Obtain up to date COVID-19 data from Our World in Data (⊕Hannah Ritchie and Roser 2020Hannah Ritchie, Lucas Rodés-Guirao, Edouard Mathieu, and Max Roser. 2020. “Coronavirus Pandemic (COVID-19).” Our World in Data.)

It is necessary to download the appropriate data sets including:

1. The data from ⊕Hannah Ritchie and Roser (2020)Hannah Ritchie, Lucas Rodés-Guirao, Edouard Mathieu, and Max Roser. 2020. “Coronavirus Pandemic (COVID-19).” Our World in Data.55 The data from COVID-19 case and death data from Our World in Data is taken directly from the data provided by Johns Hopkins University. is updated daily and can be downloaded and converted to a data.frame using the R code in this paper, or can downloaded directly from here as a .csv: https://covid.ourworldindata.org/data/owid-covid-data.csv.

2. It was also necessary to convert the data on vaccine types in use by each country (⊕wikipedia 2021wikipedia. 2021. “List of COVID-19 Vaccine Authorizations.” https://en.wikipedia.org/w/index.php?title=List_of_COVID-19_vaccine_authorizations&oldid=1049981876.)66 The data for vaccine brands and their use in different countries comes from hundreds of sources that have been aggregated by Wikipedia into a comprehensive list to a .csv format, this was done with standard spreadsheet techniques in Google Sheets. This data set I have made publicly available here: https://docs.google.com/spreadsheets/d/1egKoaLyAt_9JoWKqr8uZDxw3xIj9J-Fu33S9sMRL4XQ/edit?usp=sharing

# Folder where data is stored, **CHANGE THIS AS NECESSARY**
data_folder <- file.path("./indices[USED]/")

#*** UNCOMMENT FOR NEW DATA ***
# Download most recent OWID COVID-19 data set 
#url <- "https://covid.ourworldindata.org/data/owid-covid-data.csv"
#name <- "owid-covid-data.csv"
#download.file(url = url, destfile = paste0(data_folder,name))

## Load the reports ##
# Set directory to location of CSV files first
setwd(data_folder)
# Read files without csv
filenames <- gsub("\\.csv$","", list.files(pattern="\\.csv$"))
# Load all files
for(i in filenames){
  assign(i, read.csv(paste(i, ".csv", sep=""),fileEncoding="latin1"))
}

Clean data and merge datasets

Once all the data is obtained it can be ‘cleaned’ for faster data processing. This involves removing unnecessary variables from the data sets and renaming columns so all variable names are in agreement. This can be done with the R code included in the supplmentary material for this report.

Run Causal Analysis for all dependent variables

The R package CausalImpact (⊕Brodersen et al. 2015Brodersen, Kay H., Fabian Gallusser, Jim Koehler, Nicolas Remy, and Steven L. Scott. 2015. “Inferring Causal Impact Using Bayesian Structural Time-Series Models.” Annals of Applied Statistics 9: 247–74. https://doi.org/10.1214/14-aoas788.) utilizes a robust series of Bayesian calculations along with predictor data sets to determine the likely trajectory of a trend line had a particular intervention not occurred and then calculates the difference between that projected trend line and the real data line. The authors of the package summarized this impressive set of calculations and its improvements upon previous methods as a,

“…method [that] generalises the widely used difference-in-differences approach to the time-series setting by explicitly modelling the counterfactual of a time series observed both before and after the intervention. It improves on existing methods in two respects: it provides a fully Bayesian time-series estimate for the effect; and it uses model averaging to construct the most appropriate synthetic control for modelling the counter factual” (⊕Brodersen et al. 2015, 247Brodersen, Kay H., Fabian Gallusser, Jim Koehler, Nicolas Remy, and Steven L. Scott. 2015. “Inferring Causal Impact Using Bayesian Structural Time-Series Models.” Annals of Applied Statistics 9: 247–74. https://doi.org/10.1214/14-aoas788.).

Effectively, this allows us to look at the past 12-16 months (each country is slightly different) before vaccine administration began, this is called the pre-intervention period, and utilize that data to project where \(y1\) (total deaths per million) and \(y2\) (total cases per million) would have been had the intervention of \(X\) (vaccine administration) not occurred, what the authors call a “counterfactual” (⊕Brodersen et al. 2015, 248–49Brodersen, Kay H., Fabian Gallusser, Jim Koehler, Nicolas Remy, and Steven L. Scott. 2015. “Inferring Causal Impact Using Bayesian Structural Time-Series Models.” Annals of Applied Statistics 9: 247–74. https://doi.org/10.1214/14-aoas788.). Utilizing this estimated counterfactual and the confidence level associated with that estimation we can then compare it with the actual data available and see if there is any difference. If the projected estimation is higher than the actual results, it will appear as a negative impact, while if the projected estimation is lower than the actual results, it will appear as a positive impact.

Another aspect of the CausalImpact package is the ability to add control variables that are combined into a “synthetic control” (⊕Brodersen et al. 2015Brodersen, Kay H., Fabian Gallusser, Jim Koehler, Nicolas Remy, and Steven L. Scott. 2015. “Inferring Causal Impact Using Bayesian Structural Time-Series Models.” Annals of Applied Statistics 9: 247–74. https://doi.org/10.1214/14-aoas788.) that closely mimic the trend line, but that are exogenous (i.e. have not been affected by the intervention); this allows for even more accurate predictions and normally a lower p-value and a lower standard deviation. To obtain control variable data sets the authors of CausalImpact recommend utilizing similar data that did not receive the same treatment, such as a different region or country, any trend line that can closely mimic or even be constant alongside the trend line one is testing (e.g. similar data from an area with no intervention, price charts, stock prices, temperature records, etc.). As there are virtually zero countries that have been unaffected by the vaccines, finding a set of control variables is difficult, though not impossible. Ultimately, this study chose to utilize the data of four countries in Africa (Burkina Faso, Chad, DRC, South Sudan) that were chosen specifically for their low average severity indices since vaccine administration began (i.e. low levels of mandatory mask wearing, social distancing, crowd limitations, travel restrictions, etc.) and for their low vaccination rates. These countries in the author’s estimation best represent a “natural” progression of the virus with limited vaccine intervention on par with most other nations, making them the least likely to display problems of endogeneity while still acting as valid control groups.

