DERECHO TRIBUTARIO Y CONSTITUCIONAL DERECHO Y NUEVAS TECNOLOGIAS ACTUALIDAD JURIDICA Y ECONOMICA MEDIOAMBIENTE
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Saturday, January 8, 2022
ANALISIS BIG DATA MUNDIAL DE EFECTOS DE LAS VACUNAS EN LOS FALLECIMIENTOS Y CASOS ASOCIADOS A COVID-19
ABSTRACT
Policy makers and mainstream news anchors have promised the public that the COVID-
19 vaccine rollout worldwide would reduce symptoms, and thereby cases and deaths associated
with COVID-19. While this vaccine rollout is still in progress, there is a large
amount of public data available that permits an analysis of the effect of the vaccine rollout
on COVID-19 related cases and deaths. Has this public policy treatment produced
the desired effect?
One manner to respond to this question can begin by implementing a Bayesian causal
analysis comparing both pre- and post-treatment periods. This study analyzed publicly
available COVID-19 data from OWID (Hannah Ritchie and Roser 2020) utlizing the R
package CausalImpact (Brodersen et al. 2015) to determine the causal effect of the administration
of vaccines on two dependent variables that have been measured cumulatively
throughout the pandemic: total deaths per million (y1) and total cases per million (y2).
After eliminating all results from countries with p > 0.05, there were 128 countries for
y1 and 103 countries for y2 to analyze in this fashion, comprising 145 unique countries in
total (avg. p < 0.004).
Results indicate that the treatment (vaccine administration) has a strong and statistically
significant propensity to causally increase the values in either y1 or y2 over and above
what would have been expected with no treatment. y1 showed an increase/decrease ratio
of (+115/-13), which means 89.84% of statistically significant countries showed an
increase in total deaths per million associated with COVID-19 due directly to the causal
impact of treatment initiation. y2 showed an increase/decrease ratio of (+105/-16) which
means 86.78% of statistically significant countries showed an increase in total cases per
million of COVID-19 due directly to the causal impact of treatment initiation. Causal
impacts of the treatment on y1 ranges from -19% to +19015% with an average causal impact
of +463.13%. Causal impacts of the treatment on y2 ranges from -46% to +12240%
with an average causal impact of +260.88%. Hypothesis 1 Null can be rejected for a large
majority of countries.
This study subsequently performed correlational analyses on the causal impact results,
whose effect variables can be represented as y1.E and y2.E respectively, with the independent
numeric variables of: days elapsed since vaccine rollout began (n1), total vaccination
doses per hundred (n2), total vaccine brands/types in use (n3) and the independent
categorical variables continent (c1), country (c2), vaccine variety (c3). All categorical
variables showed statistically significant (avg. p: < 0.001) postive Wilcoxon signed rank
values (y1.E V :[c1 3.04; c2: 8.35; c3: 7.22] and y2.E V :[c1 3.04; c2: 8.33; c3: 7.19]). This
demonstrates that the distribution of y1.E and y2.E was non-uniform among categories.
The Spearman correlation between n2 and y2.E was the only numerical variable that
showed statistically significant results (y2.E ~ n2: ρ: 0.34 CI95%[0.14, 0.51], p: 4.91e-04).
This low positive correlation signifies that countries with higher vaccination rates do not
have lower values for y2.E, slightly the opposite in fact. Still, the specifics of the reasons
behind these differences between countries, continents, and vaccine types is inconclusive
and should be studied further as more data become available. Hypothesis 2 Null can be
rejected for c1, c2, c3 and n2 and cannot be rejected for n1, and n3.
The statistically significant and overwhelmingly positive causal impact after vaccine deployment
on the dependent variables total deaths and total cases per million should be
highly worrisome for policy makers. They indicate a marked increase in both COVID-19
related cases and death due directly to a vaccine deployment that was originally sold to
the public as the “key to gain back our freedoms.” The effect of vaccines on total cases
per million and its low positive association with total vaccinations per hundred signifies a
limited impact of vaccines on lowering COVID-19 associated cases. These results should
encourage local policy makers to make policy decisions based on data, not narrative, and
based on local conditions, not global or national mandates. These results should also encourage
policy makers to begin looking for other avenues out of the pandemic aside from
mass vaccination campaigns.
Some variables that could be included in future analyses might include vaccine lot by
country, the degree of prevalence of previous antibodies against SARS-CoV or SARSCoV-
2 in the population before vaccine administration begins, and the Causal Impact of
ivermectin on the same variables used in this study.
Keywords CausalImpact, causation, vaccines, BigData, COVID-19, gene therapy
Peter McCullough, MD MPH
@P_McCulloughMD
19h
This analysis is exhaustive analyzing data from around the world. The global program has made the pandemic worse for populations not better. Now is the time to stop and re-calibrate global health.
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