ArticlePDF AvailableLiterature Review


Ecological and evolutionary processes govern the fitness, propagation, and interactions of organisms through space and time, and viruses are no exception. While COVID-19 research has primarily emphasized virological, clinical, and epidemiological perspectives, crucial aspects of the pandemic are fundamentally ecological or evolutionary. Here, we highlight five conceptual domains of ecology and evolution – invasion, consumer-resource interactions, spatial ecology, diversity, and adaptation – that illuminate (sometimes unexpectedly) the emergence and spread of SARS-CoV-2. We describe the applications of these concepts across levels of biological organization and spatial scales, including within individual hosts, host populations, and multi-species communities. Together, these perspectives illustrate the integrative power of ecological and evolutionary ideas and highlight the benefits of interdisciplinary thinking for understanding emerging viruses.
Since January 2020 Elsevier has created a COVID-19 resource centre with
free information in English and Mandarin on the novel coronavirus COVID-
19. The COVID-19 resource centre is hosted on Elsevier Connect, the
company's public news and information website.
Elsevier hereby grants permission to make all its COVID-19-related
research that is available on the COVID-19 resource centre - including this
research content - immediately available in PubMed Central and other
publicly funded repositories, such as the WHO COVID database with rights
for unrestricted research re-use and analyses in any form or by any means
with acknowledgement of the original source. These permissions are
granted for free by Elsevier for as long as the COVID-19 resource centre
remains active.
SARS-CoV-2: Cross-scale Insights from
Ecology and Evolution
Celine E. Snedden,
Sara K. Makanani,
Shawn T. Schwartz,
Amandine Gamble,
Rachel V. Blakey,
Benny Borremans,
Sarah K. Helman,
Luisa Espericueta,
Alondra Valencia,
Andrew Endo,
Michael E. Alfaro,
and James O. Lloyd-Smith
Ecological and evolutionary processes govern the tness, propagation, and interac-
tions of organisms through space and time, and viruses are no exception. While
coronavirus disease 2019 (COVID-19) research has primarily emphasized virologi-
cal, clinical, and epidemiological perspectives, crucial aspects of the pandemic
are fundamentally ecological or evolutionary. Here, we highlight ve conceptual
domains of ecology and evolution invasion, consumer-resource interactions, spa-
tial ecology, diversity, and adaptation that illuminate (sometimes unexpectedly)
the emergence and spread of severe acute respiratory syndrome coronavirus 2
(SARS-CoV-2). We describe the applications of these concepts across levels of
biological organization and spatial scales, including within individual hosts, host
populations, and multispecies communities. Together, these perspectives illustrate
the integrative power of ecological and evolutionary ideas and highlight the benets
of interdisciplinary thinking for understanding emerging viruses.
The Integrative Power of Ecological and Evolutionary Concepts for
Understanding Emerging Viruses
Zoonotic pathogens, namely those transmitted from vertebrate animals into humans, comprise
a majority of the infectious diseases that plague humankind. Examples range from pathogens
that infect humans exclusively via spillover (see Glossary)fromanimalreservoir hosts
(e.g., rabies virus, Leptospira interrogans, West Nile virus) to those that spread among humans
for decades after a successful spillover event (e.g., HIV-1, inuenza A virus) [1]. Most recently,
the emergence of SARS-CoV-2 triggered the COVID-19 pandemic, up-ended global society,
and stimulated an unprecedented burst of research spanning multiple disciplines. Much of
this research addresses the growth and change of SARS-CoV-2, which are population
processes that are deeply rooted in ecology and evolutionary biology [2,3]. These disciplines
have proven their utility for combating infectious diseases by informing public policy [4,5],
identifying potential reservoir hosts [6], and directing vaccine research [7,8]. Yet despite their
inherent power to integrate ndings from other disciplines, ecological and evolutionary ideas
have not been fully appreciated in the current SARS-CoV-2 literature. Vast opportunity remains
to explore their fruitful applications across levels of biological organization (henceforth referred
to as scales), namely within individual hosts, host populations, and multispecies communities.
By recognizing parallels in the patterns and processes governing viral dynamics at these
different scales, the scientic community can harness existing knowledge in ecology and
evolutionary biology to drive progress in understanding, mitigating, and preventing the
emergence of infectious diseases.
To advance this aim, here we illustrate how ve conceptual domains of ecology and evolutionary
biology can shed light on the emergence of novel viruses, including SARS-CoV-2, across the
Foundational concepts from ecology
and evolution can elucidate the
emergence and spread of severe
acute respiratory syndrome coronavi-
rus 2 (SARS-CoV-2), and all viruses,
across multiple scales.