These carefully selected control variables as a synthetic control combined with the formula presented by ⊕Brodersen et al. (2015)Brodersen, Kay H., Fabian Gallusser, Jim Koehler, Nicolas Remy, and Steven L. Scott. 2015. “Inferring Causal Impact Using Bayesian Structural Time-Series Models.” Annals of Applied Statistics 9: 247–74. https://doi.org/10.1214/14-aoas788., which utilizes the data pre-intervention as part of its equation to predict the counterfactual, means that the other synthetic control effectively being utilized internally in the equation is a dynamic calculation for each country based on its own individual experience of COVID-19’s impacts on total deaths and cases per million sans vaccines for, as mentioned above, approximately 12-16 months.

As ⊕Brodersen et al. (2015)Brodersen, Kay H., Fabian Gallusser, Jim Koehler, Nicolas Remy, and Steven L. Scott. 2015. “Inferring Causal Impact Using Bayesian Structural Time-Series Models.” Annals of Applied Statistics 9: 247–74. https://doi.org/10.1214/14-aoas788. explained,

“The approach described in this paper inherits three main characteristics from the state-space paradigm. First, it allows us to flexibly accommodate different kinds of assumptions about the latent state and emission processes underlying the observed data, including local trends and seasonality. Second, we use a fully Bayesian approach to inferring the temporal evolution of counterfactual activity and incremental impact. One advantage of this is the flexibility with which posterior inferences can be summarised. Third, we use a regression component that precludes a rigid commitment to a particular set of controls by integrating out our posterior uncertainty about the influence of each predictor as well as our uncertainty about which predictors to include in the first place, which avoids overfitting” (⊕Brodersen et al. 2015, 251Brodersen, Kay H., Fabian Gallusser, Jim Koehler, Nicolas Remy, and Steven L. Scott. 2015. “Inferring Causal Impact Using Bayesian Structural Time-Series Models.” Annals of Applied Statistics 9: 247–74. https://doi.org/10.1214/14-aoas788.)

In other words, by utilizing the data for total deaths and cases per million from before vaccines existed and combining that with a synthetic control of countries largely non-participatory in the COVID-19 vaccine program, the R package CausalImpact is able to produce a high degree of certainty in the results.77 average p-value for \(y1.E\) = 0.0039783; average p-value for \(y2.E\) = 0.00375 The control countries’ average severity indices since vaccine administration and vaccination rates are listed in Table 1.

This analysis was carried out on all countries in the data set that met the following criteria: (1) more than five observations (i.e. dates), (2) a longer pre intervention period than post intervention period, and (3) no NAs in the observations necessary for calculation.

# CAUSAL ANALYSIS ---------------------------------------------------------
# Variables for Function

# Select data frame  
df0 <- df_owid
# Create a countries list
countries <- c(unique(df_owid$ISO))
# Folder where plots will be saved
plots.loc <- file.path("./plots/causalImpact/totalCases/run6/")

## DEPENDENT VARIABLES

###############################################################################
# Here you must declare your variables. It is necessary to re-declare variables
# for each y variable that you intend to test. The only variable necessary to 
# change is where it says ** CHANGE THIS **, otherwise leave the code as is.
###############################################################################

# Declare function name and variables
impactReports <- function(df0, countries, plots.loc) {

# Begin for loop
for (i in countries) {

# Declare Data Frame to Use with Time Series
df <- df0
df <- dplyr::filter(df, ISO == i) # Use ISO codes to loop every country

# Assure the Date column is in the correct format
dates <- as.character(df$date) # change to text
df$date = as.Date(dates, format="%Y-%m-%d") # change to date

# Treatment - Independent Variable
vacc <- dplyr::select(df,date,total_vaccinations_per_hundred) # INDEPENDENT VARIABLE
vacc <- unique(vacc) # Make sure there are no duplicate observations
vacc <- na.omit(vacc) # Make sure there are no observations with NA

# Dependent Variable

## Dependent Variable ** CHANGE THIS **
df <- dplyr::select(df,date,total_cases_per_million)

df <- unique(df) # only choose unique values
df <- na.omit(df) # remove all

## Dependent Variable ** CHANGE THIS **
dfy <- df$total_cases_per_million 

# Obtain length of Dependent Variable for time-series calculations
dfyN <- as.numeric(length(dfy))

# Add in data sets that will be used for creating the Synthetic Control data
# This paper ultimately used the four countries mentioned, however the research
# process also included analyzing the results of other potential control candidates.
# Thus, included here are four other countries that were analyzed in this fashion,
# their conclusion ultimately not being used because of lower confidence levels and/or
# higher standards of deviation suggesting a less reliable synthetic control model.
# This data is included here for full transparency and for any other researchers that
# may like to analyze these other potential control countries and their results. 

# NOT INCLUDED IN THIS PAPER, BUT INCLUDED IN FULL DATASET
#dfy1 <-  BDI$total_deaths_per_million[1:dfyN] # Burundi
#dfy3 <-  HTI$total_deaths_per_million[1:dfyN]# Haiti
#dfy5 <-  YEM$total_deaths_per_million[1:dfyN] # Yemen
#dfy7 <-  TZA$total_deaths_per_million[1:dfyN] # Tanzania

# SELECTED CONTROL COUNTRIES FOR USE IN CAUSAL IMPACT ANALYSIS
dfy2 <-  COD$total_cases_per_million[1:dfyN] # DRC
dfy4 <-  SSD$total_cases_per_million[1:dfyN] # South Sudan
dfy6 <-  TCD$total_cases_per_million[1:dfyN] # Chad
dfy8 <-  BFA$total_cases_per_million[1:dfyN] # Burkina Faso

# Combine data frames of control countries
dfy <- cbind(dfy,dfy2,dfy4,
             dfy6,dfy8)

# Remove any NA values
dfy <- na.omit(dfy)

# Obtain new length of data frame of the Dependent Variable
dfyN2 <- as.numeric(nrow(dfy))

# Create time-series from this dataframe
dfx <- df$date[1:dfyN2]  # Time Series   

# Make sure all data has more than 5 observations
if (length(dfx) < 5 | nrow(dfy) < 5 | nrow(vacc) < 5) { 
 
   # Announce stop and move to next if logic unfulfilled 
cat(paste0("###### stopping ",i," at step 2 #######")) 
  next
} else {

#Declare Data Frame Variables
df.ts <- dfx # Dates as characters
df.score <-dfy  # Y
df.time <- df$d # Day number

#Calculate length of data frame
days <- as.Date(max(df.ts)) - as.Date(min(df.ts)) # Find length of data frame
days <- as.numeric(days) # Make the string a number

