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\begin{document}

\title{How the ecology and evolution of the COVID-19 pandemic changed learning}



\author[1]{Marcus Lashley}%
\author[1]{Miguel Acevedo}%
\author[2]{Sehoya Cotner}%
\author[3]{Christopher Lortie}%
\affil[1]{University of Florida}%
\affil[2]{University of Minnesota Twin Cities}%
\affil[3]{York University - Keele Campus}%


\vspace{-1em}



  
  \date{May 25, 2024}


\begingroup
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\selectlanguage{english}
\begin{abstract}
The coronavirus disease 2019 (COVID-19) pandemic introduced an abrupt
change in human behavior globally. Here, we discuss unique insights into
the eco-evolutionary role of pathogens in ecosystems and present data
that indicates the pandemic can fundamentally change our learning
choices. This pathogen has indirectly affected many organisms and
processes by globally changing the behavior of humans to avoid being
infected. The pandemic also changed our learning behavior by affecting
the relative importance of information and forcing teaching and learning
into a framework that accommodates human behavioral measures to avoid
disease transmission. Not only are these indirect effects on the
environment occurring through a unique mechanistic pathway in ecology,
the pandemic along with its effects on us provides a profound example of
the role risk can play in the transmission of information between the
at-risk. Ultimately, these changes in our learning behavior led to this
special issue ``Taking learning online in Ecology and Evolution.'' The
special issue was a call to the community to take learning in new
directions, including online and distributed experiences. The topics
examined include a significant component of DIY ecology and evolution
that is experiential and but done individually, opportunities to use
online tools and apps to be more inclusive, student-focused strategies
for teaching online, how to reinvent conferences, strategies to retain
experiential learning safely, emerging forms of teaching such as citizen
science, apps and podcasting, and ideas on how to accommodate ever
changing constraints in the college classroom, to name a few. The
collective consensus in our fields is that these times are challenging
but we can continue to improve and innovate on existing developments,
and more broadly and importantly, this situation may provide an
opportunity to reset some of the existing practices that fail to promote
an effective and inclusive learning environment.%
\end{abstract}%



\sloppy


A unique pathway for indirect effects of COVID-19

Theory in ecology and evolution helps us understand patterns and
processes in natural systems and in human society. The coronavirus
disease 2019 (COVID-19) pandemic is a profound example of a pathogen
generating indirect effects by altering host behavior. Interestingly,
this may be the only example of those indirect effects generated by a
pathogen being primarily related to behavioral changes of individuals of
the host species who were not infected. Six months since the first
documented infection, the pathogen has infected only a small proportion
of the global population (15 million confirmed cases as of July 30,
2020; Dong et al. 2020), yet it has resulted in widespread changes in
the way billions of people live (e.g., Corlett et al. 2020, G\selectlanguage{ngerman}össling et
al. 2020, Rutz et al. 2020). These behavioral changes, arising primarily
to avoid infection, have had cascading indirect effects of the pathogen
on the environment such as decreased concentrations of some greenhouse
gasses in the atmosphere, changes in air quality and environmental
pollution, and decreases in anthropogenic sound (e.g., Corlett et al.
2020, Chen et al. 2020, Isaifan 2020, Zambrano-Monserrate et al. 2020).
Plus, the decrease in human activity (i.e., anthropause) has provided a
strong natural experimental framework for researchers to uncover how
humans affect wildlife communities (Rutz et al. 2020). Based on
ecological and evolutionary theory, these indirect effects from
pathogens changing the behavior of their hosts should be prevalent in
nature because we know organisms--humans and many other
species--commonly change their behavior in response to pathogens--either
as a result of getting infected, or to avoid getting infected (Curtis
2014, Weinstein et al. 2018). However, a recent and comprehensive review
(i.e., Buck and Ripple 2017) only uncovered one example where a pathogen
generated indirect effects by infecting the host (i.e.., a pathogen
indirectly affected prey abundance by reducing feeding rate in the
predatory crawfish it infected; Haddaway et al. 2012) and found no
published examples of indirect effects from a pathogen generated by
behavioral avoidance of getting infected (Buck and Ripple 2017). Indeed,
indirect effects stemming from host avoidance of parasites is rare as
well (Buck 2019). Given that the majority of people changing their
behavior has not been infected, COVID-19 has provided a compelling and
rare example of indirect effects by a pathogen being caused by
behavioral avoidance of infection. Likewise, COVID-19 provides us with a
stark reminder that humans are a part of, and not separate from, the
principle eco-evolutionary processes that shape our world. We as
ecologists and evolutionary biologists can use the pandemic to advance
our knowledge base, and also use our knowledge base to inform responses
to the pandemic.