Ecological and evolutionary methods
that characterize population dynamics
of organisms are potent tools to
investigate viral growth and spread within
individual hosts, or epidemic growth in
host populations.
The eld of macroevolution classically
studies the diversication and adaptation
of multicellular organisms, but major op-
portunities exist to apply macroevolution-
ary concepts to the evolution of viruses.
Concepts from spatial ecology, from
source-sink dynamics to synchrony,
can help us to understand patterns and
processes in the emergence of viruses.
Interdisciplinary research across the life
sciences can reveal otherwise unattain-
able insights into emerging infectious
diseases, posing new hypotheses and
rening existing knowledge in traditional
Department of Ecology and
Evolutionary Biology, University of
California, Los Angeles, CA, USA
La Kretz Center for California
Conservation Science, Institute of the
Environment and Sustainability,
University of California, La Kretz Hall,
Los Angeles, CA, USA
I-BioStat, Data Science Institute,
Hasselt University, Hasselt, Belgium
Evolutionary Ecology Group, University
of Antwerp, Antwerp, Belgium
These authors contributed equally to
this work
Trends in Microbiology, Month 2021, Vol. xx, No. xx 1
© 2021 Elsevier Ltd. All rights reserved.
Trends in
TIMI 1956 No. of Pages 13
within-host, population, and multispecies community scales (Figure 1, Key Figure). Though these
conceptual domains (hereafter, concepts) are inextricably linked, we present them separately
(each partitioned into discrete paragraphs by scale) and provide graphical representation of
their connections (Figure 2). In parallel, we emphasize tools and methods developed in ecology
and evolutionary biology that can unlock insights for understanding this, or any, pandemic
(Boxes 1 and 2). Our goal is not to provide an exhaustive review of the ballooning COVID-19
literature but instead to translate and apply relevant ideas from ecology and evolutionary biology
in a manner accessible to a wide audience. In particular, we aim to: (i) demonstrate the integra-
tive power of ecological and evolutionary ideas for scientists from different disciplines
(e.g., microbiology, mathematics, public health), (ii) excite students about the broad applica-
tions of ecology and evolution across the life sciences, and (iii) prompt established ecologists
Key Figure
Cross-scale Applications of Ecological and Evolutionary Concepts to Viruses, with Examples from
Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2)
Figure 1. A series of descriptions highlighting ve core ecological and evolutionary principles at multiple scales, including within host (individual; blue), within po pulations (population;
purple), and across species (community; pink). Bolded content reects the general applications of each concept to viruses. Italicized content reects specic examples relevant to
SARS-CoV-2 and SARS-like coronaviruses (CoVs). The references for each concept and example can be found in the corresponding paragraph of the text.
*Correspondence: (M.E. Alfaro)
(J.O. Lloyd-Smith).
Trends in Microbiology
2Trends in Microbiology, Month 2021, Vol. xx, No. xx
and evolutionary biologists to recognize novel opportunities to apply their expertise across
disciplines and scales to ght this and future pandemics.
Ecological Dimensions of Viral Emergence
The eld of ecology focuses on the distribution, abundance, and interactions of organisms across
space and time, and is traditionally subdivided by levels of organization, which include the popu-
lation scale (i.e., the study of one species in a given region) and the community scale (i.e., the
study of multiple species). Less conventionally, an individual organism can be viewed as its
own within-host ecosystem, where host cells and microorganisms interact in a landscape of
host tissues. In the context of infectious diseases, ecological principles govern the population
dynamics of many relevant entities (including viruses, cells, and host individuals), and these
population processes can naturally be delineated at different scales [9]. Below, we introduce
each concept at the population scale, as it provides the most intuitive platform for discussion,
followed by applications at the within-host and within-community scales.
Invasion Processes and Emerging Viruses
The success of a virus in a new target population is governed by processes of ecological invasion,
which manifest in phases of introduction, establishment, and spread [1012]. For example, when
the COVID-19 pandemic began, infected travelers from China transported SARS-CoV-2 to coun-
tries across the globe, including Germany, Italy, and the USA [13]. Once introduced, successful
establishment of a virus requires local transmission, which depends on viral shedding, host con-
tact patterns, and host population susceptibility [10,14]. The likelihood of establishment
increases if multiple introduction events occur or if many individuals arrive simultaneously, as
described by the propagule pressure hypothesis [15]. In the event of sustained community
transmission, the virus can be considered an invasive species in this local host population. Sub-
sequent dispersal propagates the virus further by initiating similar invasion processes in other
connected regions [10]. SARS-CoV-2 showcases that these invasion waves can ultimately trigger
a global pandemic.
