Content uploaded by Kyle Beattie
Author content
All content in this area was uploaded by Kyle Beattie on Nov 16, 2021
Content may be subject to copyright.
A preview of the PDF is not available
Worldwide Bayesian Causal Impact Analysis of Vaccine Administration on Deaths and Cases Associated with COVID-19: A BigData Analysis of 145 Countries
Preprints and early-stage research may not have been peer reviewed yet.
Abstract and Figures
*** THIS PAPER HAS BEEN PLACED HERE FOR PUBLIC PEER-REVIEW ***
*** After public peer-review an attempt will be made for journal submission, any suggestions for interested journals are welcome. ***
*** All comments, corrections, questions, criticism, or other feedback can be emailed to the author, or placed under Comments below. Thank you all in advance for your help in assessing and improving this study's methods and design.***
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 utlizing the R package CausalImpact 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: rho: 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 SARS-CoV-2 in the population before vaccine administration begins, and the Causal Impact of ivermectin on the same variables used in this study.
Figures - uploaded by Kyle Beattie
Author content
All figure content in this area was uploaded by Kyle Beattie
Content may be subject to copyright.
... In other words, vaccines appear to have contributed (directly or indirectly) to an increase in other causes of death (than covid), in young populations. Those previous conclusions are now consistent with statistical results brought by a dozen of preprints and papers [1,3,5,16,19,20,28,30,33,34,38]. However, they are seemingly in contradiction with several major papers such as [26,42] as well as [6,21,22,41] that defend (based on all-cause mortality) a positive benet/risk balance. ...
... Several other studies have implicated COVID-19 vaccination in increased all-cause mortality. These studies have used different methods, countries, age groups, and time periods covering different COVID-19 variants, different vaccine histories, and time following vaccination [157][158][159][160][161][162][163]. A common theme is the involvement of mRNA vaccines, and poor outcomes in children and young adults. ...
All vaccines exhibit both specific and non-specific effects. The specific effects are measured by the efficacy against the target pathogen, while the non-specific effects can be detected by the change in all-cause mortality. All-cause mortality data (gender, age band, vaccination history, month of death) between January 2021 and May 2022 was compiled by the Office for National Statistics. COVID-19 vaccination gave good protection on many occasions but less so for younger ages. Each gender and age group shows its own unique vaccination benefit/disbenefit time profile. Individuals are free to make vaccination decisions. For example, women aged 18-39 show a cohort who do not progress beyond the first or second dose. The all-cause mortality outcomes for the Omicron variant showed a very poor response to vaccination with 70% of sex/age/vaccination stage/month combinations increasing all-cause mortality, probably due to unfavorable antigenic distance between the first-generation vaccines and this variant, and additional non-specific effects. The all-cause mortality outcomes of COVID-19 vaccination is far more nuanced than have been widely appreciated, and virus vector appear better than the mRNA vaccines in this specific respect. The latter are seemingly more likely to increase all-cause mortality especially in younger age groups. An extensive discussion/literature review is included to provide potential explanations for the observed unexpected vaccine effects. Full text and Supplementary material at: https://www.preprints.org/manuscript/202304.0248/v1
Note that we are about to submit a version of this paper looking at the effects on non-COVID-19 all-cause mortality (NCACM).After that we aim to return to the all-cause mortality paper.
... There are plenty of studies which revealed that in certain cases, various prophylactic measures used to prevent the spread of SARS-CoV-2 and other viruses and bacteria, could have negative effects on the human body, could have low efficacy or may even be useless in terms of prevention of diseases [37][38][39][40]. ...