The pandemic has changed what we seek to learn

Online learning is a deep and rich field of research, tools, and ideas.
The use of the term `online learning' is not universal and can describe
different concepts (Moore et al. 2011). It can refer to learning
entirely online or more broadly the use of online tools and virtual
spaces to engage and support learning (Wallace 2003). The pandemic has
caused many shifts including behavioral responses to interest in online
learning, teaching, and information technology. Some of these changes
are driven by necessity, including emergency delivery of teaching
content online but also may stem from fear of infection. Further,
interest in online learning can shift because of emotional drivers; to
explore this idea, we can turn to data on human web searches (i.e.,
google trends). Predicting human behaviour using web searches is a
well-established field of research in consumer studies (Preis et al.
2013) and other disciplines (Nuti et al. 2014), and the evidence
suggests that volume of searches provides a useful predictive guide to
the near future in behaviour (Goel et al. 2010). This tool estimates the
relative interest (or trends) of internet users globally by comparing
the relative frequencies of search queries between different time
periods, allowing us to infer change in human priorities. There are
numerous examples of this approach, and current examples have involved
using Google Trends data to examine indicators of private consumption
(Vosen and Schmidt 2011), predicted cryptocurrency market values
(Kristoufek 2013), trading behavior in financial markets (Preis et al.
2013), human health and healthcare (Heerfordt and Heerfordt 2020, Nuti
et al. 2014), and--more pertinent to this discussion--shifts in consumer
choices during the pandemic (Claudia et al. 2020). Specifically, the
Google Trends tool provides data and trend analyses by key terms through
time. While the overall predictive accuracy of Google Trends has been
challenged by periods of unusually high search prevalences (i.e.
ephemeral spikes; Butler 2013), recent developments have improved its
predictive accuracy (Kandula and Shaman 2019). Our primary aim here was
to compare the dynamics of relative interests in different types of
information during the pandemic globally using Google Trends (Choi and
Varian 2012). Understanding that limitations exist with Google Trends,
we saw an opportunity to frame the concepts developed in this special
issue within this larger, global conversation and engagement with
information to understand how we fit in to the ecology and evolution of
the pandemic but also how our learning environment must change in
response.

To do this, we first examined the relative interests in topics related
to risk (``death''), resources (``money''), and reproduction (``love''),
three currencies we often value in natural systems in our fields. Google
trends indicated dramatic shifts in relative interest between these
topics at the onset of the COVID-19 pandemic. Google searches between
the same January-June period in 2019 and then again in 2020 showed that
our relative interests in reproduction (google search global topics
related to ``love''), risks (google search global topics related to
``death''), and resources (google search global topics related to
``money'') dramatically shifted in 2020 tracking increases in confirmed
infections (\emph{https://github.com/maacevedo/covid19gtrends}). While
in 2019, topics of ``love'' were the most frequently searched of these
three, in 2020, with an increasing number of infections, ``love'' was
replaced by topics related to ``death'' (Figure 1). A shift in relative
importance of reproduction and risk of mortality can be expected based
on ecological and evolutionary theory on the effects of predation risk
(Lima and Dill 1990) and risk of infection (Weinstein et al. 2018), but
we found it interesting that this pattern seems to have emerged in
global patterns of online searches related to the pandemic. These
changes may stem from the desire to learn about COVID-19 so that one can
better calculate the risk it poses and determine the appropriate
behavioral response--especially given that other authors have attributed
to the pandemic a heightened level of fear in people (Ahorsu et al.
2020, Lin 2020). In essence, risks from COVID-19 seem to have, at least
temporarily, changed the relative importance of the information we seek
to learn. Changes in density and behavior (Lima and Dill 1990, Brown et
al 1999) in response to fear of predation have been common in ecological
literature. However, demonstrating that individuals of any species
fundamentally change how they value different types of information is
difficult to measure (Clinchy et al. 2011). Understanding these patterns
and processes in humans can inform our understanding of the role emotion
can play in natural communities (Clinchy et al. 2011, Frey et al. 2014).
These data provide solid evidence that understanding how humans interact
with their environment and other organisms may provide us with rare
opportunities to identify ecological and evolutionary mechanisms not
easily measured in other organisms.

