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Positive association between COVID-19
deaths and influenza 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 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.
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 influenza 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 confirmed 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 influencing 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
inflammation 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://files.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 immunity”is 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-specific 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 influenza vaccination rates (IVRs) and
COVID-19 related mortality and morbidity. Marín-Hernández, Schwartz & Nixon (2020)
also showed epidemiological evidence of an association between higher influenza vaccine
uptake by elderly people and lower percentage of COVID-19 deaths in Italy. In a study
analyzing 92,664 clinically and molecularly confirmed COVID-19 cases in Brazil, Fink
et al. (2020) reported that patients who received a recent flu 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 influenzae type-B, measles-mumps-rubella,
varicella, pneumococcal conjugate (PCV13), geriatric flu, 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 influenza
vaccination was significantly associated with a higher risk of some other respiratory
diseases, due to virus interference. In a specific examination of non-influenza 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 significantly 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 influenza 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 identification 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/influenza-vaccination-rates.htm,https://oecdcode.org/
disclaimers/israel.html and https://www.statista.com/chart/16575/global-flu-immunization-
rates-vary/ (retrieved on July 25, 2020). Vietnam’s 2017 IVR was recorded from Nguyen
et al. (2020), and Singapore’s 2016/2017 IVR from https://www.todayonline.com/
commentary/why-singapores-adult-vaccination-rate-so-low.
To analyze the data, I first calculated the non-parametric Spearman rank correlation
coefficient (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
significant based on r
s
and its p-value, I did not modified (corrected) the DPMI data set.
Then, I created regression curves by Generalized additive model (GAM) using the
“ggplot2”package 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 influence 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 “rgeos”and “rworldmap”
packages, along with the “getMap”and “gCentroid”functions, implemented in
Wehenkel (2020), PeerJ, DOI 10.7717/peerj.10112 3/18
Table 1 Raw data (part 1). Countries with their influenza 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/influenza-vaccination-rates.htm,https://oecdcode.org/disclaimers/israel.html and https://www.statista.com/chart/16575/
global-flu-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/fields/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-capital–PIiAE4MM36/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.timesofisrael.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/fields/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-confirmed/
Norway None Lockdown 3/12/20 https://www.nrk.no/norge/alle-utdanningsinstitusjoner-stenges-_-flere-
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
policy—The 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-first-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 “party”package and the
non-parametric random forest function “cforest”, along with Out of bag score (with the
default option “controls = cforest_unbiased”and the conditional permutation importance
“varimp(obj, conditional = TRUE)”). Following the permutation principle of the
“mean decrease in accuracy”importance, 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 (10–20
in parts of Europe and 100–140, 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
influenza 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
influenza 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/fig-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
j≤0.57;
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
“caret”together 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-fit of the regression model using the (pseudo) coefficient 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 significantly 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 influenza
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 influenza 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/fig-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 significant (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 significant.
In the IVR interval from 7% to 50%, the association was not significant, 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. 1–3).
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 beneficial
effect of influenza 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 = influenza 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 1–3).
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 significant after Bonferroni correction (a= 0.0019).
Wehenkel (2020), PeerJ, DOI 10.7717/peerj.10112 10/18
prevention of potential influenza-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 efficient 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 influenza 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 influenza 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 = influenza 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 1–3).
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 significant 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
confirmed by an unbiased ranking variable importance (Figs. 4 and 5) using RF models.
