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Global COVID-19 Pandemic Outcomes:
Dissecting a Failed Strategy
James A. Thorp, MD SSM Health System, St. Louis, MO Department of ObGyn andDivision of Maternal Fetal Medicine. Gulf Breeze, Florida
Corresponding author jathorp@bellsouth.net, jathorpMFM@gmail.com,
Margery M. Thorp, JD MACP
Law Firm of Margery Thorp Esq PLLC, Gulf Breeze Florida
Elise M. Thorp, BS, FNTP, Private Independent Researcher, Williamston, Michigan
Ajovi Scott-Emuakpor, MD-PhD, Michigan State University, Lansing, Michigan
Department of Pediatrics, Division of Pediatric Oncology and Genetics
K. E. Thorp, MD Sparrow Hospital Lansing, Michigan
Department of Radiology, Division of Interventional Radiology

Abstract
Objectives to assess COVID-19 mortality rates per country population. To determine
what if any independent country-specific variables from 9 different databases werecorrelated.
Design population based retrospective cohort study.
Setting analysis of global COVID-19 treatment and containment strategies using datafrom 9 worldwide websites.
Participants 108 countries worldwide.
Interventions none.
Main Outcome Measures were COVID-19 death rates per country population analyzed
by univariate and multivariate analysis. The main outcome parameters were to
determine if there are any correlations between the percentage of countrywide COVID19 deaths/population by the countries’ percent vaccinated. Secondary outcome
measures include the effect of other independent variables on COVID-19 death rates
per country population including: health expenditures per capita, annual income percapita, COVID-19 tests per 1000 people, stringency index (a measure of each countries
containment strategies), hydroxychloroquine score (a measure of each countries use),ivermectin score (a measure of each countries use), hypertension, obesity, diabetes,and specific countries and geographic locations.
Results

1© 2022 by the author(s). Distributed under a Creative Commons CC BY license.

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COVID-19 vaccination rates ranged from 0-99% in 108 countries. Univariate analysis
demonstrates the following independent variables to correlate with COVID-19
deaths/population (correlation coefficient, p value): countrywide COVID-19 vaccination
rates (+0.2936, p=0.002); healthcare costs per capita (+0.3212, p=0.0007), income percapita (+0.3051, p=0.0013), COVID-19 tests per 1000 population (+0.6981 p=0.0307);
stringency index (+0.3098, p=0.0011); hydroxychloroquine index (-0.1337, p=0.0678);
and ivermectin index (-0.1383, p=0.1535).
Conclusions Increasing rates of COVID-19 vaccination are associated with increase
COVID-19 death rates per country population (p=0.002). Other variables associatedinclude healthcare costs per capita (+0.3212, p=0.0007), income per capita (+0.3051,
p=0.0013), COVID-19 tests per 1000 population (+0.6981 p=0.0307); and stringency
index (+0.3098, p=0.0011).
Key Words COVID-19 death rates, COVID-19 vaccines, hydroxychloroquine,
ivermectin, rates of COVID-19 testing, containment measures, social distancing, travel
restrictions, COVID-19 testing.

James A Thorp MD is the corresponding author jathorp@bellsouth.net,
jathorpMFM@gmail.com,

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Introduction
With global infection rates declining to the lowest levels in over a year, and many
countries having discontinued social containment policies which, in many cases, hadbeen in place since 2020, it would appear as though the pandemic is winding down.
Since its beginning nearly every facet of the pandemic has been chronicled andsubjected to deep analysis in one way or another. Undoubtedly this will be the mostthoroughly documented pandemic in history.
Despite such intense scrutiny, analysis of outcomes will ultimately be hampered by lackof valid historical comparisons, both in terms of the unparalleled modern data gathering
capacity as well as the unique nature of SARS-CoV-2 causal agent. Given such
limitations how is one to draw meaningful conclusions regarding the effectiveness of the
global response? What are the take-home lessons? How will the present COVID-19
response be used to guide future pandemic interventions?
Measures were not by any means applied uniformly across the globe. Broad disparities
were observed in how individual countries responded to the pandemic based not only
upon availability of resources, per capita wealth, composition of the population, but theability or inclination of sovereign nations to impose and enforce containment strategies.
To further complicate matters there were wide variations from country to country in thepercent of individuals that ultimately received the vaccines. Finally, there wereconfounding variables independent of vaccine status, particularly in less developed,
socioeconomically disadvantaged nations such as in Africa: namely, the broad use ofprophylactic agents such as hydroxychloroquine and ivermectin.
To address such striking incongruities in the pandemic response we identified a host ofvariables by which to compare global outcomes on a country-by-country basis andsubjected obtained data to statistical analysis.