The pandemic has transformed how we approach teaching and learning

Avoidance of COVID-19 infection has led to a wide-spread change in the
way we approach teaching and learning. Psychological stressors affecting
cognition in animals can be similar in humans (Clinchy et al. 2013).
While there is less information on how fear affects how or what animals
teach each other, COVID-19 has certainly changed human behavior in this
capacity. The pandemic has caused an abrupt shift in content delivery
across disciplines and globally, an alignment in interest among
educators that is unprecedented (e.g., Bao 2020, Basilaia and Kvavadze
2020, Crawford et al 2020, Zhang et al. 2020). This alignment may have
been predicted from ecological theory as risk (e.g., risk of predation,
risk of infection) tends to constrain decisions to what is safe for the
at-risk (Hutchinson and Gigerenzer 2005, Kemp 2005, Winnie et al. 2006).
Afterall, the fitness consequence of being eaten (or fatally infected in
this case) are more severe than missing an opportunity to learn, or
procure resources or reproduce for that matter. Because the pandemic
forced the abrupt shutdown of universities for safety reasons, in effect
it forced all disciplines to move online (Crawford et al. 2020) for
``emergency remote instruction'' (Hodges et al. 2020). Thus, among
educators, COVID-19 has simplified how we approach learning in a sense
by giving us all a common goal of moving to emergency remote
instruction.

Although measures may have been similarly applied across disciplines,
the effects of moving online across disciplines are asymmetrical.
Disciplines that require human contact such as medicine (Ahmed et al.
2020a, Rose 2020) are perhaps most severely affected. However,
field-based disciplines such as ecology, evolution, and conservation
biology are also dramatically affected (Corlett et al. 2020). Although
the pandemic has affected us all, different institutionalized and
individual strategies to accommodate the threat and burden on education
have emerged (e.g., Bao 2020, Basilaia and Kvavadze 2020, Crawford et al
2020, Zhang et al. 2020). The unifying theme is that education has
transitioned, rapidly and without time for much preparation, to online
delivery, regardless of other nuances in responses or the discipline
being affected. Interestingly, like the famous hockey stick graph in
climate change literature (i.e., Mann et al. 1999), this unification of
interest in online learning is reflected in an abrupt upturn in google
search interest--one which nearly perfectly parallels that of new
COVID-19 infections per week, at least up until mid-May when classes end
for the academic year in many parts of the world (Figure 2). This
correlation suggests that interest in strategies for effective remote
instruction increased as risk from COVID-19 increased; again, we used
google search interest and number of new infections as proxies for
interest in the topic overall and perceived risk of infection.

Although aspects of our response have been unified, the pandemic has
also highlighted inequities in many aspects of our daily experiences
(Ahmed et al. 2020b), such as access to healthcare (Hooper et al 2020),
and job and housing security (Yancy 2020; Raifman and Raifman 2020).
COVID-19 has also magnified some educational inequities, whereby those
previously disadvantaged or at risk of attrition find themselves even
more disadvantaged and at greater risk. Students from lower
socioeconomic backgrounds are more likely to face challenges from e.g.,
unreliable internet, added demands of child- and elder-care, job loss,
or the need to work as ``essential'' employees--furthering their risk of
contracting COVID-19 (Winter 2020, CDC 2020) . In many places where we
work, low socioeconomic status overlaps strongly with ethnic and racial
categories; consequently, the students we most need to retain are also
the most likely to be at greatest risk, including from an educational
standpoint with inclusion and retention, during the pandemic. Thus, an
added challenge for educators is to not only teach using novel delivery
modalities, but also to redouble our efforts at inclusive teaching.