#Declare time points from data  
time.points <- try(seq.Date(as.Date(min(df.ts)), by = 1, length.out = days))

#Declare time series variables
SCORE.Y <- ts(df.score) # Dependent Variable
TIME.C <- ts(df.time) # Time Series

#Bind them into test groups
test <- try(zoo(cbind(SCORE.Y, TIME.C), time.points))

# Announce start of next step
cat(paste0("###### starting ",i," step 3 #######")) 

df <- na.omit(vacc) # Remove any NAs

if (anyNA(df) == TRUE) {
  
cat(paste0("###### stopping ",i," at step 4 #######")) 
  next
  } else {
    
# Treatment Period 
treatmentS <- (min(df$date)) # start of treatment period (i.e. first vaccines administered)
treatmentE <- max(df$date) # end of treatment period (i.e. ongoing)

# Pre-Treatment Period 
preperS <- min(dfx) # start of pre-treatment period
preperE <- treatmentS - 1 # end of pre-treatment period
# Post-Treatment Period 
postperS <- (treatmentS) # start of treatment period (i.e. first vaccines administered)
postperE <- treatmentE # end of treatment period
# Use when you need an exact date
pre.period <- c(preperS,preperE) # declare pre period start and end dates
post.period <- c(postperS,postperE) # declare post period start and end dates

# assure pre-period is longer than post-period
if ((preperE - preperS) < (postperE - postperS) | anyNA(test) == TRUE) { 

   # Stop and move to next country if logic unfulfilled
  cat(paste0("###### stopping ",i," at step 5 #######")) 
  next
  
} else {
cat(paste0("###### calculating ",i,"#######")) # Announce beginning of calculation
impact <- try(CausalImpact(test, pre.period, post.period)) # Calculate Impact
Sys.sleep(1)
x <- "Total Vaccinations Per Million" # CHANGE ONLY IF YOU CHANGE X VARIABLE
y <- "Total Cases Per Million" # ** CHANGE THIS  FOR EACH Y VARIABLE**
cat(paste0("###### plotting ",i,"#######")) # Announce beginning of plotting
try(print(plot(impact) +     # Print the plot
  ggplot2::labs(
  title = paste0(i, # Title the plot
                ": Causal Impact Plot",
                " | ", 
                x,
                " effect on ",
                y),
  caption = 
paste("Source: Data collected from OWID, analyzed and plotted by Kyle Beattie using RStudio as of", 
      date())
) +
  theme_stata())) # Choose a theme
Sys.sleep(1) # Let plot be created
cat(paste0("###### saving plots and reports ",i," #######")) # Announce saving of plot
setwd(plots.loc) # Set the drive to the plots folder
try(savePlotAsImage(paste0(i,      # Save Plot As TITLE
                       "_:_Causal_Impact_Plot",
                       "_|_", 
                       x,
                       "_effect_on_",
                       y,
                       "_Report.png"), 
                format = "png", 
                width =  1920, # set ratio sizes
                height = 1080))
Sys.sleep(1) # Let plot be saved
try(summary(impact, "report")) # Produce Summary Report
try(summary(impact)) # Produce Summary Report 2
report<<-try(capture.output(summary(impact, "report"))) # Capture Report
report2<<-try(capture.output(summary(impact)))
try(cat(report, file = paste0(i,     # Write report to txt
                          ": Causal Impact Plot",
                          " | ", 
                          x,
                          " effect on ",
                          y,"Report.txt"), append = TRUE))

try(stargazer(report2, 
          summary = FALSE,
          type = "text",
          out = paste0(plots.loc,i,     # Write report to txt
                          ": Causal Impact Plot",
                          " | ", 
                          x,
                          " effect on ",
                          y,"Report2.txt")))
               
     #    }         
        }       
      }
    }
  }
}

# RUN FUNCTION ------------------------------------------------------------

# Run this function to produce all the plots and reports above, use try() to pass over errors
try(impactReports(df0,countries,plots.loc), silent = TRUE)

Extract Causal Analysis results and merge with main dataset

After completing the causal analysis on all countries and printing a report and plot for each country, the data was filtered and cleaned by removing all results with p > 0.05 and turning all causal impact results into both whole numbers (ex. y1.effect_percent for Japan = 48%) and decimals (ex. y1.effect_dec for Japan = 0.48), both representing the percentage of positive or negative impact in two different forms. This data was then integrated back with the original data frame for further analyses and data presentation.

# ANALYZE CAUSAL IMPACT OUTPUT -------------------------------------------------

############################# FUNCTION VARIABLES ############################
# loc_i = the location of text files for import (as file.path)
# loc_o = the location to output csv and txt files (as file.path)
# name = name for csv and txt documents (as character spaces ok)
# y = dependent variable (as character no spaces)
############################################################################ 

### Extract necessary data from the Causal Impact Analysis Reports ###

 dat.extr.CausalReports <- function(loc_i,loc_o,name,y) {
   
   ## Data location
   data_folder <- file.path(loc_i)
   # Set directory to location of CSV files first
   setwd(data_folder)
   # Read files without csv
   filenames <- gsub("\\.txt$","", list.files(pattern="\\.txt$"))
   # Create data frame first
   df <- data.frame(matrix(ncol = 2, nrow = 0))
   # Load all files
   for(i in filenames){
     df0 <- assign(i, readtext(paste(i, ".txt", sep="")))
     df <- rbind(df,df0)
   }
   
   ## ISO
   # Extract first three characters to obtain ISO
   df$ISO <- substr(df$doc_id, 1, 3) 
   
   ## p-value
   # Extract all three digit values
   df$p <- str_extract(df$text,"p = 0.\\d{3}") 
    # Extract all two digit values
   df$p2 <- str_extract(df$text, "p = 0.\\d{2}")
   # Replace NAs in three digit values with two digit values
   df$p[is.na(df$p)] <- df$p2[is.na(df$p)] 
   # Remove two digit column
   df <- dplyr::select(df, -p2) 
   # Remove p = sign and convert to numeric
   df$p <- gsub("p = ", "", df$p) %>% as.numeric() 
   # Remove all non-statistically significant observations
   df <- filter(df, p < 0.05 ) 
   
   ## % Effect
   # separate this part of the text
   df$effect <- str_extract(df$text, "the response variable showed .*?%") 
   # remove this part leaving only the %
   df$effect <- gsub("the response variable","",df$effect) 
   # remove this text or that
   df$effect <- gsub("^( showed an increase of | showed a decrease of )", "", df$effect) 
   # combine text to make an easy readout for the public
   df$effect_txt <- paste(df$ISO,": Vaccine effect on",y,df$effect) 
   
