of the MAHA movement’s efforts to reform academic science, and this article is an exemplary demonstration of why so many people - scientists, managers, & lay people - feel let down by elitist academic “expert” cabals during COVID who could only present their paradigm at the detriment of the managers and public that needed to know the full range of perspectives and uncertainty to make their own, informed decisions.
This has nothing to do with liberal or conservative and everything to do with what constitutes good science. The specific epidemiological problems in the paper are well-documented in my piece. Happy to discuss any of them.
It has everything to do with partisanship because you are selective in which sides of polarized epidemiology you choose to apply these standards to.
The Santa Clara serosurvey was fair science attempting to fill a critical data gap early in the pandemic. None of your critiques are specific to the serosurvey but rather are general limitations, future work, and alternative analyses that aren’t conclusive. Nobody, not even the study’s authors, claim their study is the end of science on the matter, but rather they found early evidence of lower IFRs, much lower than the 1% estimates at the time, estimates which were also disproven by contemporary serosurveys (Gangelt) and alternative analyses (my own ILI paper).
The manner of your arguments against Jay remind me of similar UW professors ranting against John Ioannidis’ paper finding Twitter-influencer scientists tended to have lower measures of academic impact. At the time, Carl Bergstrom and other polarizing scientists tried to argue Dr Ioannidis violated an unwritten research ethical protocol against using Twitter handles, but myself and world-renowned research ethics scholars noted there is no such ethics. I argued that selective ethics is anything but, and that argument comes to mind again as I read your selective demands for unreasonable research quality in what feels like a transparently partisan effort to oppose the NIH director chosen by a conservative president.
Conservatives will understandably view these selective ethics as evidence of academic political bias, and it will entrench their belief that the academy shouldn’t receive federal funds.
It may surprise you to learn that I actually care about the academy, so much that I feel the most important fights are against polarizing science and scientists using their professional credentials to tilt the scales of our political debates. These actions “invade the jury” of public deliberation and slowly whittle away at bipartisan support for academic science.
Epidemiologist and liberal-leaning scientist here. The analysis you’ve provided reveals a profound bias you have against the authors. Ioannidis’ STAT piece and other articles at the time didn’t advocate necessarily for low IFR but noted we are making consequential - and potentially harmful - decisions in the face of uncertainty. In that more truthful portrayal of the authors’ work, their efforts to produce data with honest methods at a time when data was scarce suggests they were acting on their own insights much like all scientists do when they pursue hypotheses in broader theories and paradigms in which they’ve played some role.
The more you write about this, the more I’m concerned that you and many scientists in the field are failing to learn the lesson of COVID-19. The lesson isn’t “GBD bad” but that our health scientific ecosystem is unhealthy due to the ways scientists deviate from good standards of science.
From the Proximal Origin paper claiming a lab origin was “implausible” while the authors privately believed it was “so friggin likely” (and ghostwritten by people with a true conflict of interest: the funders of virology research in Wuhan) to the Imperial College forecasts of early 2020 that created the scientific opposition of many of these scientists, myself, and others by misleading managers with models that truly lacked the consequential sensitivity analyses models typically require (and not ad hoc sensitivity analyses burdensome reviewers like yourself are imposing selectively on the SC serosurvey but not to the Manus hospital analysis, vaccine cost-benefit analyses, and more), it’s becoming clear that the rift from COVID will endure for the rest of our lives. As a young scientist, I look forward to arguing against every point you raised until the day I die, and thus the public will see warring scientists, unsure who to trust, because nobody with platforms seems willing to do the hard work of bridging divides.
Just make note that scientists and science writers like you make liberal epidemiologists like me more supportive of the MAHA movement’s efforts to reform academic science, and this article is an exemplary demonstration of why so many people - scientists, managers, & lay people - feel let down by elitist academic “expert” cabals during COVID who could only present their paradigm at the detriment of the managers and public that needed to know the full range of perspectives and uncertainty to make their own, informed decisions.
This has nothing to do with liberal or conservative and everything to do with what constitutes good science. The specific epidemiological problems in the paper are well-documented in my piece. Happy to discuss any of them.
It has everything to do with partisanship because you are selective in which sides of polarized epidemiology you choose to apply these standards to.
The Santa Clara serosurvey was fair science attempting to fill a critical data gap early in the pandemic. None of your critiques are specific to the serosurvey but rather are general limitations, future work, and alternative analyses that aren’t conclusive. Nobody, not even the study’s authors, claim their study is the end of science on the matter, but rather they found early evidence of lower IFRs, much lower than the 1% estimates at the time, estimates which were also disproven by contemporary serosurveys (Gangelt) and alternative analyses (my own ILI paper).
The manner of your arguments against Jay remind me of similar UW professors ranting against John Ioannidis’ paper finding Twitter-influencer scientists tended to have lower measures of academic impact. At the time, Carl Bergstrom and other polarizing scientists tried to argue Dr Ioannidis violated an unwritten research ethical protocol against using Twitter handles, but myself and world-renowned research ethics scholars noted there is no such ethics. I argued that selective ethics is anything but, and that argument comes to mind again as I read your selective demands for unreasonable research quality in what feels like a transparently partisan effort to oppose the NIH director chosen by a conservative president.
Conservatives will understandably view these selective ethics as evidence of academic political bias, and it will entrench their belief that the academy shouldn’t receive federal funds.
It may surprise you to learn that I actually care about the academy, so much that I feel the most important fights are against polarizing science and scientists using their professional credentials to tilt the scales of our political debates. These actions “invade the jury” of public deliberation and slowly whittle away at bipartisan support for academic science.