The study was carried out to evaluate the dynamics of monthly numbers of cases, deaths, tests and case fatality ratio worldwide during three phases of the COVID-19 pandemic. Material and methods: Twenty-three sets of databases, dated the 22nd of each month from January 2020 to November 2021, for 213 countries were collected from the Worldometer website. The number of cases, deaths, tests, case fatality ratio, infection fatality ratio, etc. were counted for various periods of time for each of the 213 countries, then the results related to different periods of time were compared. The analysis of main epidemiological parameters resulted in division of three phases of the global pandemic evolution. The first phase (23.01.20-22.07.20), the second phase (23.07.20-22.01.21) and the third phase (23.01.21-22.07.21) were different in terms of the number of tests performed, new cases and mortality due to COVID-19. By the end of second phase, the worldwide statistics indicated end of the pandemic, but the third phase was characterized by sudden rise in number of new cases and deaths. The most dramatic evolution of epidemic curve occurred in the countries where physicians had successfully confronted COVID-19 during the first two phases of the pandemic. Despite the decrease in the overall numbers deaths during the latest months analyzed, additional study is necessary to identify causes of new cases and deaths during the third phase of the pandemic. It can be suggested that preventive and therapeutic protocols should be changed from the 'standard' to 'personalized' types.
... On November 15, 2021, a preprint by Beattie describing Bayesian big data analysis of 145 countries claimed that vaccines were doing the opposite of what they were supposed to do [8]. ...
This working paper supplements a five-part timeline describing ivermectin-related events in the COVID-19 pandemic (February 1, 2022 to April 15, 2022). *** Other parts: *** Part 0: https://www.researchgate.net/publication/348077948 *** Part 1: https://doi.org/10.13140/RG.2.2.13705.36966 *** Part 2: https://doi.org/10.13140/RG.2.2.16973.36326 *** Part 3: https://doi.org/10.13140/RG.2.2.23081.72805 *** Part 4: https://doi.org/10.13140/RG.2.2.26000.53767 *** Part 5: https://doi.org/10.13140/RG.2.2.35015.16807 ***
... Slična, zasad nerecenzirana analiza podataka iz 145 država došla je do zaključka da je stopa procijepljenosti statistički značajno povezana s brojem slučajeva covid-19 i smrti povezanih s covid-19 u velikom dijelu država (87 %). Konkretno, cijepljenje je prema analizi bilo povezano s rastom od 261 % u broju slučajeva covid-19 te 463 % u broju smrti[230].Imajući na umu dosadašnje informacije iz teksta, nije primjerice jasno što je Marija Bubaš, pomoćnica ravnatelja Hrvatskog zavoda za javno zdravstvo, htjela reći kad je izjavila: "Sva [cjepiva] koja dolaze i dolazit će u Hrvatsku, za koja se Hrvatska predbilježila i za koja je zainteresirana, a osobito ova tri cjepiva koja jesu tu, sto posto 3 štite od umiranja u slučaju zaraze bolešću COVID-19", dodajući: "Nadam se da svi žele život i da im je život dragocjen toliko da će se odlučiti za cjepivo koje im prvo dođe pod ruku"[263]. Na kojim je informacijama temeljena ova izjava? ...
U hrvatskim je medijima sve više govora o cijepljenju djece protiv covid-19, unatoč maloj ulozi djece u prijenosu novog koronavirusa i njihovom malom riziku od teških simptoma, postojanju drugih oblika prevencije, činjenici da klinička ispitivanja nisu dovršena, raznih problema u provedenim ispitivanjima i rastućoj zabrinutosti oko sigurnosti cjepiva i mogućih štetnih učinaka. Cilj je ovog kratkog pregleda odabrane znanstvene literature potaknuti kvalitetnu javnu raspravu prije donošenja potencijalno ishitrenih odluka.