Materials & Methods
To assess outcome variability, we assembled pandemic related data on a country-bycountry basis using population size, number of diagnosed COVID-19 cases, and thenumber of registered deaths, from which we calculated COVID-19 mortality/population.
These, in turn, were compared to the percentage of individuals in the population who
had been vaccinated. All data was obtained from the Johns Hopkins University
Coronavirus Resource Center.1 Data was collected between August 21 and September
21, 2022.
To evaluate potential confounding factors, we constructed a list of country-specificparameters that could potentially influence or modify outcomes in a particular locale:
hypertension rates; obesity rates; diabetes rates; percent of the population >65 years ofage; percent of the population <14 years of age; per capita health expenditures per
capita; and net annual per capita income. These were obtained from official sources.2-10

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To evaluate the effectiveness of social containment measures we developed astringency index based on a binary (0,1) weighting of 11 parameters: mandates,
masking, social distancing, curfews, quarantine, business/school closings, banning or
limiting public gatherings, lockdowns, travel ban, contact tracing, and PCR testing. The
highest possible stringency score was thus 11 while countries with more lax policies
were correspondingly lower. Data were obtained via internet search from the 9
websites.1-10
To evaluate the potential influence of background hydroxychloroquine and/or ivermectin
use we developed an HCQ score and an IVM score based upon a 5-tiered ranking
scale: no use (0); sporadic-to-limited use (1); limited-to-moderate use (2) moderate use(3); moderate-to-widespread use (4); country-wide use (5). Data were obtained via
internet search.
Finally, as a means of cross-correlating various outcomes with global testing initiatives,
we assayed the number of COVID-19 tests per 1000 people on a country-by-country
basis.9
Statistical analysis was performed using MedCalc® Statistical Software version 20.115(MedCalc Software Ltd, Ostend, Belgium; https://www.medcalc.org; 2022). Univariate
analytics were performed using correlation coefficients. Shapiro-Wilk test
is used for determining normal distributions and log transformation was performed whennecessary. A forward regression modeling used p values for entry if < 0.05 and removal
if p >0.10.
Results
Table 1 notes the descriptive statistics for the final variables in our model. A total of 108
countries were studied. The specific geographic locations were compared to all othersby assigning a binary variable (0,1) with 1 assigned to the specific geographic locationof interest (country or country groupings) and 0 to all other locations. None of thevariables were uniformly distributed as per Shapiro Wilk test (p < 0.01) and logarithmic
data transformation and non-parametric analysis were explored. There were missing
data in only one variable (5 missing, n=103) in the number of COVID-19 tests per 1000
population as this data was not available for 5 countries.
The range of countrywide COVID-19 vaccination rates ranged from 0 to 99% (n=108)
with a mean ± 1 standard deviation [SD] of 52.2 ± 0.3. The COVID-19 deaths per
population ranged from 0.00001 to 0.653 (n=108) with a mean (± 1 standard deviation[SD]) of 0.1309 ± 0.1413 and was not uniformly distributed (p<0.01). Only one country
reports a vaccination rate of zero and that is Burundi and their COVID-19 death rate per
population was quite low at 0.07 which is at the 5th %ile of all the other countries. For
comparative purposes, the 12 countries having the lowest vaccination rates are
depicted in Table 2 and the 12 with the highest rates in Table 3.

