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Positive association between COVID-19 deaths and influenza vaccination rates in elderly people worldwide

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Background. The coronavirus disease 2019 (COVID-19) pandemic, caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), is an ongoing global health crisis, directly and indirectly impacting all spheres of human life. Some pharmacological measures have been proposed to prevent COVID-19 or reduce its severity, such as vaccinations. Previous reports indicate that influenza vaccination appears to be negatively correlated with COVID-19-associated mortality, perhaps as a result of heterologous immunity or changes in innate immunity. The understanding of such trends in correlations could prevent deaths from COVID-19 in the future. The aim of this study was therefore to analyze the association between COVID-19 related deaths and influenza vaccination rate (IVR) in elderly people worldwide. Methods. To determine the association between COVID-19 deaths and influenza vaccination, available data sets from countries with more than 0.5 million inhabitants were analyzed (in total 39 countries).To accurately estimate the influence of IVR on COVID-19 deaths and mitigate effects of confounding variables, a sophisticated ranking of the importance of different variables was performed, including as predictor variables IVR and some potentially important geographical and socioeconomic variables as well as variables related to non-pharmaceutical intervention. The associations were measured by non-parametric Spearman rank correlation coefficients and random forest functions. Results. The results showed a positive association between COVID-19 deaths and IVR of people ≥ 65 years-old. There is a significant increase in COVID-19 deaths from eastern to western regions in the world. Further exploration is needed to explain these findings, and additional work on this line of research may lead to prevention of deaths associated with COVID-19.
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Positive association between COVID-19
deaths and inuenza vaccination rates in
elderly people worldwide
Christian Wehenkel
Instituto de Silvicultura e Industria de la Madera, Universidad Juárez del Estado de Durango,
Durango, Mexico
ABSTRACT
Background: The coronavirus disease 2019 (COVID-19) pandemic, caused by
severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), is an ongoing global
health crisis, directly and indirectly impacting all spheres of human life. Some
pharmacological measures have been proposed to prevent COVID-19 or reduce
its severity, such as vaccinations. Previous reports indicate that inuenza vaccination
appears to be negatively correlated with COVID-19-associated mortality,
perhaps as a result of heterologous immunity or changes in innate immunity.
The understanding of such trends in correlations could prevent deaths from
COVID-19 in the future. The aim of this study was therefore to analyze the
association between COVID-19 related deaths and inuenza vaccination rate (IVR)
in elderly people worldwide.
Methods: To determine the association between COVID-19 deaths and inuenza
vaccination, available data sets from countries with more than 0.5 million inhabitants
were analyzed (in total 39 countries). To accurately estimate the inuence of IVR on
COVID-19 deaths and mitigate effects of confounding variables, a sophisticated
ranking of the importance of different variables was performed, including as
predictor variables IVR and some potentially important geographical and
socioeconomic variables as well as variables related to non-pharmaceutical
intervention. The associations were measured by non-parametric Spearman rank
correlation coefcients and random forest functions.
Results: The results showed a positive association between COVID-19 deaths and
IVR of people 65 years-old. There is a signicant increase in COVID-19 deaths
from eastern to western regions in the world. Further exploration is needed to explain
these ndings, and additional work on this line of research may lead to prevention of
deaths associated with COVID-19.
Subjects Epidemiology, Global Health, Immunology, Infectious Diseases, Public Health
Keywords SARS-CoV-2, Global health crisis, Risk factors, Virus interference,
Geographical longitude, Lockdown, Face mask use
INTRODUCTION
The coronavirus disease 2019 (COVID-19) pandemic, caused by severe acute respiratory
syndrome coronavirus 2 (SARS-CoV-2), is an ongoing global health crisis (Yuen et al.,
2020), directly and indirectly impacting all spheres of human life (Ozili & Arun, 2020).
How to cite this article Wehenkel C. 2020. Positive associatio n between COVID-19 deaths and inuenza vaccination rates in elderly people
worldwide. PeerJ 8:e10112 DOI 10.7717/peerj.10112
Submitted 5 August 2020
Accepted 16 September 2020
Published 1 October 2020
Corresponding author
Christian Wehenkel,
wehenkel@ujed.mx
Academic editor
Antonio Palazón-Bru
Additional Information and
Declarations can be found on
page 15
DOI 10.7717/peerj.10112
Copyright
2020 Wehenkel
Distributed under
Creative Commons CC-BY 4.0
More than 31,000,000 conrmed cases including more than 970,000 deaths have been
documented worldwide, affecting 213 countries and territories around the world
(https://covid19.who.int/).
Determining the factors inuencing the severity of COVID-19 is important
(Armengaud et al., 2020). Although COVID-19 disease does not only affect elderly people,
the severity of symptoms increases with age (https://www.cdc.gov/coronavirus/2019-ncov/
need-extra-precautions/older-adults.html;Le Couteur, Anderson & Newman, 2020).
Several other risk factors have been found for severe COVID-19, such as comorbidities,
dyspnea, chest pain, cough, expectoration, decreased lymphocytes, and increased
inammation indicators (Li et al., 2020). Low socioeconomic status is an additional risk
factor (Yancy, 2020).
In response to the increasing numbers of COVID-19 cases and deaths, numerous
non-pharmaceutical interventions have been implemented, including social distancing,
border closures, school closures, measures to isolate symptomatic individuals and their
contacts, and large-scale lockdowns of populations (Courtemanche et al., 2020;Flaxman
et al., 2020). Some pharmacological measures have also (often controversially) been
proposed in order to prevent COVID-19 disease or reduce its severity, such as the use of
remdesivir (Beigel et al., 2020), dexamethasone (RECOVERY Collaborative Group, 2020),
adjunctive therapies (https://les.covid19treatmentguidelines.nih.gov/guidelines/section/
section_85.pdf) and COVID-19 candidate vaccines (Graham, 2020,https://www.who.int/
publications/m/item/draft-landscape-of-covid-19-candidate-vaccines).
The term heterologous immunityis applied when an infection by one pathogen can
induce and/or alter the immune response against another unrelated pathogen.
Heterologous immunity can improve or decrease protective immunity against a given
pathogen, and/or cause severe immunopathology or tolerance to self-antigens.
Heterologous immunity can also result in non-specic effects (also called heterologous
effects) of vaccines which affect unrelated infections and diseases, such as extending the
protective outcomes of vaccinations (Goodridge et al., 2016;Agrawal, 2019). Arokiaraj
(2020) reported a negative correlation between inuenza vaccination rates (IVRs) and
COVID-19 related mortality and morbidity. Marín-Hernández, Schwartz & Nixon (2020)
also showed epidemiological evidence of an association between higher inuenza vaccine
uptake by elderly people and lower percentage of COVID-19 deaths in Italy. In a study
analyzing 92,664 clinically and molecularly conrmed COVID-19 cases in Brazil, Fink
et al. (2020) reported that patients who received a recent u vaccine experienced on
average 17% lower odds of death. Moreover, Pawlowski et al. (2020) analyzed the
immunization records of 137,037 individuals who tested positive in a SARS-CoV-2
PCR. They found that polio, Hemophilus inuenzae type-B, measles-mumps-rubella,
varicella, pneumococcal conjugate (PCV13), geriatric u, and hepatitis A/hepatitis B
(HepA-HepB) vaccines, which had been administered in the past 1, 2, and 5 years, were
associated with decreased SARS-CoV-2 infection rates.
By contrast, in a study with 6,120 subjects, Wolff (2020) reported that inuenza
vaccination was signicantly associated with a higher risk of some other respiratory
diseases, due to virus interference. In a specic examination of non-inuenza viruses,
Wehenkel (2020), PeerJ, DOI 10.7717/peerj.10112 2/18
the odds of coronavirus infection (but not the COVID-19 virus) in vaccinated individuals
were signicantly higher, when compared to unvaccinated individuals (odds ratio = 1.36).
Given that heterologous immunity could improve protective immunity against
COVID-19 and, thus, prevent COVID-19 deaths in the future, the aim in this study was to
analyze the possible association between COVID-19 deaths and the IVR in elderly people
worldwide. A negative association was expected.
MATERIALS AND METHODS
To look for an association between COVID-19 deaths and inuenza vaccination,
I analyzed available data sets from 39 countries, each with 0.5 million inhabitants.
In smaller states (i.e., <0.5 million inhabitants), the rate of erroneous identication of
COVID-19 deaths may be particularly high due to the lack of expertise, measuring devices
and experience. Moreover, in such microstates small absolute changes in COVID-19
deaths may result in extreme values of relative indices, such as COVID-19 deaths per
million inhabitants (DPMI) and COVID-19 Case Fatality Ratio (CFR).
I analyzed the variables DPMI and CFR, based on documented COVID-19 cases per
million inhabitants (CPMI) in 2020, COVID-19 tests per million inhabitants, and IVR (%)
in people 65 years old in 2019 or latest available data (Table 1). I recorded the DPMI,
CPMI and CFR data from the public web site https://www.worldometers.info/coronavirus/.
Then, I calculated CFR as the rate of DPMI per CPMI. IVR data were also taken from
https://data.oecd.org/healthcare/inuenza-vaccination-rates.htm,https://oecdcode.org/
disclaimers/israel.html and https://www.statista.com/chart/16575/global-u-immunization-
rates-vary/ (retrieved on July 25, 2020). Vietnams 2017 IVR was recorded from Nguyen
et al. (2020), and Singapores 2016/2017 IVR from https://www.todayonline.com/
commentary/why-singapores-adult-vaccination-rate-so-low.
To analyze the data, I rst calculated the non-parametric Spearman rank correlation
coefcient (r
s
) and its R2
Sand respective p-value (2-tailed) to determine any association
between DPMI and CFR with IVR, using R(R Core Team, 2017). As the relationship
between DPMI and the number of people tested for COVID-19 was not statistically
signicant based on r
s
and its p-value, I did not modied (corrected) the DPMI data set.
Then, I created regression curves by Generalized additive model (GAM) using the
ggplot2package and function (method = gam)(Wickham, Chang & Wickham, 2013),
also in R.