Background. January 2021 marked one year since the start of COVID-19 pandemic: it is the time of intermediate conclusions. Objective. To evaluate CFR and IFR due to COVID-19 in various countries and territories, and to study if parameters of a population age affect CFR and IFR. Material and Methods. The databases of 219 countries were collected on the Worldometers, Index Mundi, Country Meters and World Bank websites. The processing of data was divided into two parts: the first part dealt with the calculation and analysis of CFR while the second, the calculation and analysis of IFR. Results. The calculations revealed that in 74 out of 219 countries, CFR was less than 1.00 %, in 69 countries it varied between 1.00 % and 2.00 %, and in 76 countries it was more than 2.00 %. The calculation of IFR revealed that in 183 countries, IFR was less than 1.00 %, in 22 countries IFR was between 1.00 % and 2.00 %, and only in 14 out of 219 countries IFR was more than 2.00 %. A correlation between IFR and parameters of a population age was found: the less median age and the percentage of 'aged' people – the less value of IFR, although, there was no correlation between parameters of a population age and CFR. Conclusion. The global health care system has gone through a year of serious trial caused by COVID-19 and appeared to have emerged victorious. In the majority of countries analyzed, the parameters of mortality due to COVID-19 were at a low level. So, there seems to be an objective basis for optimism and hope for an early end to the pandemic.
- Background. Since the previous study dealing with the case fatality ratio and infection fatality ratio caused by COVID-19, the author has received many comments that prompted the question: "Why did an optimistic prognosis fail?" To answer this question, a more detailed and expanded analysis was carried out in a new study. - Objective. To evaluate the dynamics of monthly numbers of cases, deaths, tests and CFR worldwide during three phases of the COVID-19 pandemic. - Material and Methods. Twenty three sets of databases, dated the 22nd of each month from January 2020 to November 2021, for 213 countries were collected from the Worldometer website. The number of cases, deaths, tests, CFR, IFR, etc. were counted for various periods of time for each of the 213 countries, then results related to different periods of time were compared. - Results. The analysis of the main epidemiological parameters led to the division of three phases of the global pandemic evolution. The first phase (23.01.20-22.07.20), the second phase (23.07.20-22.01.21) and the third phase (23.01.21-22.07.21) were different in terms of the number of tests performed, new cases, and mortality due to COVID-19. By the end of the secondphase, the worldwide statistics indicated the imminent end of the pandemic, but the third phase was characterized by a sudden rise in the number of new cases and deaths that could not be explained rationally. The most dramatic evolution of the epidemic curve occurred in the countries where doctors had successfully battled COVID-19 during the first two phases of the pandemic. - Conclusions. Despite the decrease in overall death numbers during the latest months analyzed, additional study is necessary to identify the cause for the increase in the number of new cases and deaths during the third phase of the pandemic. Only complete information regarding the positive and negative impact of medical and non-medical methods of diagnostics and prophylaxis of COVID-19 can help to organize effective measures to end the current pandemic and prevent a similar one from occurring in the future. Presumably, there are several causes of the negative evolution of the current pandemic, including the overreliance on PCR tests, application of non-specialized premises for quarantine and treatment, decrease in herd and individual immunity, inadequate change of therapeutic protocols, and ignoring prophylactic treatment. It can be suggested that the use of immunemodulatory drugs, for example, thymus extract or thymic peptides, in groups of people with compromised immunity is necessary, and prophylactic and therapeutic protocols should be changed from the 'standard' types to 'personalized' ones.
BACKGROUND: Waning of vaccine protection against SARS-CoV-2 infection or COVID-19 disease is a concern. This study investigated persistence of BNT162b2 (Pfizer-BioNTech) vaccine effectiveness against infection and disease in Qatar, where the Beta and Delta variants have dominated incidence and PCR testing is done at a mass scale.
METHODS: A matched test-negative, case-control study design was used to estimate vaccine effectiveness against SARS-CoV-2 infection and against any severe, critical, or fatal COVID-19 disease, between January 1, 2021 to August 15, 2021.
RESULTS: Estimated BNT162b2 effectiveness against any infection, asymptomatic or symptomatic, was negligible for the first two weeks after the first dose, increased to 36.5% (95% CI: 33.1-39.8) in the third week after the first dose, and reached its peak at 72.1% (95% CI: 70.9-73.2) in the first five weeks after the second dose. Effectiveness declined gradually thereafter, with the decline accelerating ≥15 weeks after the second dose, reaching diminished levels of protection by the 20th week. Effectiveness against symptomatic infection was higher than against asymptomatic infection, but still waned in the same fashion. Effectiveness against any severe, critical, or fatal disease increased rapidly to 67.7% (95% CI: 59.1-74.7) by the third week after the first dose, and reached 95.4% (95% CI: 93.4-96.9) in the first five weeks after the second dose, where it persisted at about this level for six months.