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Univariate Analysis
Figure 1 depicts a scatter plot of COVID-19 deaths per population by the country’s
vaccination rates. As the data was not uniform (p<0.01), a log transformation was
performed. The correlation coefficient was +0.2936 (n=108, p=0.0020, 95% confidence
interval [CI] 0.1108 to 0.4572). The higher the countrywide COVID-19 vaccination rates,
the higher the COVID-19 deaths per country population (p=0.0020).
Figure 2 depicts a scatter plot of COVID-19 deaths per country population versus
healthcare costs per capita (US dollars). As the data was not uniform (p<0.01), a logtransformation was performed. The correlation coefficient was +0.3212 (n=108,p<0.0007, 95% CI 0.1408 to 0.4810). The higher the country’s healthcare costs per
capita, the higher the COVID-19 deaths per country population (p=0.0007).
Figure 3 depicts a scatter plot of COVID-19 deaths per country population versus
annual income per capita (US dollars). As the data was not uniform (p=0.0013), a logtransformation was performed. The correlation coefficient was +0.3051 (n=108,
p=0.0013, 95% CI 0.1232 to 0.4671). The higher the annual income per capita, thehigher the COVID-19 deaths per country population (p=0.0013).
Figure 4 depicts a scatter plot of COVID-19 deaths per country population versus the
COVID-19 tests per 1000 population. As the data was not uniform (p<0.01), a logtransformation was performed. The correlation coefficient was 0.6981 (n=103,
p=0.0307, 95% CI 0.02045 to 0.3906). The higher the countrywide COVID-19 testing,the higher the COVID-19 deaths per country population (p=0.037).
Figure 5 depicts a scatter plot of COVID-19 deaths per population versus stringency
index (1-11). As the data was not uniform (p<0.01), a log transformation was performed.
The correlation coefficient was +0.1973 (n=108, p=0.047, 95% CI 0.0.008654 to
0.3724). The higher the country’s stringency index, the higher the COVID-19 deaths per
country population (p=0.047. Stringency index is based upon a binary (0,1) weighting of
11 parameters: mandates, masking, social distancing, curfews, quarantine,
business/school closings, banning or limiting public gatherings, lockdowns, travel ban,contact tracing, and PCR testing. The highest possible stringency score was thus 11
while countries with more lax policies were correspondingly lower. Data were obtained
via internet search from the 9 websites.1-10
Figure 6 depicts a scatter plot of COVID-19 deaths per population versus thehydroxychloroquine (HCQ) index. As the data was not uniform (p<0.01), a logtransformation was performed. The correlation coefficient was -0.1337 (n=108,
p=0.0678, 95% CI -0.3147 to 0.05672). Higher countrywide HCQ index is associatedwith lower COVID-19 deaths per country population (p=0.07).
Figure 7 depicts a scatter plot of COVID-19 deaths per population versus the ivermectin(IVM) index. As the data was not uniform (p<0.01), a log transformation was performed.
The correlation coefficient was -0.1383 (n=108, p=0.1535, 95% CI -0.3189 to +0.5204).

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IVM index is trended with lower COVID-19 deaths per country population but was notstatistically significant (p=0.1535).

Multivariate Analysis
Table 4 notes the forward regression model used COVID-19 deaths / population as thedependent variable and only two variables were retained (t-value, p value): East Asia(+2.932, 0.041) and Europe (+4.648, <0.0001). The following independent variables
were eliminated (enter variable if p<0.05 and remove variable if p>0.1): Africa, Australia,
Canada, Japan, Mexico, Middle East, diabetes rate, hypertension rate, obesity rate,
Russia, Sweden, USA, Age > 65, Age < 14, India, and Central & South America.