As the analysis included countries with different socioeconomic status, demographic
structure, urban/rural settings, time of arrival of the pandemic and national control
strategies, there may be complex interactions between IVR and other correlated predictor
variables. With the aim of accurately estimating the inuence of IVR on DPMI and CFR
and mitigating the effects of confounding variables, I performed variable importance
ranking, including as predictor variables IVR and some potentially important
geographical, socioeconomic and non-pharmaceutical-intervention variables (Escobar,
Molina-Cruz & Barillas-Mury, 2020). I used the centroid longitudes () and latitudes ()of
each country as geographical variables calculated by the rgeosand rworldmap
packages, along with the getMapand gCentroidfunctions, implemented in
Wehenkel (2020), PeerJ, DOI 10.7717/peerj.10112 3/18
Table 1 Raw data (part 1). Countries with their inuenza vaccination rate (IVR) (%) of people aged 65 and older in 2019 or latest available,
COVID-19 deaths per million inhabitants (DPMI), COVID-19 Case Fatality Ratio (CFR) based on documented COVID-19 cases per million
inhabitants (CPMI) in 2020, COVID-19 tests per million inhabitants.
Country IVR*
(%)
Year of
IVR
DPMI
+
(N per M)
CPMI
+
(N per M)
CFR
+
COVID-19 tests
+
Continent
Australia 73.0 2018/2019 6 547 0.011 151,037 Australia and
Ozeanien
Belgium 59.1 2019 847 5,624 0.151 130,601 Europe
Brazil 71.8 2018/2019 402 11,078 0.036 23,094 America
Canada 59.0 2019 235 3,006 0.078 98,442 America
Chile 68.3 2019 472 17,964 0.026 78,678 America
China 7.0 2018/2019 3 58 0.052 62,814 Asia
Croatia 23.0 2017 31 1,168 0.027 26,932 Europe
Czech Republic 21.5 2019 34 1,413 0.024 61,332 Europe
Denmark 52.0 2019 106 2,319 0.046 243,677 Europe
Estonia 10.2 2019 52 1,532 0.034 87,692 Europe
Finland 49.5 2019 59 1,333 0.044 59,654 Europe
France 51.0 2019 462 2,765 0.167 45,683 Europe
Germany 34.8 2019 110 2,460 0.045 88,528 Europe
Greece 56.2 2019 19 400 0.048 42,244 Europe
Hungary 24.1 2019 62 458 0.135 33,116 Europe
Ireland 68.5 2019 357 5,235 0.068 121,496 Europe
Israel 59.8 2019 49 6,577 0.007 173,662 Europe
Italy 53.1 2019 581 4,067 0.143 107,848 Europe
Japan 48.0 2019 8 221 0.036 5,516 Asia
Latvia 11.7 2019 16 640 0.025 100,009 Europe
Lithuania 14.8 2019 29 736 0.039 182,847 Europe
Luxembourg 39.8 2019 179 9,665 0.019 618,326 Europe
Mexico 82.3 2018/2019 331 2,932 0.113 6,946 America
Netherlands 62.7 2019 358 3,077 0.116 49,709 Europe
New Zealand 62.0 2019 4 311 0.013 90,746 Australia and
Ozeanien
Norway 38.2 2019 47 1,677 0.028 77,531 Europe
Portugal 60.8 2019 168 4,900 0.034 149,941 Europe
Romania 16.1 2017 112 2,272 0.049 56,571 Europe
Singapore** 14.0 2016/2017 5 8,523 0.001 199,896 Asia
Slovak Republic 12.5 2019 5 392 0.013 46,285 Europe
Slovenia 12.9 2019 55 994 0.055 61,108 Europa
South Korea 85.1 2019 6 275 0.022 29,619 Asia
Spain 54.9 2019 608 6,833 0.089 135,188 Europe
Sweden 52.2 2019 562 7,819 0.072 74,353 Europe
Thailand 12.0 2018/2019 0.8 47 0.017 9,817 Asia
Turkey 7.0 2019 66 2,668 0.025 53,707 Europe
United Kingdom 72.0 2019 673 4,398 0.153 214,532 Europe
United States 68.7 2019 450 12,929 0.035 159,672 America
Vietnam*** 12.0 2017 0 4 0.000 2,824 Asia
Notes:
*
Taken from https://data.oecd.org/healthcare/inuenza-vaccination-rates.htm,https://oecdcode.org/disclaimers/israel.html and https://www.statista.com/chart/16575/
global-u-immunization-rates-vary/ on July 25, 2020.
**
From https://www.todayonline.com/commentary/why-singapores-adult-vaccination-rate-so-low.
***
From Nguyen et al. (2020).
+
From https://www.worldometers.info/coronavirus/ on July 25, 2020.
Wehenkel (2020), PeerJ, DOI 10.7717/peerj.10112 4/18
Table 2 Raw data (part 2). Countries with their centroid coordinates (longitude (Long) and latitude
(Lat)), Degree of urbanization in 2020, Human Development Index (HDI) in 2018, Percent elder people
in 2019 and Population density in 2018.
Country Long
()
Lat
()
Degree of
urbanization
(2020)*
HDI
(2018)**
Percent elder
people (%)
(2019)***
Population density
(people per km
2
of
land area) (2018)****
Australia 134.5 25.7 86.2 0.938 15.92 3.2
Belgium 4.6 50.6 98.1 0.919 19.01 377.4
Brazil 53.1 10.8 87.1 0.761 9.25 25.1
Canada 98.3 61.4 81.6 0.922 17.65 4.1
Chile 71.4 37.7 87.7 0.847 11.88 25.2
China 103.8 36.6 61.4 0.758 11.47 148.3
Croatia 16.4 45.1 57.6 0.837 20.86 73.0
Czech Republic 15.3 49.7 74.1 0.891 19.80 137.7
Denmark 10.0 56.0 88.1 0.930 19.97 138.0
Estonia 25.5 58.7 69.2 0.882 19.99 30.4
Finland 26.3 64.5 85.5 0.925 22.14 18.1
France 2.5 46.2 81.0 0.891 20.39 122.3
Germany 15.3 49.7 77.5 0.939 21.56 237.3
Greece 23.0 39.1 79.7 0.872 21.94 83.3
Hungary 19.4 47.2 71.9 0.845 19.69 108.0
Ireland 8.1 53.2 63.7 0.942 14.22 70.7
Israel 35.0 31.5 92.6 0.906 12.21 410.5
Italy 12.1 42.8 71.0 0.883 23.01 205.4
Japan 138.0 37.6 91.8 0.915 28.00 347.1
Latvia 24.9 56.9 68.3 0.854 20.34 31.0
Lithuania 23.9 55.3 68.0 0.869 20.16 44.7
Luxembourg 6.1 49.8 91.5 0.909 14.27 250.2
Mexico 102.5 23.9 80.7 0.767 7.42 64.9
Netherlands 5.3 52.1 92.2 0.933 19.61 511.5
New Zealand 171.5 41.8 86.7 0.921 15.99 18.4
Norway 15.3 68.8 83.0 0.954 17.27 14.5
Portugal 8.5 39.6 66.3 0.850 22.36 112.3
Romania 25.0 45.9 56.4 0.816 18.79 84.6
Singapore 103.8 1.4 100.0 0.935 12.39 7953.0
Slovak
Republic
19.5 48.7 53.8 0.857 16.17 113.3
Slovenia 14.8 46.1 55.1 0.902 20.19 103.0
South Korea 127.8 36.4 81.4 0.906 15.06 529.4
Spain 3.6 40.2 80.8 0.893 19.65 93.7
Sweden 16.7 62.8 88.0 0.937 20.20 25.0
Thailand 101.0 15.1 51.4 0.765 12.41 135.9
Turkey 35.2 39.1 76.1 0.806 8.73 107.0
United
Kingdom
2.9 54.1 83.9 0.920 18.51 274.7
United States 112.5 45.7 82.7 0.920 16.21 35.7
Vietnam 106.3 16.6 37.3 0.693 7.55 308.1
Notes:
*
https://www.cia.gov/library/publications/the-world-factbook/elds/349.html.
**
http://hdr.undp.org/en/composite/HDI.
***
https://data.worldbank.org/indicator/SP.POP.65UP.TO.ZS?name_desc=false.
****
https://data.worldbank.org/indicator/EN.POP.DNST, all retrieved on July 13, 2020.
Wehenkel (2020), PeerJ, DOI 10.7717/peerj.10112 5/18
Table 3 Raw data (part 3). Countries with some Covid-19 measures (degree of mask requirements in public, lockdown degree and lockdown
beginning).
Country Degree of mask
requirement*
Lockdown
degree
Lockdown
beginning
Sources about lockdown
(retrieved on Aug 13, 2020)
Australia Parts of Country Lockdown 3/23/20 https://www.straitstimes.com/asia/australianz/australia-starts-lockdown-
measures-as-coronavirus-cases-jump
Belgium Full Country Lockdown 3/17/20 https://www.euractiv.com/section/coronavirus/news/belgium-enters-lockdown-
over-coronavirus-crisis-until-5-april/
Brazil Parts of Country Lockdown 5/5/20 https://www.reuters.com/article/us-health-coronavirus-brazil-lockdown/major-
brazilian-cities-set-lockdowns-as-virus-spreads-idUSKBN22H2V3
Canada Parts of Country Partial
lockdown
3/17/20 https://www.manitoulin.ca/updated-canada-goes-on-covid-19-lockdown/
Chile Full Country Partial
lockdown
3/25/20 https://www.gob.cl/noticias/ministerio-de-salud-anuncia-cuarentena-total-para-
siete-comunas-de-la-region-metropolitana/
China None, but voluntary
Universal Mask Usage
Lockdown 1/23/20 https://www.who.int/bulletin/volumes/98/7/20-254045/en/
Croatia Full Country Lockdown 3/18/20 https://www.telegram.hr/zivot/koronavirus-krizni-stozer-danas-ce-objaviti-sto-
se-sve-zatvara-u-hrvatskoj/;https://m.vecernji.hr/vijesti/oxford-hrvatska-ima-
najstroze-mjere-u-europi-iza-su-samo-srbija-i-sirija-1389281
Czech
Republic
Full Country Lockdown 3/16/20 https://archiv.radio.cz/en/section/breaking/czech-republic-severely-limits-
freedom-of-movement-in-order-to-slow-down-coronavirus-spread
Denmark None Lockdown 3/13/20 https://nyheder.tv2.dk/samfund/2020-03-11-danmark-lukker-ned-her-er-
regeringens-nye-tiltag
Estonia None, but Recommends
Masks
No
lockdown
https://www.euronews.com/2020/05/13/coronavirus-lockdown-latvia-lithuania-
and-estonia-re-open-borders-to-each-other
Finland None, but Recommends
Masks
Partial
lockdown
3/28/20 https://newseu.cgtn.com/news/2020-04-16/Finland-to-lift-coronavirus-
lockdown-in-region-around-capitalPIiAE4MM36/index.html
France Full Country Lockdown 3/17/20 https://www.leparisien.fr/societe/coronavirus-etat-d-urgence-aux-etats-unis-800-
nouveaux-cas-en-france-79-morts-au-total-suivez-notre-direct-14-03-2020-
8279826.php
Germany Full Country Lockdown 3/23/20 https://www.welt.de/politik/deutschland/article206725829/Coronavirus-
Deutschland-Kontaktverbote-zu-mehr-als-zwei-Personen-Friseure-zu.html
Greece Full Country Lockdown 3/23/20 https://www.in.gr/2020/04/23/politics/se-ekseliksi-enimerosi-tou-kyvernitikou-
ekprosopou-steliou-petsa-2/
Hungary Parts of Country Lockdown 3/28/20 https://www.theguardian.com/world/2020/mar/30/hungary-jail-for-coronavirus-
misinformation-viktor-orban
Ireland No, but Recommends
Masks
Lockdown 3/12/20 https://www.irishtimes.com/news/health/coronavirus-schools-colleges-and-
childcare-facilities-in-ireland-to-shut-1.4200977
Israel Full Country Lockdown 4/1/20 https://www.haaretz.com/israel-news/coronavirus-israeli-health-minister-
netanyahu-mossad-chief-quarantine-1.8720108;https://www.timesosrael.com/
israelis-will-be-required-to-wear-face-masks-outdoors-under-new-order/
Italy Full Country Lockdown 2/25/20 https://metro.co.uk/2020/02/25/towns-italy-lockdown-coronavirus-12298246/
Japan No, but Universal Mask
Usage
No
lockdown
https://asia.nikkei.com/Spotlight/Coronavirus/Japan-quietly-reopens-as-much-
of-world-locks-down
Latvia Full Country No
lockdown
https://www.euronews.com/2020/05/13/coronavirus-lockdown-latvia-lithuania-
and-estonia-re-open-borders-to-each-other
Lithuania Full Country No
lockdown
https://www.euronews.com/2020/05/13/coronavirus-lockdown-latvia-lithuania-
and-estonia-re-open-borders-to-each-other
Wehenkel (2020), PeerJ, DOI 10.7717/peerj.10112 6/18
R(version 3.3.4; R Core Team, 2017). For each country considered, the study recorded
socioeconomic variables as the degree of urbanization (DUR) in 2020 (https://www.cia.