   ## Interval
   # Extract only % numbers between []
   df$interval <- str_extract(df$text, "\\[(\\+|\\-)\\d*%.*?\\]") 
   
   ## Effect as Decimal
   # Remove all symbols and convert to numeric
   df$effect_dec <- gsub("%","",df$effect) %>% as.numeric() 
   # Turn percentage to decimal
   df$effect_dec <- df$effect_dec / 100 
   
   ## Merge the effect changes and p values with a new data frame
   # Select only necessary columns
   df5 <- dplyr::select(df,ISO,p,effect,effect_dec) 
   setnames(df5, # Change columns names for the main data
            c("p","effect","effect_dec"),
            c(paste0(y,".p"),paste0(y,".effect"),paste0(y,".effect_dec")))
   
# Write Data Frame to CSV, change data frame, path, and name variables as necessary
write.csv(df5,                            
            paste0(loc_o,name,date(),".csv"), 
            na = "",   
            fileEncoding = "UTF-8")
   
# Write statistical table to txt
stargazer(df5,                 # Export txt
         summary = FALSE,
         type = "text",
         out = paste0(loc_o,name,date(),".txt"))
 }

# RUN FUNCTION
# Variables
loc_i <- file.path("./plots/causalImpact/totalCases/run6/")
loc_o <- file.path("./plots/causalImpact/data_output/")
name <- "CausalAnalysis-vaccines-by-totalCases"
y <- "y2"

# FUNCTION
dat.extr.CausalReports(loc_i,loc_o,name,y)

# Analyze output
df <- read.csv("./plots/causalImpact/data_output/CausalAnalysis-
               vaccines-by-totalCasesTue Oct  28 11:50:13 2021.csv") %>%
  as.data.frame()
cntP <- str_count(df$y2.effect,"\\+") # count number of positive occurrences
count(cntP) # results

Correlation Analysis of Causal Analysis Results

After integrating the results of the Causal Impact Analysis with the original data frame, Research Question 2 was addressed through follow-up correlational analyses. These were calculated with the resulting dependent variables from the Causal Impact Analysis (\(y1.E\): y1.effect_percent = effect of vaccine intervention on total deaths per million) and (\(y2.E\): y2.effect_percent = effect of vaccine intervention on total cases per million) utilizing ggstatsplot (⊕Patil 2021Patil, Indrajeet. 2021. “Visualizations with statistical details: The ’ggstatsplot’ approach.” Journal of Open Source Software 6 (61): 3167. https://doi.org/10.21105/joss.03167.) for all variables listed in Research Question 2.

Plot Results

Scatter plot and correlational matrix results were used to analyze the statistical significance of the correlation between the dependent variables and the independent numerical variables. The scatter plots and correlational matrices include the \(\rho\) (Spearman) scores and their respective p-values. Dot plots were utilized to show the distribution of the dependent variables with the independent categorical variables, they include the Wilcoxon signed rank value and respective p-value.

Causal Impact Results

The results of the CausalImpact package were produced as both figures and as automatic report summaries for each country. All figures are included as attached PDFs to this report and all figures can be requested in high quality 1920x1280 .png files from the author free of charge. All report summaries are included as .txt files in the supplementary data to this report.

The following figures and report summaries represent a sample of more than 150 figures produced by this code and data. These examples highlight a variety of countries with decreases, average increases, and substantial increases in deaths and cases associated with COVID-19 as a causal impact of vaccine deployment. Included below select figures is also an example of the report summary that was produced by CausalImpact, these report summaries assist in explaining how to interpret each figure from a statistical perspective.

To read these figures one should analyze the three different graphs that are labeled on the right \(y\) axis as Original, Pointwise, and Cumulative. The Original graph represents the actual recorded data as the solid black line; the dashed blue line represents the counterfactual, the predicted trend line had the intervention of vaccine deployment not occurred; and the light blue fill around the counterfactual shows the degree of potential statistical variance, less light blue fill signifies a more accurate counterfactual. The moment of vaccine deployment varies between countries and is represented by the vertical gray dashed line. The Pointwise graph shows all of the positive or negative causal impacts by calculating the difference between the counterfactual and the recorded data. Finally, the Cumulative graph sums all of the positive or negative causal impacts since the intervention began to show an upward (positive), downward (negative) or neutral (near zero) causal impact.

y1.E: Total Causal Impact from Vaccine Administration on Total Deaths Per Million

y2.E: Total Causal Impact from Vaccine Administration on Total Cases Per Million

Density Plots

The following density plots and tables present a larger view of the results. The density plots present data for each continent for all countries with results up to +500%.

Tables

The two tables here represent all the countries with statistically significant results, they show the percentage change in \(y1\) and \(y2\) as a direct causal impact of vaccine deployment.

Causal Impact of Vaccine Deployment on Total Deaths per Million associated with COVID-19