Discussion
The findings of this study demonstrate COVID-19 death rates per population are
positively correlated with countrywide COVID-19 vaccination rates (p=0.002); the higher
the percentage of a countries’ vaccination rate, the higher is the COVID-19 death
rate/population. Other positive correlations of secondary measures include healthcare
expenditure per capita (p=0.0007), national average income per capita (p=0.0013), rateof COVID-19 testing per 1000 population (p=0.0307), and stringency index (p=0.047).
We undertook this analysis to resolve outstanding questions regarding the efficacy of
global pandemic management strategies, namely, social containment measures suchas masking and lockdowns, widespread testing measures and the vaccine initiative. We
sought to address disparities concerning stated efficacies of these measures in relation
to reported global and regional case numbers which, in the end, seem to tell a different
story.
One question assumes primacy: how effective was the highly centralized pandemic
management strategy and, moreover, is it a viable approach in future pandemics? Itshould not be overlooked that this is the first pandemic in which widescale orchestrated
efforts were implemented. Is the world in better stead because these strategies were
employed?
Comparing reported case numbers at three points during the pandemic - early Januaryof 2020 2021, and 2022 - a pattern emerges of unimpeded spread regardless ofenacted measures: on January 1, 2020, as the pandemic was emerging (and before
data were even available) global case numbers were (perhaps) in the thousands.
During the first week of January 2021, after nearly ten months of containment
measures, 4,985,723 new cases were reported globally. During the first week ofJanuary 2022, after a year of containment measures and vaccine initiatives, there were
16,138,104 new cases translating to a 3.2-fold increase. By late-January at the peak of
the Omicron surge numbers had skyrocketed even further to 23,205,305 equating to a
4.6X increase (Figure 8).106

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Similar trends were seen in the US: in the first week of January 2021 the US tallied
1,667,173 new cases. During the first week of January 2022 these numbers had
climbed to 4,682,921, about a 2.8-fold increase. By mid-January the total hit 5,650,958
new cases corresponding to a 3.4-fold increment. Based on such data to even hint ofbeneficial outcomes related to social containment or mass vaccination is unfounded andin fact may increase the risk of COVID-19 deaths per population. This is corroborated
by the findings of this study as evidenced in Figure 5: the higher the countrywide
stringency index, the higher were the COVID-19 deaths per country population(p=0.047).
The reasons behind the uncontrolled spread are well established: it is estimated that atleast 50% of viral transmission occurs through asymptomatic or pre-symptomatic
carriers.11-18 This is to say that policies intended to curtail viral spread were, from the
beginning, doomed to fail. This, in turn, calls into question the value of mass
populational testing which, even under optimal circumstances, has a sensitivity of about
80% and, in the real world, no more than 50-60%. These facts argue against testingmeasures favorably impacting transmission dynamics.19-22
A similar case can be made regarding the vaccines. Mass deployment began in early2021 and by Spring a handful of countries, Israel in particular, were nearing the
hypothetical threshold for herd immunity and yet, once the Delta variant emerged, it
spread like wildfire independent of a country's vaccine status.23,24 It was only later
recognized that the vaccines don't confer immunity but simply induce short-term
protection by stimulating an antibody response. Therefore, booster doses became
necessary.25-33
Despite an aggressive booster campaign, the same results occurred when the Omicron
variant emerged in Fall of 2021. By January 2022 case rates, both globally and in theUS, reached the highest levels of the pandemic, far surpassing those at the beginningof 2021 when the vaccines were being rolled out. A large percentage of those infected
had received both vaccination and booster jabs. It is difficult to explain these results on
any basis other than primary failure of containment and vaccine strategies. 34-36
The failure to significantly impact pandemic outcomes incriminates top-down centralized
management strategies by entities such as WHO and large countries like the US. In
March 2020, WHO Director-General Ghebreysus announced “we have a simple
message for all countries: test, test, test.” Testing, he claimed, was essential to containthe spread of the virus. But how does a bureaucrat in New York City know what ishappening on the ground in Brazil, Indonesia, or Nigeria? The one-size-fits-all pandemic
management strategy was an unqualified disaster. The only viable solution is radical
decentralization of authority and policymaking.
The African experience is a powerful testament to this approach. For decades
throughout Africa widespread use of hydroxychloroquine (HCQ) and chloroquine (CQ)
has been a staple for the prevention and treatment of malaria. Not only had their7