The goals of this special issue

This pandemic has forced many--if not most--of us to reconsider how we
communicate information. The Academic Practice section of\emph{Ecology
and Evolution} was developed to explore how we do things in our
disciplines (Moore et al. 2017). This special issue of Academic Practice
provides a forum for our community to present experiences and ideas
about the best practices for moving our disciplines online in a focused,
accessible, and clearly organized manner. The ultimate purpose was ``to
provide a rapid outlet to share timely innovations and discoveries for
online teaching and learning in ecology and evolution.'' However, the
pandemic has reached every corner of our society and will change more
than just course delivery. Thus, this special issue also covers issues
related to meetings and conferences, development of scientific products,
strategies to promote open science, how we can best promote equity,
diversity and inclusion in the transition to remote instruction, and
engaging non-academic audiences. We wanted the contributions to be a
resource for the ecology and evolutionary biology communities as a whole
as we adapt to the COVID-19 crisis, so we emphasized rapid handling of
manuscripts, short contributions, and inclusion of tools and resources
that facilitate adoption. We have already accepted contributions related
to x (e.g., cite), y (e.g., cite), z (e.g., cite), contributions
highlighting sources of inequity and strategies to be more inclusive in
online delivery (e.g., cite), and ideas for use of tools, apps, and
novel media to enhance engagement (e.g., cite). The COVID-19 crisis
demanded action, and with over 40 submissions to this special issue by
our community, you have risen to the occasion. We applaud members of our
community for contributing their ideas and experiences in this forum and
hope that this special issue provides a start to a rich literature base
to take learning online in ecology and evolution and beyond.

\textbf{Literature Cited}

Ahmed, F., Ahmed, N. E., Pissarides, C., \& Stiglitz, J. (2020b). Why
inequality could spread COVID-19. The Lancet Public Health, 5(5), e240.

Ahmed, H., Allaf, M., \& Elghazaly, H. (2020a). COVID-19 and medical
education. The Lancet Infectious Diseases.

Ahorsu, D. K., Lin, C. Y., Imani, V., Saffari, M., Griffiths, M. D., \&
Pakpour, A. H. (2020). The fear of COVID-19 scale: development and
initial validation. \emph{International journal of mental health and
addiction} .

Bao, W. (2020). COVID\selectlanguage{english}-19 and online teaching in higher education: A case
study of Peking University. \emph{Human Behavior and Emerging
Technologies} , \emph{2} (2), 113-115.

Basilaia, G., \& Kvavadze, D. (2020). Transition to online education in
schools during a SARS-CoV-2 coronavirus (COVID-19) pandemic in
Georgia.\emph{Pedagogical Research} , \emph{5} (4), 1-9.

Brown, J. S., Laundre, J. W., \& Gurung, M. (1999). The ecology of fear:
optimal foraging, game theory, and trophic interactions. \emph{Journal
of mammalogy} , \emph{80} (2), 385-399.

Buck, J. C. (2019). Indirect effects explain the role of parasites in
ecosystems. \emph{Trends in parasitology} , \emph{35} (10), 835-847.

Buck, J. C., \& Ripple, W. J. (2017). Infectious agents trigger trophic
cascades. Trends in ecology \& evolution, 32(9), 681-694.

Burivalova, Z., Butler, R. A., \& Wilcove, D. S. (2018). Analyzing
Google search data to debunk myths about the public's interest in
conservation. \emph{Frontiers in Ecology and the Environment} ,\emph{16}
(9), 509-514.

Butler, D. (2013). When Google got flu wrong: US outbreak foxes a
leading web-based method for tracking seasonal flu. \emph{Nature}
,\emph{494} (7436), 155-157.

Center for Disease Control. (2020). Health Equity Considerations and
Racial and Ethnic Minority Groups.
https://www.cdc.gov/coronavirus/2019-ncov/community/health-equity/race-ethnicity.html

Chen, K., Wang, M., Huang, C., Kinney, P. L., \& Anastas, P. T. (2020).
Air pollution reduction

and mortality benefit during the COVID-19 outbreak in China. The Lancet
Planetary Health.

Choi, H., \& Varian, H. (2012). Predicting the present with Google
Trends. Economic record, 88, 2-9.

Claudia, S., Stephan, G., Sarah, R. and Zheng, T. (2020). Google
Searches Reveal Changing Consumer Food Sourcing in the COVID-19
Pandemic. Journal of Agriculture, Food Systems, and Community
Development 9.

Clinchy, M., Schulkin, J., Zanette, L. Y., Sheriff, M. J., McGowan, P.
O., \& Boonstra, R. (2011). The neurological ecology of fear: insights
neuroscientists and ecologists have to offer one another.\emph{Frontiers
in behavioral neuroscience} , \emph{5} , 21.