The infection of an individual host can also be viewed as an invasion process, wherein exposure
introduces a founder population of potentially invasive viruses (i.e., the inoculum). The route of
this exposure determines the inoculation site, where receptor expression, immune activation, and
other factors determine tissue susceptibility [16], just as resource availability and the presence of
competitors, predators, and pathogens impact landscape suitability for introduced plant and
animal species [10,17]. These host factors vary across tissues and affect the probability of estab-
lishing local infection for a given site of deposition [16,18]. Once established, onward spread is
governed by tissue susceptibility, physical connectivity, and transport mechanisms. For instance,
SARS-CoV-2 infection typically begins in the respiratory tract, where the cellular receptor
angiotensin-converting enzyme 2 (ACE2) is highly expressed, and it has been proposed that
subsequent neuroinvasion can occur via the olfactory nerve or via the bloodstream paired with
damage to the bloodbrain barrier [19]. While the effect of a single high-dose exposure versus
multiple low-dose exposures remains largely unresolved [15,18,20], invasion theory predicts
that, for a given dose, infection is more likely when viruses deposit at different sites across a
heterogeneous tissue landscape [21].
At the community scale, the invasion analogy applies to viral host jumps, where a virus must
overcome sequential barriers to invade a novel host species [1012,22]. Given some type of
cross-species contact, viral shedding from reservoir or intermediate hosts can introduce the
virus to an individual from a recipient host species [11,22]. A variety of virological and evolutionary
factors (dictated by the within-host invasion process) inuence establishment of the virus in this
Adaptive radiation: the rapid
diversication of organisms in response
to available environmental niches.
Bottleneck: a reduction in genetic
variation resulting from a change in
populationsize that occurs for at least
one generation.
Convergent evolution:similarity in trait
or genotype that is acquired
independently in two or more lineages,
often interpreted as evidence of
Dispersal: movement of individuals
across a landscape.
Ecological opportunity: the
environmental potential available to a
newly colonizing lineage for
diversication into divergent niches.
Ecological trap: a low-quality habitat
patch, where mortalities exceed births,
that decreases overall population tness
because individuals settle in these
habitats instead of other available high-
quality habitats.
Enemy release hypothesis: this
hypothesis posits that the absence of
enemies (e.g., predators) within an
invasive speciesexotic range leads to
successful invasion.
Exaptation: a trait that evolved by
natural selection to perform a specic
function that later performs another
unrelated function.
Founder population: agroupof
individuals from a larger population that
migrate, settle, and establish a new
population in a new, uninhabited
Functional response: the relationship
between consumption rate of a
consumer (e.g., predator) and
abundance of the target resource
(e.g., prey).
Gene flow: exchange of genetic
material between connected
populations through migration.
Genetic drift: changes in allele
frequency within a population due to
random chance.
Intermediate host: a host species that
acts as a bridge to facilitate pathogen
transmission between a reservoir
species and a focal host species.
Invasive species: aspecies
introduced to an area outside its normal
range, often by human means, where it
reproduces and spreads beyond the
area in which it was released and
negatively impacts the new ecosystem.
Landscape immunity: dened in [22]
as the ecological conditions that control
Trends in Microbiology
Trends in Microbiology, Month 2021, Vol. xx, No. xx 3
host, including immune defenses and the availability of suitable cellular receptors. Even if the virus
successfully infects this individual, other barriers can limit further spread within this novel host
population (dictated by the population invasion process) [10,11]. For SARS-CoV-2, the path to
zoonotic spillover remains unknown, though the progenitor virus likely originated in bats. The di-
versity of bat viruses, if paired with contact among humans and bats, potentially via intermediate
hosts, can provide multiple opportunities for successful cross-species invasion to occur, as de-
scribed by the propagule pressure hypothesis [15,22,23]. Several groups of bat-borne viruses,
including the Henipaviruses and Ebolaviruses, are well known to have caused numerous out-
breaks via independent zoonotic transmissions [23]. Given evidence of multiple spillover events
of SARS-like coronaviruses [24], further investigation is warranted into whether SARS-CoV-2
could have been introduced to humans more than once. Once introduced, the
contrast between the robust antiviral defenses in bats and humans, combined with the immuno-
logical naïveté of the human population, may have facilitated the successful invasion of SARS-
CoV-2 as suggested by the enemy release hypothesis [17,25].
