As a retired medic in the UK, I still find this approach to public health by the new administration in the United States difficult to understand on the scientific level. Unfortunately I can see parallels with recent European history from the political perspective. Thanks for an interesting article.
Clearly the change in content is related to the new political atmosphere in Washington, but the previous ‘party line’ was also obviously tainted. Secret funding between the Wuhan lab and Faucci via Ecohealth of research that the Obama administration had banned were deliberately concealed. Social media was pressured to censor alternative narratives.
I have 3 of your books and enjoyed reading them immensely. I read them for the methods explained and examples (I have a mathematical and medical background). Concerning the Covid and medical subjects, to put is quite plainly, I think you believe what you read in the journals and official statistics and then proceed, The validity of the conclusions rest on the validity of the input data, but much of these are a house of cards.
Is the data from China believable? That must be at best doubtful.
The data on death statistics is completely mixed up by the confusion of deaths with Covid and deaths of Covid. Official policy was at some time to label anyone dying with Covid as a Covid death.
The PCR test data relates unrelaibly to Covid because of cross reactivity with other upper respiratory tract viral infections. Long cycle times of the PCR test will detect viral remnants and not active infections
Even the original vaccine publications are suspect, The original Pfizer study published in the NEJM had 3 major flaws such that it should not have been published. The vaccine efficacy could not be assessed from the data published.
I’m glad you liked the books. A few points on those specific issues - as it’s something we spent a lot of time looking at (and routinely sense checking what was coming out in various data sources):
- UK deaths were broadly consistent in 2020 regardless of metric, whether based on excess mortality, death certificate or counts of deaths within 28 days of positive test. Post-vaccine these metrics have diverged, but pre-vaccine that wasn’t the case.
- The idea that PCR generates loads of cross-reactive false positives can be discounted by looking at the many studies measuring community positivity at times of low infection levels. Same for lateral flow tests. The long cycles are more of an issue in declining epidemic (when rising, most infections are recent, so in left hand of positivity distribution) and this long-ish positivity curve can be accounted for by converting point prevalence, I.e. number testing positive today, to incidence, I.e. number of new daily infections (this is what we and others did routinely - but it was an aspect that seems to have escaped certain ‘it’s all false positives’ commentators).
- There are loads of cohort studies measuring vaccine effectiveness in highly tested populations, not just the estimates in the original trial data. Often these were more useful, because also provide estimates of protection against infection (and hence impact on transmission, if any).
Thanks for reviewing the information presented now on covid. gov. I am surprised at the effort now made to assign blame to Chinese virologists. I thought that the topic had been exhausted years ago. Did you find anything current or relevant on the site to report?
Thank you for going line by line here with actual sound references. They are retrofitting and revising Truth to serve their sad political and ideological agenda - power and control over truth and science.
As a retired medic in the UK, I still find this approach to public health by the new administration in the United States difficult to understand on the scientific level. Unfortunately I can see parallels with recent European history from the political perspective. Thanks for an interesting article.
Clearly the change in content is related to the new political atmosphere in Washington, but the previous ‘party line’ was also obviously tainted. Secret funding between the Wuhan lab and Faucci via Ecohealth of research that the Obama administration had banned were deliberately concealed. Social media was pressured to censor alternative narratives.
I have 3 of your books and enjoyed reading them immensely. I read them for the methods explained and examples (I have a mathematical and medical background). Concerning the Covid and medical subjects, to put is quite plainly, I think you believe what you read in the journals and official statistics and then proceed, The validity of the conclusions rest on the validity of the input data, but much of these are a house of cards.
Is the data from China believable? That must be at best doubtful.
The data on death statistics is completely mixed up by the confusion of deaths with Covid and deaths of Covid. Official policy was at some time to label anyone dying with Covid as a Covid death.
The PCR test data relates unrelaibly to Covid because of cross reactivity with other upper respiratory tract viral infections. Long cycle times of the PCR test will detect viral remnants and not active infections
Even the original vaccine publications are suspect, The original Pfizer study published in the NEJM had 3 major flaws such that it should not have been published. The vaccine efficacy could not be assessed from the data published.
I’m glad you liked the books. A few points on those specific issues - as it’s something we spent a lot of time looking at (and routinely sense checking what was coming out in various data sources):
- UK deaths were broadly consistent in 2020 regardless of metric, whether based on excess mortality, death certificate or counts of deaths within 28 days of positive test. Post-vaccine these metrics have diverged, but pre-vaccine that wasn’t the case.
- The idea that PCR generates loads of cross-reactive false positives can be discounted by looking at the many studies measuring community positivity at times of low infection levels. Same for lateral flow tests. The long cycles are more of an issue in declining epidemic (when rising, most infections are recent, so in left hand of positivity distribution) and this long-ish positivity curve can be accounted for by converting point prevalence, I.e. number testing positive today, to incidence, I.e. number of new daily infections (this is what we and others did routinely - but it was an aspect that seems to have escaped certain ‘it’s all false positives’ commentators).
- There are loads of cohort studies measuring vaccine effectiveness in highly tested populations, not just the estimates in the original trial data. Often these were more useful, because also provide estimates of protection against infection (and hence impact on transmission, if any).
This piece has a more detailed breakdown of how to interpret PCR positivity data: https://kucharski.substack.com/p/counting-current-covid-infections
Thanks for reviewing the information presented now on covid. gov. I am surprised at the effort now made to assign blame to Chinese virologists. I thought that the topic had been exhausted years ago. Did you find anything current or relevant on the site to report?
Science in the tradition of Lysenko.
Thank you for clarifying these statements Adam. It is important not only to scientist but especially to public.
Thank you for going line by line here with actual sound references. They are retrofitting and revising Truth to serve their sad political and ideological agenda - power and control over truth and science.