CONCLUSIONS: BNT162b2-induced protection against infection appears to wane rapidly after its peak right after the second dose, but it persists at a robust level against hospitalization and death for at least six months following the second dose.
In 2015, the Nobel Committee for Physiology or Medicine, in its only award for treatments of infectious diseases since six decades prior, honored the discovery of ivermectin (IVM), a multifaceted drug deployed against some of the world’s most devastating tropical diseases. Since March 2020, when IVM was first used against a new global scourge, COVID-19, more than 20 randomized clinical trials (RCTs) have tracked such inpatient and outpatient treatments. Six of seven meta-analyses of IVM treatment RCTs reporting in 2021 found notable reductions in COVID-19 fatalities, with a mean 31% relative risk of mortality vs. controls. The RCT using the highest IVM dose achieved a 92% reduction in mortality vs. controls (400 total subjects, p<0.001). During mass IVM treatments in Peru, excess deaths fell by a mean of 74% over 30 days in its ten states with the most extensive treatments. Reductions in deaths correlated with extent of IVM distributions in all 25 states with p<0.002. Sharp reductions in morbidity using IVM were also observed in two animal models, of SARS-CoV-2 and a related betacoronavirus. The indicated biological mechanism of IVM, competitive binding with SARS-CoV-2 spike protein, is likely non-epitope specific, possibly yielding full efficacy against emerging viral mutant strains.
The world is currently engaged in a race of vaccination vs. infection in an effort to control the Covid-19 pandemic. Some countries have already achieved high vaccination rates, offering a glimpse into the so-called "post-vaccination" world. We describe here a striking comparison between the similarly-sized and neighboring countries of Bahrain and Qatar. While both countries have achieved impressive vaccination rates, cases increased to unprecedented levels in one country, while decreasing steadily in the other. Although this could be attributed to a number of factors, we argue here that the heavy reliance on alum-adjuvanted inactivated virus vaccines may have contributed to these discrepant outcomes. We then expand the analysis to compare the top 10 vaccinated countries, and compare their outcomes based on their reliance on inactivated virus vaccines. The results remarkably align with the initial findings seen in Bahrain and Qatar. Countries that did not use inactivated virus vaccines achieved steady declines in daily Covid-19 deaths, while other countries did not. This work highlights the urgent need to further study the effectiveness of alum-adjuvanted inactivated virus vaccines for Covid-19 before expanding their use.
Graphical displays can reveal problems in a statistical model that might not be apparent from purely numerical summaries. Such visualizations can also be helpful for the reader to evaluate the validity of a model if it is reported in a scholarly publication or report. But, given the onerous costs involved, researchers often avoid preparing information-rich graphics and exploring several statistical approaches or tests available. The ggstatsplot package in the R programming language (R Core Team, 2021) provides a one-line syntax to enrich ggplot2-based visualizations with the results from statistical analysis embedded in the visualization itself. In doing so, the package helps researchers adopt a rigorous, reliable, and robust data exploratory and reporting workflow.
Pre-existing cross-reactivity to SARS-CoV-2 may occur in absence of prior viral exposure. However, this has been difficult to quantify at the population level due to a lack of reliably defined seroreactivity thresholds. Using an orthogonal antibody testing approach, we estimated that ~0.6% of non-triaged adults from the greater Vancouver area, Canada between May 17th and June 19th 2020 showed clear evidence of a prior SARS-CoV-2 infection, after adjusting for false-positive and false-negative test results. Using a highly sensitive multiplex assay and positive/negative thresholds established in infants in whom maternal antibodies have waned, we determine that more than 90% of uninfected adults showed antibody reactivity against the spike, receptor-binding domain (RBD), N-terminal domains (NTD) or the nucleocapsid (N) protein from SARS-CoV-2. This sero-reactivity was evenly distributed across age and sex, correlated with circulating coronaviruses reactivity, and was partially outcompeted by soluble circulating coronaviruses' spike. Using a custom SARS-CoV-2 peptide mapping array, we found that this antibody reactivity broadly mapped to spike, and to conserved non-structural viral proteins. We conclude that most adults display pre-existing antibody cross-reactivity against SARS-CoV-2, which further supports investigation of how this may impact the clinical severity of COVID-19 or SARS-CoV-2 vaccine responses.
