Invalid FLAWED mathematical models doomed & badly misguided pandemic response by the US (Fauci & Birx & Clown car Task Force save Giroir); Klement's Critical Realism shreds Watson et al. & SIER model
by Paul Alexander
Watson et al.'s love affair with the fatally flawed SIER modelling approach WRONGLY estimated that COVID vaccine (in 185 nations) prevented from 15 to 20 million deaths in 2021; FLAT WRONG!
Klement and Walach did a terrific job shredding the flawed modelling work of Watson et al. (SIER adoption) and their key argument surrounds the omission by Watson et al. of key causal variables. Klement and Walach argue that Watson et al. have ‘oversimplified the complex interplay between biomedical, social and cultural dimensions of health and should not be used to guide public health policy.’
Watson’s conclusions:
SOURCE:
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9225255/?utm_source=substack&utm_medium=email
Klement’s work:
Klement and Walach began by pointing to the models of Watson et al. above that claimed the Covid-19 vaccinations prevented approximately 14-20 million deaths in 2021. ‘This conclusion is based on an epidemiological susceptible-exposed-infectious-recovered (SEIR) model trained on partially simulated data and yielding a reproduction number distribution which was then applied to a counterfactual scenario in which the efficacy of vaccinations was removed. Drawing on the meta-theory of Critical Realism, we point out several caveats of this model and caution against believing in its predictions.’
Klement argued that ‘the absence of vaccinations would have significantly changed the causal tendencies of the system being modelled, yielding a different reproduction number than obtained from training the model on actually observed data. Furthermore, the model omits many important causal factors.
Therefore this model, similar to many previous SEIR models, has oversimplified the complex interplay between biomedical, social and cultural dimensions of health and should not be used to guide public health policy. In order to predict the future in epidemic situations more accurately, continuously optimized dynamic causal models which can include the not directly tangible, yet real causal mechanisms affecting public health appear to be a promising alternative to SEIR-type models.’
SOURCE:
Critically, the COVID gene injection cannot stop infection, replication, or transmission (does not sterilize the virus). It did and does not work, and along with ineffectiveness, it is deadly (both mRNA and DNA platforms). There is no randomized controlled trial evidence that shows any reductions in hospitalization or death or severe COVID from the gene injections. None have been done! Moreover, observational epidemiological studies have not statistically controlled for i)the impact of early treatment ii)the impact of natural immunity iii)the impact of co-morbidities iv)the impact of healthy vaccinee bias effect where healthy persons tend to get vaccinated in the first place.
Bear these in mind.