Clinchy, M., Sheriff, M. J., \& Zanette, L. Y. (2013). Predator\selectlanguage{english}-induced
stress and the ecology of fear. \emph{Functional Ecology} , \emph{27}
(1), 56-65.

Corlett, R. T., Primack, R. B., Devictor, V., Maas, B., Goswami, V. R.,
Bates, A. E., \ldots{} \& Cumming, G. S. (2020). Impacts of the
coronavirus pandemic on biodiversity conservation. \emph{Biological
Conservation} ,\emph{246} , 108571.

Crawford, J., Butler-Henderson, K., Rudolph, J., \& Glowatz, M. (2020).
COVID-19: 20 Countries' Higher Education Intra-Period Digital Pedagogy
Responses. \emph{Journal of Applied Teaching and Learning (JALT)}
,\emph{3} (1).

Curtis, V. A. (2014). Infection-avoidance behaviour in humans and other
animals. Trends in Immunology, 35(10), 457-464.

Dong, E., Du, H., \& Gardner, L. (2020). An interactive web-based
dashboard to track COVID-19 in real time. The Lancet infectious
diseases.

Frey, R., Hertwig, R., \& Rieskamp, J. (2014). Fear shapes information
acquisition in decisions from experience. \emph{Cognition} ,\emph{132}
(1), 90-99.

Goel, S., Hofman, J. M., Lahaie, S., Pennock, D. M. and Watts, D. J.
2010. Predicting consumer behavior with Web search. - Proceedings of the
National Academy of Sciences 107: 17486.

Gossling, S., Scott, D., \& Hall, C. M. (2020). Pandemics, tourism and
global change: a rapid assessment of COVID-19. Journal of Sustainable
Tourism, 1-20.

Haddaway, N. R., Wilcox, R. H., Heptonstall, R. E., Griffiths, H. M.,
Mortimer, R. J., Christmas, M., \& Dunn, A. M. (2012). Predatory
functional response and prey choice identify predation differences
between native/invasive and parasitised/unparasitised
crayfish.\emph{PloS one} , \emph{7} (2), e32229.

Heerfordt, C., \& Heerfordt, I. M. (2020). Has there been an increased
interest in smoking cessation during the first months of the COVID-19
pandemic? A Google Trends study. \emph{Public health} , \emph{183} , 6.

Hodges, C., Moore, S., Lockee, B., Trust, T., and Bond, A. (2020). The
Difference Between Emergency Remote Teaching and Online Learning.
Educause. March . Accessed 7/30/20.

Hooper, M. W., Napoles, A. M., \& Perez-Stable, E. J. (2020). COVID-19
and racial/ethnic disparities. Jama.

Hutchinson, J. M. C., and Gigerenzer G. (2005). Connecting behavioural
biologists and psychologists: clarifying distinctions and suggestions
for further work. Behav Processes 69:159--163

Isaifan, R. J. (2020). The dramatic impact of Coronavirus outbreak on
air quality: Has it saved as much as it has killed so far?. Global
Journal of Environmental Science and Management, 6(3), 275-288.

Kandula, S., \& Shaman, J. (2019). Reappraising the utility of Google
Flu Trends. \emph{PLoS Computational Biology} , \emph{15} (8), e1007258.

Kemp S (2005) Simple optimisation. Behav Processes 69:131--132.

Kristoufek, L. (2013). BitCoin meets Google Trends and Wikipedia:
Quantifying the relationship between phenomena of the Internet
era.\emph{Scientific reports} , \emph{3} (1), 1-7.

Lima, S. L., \& Dill, L. M. (1990). Behavioral decisions made under the
risk of predation: a review and prospectus. \emph{Canadian journal of
zoology} , \emph{68} (4), 619-640.

Lin, C. Y. (2020). Social reaction toward the 2019 novel coronavirus
(COVID-19). \emph{Social Health and Behavior} , \emph{3} (1), 1.

Mann, M. E., Bradley, R. S., \& Hughes, M. K. (1999). Northern
hemisphere temperatures during the past millennium: Inferences,
uncertainties, and limitations. \emph{Geophysical research letters}
,\emph{26} (6), 759-762.

Moore, A. J., Firn, J., \& Beckerman, A. (2017). Academic practice in
ecology and evolution: Soliciting a new category of
manuscript.\emph{Ecology and evolution} , \emph{7} (14), 5030.