The influenza vaccine may increase influenza 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-influenza respiratory virus. Alternatively, weaker temporary,
non-specific immunity after influenza 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
influenza vaccination would have been protected against influenza but not against other
viral infections, due to reduced non-specific 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, specific adjuvants in influenza vaccines should also be tested for adverse
reactions, such as additionally increased inflammation indicators (Petrovsky, 2015)in
COVID-19 patients with already strongly increased inflammation (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
influenza 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 influenza
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/fig-3
Wehenkel (2020), PeerJ, DOI 10.7717/peerj.10112 12/18
(Figs. 1–3), 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 significant increase in CP and CFR from eastern to western regions in the
world (Table 5;Figs. 4 and 5), as confirmed 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 1918–1919 influenza 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 influenza (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 “party”and the non-parametric random forest function
“cforest”in the software R; IVR = influenza 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/fig-4
Wehenkel (2020), PeerJ, DOI 10.7717/peerj.10112 13/18
temperature by week and by month prior to the first 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). Leffler 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-significant
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 “party”and the non-parametric random forest function “cforest”in the software R;
IVR = influenza 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/fig-5
Wehenkel (2020), PeerJ, DOI 10.7717/peerj.10112 14/18
Finally, the study is limited by the fact that I didn’t 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 findings 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 figures and/or tables, authored and reviewed
drafts of the paper, and approved the final draft.
Data Availability
The following information was supplied regarding data availability:
The raw data is available in Tables 1–3.
REFERENCES
Agrawal B. 2019. Heterologous immunity: role in natural and vaccine-induced resistance to
infections. Frontiers in Immunology 10:164 DOI 10.3389/fimmu.2019.02631.
Armengaud J, Delaunay‐Moisan A, Thuret J‐Y, Anken E, Acosta‐Alvear D, Aragón T, Arias C,
Blondel M, Braakman I, Collet J‐F, Courcol R, Danchin A, Deleuze J‐F, Lavigne J‐P, Lucas S,
Michiels T, Moore ERB, Nixon‐Abell J, Rossello‐Mora R, Shi Z‐L, 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 fight against Covid-19. Environmental Microbiology
22(6):1997–2000.
Arokiaraj MC. 2020. Correlation of influenza 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, Benfield 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 26:17–42.
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 confirmed 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 noninfluenza respiratory virus infections associated with receipt of
inactivated influenza vaccine. Clinical Infectious Diseases 54(12):1778–1783
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 5:293–308.
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):17720–17726 DOI 10.1073/pnas.2008410117.
Fink G, Orlova-Fink N, Schindler T, Grisi S, Ferrer AP, Daubenberger C, Brentani A. 2020.
Inactivated trivalent influenza 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 efficacy for filtering 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
584:257–261.
Goodridge HS, Ahmed SS, Curtis N, Kollmann TR, Levy O, Netea MG, Pollard AJ,
Van Crevel R, Wilson CB. 2016. Harnessing the beneficial heterologous effects of vaccination.
Nature Reviews Immunology 16(6):392–400 DOI 10.1038/nri.2016.43.
Graham BS. 2020. Rapid COVID-19 vaccine development. Science 368(6494):945–946
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):E4911–E4919
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):258–267.
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):375–386.
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.
Leffler 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):327–331.
Marín-Hernández D, Schwartz RE, Nixon DF. 2020. Epidemiological evidence for association
between higher influenza 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 influenza transmission,
immunity and pandemic threats. Influenza and Other Respiratory Viruses 3(4):143–149
DOI 10.1111/j.1750-2659.2009.00089.x.
McGill J, Heusel JW, Legge KL. 2009. Innate immune control and regulation of influenza virus
infections. Journal of Leukocyte Biology 86(4):803–812 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):12746–12751 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. Defining trained immunity and its role in health and
disease. Nature Reviews Immunology 20(6):375–388 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 influenza vaccines among health care workers in
Vietnam in 2017. Vaccine 38(8):2045–2050.
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):1059–1074 DOI 10.1007/s40264-015-0350-4.
Pawlowski C, Puranik A, Bandi H, Venkatakrishnan AJ, Agarwal V, Kennedy R, O’Horo 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):762–768 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-19—preliminary 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):8615–8618 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, 303–327.
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. Influenza vaccination and respiratory virus interference among Department of
Defense personnel during the 2017–2018 influenza season. Vaccine 38(2):350–354
DOI 10.1016/j.vaccine.2019.10.005.
Yancy CW. 2020. COVID-19 and African Americans. JAMA 323(19):1891–1892
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):1–5DOI 10.1186/s13578-019-0370-3.
Wehenkel (2020), PeerJ, DOI 10.7717/peerj.10112 18/18