ConsumerResource Interactions between Viruses, Hosts, and Intervention Strategies
The population dynamics of a virus invariably depend on consumerresource interactions in
which consumers rely on, and directly impact, resource availability. These interactions are a
critical component of ecological community structure and provide the foundation for classical
epidemiological models, where populations of infected hosts grow by 'consuming' susceptible
individuals (i.e., the resource). In fact, the simplest epidemic models and predatorprey models
are mathematically equivalent (Box 1)[26] and thus share fundamental features such as the
tendency to cycle. Vaccination reduces susceptible availability and hence lowers infection preva-
lence [4,27], just as a loss of prey reduces predator abundance. Less obviously, nonpharmaceutical
interventions can be analyzed in the consumerresource framework where contact tracers can be
viewed as hyperpredators that remove (i.e., consume) infected individuals from a population, and
physical distancing alters how the infection rate depends on susceptible abundance (i.e., the
functional response). Such epidemiological models can provide prompt insights into the popula-
tion dynamics of an emerging virus under various assumptions (e.g., quarantine compliance, vacci-
nation rates), as evidenced in the SARS-CoV-2 literature (e.g., [27]). While obtaining accurate
predictions from these models requires reliable parameter estimates from high-quality datasets,
strategies developed in ecology can account for and leverage imperfect data (Box 1).
The interactions between viruses and cells within an individual host can also be treated as a
network of consumerresource interactions [28]. In the simplest case, viruses (i.e., the consumer)
infect susceptible cells (i.e., the resource), thus decreasing susceptible cell population size while
increasing viral population size (Box 1)[29]. This conceptual framework can also incorporate the
immune system, which can consume viral particles and infected cells [30] or block viral consump-
tion of susceptible cells by stimulating an antiviral state [25]. Models that incorporate these inter-
actions improve our understanding of the immune system and our ability to control disease
progression [29]. For instance, they can explore the impacts of target cell depletion on viral
load and within-host spread [28].
Consumerresource interactions inuence community-level dynamics of virus emergence by
providing opportunities for spillover events and potential pandemics [2]. For instance, the hunting,
handling, and consumption of livestock and wildlife can expose humans to zoonotic viruses
through contact with infected tissues [11,31]. Three familiar examples include SARS-CoV, HIV-1,
and 2009 H1N1 pandemic inuenza virus (H1N1pdm), which are linked to palm civets in Chinese
wildlife markets, hunted chimpanzees in Central Africa, and pig farms in Mexico, respectively
[31,32]. Though the precise origin of SARS-CoV-2 remains unclear, the trade and consumption
pathogen populations while
strengthening the immune functions of
wild animals in an ecosystem.
Population dynamics: the study of
how and why population size and
structure change over time.
Propagule pressure hypothesis: this
hypothesis posits that a greater number
of individuals in a single release event or
a higher frequency of release eventsover
time increases the likelihood of invasion
Reassortment: atypeof
recombination exclusive to segmented
viruses in which coinfection of a host cell
results in the exchange of gene
segments between similar virus strains.
Recombination: the process by which
segments of genomic material are
broken and exchanged during genome
replication, creating new combinations
of alleles.
Reservoir host: ahostspeciesin
which a pathogen circulates
continuously without reintroduction and
which can transmit the pathogen to
other hosts.
Spillover: the transmission of a
pathogen from one host species to
another; zoonotic spillover species
transmission from a vertebrate animal to
Trends in Microbiology
4Trends in Microbiology, Month 2021, Vol. xx, No. xx
of exotic animals in Chinese wildlife markets have been suspected to be involved [33]. Interactions
with farmed animals can also facilitate human-to-animal transmission of viruses, as most recently
evidenced by SARS-CoV-2 outbreaks in mink farms [34]. Many countries have also reported
human-to-pig H1N1pdm transmission, where further reassortment could generate another virus
capable of infecting humans [35]. These examples highlight the inherent health risks (for both
humans and animals) associated with animal products, and they emphasize the importance of
developing, implementing, and managing more responsible biosecurity regulations for livestock
and wildlife trade [36].
Ecological Principles Governing Virus Spatial Dynamics
Spatial ecology describes the spread, persistence, and interactions of individuals across land-
scapes consisting of habitat patches connected by dispersal. Individuals moving from source
habitats, where birth rates exceed mortality rates, can colonize new habitats or sustain popula-
tions in sink habitats, where mortalities exceed births [37]. This sourcesink framework directly
applies to spatial spread of disease, where epidemiologists call sources 'supercritical' and
sinks 'subcritical' for pathogen transmission. For example, during the early COVID-19 epidemic
in China, infected travelers from Wuhan (i.e., the source, with positive epidemic growth) sparked
outbreaks in many other Chinese cities (i.e., the new habitats). However, prompt implementation
of local control measures in these cities reduced growth rates to become negative (i.e., they
became sinks), so those outbreaks died out once the source outbreak was controlled [38]. In
such heterogeneous landscapes, synchronous dynamics, wherein populations rise and fall
concurrently, increase the likelihood of population-wide extinction. Such synchrony can arise
from correlated exogenous factors (e.g., climate conditions) and/or sufcient dispersal [39]. For
viruses, if prevalence declines simultaneously in connected patches, the absence of high-
prevalence sources prevents dispersing hosts from recolonizing locally extinct patches [39,40].