Background
Despite high vaccine coverage and effectiveness, the incidence of symptomatic infection with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has been increasing in Israel. Whether the increasing incidence of infection is due to waning immunity after the receipt of two doses of the BNT162b2 vaccine is unclear.
Methods
We conducted a 6-month longitudinal prospective study involving vaccinated health care workers who were tested monthly for the presence of anti-spike IgG and neutralizing antibodies. Linear mixed models were used to assess the dynamics of antibody levels and to determine predictors of antibody levels at 6 months.
Results
The study included 4868 participants, with 3808 being included in the linear mixed-model analyses. The level of IgG antibodies decreased at a consistent rate, whereas the neutralizing antibody level decreased rapidly for the first 3 months with a relatively slow decrease thereafter. Although IgG antibody levels were highly correlated with neutralizing antibody titers (Spearman’s rank correlation between 0.68 and 0.75), the regression relationship between the IgG and neutralizing antibody levels depended on the time since receipt of the second vaccine dose. Six months after receipt of the second dose, neutralizing antibody titers were substantially lower among men than among women (ratio of means, 0.64; 95% confidence interval [CI], 0.55 to 0.75), lower among persons 65 years of age or older than among those 18 to less than 45 years of age (ratio of means, 0.58; 95% CI, 0.48 to 0.70), and lower among participants with immunosuppression than among those without immunosuppression (ratio of means, 0.30; 95% CI, 0.20 to 0.46).
Conclusions
Six months after receipt of the second dose of the BNT162b2 vaccine, humoral response was substantially decreased, especially among men, among persons 65 years of age or older, and among persons with immunosuppression.
The world tried to control the spread of COVID‐19 at national and regional levels through various mitigation strategies. In the first wave of infections the most extreme strategies included large scale national and regional lockdowns or stay‐at‐home orders. One major side‐effect of large scale lockdowns was the shuttering of the economy, leading to massive layoffs, loss of income and livelihood. Lockdowns were justified in part by scientific models (computer forecast and simulations) that assumed exponential growth in infections and predicted millions of fatalities without these “Non‐pharmaceutical interventions” (NPI). Some scientists questioned these assumptions. Regions that followed other softer mitigation strategies such as work from home, crowd limits, use of masks, individual quarantining, basic social‐distancing, testing and tracing – at least in the first wave of infections‐ saw similar health outcomes. Clear results were confusing, complicated and difficult to assess. Ultimately, in the US, what kind of mitigation strategy was enforced became a political decision only partly based on scientific models. We do not test for what levels of NPI are necessary for appropriate management of the first wave of the pandemic. Rather we use the “inverse‐fitting Gompertz function” methodology suggested by anti‐lockdown advocate and Nobel Laureate Dr. Levitts to estimate the rate of growth/decline in COVID‐19 infections as well to determine when disease peaking occurred. Our estimates may help predict levels of first wave infections in the future and help a region to monitor new outbreaks prior to opening its economy. The inverse fitting function is applied to the first wave of infections in the U.S. and in the hard hit New York and New Jersey regions for the time period March to June 2020. This is the earliest days of pandemic in the US. The estimates for the rates of growth/decline are computed and used to predict underlying future infections, so that decision makers can monitor the disease threat as they open their economies. This preliminary and exploratory analysis and findings are discussed briefly and presented primarily in charts and tables but the following waves of disease diffusion are not included and certainly were not anticipated.