gov/library/publications/the-world-factbook/elds/349.html), the population density (PD)
in 2018 (https://data.worldbank.org/indicator/EN.POP.DNST), the Human Development
Index (HDI) in 2018 (http://hdr.undp.org/en/composite/HDI) and the percentage of
elderly people (PEP) in 2019 (https://data.worldbank.org/indicator/SP.POP.65UP.TO.ZS?
name_desc=false), which were all retrieved on July 13, 2020 (Table 2). Finally, I recorded
Table 3 (continued)
Country Degree of mask
requirement*
Lockdown
degree
Lockdown
beginning
Sources about lockdown
(retrieved on Aug 13, 2020)
Luxembourg Full Country Lockdown 4/15/20 https://www.tageblatt.lu/headlines/pressekonferenz-nach-dem-regierungsrat-
kommt-das-ende-des-lockdowns/
Mexico Full Country Lockdown 3/23/20 https://www.eluniversal.com.mx/english/mexico-city-closes-museums-bars-
nightclubs-and-movie-theaters-bid-halt-coronavirus-spread
Netherlands Full Country Lockdown 3/12/20 Maarten Keulemans (12 March 2020). "Are we doing enough? RIVM boss Van
Dissel: As soon as something indicates infection in the family: isolation". de
Volkskrant (in Dutch). Retrieved 13 March 2020.
New
Zealand
None Lockdown 3/26/20 https://www.newstalkzb.co.nz/news/national/coronavirus-covid-19-state-of-
emergency-declared-in-new-zealand-50-new-cases-conrmed/
Norway None Lockdown 3/12/20 https://www.nrk.no/norge/alle-utdanningsinstitusjoner-stenges-_-ere-
arrangementer-og-virksomheter-far-forbud-1.14940952
Portugal Full Country Lockdown 3/19/20 http://www.presidencia.pt/?idc=22&idi=176060
Romania Full Country Lockdown 3/25/20 http://www.ms.ro/2020/03/25/buletin-informativ-25-03-2020/
Singapore Full Country Lockdown 4/7/20 https://www.channelnewsasia.com/news/business/suntec-city-waives-april-rent-
for-tenants-covid-19-12614802
Slovak
Republic
Full Country Lockdown 3/12/20 https://spectator.sme.sk/c/22356193/emergency-situation-applies-from-thursday-
morning.html?ref=njctse
Slovenia Full Country Lockdown 3/13/20 https://www.rtvslo.si/zdravje/novi-koronavirus/katalonija-zeli-razglasiti-
karanteno-za-celotno-pokrajino/517068
South Korea None, but voluntary
Universal Mask Usage
No
lockdown
https://www.sciencemag.org/news/2020/03/coronavirus-cases-have-dropped-
sharply-south-korea-whats-secret-its-success
Spain Full Country Lockdown 3/14/20 https://administracion.gob.es/pag_Home/atencionCiudadana/Estado-de-alarma-
crisis-sanitaria.html#.Xn3xj0dKjIU
Sweden None No
lockdown
Sayers, Freddy (17 April 2020). Swedish expert: why lockdowns are the wrong
policyThe Post. UnHerd.
Thailand Full Country Lockdown 4/3/20 https://www.bangkokpost.com/thailand/general/1891910/curfew-starts-today
Turkey Full Country Partial
lockdown
3/21/20 ttps://www.bbc.com/news/world-europe-52831017;https://www.aa.com.tr/tr/
koronavirus/cumhurbaskanligi-sozcusu-kalin-ilk-orta-ve-liseler-1-hafta-
universiteler-3-hafta-tatil-edilecek/1763918
United
Kingdom
Full Country Lockdown 3/23/20 https://www.thesun.co.uk/news/11304061/uk-coronavirus-lockdown-month-
lasted-start-end/
United
States
Parts of Country Lockdown 3/19/20 https://www.wsj.com/articles/china-reports-no-new-domestic-coronavirus-
infections-for-the-rst-time-since-outbreak-started-11584611233
Vietnam Full Country Lockdown 4/1/20 https://e.vnexpress.net/news/news/covid-19-lockdown-hanoi-hospital-lacks-
food-necessities-for-3-500-inmates-4077071.html
Note:
*
https://masks4all.co/what-countries-require-masks-in-public/ (retrieved on Aug 13, 2020).
Wehenkel (2020), PeerJ, DOI 10.7717/peerj.10112 7/18
two aspects as COVID-19 prevention measures, that is, the degree of requirement to use
masks (mask) in public (with three degrees: none, parts of country, full country)
(https://masks4all.co/what-countries-require-masks-in-public/) and the lockdown degree
(lockdown) (with three levels: no lockdown, partial lockdown, nationwide lockdown); all
of these sources and the noted in Table 3 were consulted on Aug 13, 2020.
Variable importance ranking was carried out using the partypackage and the
non-parametric random forest function cforest, along with Out of bag score (with the
default option controls = cforest_unbiasedand the conditional permutation importance
varimp(obj, conditional = TRUE)). Following the permutation principle of the
mean decrease in accuracyimportance, this machine learning algorithm guarantees
unbiased variable importance for predictor variables of different types (Strobl et al., 2008).
To mitigate the effects of confounding factors, IVR, DPMI and CFR evaluations
were also conducted for countries with similar social conditions (>50% of DUR, HDI
of >0.80, >15% of PEP, and PD between 25 and 350 inhabitants per km
2
)(Escobar,
Molina-Cruz & Barillas-Mury, 2020) and for countries with similar longitudes (1020
in parts of Europe and 100140, East and Southeast Asia along with Australia and
New Zealand).
Figure 1 Association of COVID-19 deaths per million inhabitants (DPMI) up to July 25, 2020 with
inuenza vaccination rate (IVR) of people aged 65 and older in 2019 or latest data available in
Europe. Association of COVID-19 deaths per million inhabitants (DPMI) up to July 25, 2020 with
inuenza vaccination rate (IVR) of people aged 65 and older in 2019 or latest data available in Europe
(26 countries with more than 0.5 million inhabitants). The mean (blue line) and standard deviation (grey
area) are based on generalized additive models (GAM); r
s
(IVR × DPMI) = +0.687 with p= 0.00015.
Full-size
DOI: 10.7717/peerj.10112/g-1
Wehenkel (2020), PeerJ, DOI 10.7717/peerj.10112 8/18
As IVR and the other eight predictor variables were not strongly correlated (jr
s
j0.57;
r
s
(IVR × DUR) = +0.52; r
s
(IVR × Long) = 0.46; r
s
(IVR × HDI) = 0.36), therefore,
I included these variables in non-parametric Random Forest (RF) models of DPMI and
CFR, including a 5-fold cross validation approach, repeated 30 times using the package
carettogether with the function train(Venables & Ripley, 1999;Williams et al.,
2018,http://topepo.github.io/caret/index.html)inRsoftware. Finally, I evaluated the
goodness-of-t of the regression model using the (pseudo) coefcient of determination
(R
2
) and the root mean square error (RMSE).
RESULTS
For the 26 European countries considered, the results indicated that COVID-19 DPMI and
the COVID-19 CFR were positively and statistically signicantly associated with IVR in
people 65 years-old in 2019 or latest data available (r
s
(IVR × DPMI) = +0.62 with
p= 0.0008, R2
s(IVR × DPMI) = 0.38; r
s
(IVR × CFR) = +0.50 with p= 0.01, R2
S(IVR ×
CFR) = 0.25) (Figs. 1 and 2;Table 4). In evaluations including only countries with similar
social conditions, r
s
(IVR × DPMI) was equal to +0.65 (p= 0.002, N= 20) and r
s
(IVR × CFR) +0.48 (p= 0.03, N= 20). In analyses including only countries with similar
longitude of the country centroid (Long), r
s
(IVR × DPMI) was equal to +0.83 (p= 0.003,
Figure 2 Association of COVID-19 Case Fatality Ratio (CFR) up to July 25, 2020 with inuenza
vaccination rate (IVR) of people aged 65 and older in 2019 or latest data available in Europe.
Association of COVID-19 Case Fatality Ratio (CFR) up to July 25, 2020 with inuenza vaccination
rate (IVR) of people aged 65 and older in 2019 or latest data available in Europe (26 countries with more
than 0.5 million inhabitants). The mean (blue line) and standard deviation (grey area) are based on
generalized additive models (GAM); r
s
(IVR × CFR) = +0.629 with p= 0.00075.
