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.

INGENIERIA DEL PÚBLICO: GRANDES DATOS, VIGILANCIA Y POLITICA COMPUTACIONAL

Engineering the public: Big data, surveillance and computational politics

Digital technologies have given rise to a new combination of big data
and computational practices which allow for massive, latent data
collection and sophisticated computational modeling, increasing the
capacity of those with resources and access to use these tools to carry
out highly effective, opaque and unaccountable campaigns of persuasion
and social engineering in political, civic and commercial spheres. I
examine six intertwined dynamics that pertain to the rise of
computational politics: the rise of big data, the shift away from
demographics to individualized targeting, the opacity and power of
computational modeling, the use of persuasive behavioral science,
digital media enabling dynamic real-time experimentation, and the growth
of new power brokers who own the data or social media environments. I
then examine the consequences of these new mechanisms on the public
sphere and political campaigns.

On the surface, this century has ushered in new digital technologies
that brought about new opportunities for participation and collective
action by citizens. Social movements around the world, ranging from the
Arab uprisings to the Occupy movement in the United States (Gitlin,
2012), have made use of these new technologies to organize dissent
against existing local, national and global power [11].

Such effects are real and surely they are part of the story of the
rise of the Internet. However, history of most technologies shows that
those with power find ways to harness the power of new technologies and
turn it into a means to further their own power (Spar, 2001). From the
telegraph to the radio, the initial period of disruption was followed by
a period of consolidation in which challengers were incorporated into
transformed power structures, and disruption gave rise to entrenchment.
There are reasons to think that the Internet’s trajectory may have some
differences though there is little reason to think that it will escape
all historical norms.

The dynamics outlined in this paper for computational politics
require access to expensive proprietary databases, often controlled by
private platforms, and the equipment and expertise required to
effectively use this data. At a minimum, this environment favors
incumbents who already have troves of data, and favors entrenched and
moneyed candidates within parties, as well as the data–rich among
existing parties. The trends are clear. The selling of politicians — as
if they were “products” — will become more expansive and improved, if
more expensive. In this light, it is not a complete coincidence that the
“chief data scientist” for the Obama 2012 campaign was previously
employed by a supermarket to “maximize the efficiency of sales
promotions.” And while the data advantage is held, for the moment, by
the Democratic party in the United States, it will likely available to
the highest bidder in future campaigns.

A recent peek into public’s unease with algorithmic manipulation was
afforded by the massive negative reaction to a study conducted by
Facebook and Cornell which experimentally manipulated the emotional
tenor of the hundreds of thousands of people’s newsfeed in an effort to
see if emotional contagion could occur online (Kramer, et al.,
2014). The authors stated their results “indicate that emotions
expressed by others on Facebook influence our own emotions, constituting
experimental evidence for massive–scale contagion via social networks.”
While both the level of stimulus and the corresponding effect size were
on the small side, the broad and negative reaction suggests that
algorithmic manipulation generates discomfort exactly because it is
opaque, powerful and possibly non–consensual (study authors pointed to
the Facebook’s terms–of–service as indication of consent) in an
environment of information asymmetry.

The methods of computational politics will, and already are, also
used in other spheres such as marketing, corporate campaigns, lobbying
and more. The six dynamics outlined in this paper — availability of big
data, shift to individual targeting, the potential and opacity of
modeling, the rise of behavioral science in the service of persuasion,
dynamic experimentation, and the growth of new power brokers on the
Internet who control the data and algorithms — will affect many aspects
of life in this century. More direct research, as well as critical and
conceptual analysis, is crucial to increase both our understanding and
awareness of this information environment, as well as to consider policy
implications and responses. Similar to campaign finance laws, it may be
that data use in elections needs regulatory oversight thanks to its
effects on campaigning, governance and privacy. Starting an empirically
informed, critical discussion of data politics now may be the first
important step in asserting our agency with respect to big data that is
generated by us and about us, but is increasingly being used at us