X	ISO	y1.p	y1.effect	y1.effect_dec
1	AFG	0.001	+32%	0.32
2	AGO	0.001	+29%	0.29
3	ALB	0.001	+35%	0.35
4	ARE	0.001	+71%	0.71
5	ARG	0.001	+23%	0.23
6	ATG	0.001	+279%	2.79
7	AUS	0.007	-22%	-0.22
8	AUT	0.005	+26%	0.26
9	AZE	0.004	+17%	0.17
10	BGD	0.001	+33%	0.33
11	BHS	0.019	+13%	0.13
12	BIH	0.001	+30%	0.30
13	BLR	0.001	+43%	0.43
14	BLZ	0.009	-19%	-0.19
15	BOL	0.027	+13%	0.13
16	BRA	0.001	+52%	0.52
17	BRB	0.001	+96%	0.96
18	BRN	0.001	+65%	0.65
19	BTN	0.033	+35%	0.35
20	BWA	0.001	+168%	1.68
21	CAF	0.046	-12%	-0.12
22	CAN	0.001	+31%	0.31
23	CHL	0.001	+29%	0.29
24	CHN	0.026	-20%	-0.20
25	CIV	0.001	+43%	0.43
26	CMR	0.001	+25%	0.25
27	COL	0.001	+25%	0.25
28	CPV	0.001	+32%	0.32
29	CUB	0.001	+245%	2.45
30	CYP	0.001	+87%	0.87
31	DEU	0.001	+127%	1.27
32	DJI	0.001	+27%	0.27
33	DZA	0.007	-7%	-0.07
34	ECU	0.030	+11%	0.11
35	EGY	0.001	+20%	0.20
36	ESP	0.001	+16%	0.16
37	ETH	0.001	+19%	0.19
38	FIN	0.001	+35%	0.35
39	FJI	0.001	+2499%	24.99
40	FRA	0.001	+28%	0.28
41	GAB	0.001	+26%	0.26
42	GBR	0.001	+35%	0.35
43	GEO	0.001	+23%	0.23
44	GHA	0.001	+26%	0.26
45	GIN	0.001	+56%	0.56
46	GNB	0.003	+13%	0.13
47	GRD	0.001	+1180%	11.80
48	GTM	0.044	+8%	0.08
49	GUY	0.001	+64%	0.64
50	HKG	0.030	-13%	-0.13
51	HND	0.001	+26%	0.26
52	HRV	0.001	+43%	0.43
53	HTI	0.002	+11%	0.11
54	HUN	0.001	+99%	0.99
55	IDN	0.001	+100%	1.00
56	IND	0.001	+29%	0.29
57	IRL	0.001	+33%	0.33
58	ITA	0.001	+24%	0.24
59	JAM	0.001	+91%	0.91
60	JOR	0.001	+56%	0.56
61	JPN	0.001	+48%	0.48
62	KAZ	0.001	+94%	0.94
63	KEN	0.001	+33%	0.33
64	KWT	0.001	+24%	0.24
65	LBN	0.001	+74%	0.74
66	LBR	0.001	+75%	0.75
67	LCA	0.001	+250%	2.50
68	LKA	0.001	+437%	4.37
69	MAR	0.047	-12%	-0.12
70	MCO	0.001	+741%	7.41
71	MDA	0.001	+17%	0.17
72	MDG	0.021	+11%	0.11
73	MDV	0.001	+108%	1.08
74	MEX	0.001	+28%	0.28
75	MKD	0.001	+28%	0.28
76	MLI	0.018	+13%	0.13
77	MLT	0.005	+19%	0.19
78	MMR	0.001	+71%	0.71
79	MNE	0.001	+30%	0.30
80	MNG	0.001	+19015%	190.15
81	MOZ	0.001	+64%	0.64
82	MUS	0.001	+69%	0.69
83	MWI	0.003	+22%	0.22
84	MYS	0.001	+212%	2.12
85	NAM	0.001	+227%	2.27
86	NER	0.029	-12%	-0.12
87	NIC	0.005	-18%	-0.18
88	NPL	0.001	+133%	1.33
89	NZL	0.002	-19%	-0.19
90	OMN	0.001	+32%	0.32
91	PAK	0.001	+28%	0.28
92	PER	0.001	+20%	0.20
93	PHL	0.001	+38%	0.38
94	PNG	0.001	+263%	2.63
95	PRY	0.001	+156%	1.56
96	PSE	0.004	+14%	0.14
97	ROU	0.001	+34%	0.34
98	RUS	0.001	+77%	0.77
99	RWA	0.001	+107%	1.07
100	SAU	0.018	-10%	-0.10
101	SEN	0.001	+43%	0.43
102	SGP	0.025	-17%	-0.17
103	SOM	0.005	+24%	0.24
104	SRB	0.001	+32%	0.32
105	SUR	0.001	+103%	1.03
106	SVK	0.001	+276%	2.76
107	SVN	0.001	+27%	0.27
108	SWE	0.001	+22%	0.22
109	SYC	0.001	+10680%	106.80
110	SYR	0.001	+31%	0.31
111	TGO	0.001	+24%	0.24
112	THA	0.001	+887%	8.87
113	TLS	0.002	+2356%	23.56
114	TTO	0.001	+266%	2.66
115	TUN	0.001	+56%	0.56
116	TUR	0.001	+32%	0.32
117	TWN	0.001	+2767%	27.67
118	TZA	0.001	+427%	4.27
119	UGA	0.001	+235%	2.35
120	UKR	0.001	+43%	0.43
121	URY	0.001	+507%	5.07
122	USA	0.001	+31%	0.31
123	VCT	0.013	+26%	0.26
124	VEN	0.001	+62%	0.62
125	VNM	0.001	+707%	7.07
126	VUT	0.025	-39%	-0.39
127	ZMB	0.001	+85%	0.85
128	ZWE	0.001	+48%	0.48

Causal Impact of Vaccine Deployment on Total Cases per Million associated with COVID-19