Full-size
DOI: 10.7717/peerj.10112/g-2
Wehenkel (2020), PeerJ, DOI 10.7717/peerj.10112 9/18
N= 10) (Long from 10to 20) and r
s
(IVR × DPMI) +0.76 (p= 0.046, N= 7) (Long from
100to 140).
At worldwide level (39 countries studied), the positive associations between DPMI
and IVR were also statistically signicant (r
s
(IVR × DPMI) = +0.49 with p= 0.0016, R2
s
(IVR × DPMI) = 0.24) (Fig. 3;Table 5). However, the relationships between IVR and CFR
were not statistically signicant.
In the IVR interval from 7% to 50%, the association was not signicant, although a trend
for DPMI and CFR to be positively associated with IVR was observed. DPMI and CFR
varied strongly when IVR was 50% or higher (Figs. 13).
Worldwide, the unbiased ranking showed the degree of importance of each variable
analyzed. The variables Long (with 55.9% and 52.3%) and IVR (with 36.3% and 24.5%)
were by far the most important of the nine variables used to predict DPMI and CFR,
respectively. The DUR in 2020 was the third most important variable, with an importance
of 5.7% for predicting DPMI. The PEP in 2019 was the third most important variable
(11.5%) in the CFR model (Figs. 4 and 5). The nine predictor variables considered in this
study explained 63% of the variation in DPMI (RMSE = 161.9) and 43% of the variation in
CFR (RMSE = 0.039).
DISCUSSION
Contrary to expectations, the present worldwide analysis and European sub-analysis do
not support the previously reported negative association between COVID-19 deaths
(DPMI) and IVR in elderly people, observed in studies in Brazil and Italy (Fink et al., 2020;
Marín-Hernández, Schwartz & Nixon, 2020). Previous studies attributed the benecial
effect of inuenza vaccination in reducing severity of COVID-19 disease to better
Table 4 Spearman correlations (r
s
) of COVID-19 deaths per million inhabitants (DPMI) with nine
predictor variables. Spearman correlations (r
s
) of COVID-19 deaths per million inhabitants (DPMI)
with the variables (var): IVR = inuenza vaccination rate (IVR, %) of people aged 65 and older in 2019 or
latest data available, Long and Lat = Longitude and Latitude of the country centroid (), DUR = Degree of
urbanization in 2020, HDI = Human Development Index in 2018, PEP = Percent elder people in 2019,
PD = Population density in 2018, Mask = the requirement degree of using masks in public (with three
degrees: none, parts of country, full country), Lockdown = lockdown degree (with three levels: no
lockdown, partial lockdown, nationwide lockdown) and their pvalues based on 26 countries in Europe
(Tables 13).
var r
s
(DPMI × var) pvalue
Long 0.65 0.0003
IVR 0.62 0.0008
DUR 0.43 0.0273
PD 0.41 0.0375
HDI 0.38 0.0533
Lockdown 0.25 0.2146
PEP 0.07 0.7387
Lat (abs) 0.02 0.9313
Mask 0 0.9949
Note:
Bold values statistically signicant after Bonferroni correction (a= 0.0019).
Wehenkel (2020), PeerJ, DOI 10.7717/peerj.10112 10/18
prevention of potential inuenza-SARS-CoV-2 coinfections (Arokiaraj, 2020) and, more
likely, to changes in innate immunity (Netea et al., 2020). The innate immune response
induced by recent vaccination could result in more rapid and efcient SARS-CoV-2
clearance, preventing progressive dissemination into lower areas of lung tissues (Fink et al.,
2020).
The negative association between the proportion of DPMI and IVR found in Italy was
explained as probably caused by (i) a higher inuenza vaccine rate occurring in higher
economic groups with overall better health, (ii) chance, (iii) a relationship with seasonal
respiratory virus infections, or (iv) an unrelated mechanistic association (Marín-
Hernández, Schwartz & Nixon, 2020). However, the induction of cross-neutralizing
antibodies and T-cells that directly target other RNA viruses like SARS-CoV-2 and
cross-protection seem unlikely, given the extraordinary diversity of inuenza viruses
(Fink et al., 2020).
Table 5 Spearman correlations (r
s
) of COVID-19 deaths per million inhabitants (DPMI) and
COVID-19 Case Fatality Ratio (CFR) with nine predictor variables. Spearman correlations (r
s
)of
COVID-19 deaths per million inhabitants (DPMI) and COVID-19 Case Fatality Ratio (CFR) with the
variables: IVR = inuenza vaccination rate (%) of people aged 65 and older in 2019 or latest available,
Long = Longitude of the centroid of the country (), Latitude of the centroid of the country (),
DUR = Degree of urbanization in 2020, HDI = Human Development Index in 2018, PEP = Percent elder
persons in 2019, PD = Population density in 2018, Mask = the requirement degree of using masks in
public (with three degrees: none, parts of country, full country), Lockdown = lockdown degree (with
three levels: no lockdown, partial lockdown, nationwide lockdown), based on 39 countries worldwide
(Tables 13).
r
s
DPMI CFR
Long 0.81 0.56
IVR 0.49 0.25
DUR 0.32 0.39
Lat (abs) 0.32 0.03
HDI 0.20 0.10
PEP 0.15 0.38
Mask 0.14 0.01
Lockdown 0.08 0.09
PD 0.07 0.01
pvalues
Long 0 0.0002
IVR 0.0016 0.1275
DUR 0.0436 0.8698
Lat (abs) 0.0451 0.0155
HDI 0.2167 0.5529
PEP 0.3523 0.0174
Mask 0.3819 0.9436
Lockdown 0.6448 0.5980
PD 0.6713 0.9347
Note:
Bold values statistically signicant after Bonferroni correction (a= 0.0019).
Wehenkel (2020), PeerJ, DOI 10.7717/peerj.10112 11/18
Therefore, the above-mentioned arguments cannot explain the positive, direct or
indirect relationship between IVR and both DPMI and CFR found in this study, which was
conrmed by an unbiased ranking variable importance (Figs. 4 and 5) using RF models.
The inuenza vaccine may increase inuenza immunity at the expense of reduced
immunity to SARS-CoV-2 by some unknown biological mechanism, as suggested by
Cowling et al. (2012) for non-inuenza respiratory virus. Alternatively, weaker temporary,
non-specic immunity after inuenza viral infection could cause this positive association
due to stimulation of the innate immune response during and for a short time after
infection (McGill, Heusel & Legge, 2009;Khaitov et al., 2009). People who had received the
inuenza vaccination would have been protected against inuenza but not against other
viral infections, due to reduced non-specic immunity in the following weeks (Cowling
et al., 2012), probably caused by virus interference (Isaacs & Lindenmann, 1957;
Seppälä et al., 2011;Wolff, 2020). Although existing human vaccine adjuvants have a high
level of safety, specic adjuvants in inuenza vaccines should also be tested for adverse
reactions, such as additionally increased inammation indicators (Petrovsky, 2015)in
COVID-19 patients with already strongly increased inammation (Qin et al., 2020).
The strong variation in DPMI and CFR from an IVR of about 50% or larger may be
the result of interactions among the different measures applied in the analyzed countries
Figure 3 Association of COVID-19 deaths per million inhabitants (DPMI) up to July 25, 2020 with
inuenza vaccination rate of people aged 65 and older in 2019 or latest data available worldwide.
Association of COVID-19 deaths per million inhabitants (DPMI) up to July 25, 2020 with inuenza
vaccination rate of people aged 65 and older in 2019 or latest data available worldwide (39 countries with
more than 0.5 million inhabitants). The mean (blue line) and standard deviation (grey area) are based on
generalized additive models (GAM); r
s
(IVR × DPMI) = +0.487 with p= 0.0017.
Full-size
DOI: 10.7717/peerj.10112/g-3
Wehenkel (2020), PeerJ, DOI 10.7717/peerj.10112 12/18
(Figs. 13), for example, initiation of interventions, emergency plans and health systems
against COVID-19. For example, Australia and South Korea had a very low DPMI and
CFR compared with Belgium and United Kingdom (Table 1).
The high correlation between the longitude of the country centroid and DPMI and CFR
emphasize a signicant increase in CP and CFR from eastern to western regions in the
world (Table 5;Figs. 4 and 5), as conrmed by Leung, Bulterys & Bulterys (2020) and
Skórka et al. (2020). Longitude could act as a proxy for variables such as lifestyle, social
behavior, genetics, geographically isolated and remote populations, which may also be
associated with CP and CFR. In the severe 19181919 inuenza pandemic, remote or
isolated populations were also affected, at least partly because of the lack of prior immunity
in locations that had not been recently affected by any form of inuenza (Mathews et al.,
2009). Therefore, crossing geographical and ecological barriers also is a key factor in
spreading diseases (Hallatschek & Fisher, 2014;Murray et al., 2015).
Both DPMI and CFR were weakly and positively correlated (p< 0.05) with the absolute
value of geographical latitude (abs(Lat)), DUR, PEP and PD (Tables 4 and 5). In a global
analysis, Escobar, Molina-Cruz & Barillas-Mury (2020) also found positive associations
between COVID-19 mortality and the percentage of population aged 65 years and
urbanization, but still more strongly with the Human Development Index. Leung,
Bulterys & Bulterys (2020) also reported positive associations between latitude,
Figure 4 Unbiased Conditional variables importance ranking to predict COVID-19 deaths per
million inhabitant. Unbiased conditional variables importance ranking (%) to predict COVID-19
deaths per million inhabitants using the package partyand the non-parametric random forest function
cforestin the software R; IVR = inuenza vaccination rate, Long = centroid longitude (), Lat = centroid
latitude (), DUR = degree of urbanization in 2020, HDI = Human Development Index in 2018,
PEP = percent of elder people in 2019, PD = population density in 2018, mask = the requirement degree
of using masks in public (with three degrees: none, parts of country, full country), lockdown = lockdown
degree (with three levels: no lockdown, partial lockdown, nationwide lockdown) of each country, at
worldwide level (39 countries studied). Full-size
DOI: 10.7717/peerj.10112/g-4
Wehenkel (2020), PeerJ, DOI 10.7717/peerj.10112 13/18
temperature by week and by month prior to the rst reported COVID-19 case. Lower
temperature at northern latitudes was a strong independent predictor of national
COVID-19 mortality.
Although countywide lockdowns and use of face masks by the general public should
reduce COVID-19 transmission (Conyon, He & Thomsen, 2020;Eikenberry et al., 2020),
the variables lockdown degree and the degree of requirement for mask use in public
were not associated with DPMI and CFR in the present study (Tables 4 and 5;Figs. 4
and 5). Lefer et al. (2020) reported in a global study that internal lockdown requirements
were not associated with mortality, but that in countries that recommended use of
face masks early on at the national level, the COVID-19 death rate was lower than
expected.