Public health ofcials can leverage this principle to limit the spread of emerging infectious diseases
if control policies are coordinated across cities and regions to promote synchronous declines in
prevalence. Unfortunately, the lack of coordination plaguing the COVID-19 response has allowed
reseeding of outbreaks in locales that had previously contained SARS-CoV-2, leading to more
cases and more interventions needed [5].
These spatial ecology concepts can also illuminate viral spread within the spatially structured
organs and tissues of an infected host. Because viral replication depends on many factors,
including temperature, immune response, and cellular receptor and protease expression, dif-
ferent tissues act as sources or sinks [41]. For example, to enter a cell, the SARS-CoV-2
spike protein must bind to the ACE2 receptor and be primed by the protease TMPRSS2, al-
though other receptors and proteases may also be involved [42]. Tissues with sufcient
coexpression of ACE2 and TMPRSS2 (e.g., nasal cavity) may act as sources that seed infection
of surrounding areas with lower expression levels (e.g., bronchioles) [16,43]. When ACE2 is
expressed without TMPRSS2 (e.g., the heart), a tissue may function as an ecological trap,
where virions bind target cells but cannot enter or replicate [3,41,43]. Interestingly, this concept
can be leveraged to design therapeutics (e.g., [44]). Physical transport mechanisms can also
create ecological traps: for instance, SARS-CoV-2 may infect the central nervous system
[19], but the viral particles produced in these tissues cannot readily transmit between hosts.
Additionally, SARS-CoV-2 largely infects the human upper respiratory tract, from which pro-
duced virions are readily expired, whereas Middle East respiratory syndrome coronavirus
(MERS-CoV) and SARS-CoV infections predominantly reside in the lower respiratory tract,
from which viral particles cannot readily be expelled from the host [45]. This difference in tissue
tropism affects the transmissibility of these coronaviruses, and likely their pandemic potential.
These examples demonstrate that models designed in spatial ecology can integrate knowledge
Trends in Microbiology
Trends in Microbiology, Month 2021, Vol. xx, No. xx 5
Box 1. Ecological Methods for Modeling Viral Dynamics and Addressing Data Limitations
Classically, ecologists study population dynamics by using mechanistic models that classify entities (e.g., individuals) into
compartments according to their states and quantify transition rates between them. Most models of disease transmission
are based on the classication of individuals into susceptible (S), infectious (I), and recovered/immune (R) states (Figure I),
though additional states are frequently included [26,27]. Other applications include within-host models of virus replication
(classied by target cells, infected cells, and free-living virus) [29] and between-farm models of infected livestock (e.g., foot-
and-mouth disease, classied by susceptible, noninfectious, infectious, and slaughtered farms) [83]. Various mathematical
frameworks can capture these dynamics and advance a wide range of scientic aims, including estimating epidemiological
parameters, assessing the effectiveness of public health strategies, and directing optimal data collection [84].
Statistical ecologists deploy another suite of tools. Bayesian joint-likelihood models are well suited to integrating multiple
datasets with different units and temporal/spatial scales and can be designed to account for mechanism (e.g., [85]) or
discover statistical patterns (e.g., [86]). Species distribution models classify and predict habitat suitability for a given
species on the basis of environmental factors and known species occurrence [87]. These methods can be used to
estimate the regional and global distribution of viruses [88], with important exceptions [89], but their application to studying
tissue tropism across the within-host landscape remains largely unexplored.
Parameterizing ecological models relies on accurate quantication of state variables (e.g., prevalence) from eld and
laboratory data. However, data are never perfect due tofactors that include sparse or irregular sampling, diagnostics with
imperfect sensitivity or specicity, human error, awed experimental design, or missing data. The possibility of false-
positive and false-negative test results is particularly important for epidemiological models (Figure I). While all of these
issues can introduce bias or other problems, ecologists have developed strategies and tools to account for them, including
occupancy models and state-space models [90,91]. Occupancy models use repeated observations (e.g., multiple swabs
per individual per day) to jointly estimate detection probability and the occupancy of a species in a landscape (or
analogously, infection prevalence) (Figure I)[92]. State-space models account for imperfect observations in time series
data by separating the dynamics of the biological process (e.g., infection dynamics) from noise or bias in the observation
process (e.g., false negatives) [91]. Extensions of these two methods can incorporate multiple infection states [93],
estimate transmission and recovery rates [86], and include multiple host or virus species [94].