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efficacy and safety been empirically well-established but they were cheap and widely
available. It seemed inevitable these two agents would figure into the African pandemic
strategy until the publication of one study. The findings in this study are consistent withthis strategy.
In May 2020 the medical journal Lancet published a meta-analysis of 96,000
hospitalized COVID-19 patients from 671 hospitals across the globe claiming that HCQ
had no benefit and was associated with increased risk of cardiac arrhythmias anddeath.37 The article, however, was fraudulent and subsequently retracted by Lancet.
But the damage was far-reaching. Not only were several ongoing clinical trials
discontinued but, in months to follow, oversight agencies such as the European
Medicines Agency (EMA), WHO and FDA issued warnings against their use. These
recommendations were met with skepticism on the African continent.37
Countries such as Egypt, Zambia, Nigeria, Tunisia, and South Africa chose to continue
clinical trials under the support of Africa Centers for Disease Control (ACDC). The
director of Nigeria’s National Agency for Food and Drug Administration and Control(NAFDAC), Dr. Mojisola Adeyeye, affirmed ongoing support of HCQ and claimed that
Nigeria would continue clinical trials despite the WHO warning: “The narrative mightchange later, but for now, we believe in hydroxychloroquine.”38
Other countries chose to ignore the edicts. The Economic Community of West African
States (ECOWAS) approved HCQ and CQ for the treatment of COVID-19 infection.
Ghana’s health minister, Kweku Agyeman-Manu, also approved HCQ for widespread
use and supported its efficacy. Similarly, Uganda continued its use in conjunction withazithromycin and claimed beneficial results. Djibouti continued to treat all COVID-19
infections with CQ/HCQ and azithromycin. Djibouitian health officials claimed that thedeath rate was only 0.5% and Dr. Maad Nasser Mohammed, top official of the COVID19 response center, claimed that the treatment regimen was the main reason for the low
death rate.
Algeria also used HCQ for COVID-19 despite the cessation of the WHO-sponsored
trials. Mohamed Bekkat, a member of the COVID-19 treatment committee, claimed thatthousands of cases had been successfully treated with the HCQ + azithromycin
combination with very few undesirable reactions. Moroccan Minister of Health Khalid
Ait Taleb vigorously defended its use in COVID-19 infections and claimed Morrocco
would continue to use CQ despite warnings from the WHO. Meanwhile, however, as
more data has emerged from countries across the globe the efficacy of HCQ and CQ inearly COVID-19 infection is widely substantiated.
Dr. Peter McCullough most eloquently communicated this important concept in his now
famous “Lesson Learned” Testimony to the Texas State Senate on June 28, 2022.39 As
McCullough emphasized the community standard of care should not emanate from a
top-down decree but quite the opposite. Community standard of care should beestablished at the ground level with experienced, local care providers treating their8Preprints (www.preprints.org) | NOT PEER-REVIEWED | Posted: 25 November 2022

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patients with their own wisdom, due diligence, and their own research. Communicationof these grassroots experiences among others then establishes a regional standard.
For decades throughout Africa widespread use of hydroxychloroquine (HCQ) and
chloroquine (CQ) has been a staple for the prevention and treatment of malaria. Not
only had their efficacy and safety been empirically well-established but they were cheap
and widely available. It seemed inevitable these two agents would figure into theAfrican pandemic strategy until the publication of one study. The greater availability of
HCQ by country, the less the COVID-19 deaths per country population (-0.1337,
p=0.0678).
The centralized statistically based management strategies implemented during the
COVID-19 pandemic utterly failed to achieve their stated goals. The socioeconomic andindividual consequences of the failed containment strategies such as lockdowns,
banning of public gatherings, and business closures far outweighed any possible benefit
in terms of loss of life or social well-being. Our analysis demonstrates negative
associations between containment strategies, widespread populational testing, massvaccination and disease outcomes, that is these interventions were associated withmore COVID-19 deaths per country population. Evidence suggests that future mass
infectious outbreaks would be managed more efficiently and effectively on the ground atregional levels where consequences are most directly felt.39,40
Limitations of this study are obvious: the conclusions made in this study are only as
reliable as the validity of the data abstracted from the 9 sources that we used toassemble our database.1-10 There is wide variation in case reporting from country tocountry and global outcomes could be potentially limited by inconsistencies. Multiple
regression analytics may be limited by non-uniform data, collinearity andheteroskedasticity.