Although countywide lockdowns were proclaimed in many countries, the restrictive
measures and their implementations differed in degree, strictness and implementation
date in relation to the advance of the disease (see references in Table 3). Also, although
many countries have required masks in public, the mask quality and correct use may differ
from country to country. In this regard, Fischer et al. (2020) found that the use of
ineffective masks could be counterproductive. This could explain the non-signicant
differences between the means of DPMI among countries with and without one or both
requirements, lockdown and masks.
Figure 5 Conditional variables importance ranking to predict COVID-19 Case Fatality Ratio.
Unbiased conditional variables importance ranking (%) to predict COVID-19 Case Fatality Ratio
using the package partyand the non-parametric random forest function cforestin the software R;
IVR = inuenza vaccination rate, Long = centroid longitude (), Lat = centroid latitude (), DUR = degree
of urbanization in 2020, HDI = Human Development Index in 2018, PEP = percent of elder people in
2019, PD = population density in 2018, mask = the requirement degree of using masks in public (with
three degrees: none, parts of country, full country), lockdown = lockdown degree (with three levels: no
lockdown, partial lockdown, nationwide lockdown) of each country, at worldwide level (39 countries
studied). Full-size
DOI: 10.7717/peerj.10112/g-5
Wehenkel (2020), PeerJ, DOI 10.7717/peerj.10112 14/18
Finally, the study is limited by the fact that I didnt normalize the time of arrival of the
pandemic. Moreover, the associations found may change in the future because the
COVID-19 pandemic was not over at the end of the study.
CONCLUSIONS
Given the positive relationship between IVR and the number of deaths per million found
in this study, further exploration would be valuable to explain these ndings and to make
conclusions. Additional work on this line of research may also yield results to improve
prevention of COVID-19 deaths.
ACKNOWLEDGEMENTS
I am grateful to María del Socorro González-Elizondo and José Ciro Hernández-Díaz for
their comments on the manuscript, and to Dr. Daniela Marín-Hernández and an
anonymous reviewer for their careful review and insightful comments.
ADDITIONAL INFORMATION AND DECLARATIONS
Funding
The author received no funding for this work.
Competing Interests
Christian Wehenkel is an Academic Editor for PeerJ.
Author Contributions
Christian Wehenkel conceived and designed the experiments, performed the
experiments, analyzed the data, prepared gures and/or tables, authored and reviewed
drafts of the paper, and approved the nal draft.
Data Availability
The following information was supplied regarding data availability:
The raw data is available in Tables 13.
REFERENCES
Agrawal B. 2019. Heterologous immunity: role in natural and vaccine-induced resistance to
infections. Frontiers in Immunology 10:164 DOI 10.3389/mmu.2019.02631.
Armengaud J, DelaunayMoisan A, Thuret JY, Anken E, AcostaAlvear D, Aragón T, Arias C,
Blondel M, Braakman I, Collet JF, Courcol R, Danchin A, Deleuze JF, Lavigne JP, Lucas S,
Michiels T, Moore ERB, NixonAbell J, RosselloMora R, Shi ZL, Siccardi AG, Sitia R,
Tillett D, Timmis KN, Toledano MB, Sluijs P, Vicenzi E. 2020. The importance of naturally
attenuated SARS-Cov-2 in the ght against Covid-19. Environmental Microbiology
22(6):19972000.
Arokiaraj MC. 2020. Correlation of inuenza vaccination and the COVID-19 severity. Available at
https://ssrn.com/abstract=3572814.
Beigel JH, Tomashek KM, Dodd LE, Mehta AK, Zingman BS, Kalil AC, Hohmann E, Chu HY,
Luetkemeyer A, Kline S, Lopez de Castilla D, Finberg RW, Dierberg K, Tapson V, Hsieh L,
Wehenkel (2020), PeerJ, DOI 10.7717/peerj.10112 15/18
Patterson TF, Paredes R, Sweeney DA, Short WR, Touloumi G, Lye DC, Ohmagari N,
Oh M-D, Ruiz-Palacios GM, Beneld T, Fätkenheuer G, Kortepeter MG, Atmar RL,
Creech CB, Lundgren J, Babiker AG, Pett S, Neaton JD, Burgess TH, Bonnett T, Green M,
Makowski M, Osinusi A, Nayak S, Lane HC. 2020. Remdesivir for the treatment of Covid-19-
preliminary report. New England Journal of Medicine DOI 10.1056/NEJMoa2007764.
Conyon MJ, He L, Thomsen S. 2020. Lockdowns and COVID-19 deaths in Scandinavia.
Covid Economics 26:1742.
Courtemanche C, Garuccio J, Le A, Pinkston J, Yelowitz A. 2020. Strong social distancing
measures in the United States reduced the COVID-19 growth rate: study evaluates the impact of
social distancing measures on the growth rate of conrmed COVID-19 cases across the United
States. Health Affairs 10:1377.
Cowling BJ, Fang VJ, Nishiura H, Chan K-H, Ng S, Ip DKM, Chiu SS, Leung GM, Peiris JSM.
2012. Increased risk of noninuenza respiratory virus infections associated with receipt of
inactivated inuenza vaccine. Clinical Infectious Diseases 54(12):17781783
DOI 10.1093/cid/cis307.
Eikenberry SE, Mancuso M, Iboi E, Phan T, Eikenberry K, Kuang Y, Kostelich E, Gumel AB.
2020. To mask or not to mask: modeling the potential for face mask use by the general public to
curtail the COVID-19 pandemic. Infectious Disease Modelling 5:293308.
Escobar LE, Molina-Cruz A, Barillas-Mury C. 2020. BCG vaccine protection from severe
coronavirus disease 2019 (COVID-19). Proceedings of the National Academy of Sciences
117(30):1772017726 DOI 10.1073/pnas.2008410117.
Fink G, Orlova-Fink N, Schindler T, Grisi S, Ferrer AP, Daubenberger C, Brentani A. 2020.
Inactivated trivalent inuenza vaccine is associated with lower mortality among Covid-19
patients in Brazil. medRxiv DOI 10.1101/2020.06.29.20142505.
Fischer EP, Fischer MC, Grass D, Henrion I, Warren WS, Westman E. 2020. Low-cost
measurement of facemask efcacy for ltering expelled droplets during speech. Science Advances
6(36):eabd3083 DOI 10.1126/sciadv.abd3083.
Flaxman S, Mishra S, Gandy A, Unwin HJT, Mellan TA, Coupland H, Whittaker C, Zhu H,
Berah T, Eaton JW, Monod M, Ghani AC, Donnelly CA, Riley S, Vollmer MAC,
Ferguson NM, Okell LC, Bhatt S, Imperial College COVID-19 Response Team. 2020.
Estimating the effects of non-pharmaceutical interventions on COVID-19 in Europe. Nature
584:257261.
Goodridge HS, Ahmed SS, Curtis N, Kollmann TR, Levy O, Netea MG, Pollard AJ,
Van Crevel R, Wilson CB. 2016. Harnessing the benecial heterologous effects of vaccination.
Nature Reviews Immunology 16(6):392400 DOI 10.1038/nri.2016.43.
Graham BS. 2020. Rapid COVID-19 vaccine development. Science 368(6494):945946
DOI 10.1126/science.abb8923.
Hallatschek O, Fisher DS. 2014. Acceleration of evolutionary spread by long-range dispersal.
Proceedings of the National Academy of Sciences 111(46):E4911E4919
DOI 10.1073/pnas.1404663111.
Isaacs A, Lindenmann J. 1957. Virus interference. I. The interferon. Proceedings of the Royal
Society of London: Series B-Biological Sciences 147(927):258267.
Khaitov M, Laza-Stanca V, Edwards MR, Walton RP, Rohde G, Contoli M, Papi A, Stanciu LA,
Kotenko SV, Johnston SL. 2009. Respiratory virus induction of alpha-, beta-and lambda-
interferons in bronchial epithelial cells and peripheral blood mononuclear cells. Allergy
64(3):375386.
Wehenkel (2020), PeerJ, DOI 10.7717/peerj.10112 16/18
Le Couteur DG, Anderson RM, Newman AB. 2020. COVID-19 is a disease of older people.
Journals of Gerontology Series A Biological Sciences and Medical Sciences 75(9):glaa077
DOI 10.1093/gerona/glaa077.
Lefer CT, Ing EB, Lykins JD, Hogan MC, McKeown CA, Grzybowski A. 2020. Association of
country-wide coronavirus mortality with demographics, testing, lockdowns, and public wearing
of masks. medRxiv DOI 10.1101/2020.05.22.20109231.
Leung NY, Bulterys MA, Bulterys PL. 2020. Predictors of COVID-19 incidence, mortality, and
epidemic growth rate at the country level. medRxiv DOI 10.1101/2020.05.15.20101097.
Li K, Wu J, Wu F, Guo D, Chen L, Fang Z, Li C. 2020. The clinical and chest CT features
associated with severe and critical COVID-19 pneumonia. Investigative Radiology
55(6):327331.
Marín-Hernández D, Schwartz RE, Nixon DF. 2020. Epidemiological evidence for association
between higher inuenza vaccine uptake in the elderly and lower COVID-19 deaths in Italy.
Journal of Medical Virology DOI 10.1002/jmv.26120.
Mathews JD, Chesson JM, McCaw JM, McVernon J. 2009. Understanding inuenza transmission,
immunity and pandemic threats. Inuenza and Other Respiratory Viruses 3(4):143149
DOI 10.1111/j.1750-2659.2009.00089.x.
McGill J, Heusel JW, Legge KL. 2009. Innate immune control and regulation of inuenza virus
infections. Journal of Leukocyte Biology 86(4):803812 DOI 10.1189/jlb.0509368.
Murray KA, Preston N, Allen T, Zambrana-Torrelio C, Hosseini PR, Daszak P. 2015. Global
biogeography of human infectious diseases. Proceedings of the National Academy of Sciences
112(41):1274612751 DOI 10.1073/pnas.1507442112.
Netea MG, Domínguez-Andrés J, Barreiro LB, Chavakis T, Divangahi M, Fuchs E, Joosten LAB,
Van der Meer JWM, Mhlanga MM, Mulder WJM, Riksen NP, Schlitzer A, Schultze JL,
Benn CS, Sun JC, Xavier RJ, Latz E. 2020. Dening trained immunity and its role in health and
disease. Nature Reviews Immunology 20(6):375388 DOI 10.1038/s41577-020-0285-6.