X	ISO	y2.p	y2.effect	y2.effect_dec
1	AFG	0.006	-12%	-0.12
2	AGO	0.001	+52%	0.52
3	ALB	0.001	+54%	0.54
4	ARE	0.001	+72%	0.72
5	ARG	0.001	+66%	0.66
6	ATG	0.001	+184%	1.84
7	BGD	0.014	+11%	0.11
8	BHS	0.001	+47%	0.47
9	BIH	0.047	+18%	0.18
10	BLZ	0.042	-14%	-0.14
11	BOL	0.001	+22%	0.22
12	BRA	0.001	+37%	0.37
13	BRB	0.001	+91%	0.91
14	BRN	0.001	+381%	3.81
15	BTN	0.001	+90%	0.90
16	BWA	0.001	+140%	1.40
17	CAF	0.001	-38%	-0.38
18	CAN	0.001	+59%	0.59
19	CHL	0.001	+45%	0.45
20	COG	0.018	-11%	-0.11
21	COL	0.001	+34%	0.34
22	CPV	0.001	+46%	0.46
23	CUB	0.001	+168%	1.68
24	CYP	0.001	+82%	0.82
25	DEU	0.001	+52%	0.52
26	DMA	0.001	+407%	4.07
27	ECU	0.001	+24%	0.24
28	ESP	0.001	+44%	0.44
29	FIN	0.001	+59%	0.59
30	FJI	0.001	+12240%	122.40
31	FRA	0.001	+49%	0.49
32	GBR	0.001	+46%	0.46
33	GEO	0.003	+24%	0.24
34	GHA	0.002	-16%	-0.16
35	GNQ	0.001	-23%	-0.23
36	GRD	0.001	+309%	3.09
37	GTM	0.001	+30%	0.30
38	GUY	0.001	+100%	1.00
39	HND	0.001	+19%	0.19
40	HTI	0.001	-15%	-0.15
41	HUN	0.005	+27%	0.27
42	IDN	0.001	+106%	1.06
43	IND	0.001	+74%	0.74
44	IRL	0.001	+120%	1.20
45	IRN	0.001	+64%	0.64
46	IRQ	0.001	+51%	0.51
47	ITA	0.001	+39%	0.39
48	JAM	0.001	+82%	0.82
49	JOR	0.001	+53%	0.53
50	JPN	0.001	+45%	0.45
51	KAZ	0.001	+38%	0.38
52	KEN	0.001	+28%	0.28
53	KHM	0.001	+5808%	58.08
54	KNA	0.001	+1051%	10.51
55	KOR	0.001	+49%	0.49
56	KWT	0.001	+50%	0.50
57	LAO	0.001	+6955%	69.55
58	LBN	0.001	+47%	0.47
59	LBR	0.001	+20%	0.20
60	LBY	0.001	+31%	0.31
61	LCA	0.001	+125%	1.25
62	LKA	0.001	+170%	1.70
63	MCO	0.001	+124%	1.24
64	MDA	0.007	+12%	0.12
65	MDG	0.024	-16%	-0.16
66	MDV	0.001	+156%	1.56
67	MEX	0.001	+41%	0.41
68	MKD	0.003	+23%	0.23
69	MLI	0.027	+11%	0.11
70	MLT	0.001	+46%	0.46
71	MMR	0.037	+39%	0.39
72	MNE	0.001	+20%	0.20
73	MNG	0.001	+3391%	33.91
74	MOZ	0.001	+46%	0.46
75	MRT	0.001	-22%	-0.22
76	MUS	0.001	+536%	5.36
77	MYS	0.001	+102%	1.02
78	NAM	0.001	+84%	0.84
79	NGA	0.024	-15%	-0.15
80	NIC	0.001	-25%	-0.25
81	NLD	0.001	+31%	0.31
82	NPL	0.001	+39%	0.39
83	OMN	0.027	+42%	0.42
84	PAK	0.029	-7%	-0.07
85	PER	0.001	+30%	0.30
86	PHL	0.001	+101%	1.01
87	PNG	0.001	+191%	1.91
88	PRY	0.001	+117%	1.17
89	PSE	0.002	+19%	0.19
90	RUS	0.001	+22%	0.22
91	RWA	0.001	+118%	1.18
92	SAU	0.003	-28%	-0.28
93	SDN	0.001	-29%	-0.29
94	SEN	0.001	+24%	0.24
95	SGP	0.001	-46%	-0.46
96	SLE	0.017	-10%	-0.10
97	SMR	0.001	+25%	0.25
98	SRB	0.001	+32%	0.32
99	SUR	0.001	+62%	0.62
100	SVK	0.001	+29%	0.29
101	SVN	0.001	+58%	0.58
102	SWE	0.001	+60%	0.60
103	SWZ	0.001	+36%	0.36
104	SYC	0.001	+1978%	19.78
105	SYR	0.001	+29%	0.29
106	TGO	0.001	+76%	0.76
107	THA	0.001	+381%	3.81
108	TLS	0.001	+839%	8.39
109	TTO	0.001	+199%	1.99
110	TUN	0.001	+68%	0.68
111	TWN	0.001	+475%	4.75
112	TZA	0.001	+210%	2.10
113	UGA	0.001	+48%	0.48
114	UKR	0.001	+32%	0.32
115	URY	0.001	+390%	3.90
116	USA	0.001	+38%	0.38
117	VCT	0.001	+35%	0.35
118	VEN	0.001	+41%	0.41
119	VNM	0.001	+1099%	10.99
120	ZMB	0.001	+66%	0.66
121	ZWE	0.001	+50%	0.50

Categorical Variables - Dot plots of variables \(y1.E\), \(y2.E\), \(c1\), \(c2\), and \(c3\)

To test and display categorical variables, dot plots were created using the R package ggstatsplot (⊕Patil 2021Patil, Indrajeet. 2021. “Visualizations with statistical details: The ’ggstatsplot’ approach.” Journal of Open Source Software 6 (61): 3167. https://doi.org/10.21105/joss.03167.). These plots demonstrate the distribution of vaccine effect across continent, country and vaccine brands in use in each country. As this data is non-parametric, these charts also present the Wilcoxon signed rank value (\(V\)) to demonstrate whether the differences between groups is different from the null of 0. This demonstrates whether different continents, countries, or vaccine brands show different levels of \(y1.E\) or \(y2.E\) or whether the effect of vaccine administration was uniform.

Numerical Variables - Correlation Matrix and Scatterplot of \(y1.E\), \(y2.E\), \(n1\), \(n2\), and \(n3\)

As this data is non-parametric, all significant correlation calculations are presented with Spearman’s \(\rho\) (rho). This correlation matrix shows how all numerical variables relate to one another. The stronger the correlation, the bluer the box appears, if there is an X over the number that means it has a p-value > 0.05 and is not statistically significant. The only statistically significant result of importance for this study is the correlation between total vaccinations per hundred people and effect of vaccine intervention on total cases per million (\(\rho\): 0.3384496, p: 9.7^{-4}).

Increase in Death and Cases as a Causal Impact of Vaccine Administration

Countries with few COVID-19 deaths in the year 2020 appear to have fared the worst of all countries after vaccine administration (e.g Thailand, Vietnam, Mongolia, Taiwan, Seychelles, Cambodia, etc.). The causal impact results from vaccine administration seen in these countries of hundreds or thousands of percentage increases in total deaths and cases per million are also the causal impact results we can be most statistically confident in due to the direct increase of COVID-19 associated deaths and cases after vaccine administration, where prior to vaccine administration there were few or none. Notably, the results we can be least statistically confident about are many of the results suggesting a negative causal impact from vaccine administration (e.g. Saudi Arabia, China, Nigeria, Belize, etc.).