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2022 https://www.cidrap.umn.edu/news-perspective/2022/08/study-pfizer-covid-vaccineefficacy-wanes-27-days-after-dose-2-teens30) Andrews N, Stowe J, Kirsebom F, et al. COVID-19 vaccine effectiveness against the
Omicron (B.1.1.529) variant. NEJM 2022 Apr;386(16):1532-46.
https://pubmed.ncbi.nlm.nih.gov/35249272/
31) UK Health Security Agency. COVID-19 vaccine surveillance report. Week 1 January
6, 2022.
https://assets.publishing.service.gov.uk/government/uploads/system/uploads/attachmen
t_data/file/1045329/Vaccine_surveillance_report_week_1_2022.pdf32) Amanatidou E, Gkiouliava A, Pella E, et al. Breakthrough infections after COVID-19
vaccination: Insights, perspectives and challenges. Metabol Open 2022 Mar;
14:100180 https://pubmed.ncbi.nlm.nih.gov/35313532/
33) Wilhelm A, Widera M, Grikscheit K, et al. Reduced Neutralization of SARS-CoV-2
Omicron Variant by Vaccine Sera and monoclonal antibodies. MedRxiv December 8, 2021.
https://www.medrxiv.org/content/10.1101/2021.12.07.21267432v2.full.pdf

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34) Thorp KE, Thorp JA, Thorp EM. COVID-19 and the Unraveling of Experimental
Medicine - Part I. G Med Sci. 2022; 3(1): 015-045.
https://www.doi.org/10.46766/thegms.pubheal.22012306
35) Thorp KE, Thorp JA, Thorp EM. COVID-19 and the Unraveling of Experimental
Medicine - Part II. G Med Sci. 2022; 3(1):074-106
https://www.doi.org/10.46766/thegms.pubheal.22022804
36) Thorp KE, Thorp JA, Thorp EM. COVID-19 and the Unraveling of Experimental
Medicine - Part III. G Med Sci. 2022; 3(1):118-158.
https://www.doi.org/10.46766/thegms.pubheal.22042302
37) Mehra MR, Desai SS, Ruschitzka F, Patel AN. RETRACTED: Hydroxychloroquineor chloroquine with or without a macrolide for treatment of COVID-19: a multinational
registry analysis. Lancet. 2020 May 22:S0140-6736(20)31180-6. doi: 10.1016/S01406736(20)31180-6. Epub ahead of print. Retraction in: Lancet. 2020 Jun 5;:null. Erratumin: Lancet. 2020 May 30;: Erratum in: Lancet. 2020 Jul 18;396(10245):e2-e3. PMID:
32450107; PMCID: PMC7255293. https://pubmed.ncbi.nlm.nih.gov/32450107/
38) Belayneh A. Off-Label Use of Chloroquine and Hydroxychloroquine for COVID-19
Treatment in Africa Against WHO Recommendation. Res Rep Trop Med 2020;11:61-72.
https://pubmed.ncbi.nlm.nih.gov/32982538/
39) Dr. Peter McCullough testifies in the Texas State Senate on Health & Human
Services: Lessons Learned on Community Standard of Care.
https://zeeemedia.com/interview/dr-peter-mccullough-md-mph-testifies-at-the-texassenate- (accessed 11.14.2022)
40) Thorp KE, Thorp MM, Thorp EM, Thorp JA. COVID-19 & Disaster Capitalism – Part
I. G Med Sci. 2022; 3(1):159-178
https://www.doi.org/10.46766/thegms.medethics.22071901

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Table 1 Descriptive Statistics for 108 countries.
Variable