Figure I. The SIR Model of Infection Dynamics and the Occupancy Modeling Approach to Determine
Infection State. The SIR model is a fundamental mechanistic model of infection dynamics. Colored boxes represent
entities (e.g., cell, tissue, organ, person, or population) that are classied by their infection state: susceptible (S, green),
infectious (I, purple), and recovered (R, blue). The biological system (i.e., the virus, host population, and environment)
determines the transition rates between each state (represented by arrows) and whether a recovered host can become
susceptible again (broken arrow). In parallel, the occupancy modeling approach uses sampling techniques to infer an
entitys infection state at various time points. Here, we present two sample types (Observation 1 and 2) that are measured
per sampling event (marked by a red X). In this gure, we depict measurements of immune markers (e.g., antibodies) and
viral material (e.g., viral RNA), though the framework is applicable to any other observation relevant for the considered
system. Three tests are conducted per sample type per sample time, and each vial represents an individual test per
time point. Vial color denotes the test result (green, positive; purple, negative), which can correctly or incorrectly classify
infection state [T.P. (true positive); T.N. (true negative)]. The frequency of false-negative or false-positive test results
depends on the diagnostic, the sampling time, and inherent variability in infection dynamics.
Trends in Microbiology
6Trends in Microbiology, Month 2021, Vol. xx, No. xx
from molecular biology (e.g., receptor afnity), multiomics (e.g., receptor expression), and physiol-
ogy (e.g., tissue connectivity) to uncover patterns that underlie varying transmission characteristics
and pathogenicity of different viruses.
Concepts and tools from spatial ecology allow us to identify and predict landscapes at high risk
of experiencing cross-species spillover [46]. In particular, anthropogenic landscape changes
increase spillover risk by: (i) altering the abundance and distribution of wildlife hosts, with highly
modied areas potentially attracting a greater abundance of known reservoir hosts of zoonoses
(e.g., rodents and some bat species), (ii) promoting stress-induced shedding and host suscep-
tibility, and (iii) increasing contact rates among domestic animals, wildlife, and humans
[1,2,11,22,47]. While interspeciccontactsaredifcult to quantify in the wild, advances in an-
imal tracking [48], data sharing platforms (e.g., Movebank), and quantitative methods [49]can
rene our predictions of animal encounters, so additional monitoring can be directed to high-
risk locations. However, given the difculty of identifying and tracking the multitude of potential
hosts, future applications of spatial ecology to understanding and preventing cross-species
transmission may focus increasingly on resilience, rather than risk, within landscapes. Scien-
tists have called for ecological countermeasures to prevent future pandemics, including foster-
ing landscape immunity. Interdisciplinary collaborations (among disease ecologists,
conservation practitioners, immunologists, and many more) are necessary to understand
and maintain landscape immunity across diverse ecosystems and to formulate clear guidance
for policy-makers [22].
Evolutionary Dimensions of Emerging Viruses
The evolution of organisms hinges on the accumulation of heritable mutations over successive
generations, which can generate phenotypic variation. When studying virus evolution, it is essen-
tial to note that virus populations can be dened simultaneously at several nested scales (within
their hosts, within host populations, and across host species communities). Evolutionary forces
(e.g., mutation, selection) affect viral diversity and tness concurrently at all of these scales, always
mediated by the common currency of viral genomes. Due to the inherently intertwined nature of
evolutionary processes at these different scales, we explore each concept rst at the within-host
scale, where viral factors (e.g., mutation rates) and host pressures (e.g., immune responses) act
proximately to generate viral diversity and perhaps drive adaptation [2]. Then, we discuss how
processes functioning within host populations and across host species further shape the evolu-
tionary trajectory of an emerging virus.