Nguyen TTM, Lafond KE, Nguyen TX, Tran PD, Nguyen HM, Do TT, Ha NT, Seward JF,
McFarland JW. 2020. Acceptability of seasonal inuenza vaccines among health care workers in
Vietnam in 2017. Vaccine 38(8):20452050.
Ozili PK, Arun T. 2020. Spillover of COVID-19: impact on the Global Economy. Available at
https://ssrn.com/abstract=3562570.
Petrovsky N. 2015. Comparative safety of vaccine adjuvants: a summary of current evidence and
future needs. Drug Safety 38(11):10591074 DOI 10.1007/s40264-015-0350-4.
Pawlowski C, Puranik A, Bandi H, Venkatakrishnan AJ, Agarwal V, Kennedy R, OHoro JC,
Gores GJ, Williams AW, Halamka J, Badley AD, Venky Soundararajan. 2020. Exploratory
analysis of immunization records highlights decreased SARS-CoV-2 rates in individuals with
recent non-COVID-19 vaccinations. medRxiv DOI 10.1101/2020.07.27.20161976.
Qin C, Zhou L, Hu Z, Zhang S, Yang S, Tao Y, Xie C, Ma K, Shang K, Wang W, Tian D-S. 2020.
Dysregulation of immune response in patients with COVID-19 in Wuhan, China.
Clinical Infectious Diseases 71(15):762768 DOI 10.1093/cid/ciaa248.
R Core Team. 2017. R: a language and environment for statistical computing. Vienna: The R
Foundation for Statistical Computing. Available at http://www.R-project.org.
RECOVERY Collaborative Group. 2020. Dexamethasone in hospitalized patients with
Covid-19preliminary report. New England Journal of Medicine
DOI 10.1056/NEJMoa2021436.
Wehenkel (2020), PeerJ, DOI 10.7717/peerj.10112 17/18
Seppälä E, Viskari H, Hoppu S, Honkanen H, Huhtala H, Simell O, Ilonen J, Knip M, Hyöty H.
2011. Viral interference induced by live attenuated virus vaccine (OPV) can prevent otitis
media. Vaccine 29(47):86158618 DOI 10.1016/j.vaccine.2011.09.015.
Skórka P, Grzywacz B, Moro
n D, Lenda M. 2020. The macroecology of the COVID-19 pandemic
in the Anthropocene. PLOS ONE 15(7):e0236856 DOI 10.1371/journal.pone.0236856.
Strobl C, Boulesteix AL, Kneib T, Augustin T, Achim Zeileis A. 2008. Conditional variable
importance for random forests. BMC Bioinformatics 9(1):307 DOI 10.1186/1471-2105-9-307.
Venables WN, Ripley BD. 1999. Chapter 10: Tree-based methods. In: Chambers J, Eddy W,
Härdle W, Sheather S, Tierney L, eds. Modern Applied Statistics with S-PLUS. Third Edition.
New York: Springer-Verlag, 303327.
Wickham H, Chang W, Wickham MH. 2013. Package ggplot2. Computer software manual.
R package version 0.9.3.1. Available at http://cran.r-project.org/web/packages/ggplot2/ggplot2.pdf.
Williams CK, Engelhardt A, Cooper T, Mayer Z, Ziem A, Scrucca L, Kuhn MM. 2018. Package
caret.Available at https://github.com/topepo/caret/.
Wolff GG. 2020. Inuenza vaccination and respiratory virus interference among Department of
Defense personnel during the 20172018 inuenza season. Vaccine 38(2):350354
DOI 10.1016/j.vaccine.2019.10.005.
Yancy CW. 2020. COVID-19 and African Americans. JAMA 323(19):18911892
DOI 10.1001/jama.2020.6548.
Yuen KS, Ye ZW, Fung SY, Chan CP, Jin DY. 2020. SARS-CoV-2 and COVID-19: the most
important research questions. Cell & Bioscience 10(1):15DOI 10.1186/s13578-019-0370-3.
Wehenkel (2020), PeerJ, DOI 10.7717/peerj.10112 18/18
... The reported mortality attributed to COVID-19 is not "excess mortality" as usually recorded in epidemiology; therefore, the mortality figures used here could be an overestimation if basal mortality (mortality occurring in absence of epidemics) would be discounted. A map, as well as a list of countries and territories that did not mandate lock-downs can be freely downloaded from the world-wide-web [40] [41] [42] [43] [44]; and a summary of countries with-and without lock-downs was also published elsewhere [45]. The figures corresponding to infection per million inhabitants were pooled for countries that instituted lock-downs in each continent. ...
... Table 1; (a) that mandated lock-downs and (b) that did not mandate lock-downs. Open Journal of Epidemiology contrast, the European countries presented in Figure 1(b) [6] [56] that did not mandate lock-downs and allowed healthy people to remain outdoors [44] [45] do not show a bimodal curve of COVID-19 like those in Figure 1(a). Countries in Figure 1(a) show a bi-modal distribution with a first peak around mid-April 2020, and a valley either in mid-May, early May or mid-June in Spain, Greece and Slovakia, respectively while the number of active COVID-19 cases spike at the beginning of September 2020 in Spain, the middle of September in Greece, late September in France, UK, and Slovakia, and early October 2020 in Italy [6] (data not shown), demonstrating that lock-downs may have delayed but failed to prevent progression of COVID-19. ...
... Countries were selected among those that mandated earlier, longer, and strictly enforced lock-downs, or that did not instituted lock-downs or similar restrictions[44] [45], thus being considered among the best known country representatives of its class.2 Infections per million inhabitant, infection death rate (total deaths/infections × 100) shown in parenthesis ( ), were obtained from[6] on the dates of the year 2020 indicated in theTable.Population mortality rate shown by numbers between brackets [ ] were calculated by dividing total deaths[6] by the population (in millions to one decimal point) according to the 2019 census[39].3 ...
... Recently, several studies determined the potential preventive effect of influenza vaccination against COVID-19 infection [43][44][45]. However, these studies require further investigation, as many studies also show a lack of positive correlation between influenza vaccination and the mortality and morbidity rates of COVID-19 infections [46][47][48][49]. ...
Article
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Older adults are at a high risk of experiencing severe complications of influenza. Receiving a vaccination is a beneficial strategy to prevent the disease and reduce the severity of influenza illnesses. This cross-sectional questionnaire-based study aimed to evaluate the influence of sociodemographic, clinical, and mental parameters as well as other potential risk factors on refusal to vaccinate against influenza among the elderly population in Poland. Furthermore, due to the prevailing COVID-19 pandemic, we put efforts into finding any statistical correlations between the fear of COVID-19 infection in patients and their attitudes toward receiving an influenza vaccination. The study was conducted in November–December 2020 in Poland on a representative nationwide sample of 500 individuals aged > 60. Of the respondents, 62 (12.4%) and 51 (10.2%) underwent influenza vaccination in 2019 and 2020, respectively. Out of ten different factors analyzed in this study, three were significantly associated with attitudes towards influenza vaccination. Participants with net income below the national average of PLN 3000 (OR = 2.37, CI 95% [1.26–4.47]), compared to those earning more than PLN 3000, had significantly higher odds of having a negative attitude towards influenza vaccination. Furthermore, respondents with
... On the contrary, the studies performed by Martínez-Baz et al. as well as Kissling et al., did not confirm that the influenza vaccination significantly modified the risk of SARS-CoV-2 infection, indicating an absence of an effect or a small protective effect of an influenza vaccination status on COVID-19 infections [24,25]. Furthermore, Wehenkel reported a positive association between COVID-19 deaths and the influenza vaccination of people ≥ 65 years old [26]. ...
Article
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In this study, we used publicly available data from the Centrum e-Zdrowia (CeZ) Polish Databank proposing a possible correlation between influenza vaccination and mortality due to COVID-19. We limited our search to the patients with positive COVID‑19 laboratory tests from 1 January 2020 to 31 March 2021 and who filled a prescription for any influenza vaccine during the 2019–2020 influenza season. In total, we included 116,277 patients and used a generalized linear model to analyze the data. We found out that patients aged 60+ who received an influenza vaccination have a lower probability of death caused by COVID-19 in comparison to unvaccinated, and the magnitude of this difference grows with age. For people below 60 years old, we did not observe an influence of the vaccination. Our results suggest a potential protective effect of the influenza vaccine on COVID-19 mortality of the elderly. Administration of the influenza vaccine before the influenza season would reduce the burden of increased influenza incidence, the risk of influenza and COVID‑19 coinfection and render the essential medical resources accessible to cope with another wave of COVID-19. To our knowledge, this is the first study showing a correlation between influenza vaccination and the COVID-19 mortality rate in Poland.
... Tanmateix, la inmunològicament forasenyada i inoportuna pseudovacunació de la grip estacional (influenza) a l'hemisferi nord en el marc epidèmic d'un coronavirus respiratori va anul·lar la l'atenuació recíproca de la molt similar patogenicitat foto-dinàmica genòmica-clínica-simptomàtica de dues espècies microbianes de diferent gènere concurrents 20,21 , i així va contribuir enormement a la resurgència hivernal 2020-2021 de casos severs de COVID-19 en adults amb immunocompetència sub-eficient, sub-òptima i vulnerabilitat associada 22 . ...
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Regulation (EU) 2021/953 for the issuance of EU Digital COVID Certificate entered into force on 1 July 2021. It allows people who have recovered from SARS-CoV-2 natural infection to obtain a certificate of recovery, on the basis of a performed confirmatory positive RT-PCR test. It thus incurs a serious bias of omission that discriminates against millions of recovered adults and minors, who developed lasting and robust adaptive, cellular, SARS-CoV-2 specific immunity, and immunological memory during 2020 and in the first quarter of 2021. Such a protective immunity can be assessed analitically, reliably at the individual level.
... In a large scale study of over 2,000 counties in the United States, Zanettini et al. demonstrated a potential protective effect of influenza vaccination on SARS-CoV-2 mortality [29]. Conversely, Wehenkel et al. performed a macro-scale study of association between influenza vaccination rate and SARS--CoV-2 deaths in an examination of over 500,000 patients across 39 countries [30]. This study showed a positive association between COVID-19 deaths and influenza vaccination rates in elderly people 65 years of age and older. ...