Moore, J. L., Dickson-Deane, C. and Galyen, K. 2011. e-Learning, online
learning, and distance learning environments: Are they the same? - The
Internet and Higher Education 14: 129-135.

Nuti, S. V., Wayda, B., Ranasinghe, I., Wang, S., Dreyer, R. P., Chen,
S. I., \& Murugiah, K. (2014). The use of google trends in health care
research: a systematic review. \emph{PloS one} , \emph{9} (10), e109583.

Preis, T., Moat, H. S., \& Stanley, H. E. (2013). Quantifying trading
behavior in financial markets using Google Trends. \emph{Scientific
reports} , \emph{3} , 1684.

Raifman, M. A., \& Raifman, J. R. (2020). Disparities in the population
at risk of severe illness from covid-19 by race/ethnicity and income.
American Journal of Preventive Medicine.

Rose, S. (2020). Medical student education in the time of COVID-19.
Jama.

Rutz, C., Loretto, M. C., Bates, A. E., Davidson, S. C., Duarte, C. M.,
Jetz, W., \ldots{} \& Primack, R. B. (2020). COVID-19 lockdown allows
researchers to quantify the effects of human activity on
wildlife.\emph{Nature Ecology \& Evolution} , 1-4.

Wallace, R. M. 2003. Online Learning in Higher Education: a review of
research on interactions among teachers and students. - Education,
Communication \& Information 3: 241-280.

Weinstein, S. B., Buck, J. C., \& Young, H. S. (2018). A landscape of
disgust. \emph{Science} , \emph{359} (6381), 1213-1214.

Winnie, J., Christianson, D., Creel, S., \& Maxwell, B. (2006). Elk
decision-making rules are simplified in the presence of
wolves.\emph{Behavioral Ecology and Sociobiology} , \emph{61} (2), 277.

Winter, A. Coronavirus pandemic has worsened racial disparities in
education, lawmakers told. Ohio Capital Journal.

Vosen, S., \& Schmidt, T. (2011). Forecasting private consumption:
survey\selectlanguage{english}-based indicators vs. Google trends. \emph{Journal of forecasting}
, \emph{30} (6), 565-578.

Yancy, C. W. (2020). COVID-19 and African Americans. Jama.

Zambrano-Monserrate, M. A., Ruano, M. A., \& Sanchez-Alcalde, L. (2020).
Indirect effects of COVID-19 on the environment. \emph{Science of the
Total Environment} , 138813.

Zhang, W., Wang, Y., Yang, L., \& Wang, C. (2020). Suspending classes
without stopping learning: China's education emergency management policy
in the COVID-19 Outbreak.

Figure 1. The figure shows how the relative frequency of google searches
on global topics related to money, death, and love changed over time.
The panel on the left shows data from January 1 - June 30, 2019 (no
COVID-19), while the panel on the right shows data from January 1 --
June 30, 2020 (during the COVID-19 pandemic). Google Trends does not
provide the total number of searchers for a term (Burivalova et al.
2018). They provide an adjusted proportion of searches. The thicker
lines correspond to daily values, while the smaller line in the front
corresponds to detrended weekly averages. In 2019, google searches on
love were the most common followed by death and then by money. Users'
interests remained stable through time except for a spike in searches on
love around February 14, which corresponds to Saint Valentine's day. In
2020, the pattern is similar until mid March, with the exception of a
spike in searches on topics related to death around January 26, which
corresponds to increased interest in the death of basketball player Kobe
Bryant who died in a helicopter accident. Gray bars represent the global
number of confirmed COVID-19 cases (Dong et al. 2020). Note that as the
number of COVID-19 cases increases, there is a shift in the relative
interest in google searches. There is a small relative increase in
searches related to money. Death becomes the top category, switching
places with love. Data used in the figure is available at

\emph{https://github.com/maacevedo/covid19gtrends.}

Figure 2. The figure shows how interest in online learning, measured as
relative number google searches for ``online learning'' beginning
January 2019 up to June 30 2020 in the USA, increased exponentially in
parallel to the cumulative number of confirmed COVID-19 infections. Data
used in the figure is available at

\emph{https://github.com/maacevedo/covid19gtrends.}

.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/Figure-1-love-death-money/Figure-1-love-death-money}

\end{center}
\end{figure}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/Figure-2-online-learning/Figure-2-online-learning}

\end{center}
\end{figure}

\selectlanguage{english}
\FloatBarrier
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