Some might try to argue that these results indicate a rise in cases and deaths associated with COVID-19 of those who have not taken COVID-19 experimental gene therapy injections, or that perhaps these deaths are as a result of some new more contagious variant such as Delta. As to the first point, while this data is still inconclusive, especially on a worldwide scale as was this study’s focus, there is beginning to emerge a pattern of a similar amount of cases and deaths of COVID-19 in relation to the population that is vaccinated as is evidenced by public records from Public Health England and the Israeli Ministry of Health. In addition, if that counterargument were true, we would expect to see countries with higher vaccination rates also have lower (or negative) impacts from vaccine administration on rates of cases and deaths associated with COVID-19. Instead, we see the opposite, a low positive correlation (\(\rho\): 0.34, p < 0.001) between total vaccinations per hundred and the impact of vaccine administration on cases associated with COVID-19. These results concur with the fact that the vaccines only offer a low absolute risk reduction (ARR) (0.8-1.9%) (⊕Olliaro, Torreele, and Vaillant 2021Olliaro, Piero, Els Torreele, and Michel Vaillant. 2021. “COVID-19 Vaccine Efficacy and Effectiveness—the Elephant (not) in the Room.” The Lancet Microbe.) in the first place and have been shown to wane over time to an even lower ARR (⊕Levin et al. 2021Levin, Einav G, Yaniv Lustig, Carmit Cohen, Ronen Fluss, Victoria Indenbaum, Sharon Amit, Ram Doolman, et al. 2021. “Waning Immune Humoral Response to BNT162b2 Covid-19 Vaccine over 6 Months.” New England Journal of Medicine. https://doi.org/10.1056/nejmoa2114583.; ⊕Chemaitelly et al. 2021Chemaitelly, Hiam, Patrick Tang, Mohammad R Hasan, Sawsan AlMukdad, Hadi M Yassine, Fatiha M Benslimane, Hebah A Al Khatib, et al. 2021. “Waning of BNT162b2 Vaccine Protection Against SARS-CoV-2 Infection in Qatar.” New England Journal of Medicine. https://doi.org/10.1101/2021.08.25.21262584.; ⊕Wang et al. 2021Wang, Zijun, Fabian Schmidt, Yiska Weisblum, Frauke Muecksch, Christopher O Barnes, Shlomo Finkin, Dennis Schaefer-Babajew, et al. 2021. “mRNA Vaccine-Elicited Antibodies to SARS-CoV-2 and Circulating Variants.” Nature 592 (7855): 616–22. https://doi.org/10.1101/2021.01.15.426911.).

To the latter point, the calculations in the CausalImpact package and the code presented above accounted for the differing dates when the vaccine administration started in each country and it is not likely that the Delta variant arrived in each of these countries precisely at the time each vaccine administration also started. Rather, it is more likely that the vaccine administration causes a bottleneck effect in each region and helps to create even more deadly variants as ⊕Ausschuss et al. (2021)Ausschuss, Stiftung Corona, Dr. Reiner Fuellmich, Dr. Wolfgang Woodard, and Dr. Luc Montagnier. 2021. “Prof. Dr. Med. Luc Montagnier (IN ENGLISH) Full Interview June 2021.” https://www.youtube.com/watch?v=Nd5yFvU3mpg., ⊕Bossche (2021)Bossche, Geert Vanden. 2021. “Open Letter to All Authorities, Scientists and Experts Around the World.” Geert Vanden Bossche. https://www.geertvandenbossche.org/post/opencall., and ⊕Ricke and Malone (2020)Ricke, Darrell, and Robert W Malone. 2020. “Medical Countermeasures Analysis of 2019-nCoV and Vaccine Risks for Antibody-Dependent Enhancement (ADE).” Available at SSRN 3546070. all warned, which may translate into a rise in cases and deaths associated with COVID-19 as a result of the causal impact of vaccine administration.

Farr’s Law, Gompertz Function

As noted by other authors (⊕Pacheco-Barrios et al. 2020Pacheco-Barrios, Kevin, Alejandra Cardenas-Rojas, Stefano Giannoni-Luza, and Felipe Fregni. 2020. “COVID-19 Pandemic and Farr’s Law: A Global Comparison and Prediction of Outbreak Acceleration and Deceleration Rates.” PloS One 15 (9): e0239175. https://doi.org/10.1371/journal.pone.0239175.), many countries that reported high cases and deaths associated with COVID-19 during 2020 and early 2021 showed a standard example of Farr’s Law and/or the Gompertz Function when viewed cumulatively per capita as an inverse Gompertz function (⊕Haynes and Kulkarni 2021Haynes, Kingsley E, and Rajendra Kulkarni. 2021. “Modeling Region Based Regimes for COVID-19 Mitigation: An Inverse Gompertz Approach to Coronavirus Infections in the USA, New York, and New Jersey.” Regional Science Policy & Practice.) or when predicted as a “best straight line” (⊕Levitt, Scaiewicz, and Zonta 2020Levitt, Michael, Andrea Scaiewicz, and Francesco Zonta. 2020. “Predicting the Trajectory of Any Covid19 Epidemic from the Best Straight Line.” medRxiv. https://doi.org/10.1101/2020.06.26.20140814.). In other words, these countries appeared to have largely achieved natural immunity by late spring of 2021 in the Northern Hemisphere, which is why many of their trend lines go flat for a time. Unfortunately, once the vaccine administration started for the general population, or shortly thereafter, those trend lines began to increase again in many countries, and unnaturally so in the middle or towards the end of summer in the Northern hemisphere or in countries where previously there had been very few if any cases or deaths. Normally, seasonal die off from pneumonia, influenza, or COVID-19 is in the winter, so this spike that appears in many countries after vaccine administration at this time of the year or in countries with no previous outbreaks is highly suspect as not being a natural trend, but rather vaccine-induced.

These results are consistent with the waning effect of COVID-19 vaccine protection mentioned above, the amount of “breakthrough” cases we are currently witnessing (⊕Musser et al. 2021Musser, James M, Paul A Christensen, Randall J Olsen, Scott Wesley Long, Sishir Subedi, James J Davis, Parsa Hodjat, Debbie R Walley, Jacob C Kinskey, and Jimmy D Gollihar. 2021. “Delta Variants of SARS-CoV-2 Cause Significantly Increased Vaccine Breakthrough COVID-19 Cases in Houston, Texas.” medRxiv.), and the overwhelming and historically unprecedented quantity of reports of vaccine adverse events in the Vaccine Adverse Events Reporting System (VAERS), over 16,000 reported deaths as of writing this report (⊕Health and Services 2021Health, and Human Services. 2021. “Vaccine Adverse Event Reporting System 2021 Dataset.” https://vaers.hhs.gov/data.html.), suggesting a highly untested vaccine. At the same time, the robust, durable, and long-lasting natural immunity that occurs with infection from SARS-CoV or SARS-CoV-2 (⊕Majdoubi et al. 2021Majdoubi, Abdelilah, Christina Michalski, Sarah E O’Connell, Sarah Dada, Sandeep Narpala, Jean Gelinas, Disha Mehta, et al. 2021. “A Majority of Uninfected Adults Show Preexisting Antibody Reactivity Against SARS-CoV-2.” JCI Insight 6 (8).) combined with extremely low absolute risk reduction (0.8-1.9%) (⊕Olliaro, Torreele, and Vaillant 2021Olliaro, Piero, Els Torreele, and Michel Vaillant. 2021. “COVID-19 Vaccine Efficacy and Effectiveness—the Elephant (not) in the Room.” The Lancet Microbe.) from available vaccines, make the risks of these vaccines likely outweigh the benefits for most if not all of the population.