Evolutionary Controls of Viral Diversity
Genetic diversity accumulates over many generations through mutation and recombination,
and the frequency of these processes varies across viral lineages. Most RNA viruses have re-
markably high mutation rates due to a low-delity RNA polymerase that increases the frequency
of spontaneous mutations [50,51]. However, SARS-CoV and SARS-CoV-2 utilize a high-delity,
RNA-dependent RNA proofreading mechanism, which reduces the occurrence of mutations and
helps the virus to maintain a functional genome [51,52]. For other coronaviruses (e.g., HCoV-
OC43), recombination is essential for generating diversity, which suggests that this process
may also be important for SARS-CoV-2 [52,53]. Concrete evidence of recombination seemed
conspicuously absent in SARS-CoV-2 genomes sampled during early 2020, while point muta-
tions and deletions were common [54]. However, new evidence that SARS-CoV-2 recombines
readily in vitro [53] emphasizes that diversity may initially have been insufcient to yield detectable
recombination, and further study is needed to investigate recombination in eld isolates. Addition-
ally, in vivo studies of within-host viral diversity could better characterize the relative frequencies of
mutation and recombination. Such research has been limited so far; however, two notable case
Trends in Microbiology
Trends in Microbiology, Month 2021, Vol. xx, No. xx 7
studies of immunocompromised patients reported nonsynonymous and synonymous mutations
and deletions across 15 sites arising over 150 days [55] and 30 sites over 152 days [56] but did
not appear to screen for recombination.
Just as for multicellular organisms, population genetic mechanisms (e.g., gene flow,genetic
drift) modulate the genetic diversity of viruses in a host population. The occurrence of bottle-
neck events eliminates viral variants, while high gene ow promotes homogeneity [57]. Despite
the vast ecological opportunity posed by its recent zoonotic jump and the sheer number of
COVID-19 cases worldwide, the global diversity of SARS-CoV-2 measured in late July 2020
was remarkably low, with 46 723 sequenced genomes from 99 countries diverging maximally
by 32 SNPs, all of which were considered descendants of a single lineage [54]. However, as of
January 2021, global diversity has increased enormously [58], including three novel lineages
each characterized by 17 (B.1.1.7), nine (B.1.351), and 16 (P.1) nonsynonymous mutations,
of which 38 are distinct [59,60]. Two explanations for this surge of diversity include sufcient
time elapsed since emergence for the accumulation of viral diversity and/or transmission of di-
verse variants that arose in long-term COVID-19 patients [55,59]. In principle, the diversity of
endemic human coronaviruses could provide a preview of the future evolutionary trajectory
of SARS-CoV-2, but sparse sampling (e.g., 36 HCoV-OC43 genomes examined from four
countries over 30 years [61]) makes quantitative comparisons difcult. By contrast, the massive
sequencing efforts of SARS-CoV-2 provide an exciting opportunity to detect new variants in
real time and, by utilizing techniques from macroevolution, investigate the controls on global
coronavirus diversity (Box 2).
Phylogenetic perspectives on diversication can identify genetic relationships among viruses and
provide pivotal insights into evolutionary trajectories of host jumps. Such analyses showed that
reassortment of human, avian, and porcine inuenza viruses gave rise to the 2009 H1N1 pan-
demic strain [35]. Early phylogenetic studies suggested that SARS-CoV-2 emergence involved
recombination between bat and pangolin coronaviruses, but subsequent results showed that
the lineage that gave rise to SARS-CoV-2 has been present in bats for many years [62]. However,
the detection of coronaviruses in many host species (e.g., raccoon dogs, minks), coupled with
their propensity to recombine, emphasizes that coinfection could facilitate recombination be-
tween various, potentially distinct, coronaviruses and thus generate novel viruses with pandemic
potential [34,63,64]. Further analyses of genetic diversity can shed light on the multihost epidemi-
ology of coronaviruses, distinguishing reservoir, intermediate, and dead-end hosts. For instance,
the high and stable diversity of SARS-like coronaviruses in bats supports their role as a reservoir,
while rapid growth of viral diversity in humans and civets was a hallmark of recent spillover of
SARS-CoV in 20022004 [64]. Continuing transmission between host species can further con-
tribute to viral diversity as evidenced by repeated transmission of SARS-CoV-2 between minks
and humans, with novel variants arising in minks sometimes transmitted back to humans,
which echoes observations for inuenza A [34,35].
Adaptation and Viral Evolutionary Success
The evolutionary process of viral adaptation reects selective pressures operating across multiple
scales [65]. Inside an infected host, purifying selection purges viral variants with disadvantageous
traits (e.g., structural instability), and positive selection favors those that confer an adaptive ad-
vantage (e.g., evasion of immune responses) [3,54,66]. Selection can act strongly on viral attach-
ment proteins, which mediate cellular entry and are an accessible target for the immune system
[67]. For SARS-CoV-2, mutations in the spike protein can alter viral tness by enhancing ACE2
receptor binding or facilitating immune escape. Notably, the variant lineages that arose in late
2020 shared several substitutions in the spike protein, including K417N (found in the B.1.351
Trends in Microbiology
8Trends in Microbiology, Month 2021, Vol. xx, No. xx
and P.1 lineages), E484K (B.1.1.7, B.1.351, P.1), N501Y (B.1.1.7, B.1.351, P.1), and D614G
(B.1.1.7, B.1.351, P.1) [60,66,6870]. In vitro assays suggest that both D614G and N501Y pro-
mote an up-conformation of the spike protein subunits, which increases the likelihood of binding
ACE2, exposes the cleavage site, and increases overall infectivity of a cell [69,70]. K417N exhibits
diminished neutralization by monoclonal antibodies, but only moderately increases ACE2 binding
afnity. E484K exhibits increased binding afnity to ACE2 and reduced neutralizing activity of
monoclonal antibodies [71].