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IntroductionRecently, several single center studies have suggested a protective effect of the influenza vaccine against severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2). This study utilizes a continuously updated Electronic Medical Record (EMR) network to assess the possible benefits of influenza vaccination mitigating critical adverse outcomes in SARS-CoV-2 positive patients from 56 healthcare organizations (HCOs).Methods The de-identified records of 73,346,583 patients were retrospectively screened. Two cohorts of 37,377 patients, having either received or not received influenza vaccination six months-two weeks prior to SARS-CoV-2 positive diagnosis, were created using Common Procedural Terminology (CPT) and logical observation identifiers names and codes (LOINC) codes. Adverse outcomes within 30, 60, 90, and 120 days of positive SARS-CoV-2 diagnosis were compared between cohorts. Outcomes were assessed with stringent propensity score matching including age, race, ethnicity, gender, hypertension, diabetes, hyperlipidemia, chronic obstructive pulmonary disease (COPD), obesity, heart disease, and lifestyle habits such as smoking.ResultsSARS-CoV-2-positive patients who received the influenza vaccine experienced decreased sepsis (p
... For instance, the study performed by Ragni et al. [Ragni et al. 2020 Moreover, since older adults are often eligible for free-of-charge influenza vaccination, a number of databases reporting vaccination coverages in this population are publicly available. This is probably the reason for which various ecological studies [Zanettini et al. 2021;Arokiaraj 2020;Cocco et al. 2020;Marín-Hernández et al. 2021;Wehenkel 2020] specifically focused on the population aged ≥65 and found an association between influenza vaccination and reduced risk of death by COVID-19. ...
Thesis
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In the context of the ongoing COVID-19 pandemic, whit many countries still struggling because of SARS-CoV-2 vaccine shortage, it is imperative to provide individuals with all preventive measures available, able to reduce the pressure on healthcare systems. An increasing number of epidemiological studies suggest that some routinely administered vaccines (e.g., influenza) may exercise non-specific effects on COVID-19-related outcomes. The aim of this thesis was to conduct an in-depth analysis of the association between seasonal influenza vaccination and outcomes attributable to SARS-CoV-2. This objective was achieved in two consecutive steps: (i) a systematic review and meta-analysis of the previously published reports on this topic and (ii) a retrospective observational study on a cohort of healthcare workers (HCWs). The systematic review identified a total of 33 primary research reports. The meta-analysis of 8 studies (167,579 subjects) showed that compared with non-vaccinated individuals, those immunized against seasonal influenza had a 19% [pooled adjusted odds ratio 0.81 (95% CI: 0.70–0.94)] risk reduction of testing positive for SARS-CoV-2. Influenza vaccination was also protective against different clinical outcomes (e.g., COVID-19 related hospitalization, intensive care unit admission, mortality), although the pooled effect size did not always reach an alpha <0.05. We then conducted a retrospective cohort study composed of HCWs of the San Martino Polyclinic Hospital (Genoa). In particular, we analyzed the incidence of newly diagnosed RT-PCR-confirmed SARS-CoV-2 infections in healthcare workers with regards to 2020/21 influenza vaccinal status. Following the application of inclusion and exclusion criteria, a total of 2,561 individuals that contributed to 94,438 person-day observations were analyzed. A total of 290 new positive cases were identified. The incidence of SARS-CoV-2 was 1.62 (95% CI: 1.22–2.10) and 3.91 (95% CI: 3.43–4.45) per 1,000 person-days in vaccinated and non- vaccinated HCWs (P<0.001). The multivariable Cox’s proportional hazard model adjusted for potential confounders showed generally a significant protective effect of influenza vaccination. In conclusion, our findings suggests that influenza vaccination is associated with a lower risk of COVID-19 related outcomes and underline, particularly while the global response to COVID-19 pandemic is still suboptimal, the importance of promote and carry out effective influenza vaccination campaigns, in order to reduce the clinical and socioeconomic burden of respiratory infections. Publicly available in English language at: https://unire.unige.it/handle/123456789/3602
... However, an Italian study [8] of a sample of healthcare workers found no evidence of a relationship between the influenza vaccine and either a COVID-19 diagnosis or a positive SARS-CoV-2 serology test in this group. Furthermore, Wehenkel C [9] reported a positive correlation between COVID-19 deaths and influenza vaccination in people ≥65 years-old, although the journal's editors reminded readers that correlation does not necessarily imply causation. By contrast, a 2020 non-systematic review of the published, pre-print, and gray literature [10] concluded that high rates of coverage by the influenza and 23-valent pneumococcal polysaccharide vaccines has the potential to not only reduce the burden of the targeted diseases but to also prevent a proportion of COVID-19 morbidity and mortality. ...
Article
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COVID-19 is an infectious disease caused by the novel coronavirus SARS-CoV-2. Several measures aimed at containing the spread of this virus have been recommended by international and nation public health institutions, but whether the influenza vaccine, while not protective against COVID-19, nonetheless reduces disease severity is unclear. This study evaluated the potential role of influenza vaccine in reducing the rate of hospitalization and death in COVID-19 patients. COVID-19 cases recorded in the province of Brindisi (Apulia, Southern Italy) during the first pandemic wave (February–May 2020) and occurring in patients vaccinated with the influenza vaccine during the 2019–2020 influenza season were considered. From February 2020 to May 2020, 3872 inhabitants of the province of Brindisi underwent SARS-CoV-2 PCR testing and 664 (8.7%) tested positive. A multivariate analysis showed that among COVID-19 patients neither hospitalization nor death was significantly associated with influenza vaccination (p > 0.05), whereas within this group male sex, older age, and chronic diseases were identified as risk factors for morbidity and mortality. Our study did not show an association between the influenza vaccine and complications of COVID-19. Nonetheless, influenza vaccination must be promoted as a central public health measure, because by reducing the burden on hospitals it can greatly benefit the management of COVID-19 patients.
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Background. Outdoor environmental variables, such as cold temperatures and low wind speed, have been correlated with incidence and mortality from Covid-19 (caused by the SARS-CoV-2 virus). However, as Covid-19 predominantly spreads indoors, the degree to which outdoor environmental variables might directly cause disease spread is unclear. Methods. World regions were considered to have reliable data if the excess mortality did not greatly exceed reported Covid-19 mortality. The relative risk of Covid-19 mortality for 142 regions as a function of median weekly temperature and wind speed was determined. For instance, Covid-19 mortality following warm weeks in a country was compared with mortality following cold weeks in the same country. Results. Covid-19 mortality increases with cooling from 20 C to close to freezing (0 to 4 C, p<0.001). The relation of Covid-19 mortality with temperature demonstrates a maximum close to freezing. Below -5 C, the decrease in mortality with further cooling was statistically significant (p<0.01). With warming above room temperature (20 to 24 C), there is a nonsignificant trend for mortality to increase again. A literature review demonstrated that window opening and indoor ventilation tend to increase with warming in the range from freezing to room temperature. Conclusion. The steep decline in Covid-19 mortality with warming in the range from freezing to room temperature may relate to window opening and less indoor crowding when it is comfortable outside. Below freezing, all windows are closed, and further cooling increases stack ventilation (secondary to indoor-outdoor temperature differences) and thereby tends to decrease Covid-19 mortality. Opening windows and other tools for improving indoor ventilation may decrease the spread of Covid-19.
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Background. Outdoor environmental variables, such as cold temperatures and low wind speed, have been correlated with incidence and mortality from Covid-19 (caused by the SARS-CoV-2 virus). However, as Covid-19 predominantly spreads indoors, the degree to which outdoor environmental variables might directly cause disease spread is unclear. Methods. World regions were considered to have reliable data if the excess mortality did not greatly exceed reported Covid-19 mortality. The relative risk of Covid-19 mortality for 142 regions as a function of median weekly temperature and wind speed was determined. For instance, Covid-19 mortality following warm weeks in a country was compared with mortality following cold weeks in the same country. Results. The association of Covid-19 mortality with outdoor temperature shows two peaks. The global maximum of Covid-19 mortality occurs close to freezing (0 C, 32° F). A local minimum in Covid-19 mortality is observed close to room temperature (20 C, 68° F). Conclusion. The local minimum of Covid-19 mortality at room temperature likely relates to window opening and less indoor crowding when it is comfortable outside. Below freezing, all windows are closed, and further cooling increases stack ventilation (secondary to indoor-outdoor temperature differences) and thereby decreases Covid-19 mortality. Opening windows and other tools for improving indoor ventilation may decrease the spread of Covid-19.
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La crise sanitaire mondiale déclarée depuis début 2020 avec l’apparition et diffusion du SARS- CoV-2 et de la maladie étiquetée COVID-19, place chaque individu face à des choix difficiles à faire pour sa santé et celle de ses proches. Les informations contradictoires, biaisées, censurées, compliquent encore davantage les prises de décisions « informées » ou « éclairées ». Les questions essentielles qui se posent sont de déterminer les mesures de prévention possibles, les traitements pour les personnes symptomatiques, l’abord des séquelles de l’infection ou intoxication (COVID-léger, COVID-long), le choix de se faire vacciner ou pas, la prévention des possibles effets toxiques des vaccins aussi bien pour les vaccinés que pour les non-vaccinés en présence des vaccinés. Les médecines traditionnelles amazoniennes signalent l’usage empirique du tabac brun thérapeutique comme un recours essentiel dans tous ces cas de figure, celui-ci étant conforté par les données acquises de la science et les pistes de recherche actuellement en cours sur le tabac. Nous proposons dans cet article de dégager quelques lignes directrices autour du rôle et de la place du tabac dans le cadre de l’épidémie de COVID-19 et d’en signaler les usages thérapeutiques empiriques possibles pour répondre aux différentes situations individuelles face aux choix qui s’imposent. Cet article doit être abordé comme un article d’opinion. Cette opinion repose sur notre pratique thérapeutique associée aux médecines traditionnelles amazoniennes depuis 35 ans. Le tabac constitue un usage central et quotidien de notre expérience et nous estimons, à ce titre, avoir droit à nous exprimer librement et à ce que notre parole soit prise au sérieux. Notre avis repose sur une recherche clinique observationnelle qui représente presque toujours le premier pas de la recherche expérimentale et l’occasion de découvertes riches de potentiel. Les hypothèses que nous présentons ont été confrontées à l’expérience clinique et nous exposerons quelques arguments scientifiques qui font écho à ces observations et en général aux savoirs autochtones sur les usages du tabac. Ces corrélations ne constituent pas une preuve en soi mais signalent des cohérences signifiantes qui auraient intérêt à être davantage explorées.