Africa (0,1)
Australia (0,1)
Canada (0,1)
Central & South America (%)
East Asia (0,1)
Europe (0,1)
India (0,1)
Japan (0,1)
Mexico (0,1)
Middle East (0,1)
Russia (0,1))
USA (0,1)
Population
COVID-19 tests per 1000 population
COVID-19 Vaccinated (%)
COVID-19 cases
COVID-19 deaths
Pop > 65 years (%)
Pop < 14 years (%)
Net Annual Income per Capita ($)
Health Expense per Capita ($)
Diabetes (%)
Hypertension (%)
Obesity (%)
Hydroxychloroquine Score (0 – 5)
Ivermectin Score (0 – 5)Stringency Index (0 – 11)
COVID-19 deaths / Population (%)
COVID-19 deaths / COVID-19 cases

Range

Mean ± SD

586634 – 1.412B
5 – 21272
0 - 997571 – 93.75M
38 – 1.04M
1 - 2912 - 46
174 – 64,140
20 – 10,921
1 - 31
1 - 75
2 – 370-5
0-5
1 - 110.007 –0.47
0.000763 – 0.0784

50.85M ±144.1M
1710 ± 3214
52.2 ± 0.3
5.280M±11.92M
55257±139,091
10.1 ± 7.3
26.9 ± 0.1
4272 ±15,763
337 ± 2143
8 ± 4.5
16 ± 1719 ± 92.2 ± 1.9
1.6 ± 1.9
8.9 ± 2.2
0.07551 ± 0.128
0.01484 ± 0.01281

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Table 2 Depicts the 12 HIGHEST COVID-19 vaccination rates by country and includes
the COVID-19 death rate per population, COVID-19 testing rate per 1000 populationand Healthcare Expenses per annum per capita.
COVID-19
COVID-19
COVID-19
Healthcare
VaccineDeath Rate per Testing Rate
Expenses per
Country
Rate
Population
per 1000
Annum perPopulation
CapitaUnited Arab
99%
0.02
17884
$1843
Emirates
Qatar
96%
0.023
2818
$1807
Chile
92%
0.315
2040
$1376
New Zealand
0.035
1416
$4211
91%
Portugal
87%
0.24
4160
$2221
Spain
87%
0.24
1962
$2721
Singapore
86%
0.27
1800
$2632
South Korea
86%
0.05
1935
$2625
Australia
85%
0.052
2831
$5427
Peru
85%
0.653
859
$370
Argentina
84%
0.273
810
$946
Vietnam
84%
0.014
881
$181

Table 3 Depicts the 12 LOWEST COVID-19 vaccination rates by country and includes
the COVID-19 death rate per population, COVID-19 testing rate per 1000 populationand Healthcare Expenses per annum per capita.
COVID-19
COVID-19
COVID-19
Healthcare
VaccineDeath Rate per
Testing Rate
Expenses per
Country
Rate
Population
per 1000
Annum per Capita
Population
Burundi00.0700
128
$21
Haiti
2%
0.0300
18$57
Papua New3%
0.010025$65GuineaMadagascar
5%0.0200
16$20
Cameroon
5%0.0200100$54
Senegal7%0.0200
66
$59
Sudan
10%
0.086
12
$23
Gabon
12%
0.0800
12$215
Malawi12%0.0100683$30Somalia13%0.030030$22Nigeria14%0.050029$71South Sudan
14%0.010025$2315

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Table 4 Forward regression model used COVID-19 deaths / population as thedependent variable. The following independent variables were eliminated from thismodel with enter variable if p<0.05 and remove variable if p>0.1: Africa, Australia,
Canada, Japan, Mexico, Middle East, diabetes rate, hypertension rate, obesity rate,
Russia, Sweden, USA, Age > 65, Age < 14, India, and Central & South America.

Independent Variables

Constant
East Asia (0,1)
Europe (0,1)

Coefficient

0.038070.15690.1207

16

Standard
Error

t-valuep value

0.05352
0.02597

2.932
4.648

.0041
<0.0001

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Deaths per Country Population

Figure 1 depicts a scatter plot of COVID-19 deaths per population by the country’s
vaccination rates. As the data was not uniform (p<0.01), a log transformation was
performed. The correlation coefficient was +0.2936 (n=108, p=0.0020, 95% confidence
interval [CI] 0.1108 to 0.4572). The higher the countrywide COVID-19 vaccination rates,
the higher the COVID-19 deaths per country population (p=0.002).