At the very least, these results suggest that COVID-19 vaccine administration as a public policy over 80% of the time does not have a statistically significant causal impact of lowering total deaths or cases per million, but rather a statistically significant impact in increasing total deaths or cases per million associated with COVID-19 over and above what would have been expected if no vaccines were ever administered.

Obviously, the results here will be shocking to many who have perhaps been paying more attention to official government/media narrative rather than on the ground data and evidence. However, for those that have been paying attention to the pleas, warnings, and publicly voiced frustrations of many of the brightest and bravest minds in the scientific, medical and investigative community (e.g. Dr. Luc Montagnier (⊕Ausschuss et al. 2021Ausschuss, Stiftung Corona, Dr. Reiner Fuellmich, Dr. Wolfgang Woodard, and Dr. Luc Montagnier. 2021. “Prof. Dr. Med. Luc Montagnier (IN ENGLISH) Full Interview June 2021.” https://www.youtube.com/watch?v=Nd5yFvU3mpg.), Dr. Michael Yeadon (⊕Yeadon 2020Yeadon, Michael. 2020. “Lies, Damned Lies and Health Statistics–the Deadly Danger of False Positives.” Lockdown Scept. URL Https://Lockdownsceptics. Org/Lies-Damned-Lies-and-Health-Statistics-the-Deadly-Danger-of-False-Positives/(accessed 10.6. 20).; ⊕Borger et al. 2021Borger, Pieter, Bobby Rajesh Malhotra, Michael Yeadon, Clare Craig, Kevin McKernan, Klaus Steger, Paul McSheehy, et al. 2021. “Addendum to the Corman-Drosten Review Report.”), Dr. Robert Malone (⊕Ricke and Malone 2020Ricke, Darrell, and Robert W Malone. 2020. “Medical Countermeasures Analysis of 2019-nCoV and Vaccine Risks for Antibody-Dependent Enhancement (ADE).” Available at SSRN 3546070.), Dr. Wolfgang Woodard (⊕Wodarg and Scarlattilaan 2020Wodarg, Wolfgang, and Domenico Scarlattilaan. 2020. “Petition/Motion for Administrative/Regulatory Action Regarding Confirmation of Efficacy End Points and Use of Data in Connection with the Following Clinical Trial (s): Phase III-Eudract Number: 2020-002641-2. Corona-Ausschuss. De.” http://enformtk.u-aizu.ac.jp/views/docs/EMA_FDA_Petition.pdf.), Dr. Geert Vanden Bossche (⊕Bossche 2021Bossche, Geert Vanden. 2021. “Open Letter to All Authorities, Scientists and Experts Around the World.” Geert Vanden Bossche. https://www.geertvandenbossche.org/post/opencall.), Dr. Peter McCullough (⊕Bruno et al. 2021Bruno, Roxana, Peter A Mccullough, Teresa Forcades I Vila, Alexandra Henrion-Caude, Teresa Garcia-Gasca, Galina P Zaitzeva, Sally Priester, et al. 2021. “SARS-CoV-2 Mass Vaccination: Urgent Questions on Vaccine Safety That Demand Answers from International Health Agencies, Regulatory Authorities, Governments and Vaccine Developers.” Authorea Preprints.), and over 860,000 others (⊕Kulldorff, Gupta, and Bhattacharya 2020Kulldorff, Martin, Sunetra Gupta, and Jay Bhattacharya. 2020. “Great Barrington Declaration.” Great Barrington Declaration 4.; ⊕Kampf and Kulldorff 2021Kampf, Gunter, and Martin Kulldorff. 2021. “Calling for Benefit–Risk Evaluations of COVID-19 Control Measures.” The Lancet 397 (10274): 576–77.)) or have been studying the raw data themselves, these results likely come as no surprise.

Vaccine Mandates

If they ever were ethical, which this author disagrees with strongly based on scientific, medical, and research ethics that regard the patient rights to informed consent and non-prejudcial refusal of treatment or experimentation as iterated and agreed to under the Nuremberg Code (⊕Code 1998Code, Nuremberg. 1998. “The Nuremberg Code. 1949.” The Ethics of Biomedical Research. An International Perspective. Oxford University Press, New York, 213.), Helsinki Accords (⊕Association and others 2009Association, World Medical, and others. 2009. “Declaration of Helsinki. Ethical Principles for Medical Research Involving Human Subjects.” Jahrbuch Für Wissenschaft Und Ethik 14 (1): 233–38. https://doi.org/10.1515/9783110208856.233.), and the Human Rights Declaration on Bioethics (⊕UNESCO 2019UNESCO. 2019. “Universal Declaration on Bioethics and Human Rights.” UNESCO. https://en.unesco.org/themes/ethics-science-and-technology/bioethics-and-human-rights.) as inalienable, essential, and non-negotiable, vaccine mandates under these conditions and results are beyond unethical at this point, they are clearly discriminatory and likely criminal, a determination courts and lawyers will ultimately decide.

The results of this study taken together demonstrate a product that directly causes more COVID-19 associated cases and deaths than otherwise would have existed with zero vaccines. Consequently, these experimental gene therapy injections known as COVID-19 vaccines cannot be mandated by any public policy that intends to continue following the regulations of the Nuremberg Code (⊕Code 1998Code, Nuremberg. 1998. “The Nuremberg Code. 1949.” The Ethics of Biomedical Research. An International Perspective. Oxford University Press, New York, 213.), the Helsinki Accords (⊕Association and others 2009Association, World Medical, and others. 2009. “Declaration of Helsinki. Ethical Principles for Medical Research Involving Human Subjects.” Jahrbuch Für Wissenschaft Und Ethik 14 (1): 233–38. https://doi.org/10.1515/9783110208856.233.), and the Human Rights Declaration on Bioethics (⊕UNESCO 2019UNESCO. 2019. “Universal Declaration on Bioethics and Human Rights.” UNESCO. https://en.unesco.org/themes/ethics-science-and-technology/bioethics-and-human-rights.).