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Mandates for mask use in public during the recent coronavirus disease 2019 (COVID-19) pandemic, worsened by global shortage of commercial supplies, have led to widespread use of homemade masks and mask alternatives. It is assumed that wearing such masks reduces the likelihood for an infected person to spread the disease, but many of these mask designs have not been tested in practice. We have demonstrated a simple optical measurement method to evaluate the efficacy of masks to reduce the transmission of respiratory droplets during regular speech. In proof-of-principle studies, we compared a variety of commonly available mask types and observed that some mask types approach the performance of standard surgical masks, while some mask alternatives, such as neck gaiters or bandanas, offer very little protection. Our measurement setup is inexpensive and can be built and operated by nonexperts, allowing for rapid evaluation of mask performance during speech, sneezing, or coughing.
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Severe acute respiratory syndrome coronavirus 2, the virus that causes coronavirus disease 2019 (COVID-19), has expanded rapidly throughout the world. Thus, it is important to understand how global factors linked with the functioning of the Anthropocene are responsible for the COVID-19 outbreak. We tested hypotheses that the number of COVID-19 cases, number of deaths and growth rate of recorded infections: (1) are positively associated with population density as well as (2) proportion of the human population living in urban areas as a proxies of interpersonal contact rate, (3) age of the population in a given country as an indication of that population’s susceptibility to COVID-19; (4) net migration rate and (5) number of tourists as proxies of infection pressure, and negatively associated with (5) gross domestic product which is a proxy of health care quality. Data at the country level were compiled from publicly available databases and analysed with gradient boosting regression trees after controlling for confounding factors (e.g. geographic location). We found a positive association between the number of COVID-19 cases in a given country and gross domestic product, number of tourists, and geographic longitude. The number of deaths was positively associated with gross domestic product, number of tourists in a country, and geographic longitude. The effects of gross domestic product and number of tourists were non-linear, with clear thresholds above which the number of COVID-19 cases and deaths increased rapidly. The growth rate of COVID-19 cases was positively linked to the number of tourists and gross domestic product. The growth rate of COVID-19 cases was negatively associated with the mean age of the population and geographic longitude. Growth was slower in less urbanised countries. This study demonstrates that the characteristics of the human population and high mobility, but not population density, may help explain the global spread of the virus. In addition, geography, possibly via climate, may play a role in the pandemic. The unexpected positive and strong association between gross domestic product and number of cases, deaths, and growth rate suggests that COVID-19 may be a new civilisation disease affecting rich economies.
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Multiple clinical studies are ongoing to assess whether existing vaccines may afford protection against SARS-CoV-2 infection through trained immunity. In this exploratory study, we analyze immunization records from 137,037 individuals who received SARS-CoV-2 PCR tests. We find that polio, Hemophilus influenzae type-B (HIB), measles-mumps-rubella (MMR), varicella, pneumococcal conjugate (PCV13), geriatric flu, and hepatitis A / hepatitis B (HepA-HepB) vaccines administered in the past 1, 2, and 5 years are associated with decreased SARS-CoV-2 infection rates, even after adjusting for geographic SARS-CoV-2 incidence and testing rates, demographics, comorbidities, and number of other vaccinations. Furthermore, age, race/ethnicity, and blood group stratified analyses reveal significantly lower SARS-CoV-2 rate among black individuals who have taken the PCV13 vaccine, with relative risk of 0.45 at the 5 year time horizon (n: 653, 95% CI: (0.32, 0.64), p-value: 6.9e-05). These findings suggest that additional pre-clinical and clinical studies are warranted to assess the protective effects of existing non-COVID-19 vaccines and explore underlying immunologic mechanisms. We note that the findings in this study are preliminary and are subject to change as more data becomes available and as further analysis is conducted.
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Background Coronavirus disease 2019 (Covid-19) is associated with diffuse lung damage. Glucocorticoids may modulate inflammation-mediated lung injury and thereby reduce progression to respiratory failure and death. Methods In this controlled, open-label trial comparing a range of possible treatments in patients who were hospitalized with Covid-19, we randomly assigned patients to receive oral or intravenous dexamethasone (at a dose of 6 mg once daily) for up to 10 days or to receive usual care alone. The primary outcome was 28-day mortality. Here, we report the preliminary results of this comparison. Results A total of 2104 patients were assigned to receive dexamethasone and 4321 to receive usual care. Overall, 482 patients (22.9%) in the dexamethasone group and 1110 patients (25.7%) in the usual care group died within 28 days after randomization (age-adjusted rate ratio, 0.83; 95% confidence interval [CI], 0.75 to 0.93; P<0.001). The proportional and absolute between-group differences in mortality varied considerably according to the level of respiratory support that the patients were receiving at the time of randomization. In the dexamethasone group, the incidence of death was lower than that in the usual care group among patients receiving invasive mechanical ventilation (29.3% vs. 41.4%; rate ratio, 0.64; 95% CI, 0.51 to 0.81) and among those receiving oxygen without invasive mechanical ventilation (23.3% vs. 26.2%; rate ratio, 0.82; 95% CI, 0.72 to 0.94) but not among those who were receiving no respiratory support at randomization (17.8% vs. 14.0%; rate ratio, 1.19; 95% CI, 0.91 to 1.55). Conclusions In patients hospitalized with Covid-19, the use of dexamethasone resulted in lower 28-day mortality among those who were receiving either invasive mechanical ventilation or oxygen alone at randomization but not among those receiving no respiratory support. (Funded by the Medical Research Council and National Institute for Health Research and others; RECOVERY ClinicalTrials.gov number, NCT04381936; ISRCTN number, 50189673.)
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A series of epidemiological explorations has suggested a negative association between national bacillus Calmette–Guérin (BCG) vaccination policy and the prevalence and mortality of coronavirus disease 2019 (COVID-19). However, these comparisons are difficult to validate due to broad differences between countries such as socioeconomic status, demographic structure, rural vs. urban settings, time of arrival of the pandemic, number of diagnostic tests and criteria for testing, and national control strategies to limit the spread of COVID-19. We review evidence for a potential biological basis of BCG cross-protection from severe COVID-19, and refine the epidemiological analysis to mitigate effects of potentially confounding factors (e.g., stage of the COVID-19 epidemic, development, rurality, population density, and age structure). A strong correlation between the BCG index, an estimation of the degree of universal BCG vaccination deployment in a country, and COVID-19 mortality in different socially similar European countries was observed ( r ² = 0.88; P = 8 × 10 ⁻⁷ ), indicating that every 10% increase in the BCG index was associated with a 10.4% reduction in COVID-19 mortality. Results fail to confirm the null hypothesis of no association between BCG vaccination and COVID-19 mortality, and suggest that BCG could have a protective effect. Nevertheless, the analyses are restricted to coarse-scale signals and should be considered with caution. BCG vaccination clinical trials are required to corroborate the patterns detected here, and to establish causality between BCG vaccination and protection from severe COVID-19. Public health implications of a plausible BCG cross-protection from severe COVID-19 are discussed.
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We analyzed data from 92,664 clinically and molecularly confirmed Covid-19 cases in Brazil to understand the potential associations between influenza vaccination and Covid-19 outcomes. Controlling for health facility of treatment, comorbidities as well as an extensive range of sociodemographic factors, we show that patients who received a recent influenza vaccine experienced on average 8% lower odds of needing intensive care treatment (95% CIs [0.86, 0.99]), 18% lower odds of requiring invasive respiratory support (0.74, 0.88) and 17% lower odds of death (0.75, 0.89). Large scale promotion of influenza vaccines seems advisable, especially in populations at high risk of severe SARS-CoV-2 infection. One Sentence Summary Covid-19 patients with recent influenza vaccination experience better health outcomes than non-vaccinated patients in Brazil.
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Following the emergence of a novel coronavirus¹ (SARS-CoV-2) and its spread outside of China, Europe has experienced large epidemics. In response, many European countries have implemented unprecedented non-pharmaceutical interventions such as closure of schools and national lockdowns. We study the impact of major interventions across 11 European countries for the period from the start of COVID-19 until the 4th of May 2020 when lockdowns started to be lifted. Our model calculates backwards from observed deaths to estimate transmission that occurred several weeks prior, allowing for the time lag between infection and death. We use partial pooling of information between countries with both individual and shared effects on the reproduction number. Pooling allows more information to be used, helps overcome data idiosyncrasies, and enables more timely estimates. Our model relies on fixed estimates of some epidemiological parameters such as the infection fatality rate, does not include importation or subnational variation and assumes that changes in the reproduction number are an immediate response to interventions rather than gradual changes in behavior. Amidst the ongoing pandemic, we rely on death data that is incomplete, with systematic biases in reporting, and subject to future consolidation. We estimate that, for all the countries we consider, current interventions have been sufficient to drive the reproduction number \({R}_{t}\) below 1 (probability \({R}_{t}\,\)< 1.0 is 99.9%) and achieve epidemic control. We estimate that, across all 11 countries, between 12 and 15 million individuals have been infected with SARS-CoV-2 up to 4th May, representing between 3.2% and 4.0% of the population. Our results show that major non-pharmaceutical interventions and lockdown in particular have had a large effect on reducing transmission. Continued intervention should be considered to keep transmission of SARS-CoV-2 under control.
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Background. Wide variation between countries has been noted in per-capita mortality from the disease (COVID-19) caused by the SARS-CoV-2 virus. Determinants of this variation are not fully understood. Methods. Potential predictors of country-wide per-capita coronavirus-related mortality were studied, including age, sex ratio, temperature, urbanization, viral testing, smoking, duration of infection, lockdowns, and public mask-wearing norms and policies. Multivariable linear regression analysis was performed. Results. In univariate (but not multivariable) analyses, prevalence of smoking, per-capita gross domestic product, and colder average country temperature were positively associated with coronavirus-related mortality. In a multivariable analysis of 183 countries, urbanization, the duration of the infection in the country, and percent of the population at least 60 years of age were all positively associated with per-capita mortality, while duration of mask-wearing by the public was negatively associated with mortality (all p<0.001). In countries with cultural norms or government policies supporting public mask-wearing, per-capita coronavirus mortality increased on average by just 5.4% each week, as compared with 48% each week in remaining countries. In the multivariable analysis, lockdowns tended to be associated with less mortality (p=0.31), and per-capita testing with higher reported mortality (p=0.26), though neither association was statistically significant. Conclusions. Societal norms and government policies supporting the wearing of masks by the public are independently associated with less mortality from COVID-19.
Article
The Italian COVID‐19 epidemic may finally be slowing, although the virus has spread from the North in Lombardy throughout the rest of the country. While there have been more than 233,000 confirmed cases, and a mortality rate estimated around 14%, Italy will now navigate an exit from lockdown with continued testing, monitoring, and contact tracing of any new infections. This article is protected by copyright. All rights reserved.