.

C VID

.

.
.

.2

.

.

Percent of Country Vaccinated

17

.

.

Preprints (www.preprints.org) | NOT PEER-REVIEWED | Posted: 25 November 2022

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Deaths per Country Population

Figure 2 depicts a scatter plot of COVID-19 deaths per country population versus
healthcare costs per capita (US dollars). As the data was not uniform (p<0.01), a logtransformation was performed. The correlation coefficient was +0.3212 (n=108,
p=0.0007, 95% CI 0.1408 to 0.4810). The higher the country’s healthcare costs per
capita, the higher the COVID-19 deaths per country population (p=0.0007).

.

C VID

.

.
ealth

penditures per Capita

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Deaths per Country Population

Figure 3 depicts a scatter plot of COVID-19 deaths per country population versus
annual income per capita (US dollars). As the data was not uniform (p=0.0013), a logtransformation was performed. The correlation coefficient was +0.3051 (n=108,
p=0.0013, 95% CI 0.1232 to 0.4671). The higher the annual income per capita, thehigher the COVID-19 deaths per country population (p=0.0013).

.

C VID

.

.
Annual Income per Capita

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Deaths per Coungry Population

Figure 4 depicts a scatter plot of COVID-19 deaths per country population versus the
COVID-19 tests per 1000 population. As the data was not uniform (p<0.01), a logtransformation was performed. The correlation coefficient was +0.6981 (n=103,
p=0.0307, 95% CI 0.02045 to 0.3906). The higher the countrywide COVID-19 testing,the higher the COVID-19 deaths per country population (p=0.037).

.

C VID

.

.
C VID

ests per20

peoplePreprints (www.preprints.org) | NOT PEER-REVIEWED | Posted: 25 November 2022

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Deaths per Country Population

Figure 5 depicts a scatter plot of COVID-19 deaths per population versus stringency
index (1-11). As the data was not uniform (p<0.01), a log transformation was performed.
The correlation coefficient was +0.1973 (n=108, p=0.047, 95% CI 0.0.008654 to
0.3724). The higher the country’s stringency index, the higher the COVID-19 deaths per
country population (p=0.047). Stringency index is based upon a binary (0,1) weighting of
11 parameters: mandates, masking, social distancing, curfews, quarantine,
business/school closings, banning or limiting public gatherings, lockdowns, travel ban,contact tracing, and PCR testing. The highest possible stringency score was thus 11
while countries with more lax policies were correspondingly lower. Data were obtained
via internet search from the 9 websites.1-10

.

C VID

.

.
2

2
Stringency Inde

21

Preprints (www.preprints.org) | NOT PEER-REVIEWED | Posted: 25 November 2022

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Deaths per Country Population

Figure 6 depicts a scatter plot of COVID-19 deaths per population versus thehydroxychloroquine (HCQ) index. As the data was not uniform (p<0.01), a logtransformation was performed. The correlation coefficient was -0.1337 (n=108,
p=0.0678, 95% CI -0.3147 to 0.05672). Higher countrywide HCQ index is associatedwith lower COVID-19 deaths per country population (p=0.0678).

.

C VID

.

.
2
ydro ychloro uine Score

22

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Deaths per Country Population

Figure 7 depicts a scatter plot of COVID-19 deaths per population versus the ivermectin(IVM) index. As the data was not uniform (p<0.01), a log transformation was performed.
The correlation coefficient was -0.1383 (n=108, p=0.1535, 95% CI -0.3189 to +0.5204).
IVM index is trended with lower COVID-19 deaths per country population but was notstatistically significant (p=0.1535).

.

C VID

.

.
2
Ivermectin Score

23

5

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Figure 8 depicts global monthly global cases of COVID-19 and the monthly COVID-19
deaths from the World Health Organization.

24