In host populations, selection favors viral variants that can transmit between hosts and propagate
through the population; thus, rising frequency of particular variants suggests a selective advan-
tage. However, similar patterns could arise due to founder effects or stochasticity [72], so cau-
tious interpretation is warranted. For SARS-CoV-2, D614G rose to high frequency in separate
global outbreaks and became dominant worldwide by March 2020 [68,73], and phylogenetic
analysis in the UK suggests that D614G approached xation after introduction into a region dom-
inated by the wild-type [73]. These ndings are consistent with an adaptive role for D614G, which
is further supported by evidence that it promotes increased transmissibility compared to the wild
type: (i) more efcient transmission in hamsters, (ii) increased replication in the upper respiratory
tract of humans and hamsters in vitro and in vivo, and (iii) the spike conformational mechanism
described above [68,69,74,75]. Similarly, B.1.1.7 became the dominant lineage in the UK within
3 months of its emergence in late September 2020, while B.1.351 and P.1 rose rapidly in fre-
quency in South Africa and Brazil, respectively [59,76]. Although these lineages appear to have
emerged independently in different countries, they share several key substitutions in the spike
Box 2. Macroevolutionary Theory: An Underused Toolbox for Studying Viral Diversity
Macroevolution is the study of processes that govern the origin, persistence, and extinction of species. Despite a well-
developed set of conceptual tools for understanding diversity dynamics, including models of lineage origination and
extinction that vary with time, traits, and environmental conditions, macroevolutionary approaches have scarcely been
applied in virological studies. Macroevolutionary ideas may apply fruitfully to viral diversity across scales, and even
within a single viral species. Potential examples include:
Macroevolution has revealed surprising ways that species persistence and diversication can be decoupled from
forces governing individual tness [e.g., selection has repeatedly favored traits associated with mammalian
hypercarnivory (e.g., bone-cracking) at the individual level, but these lineages are more vulnerable to extinction than
generalist clades [95]] [96,97], and may offer new perspectives on cross-scale phenomena in viruses such as the
evolution of virulence.
The concept of ecological adaptive radiation links ecological opportunity (e.g., absence of competitors when novel
habitats are colonized) to the rapid proliferation of new species adapted to distinct niches. Host jumps leading to
epidemics or pandemics could provide viruses with vast ecological opportunity to differentiate (e.g., the global popu-
lation of susceptible humans for SARS-CoV-2, as well as other new host species infected by humans), yet host
population movement and viral gene ow will work against differentiation. Adaptive radiation theory may help to predict
evolutionary trajectories of novel viruses in humans or other hosts.
Macroevolutionar y theory around adaptive radiation and clade comp etition [98] may provide new insightsinto patterns in
infectious disease emergence events, including impacts of competition with endemic viruses and the factors controlling
the total diversity of viruses that can infect humans.
Substantial challenges exist for delineating the signicant evolutionary differences between variants, strains, species, and
lineages. Macroevolutionary principlescombined with species delimitation frameworkscould help to integrate genotypic
data with phenotypic data (e.g., conserved protein domains, receptor specicity) to create a rigorous system for virus
species delineation which might offer insights into the properties of an emerging virus (e.g., potential host range, trans-
mission route) [99].
Some of these questions have been approached within emerging elds such as phylodynamics [100], but macroevolution
may help to develop much-needed frameworks for understanding the vastness of viral diversity, especially at deeper
phylogenetic and temporal scales. Furthermore, since viruses diversify much more rapidly than plant and animal species,
engagement with virology might provide macroevolutionists with heretofore nonexistent opportunities to directly observe
hypothesized processes for the assembly of biodiversity.
Trends in Microbiology
Trends in Microbiology, Month 2021, Vol. xx, No. xx 9
protein (D614G, N501Y, E484K) associated with within-host advantages and, putatively, in-
creased transmissibility [59,71,77]. Such convergent evolution is a classic sign of adaptation
[58,78], and viruses may provide an unexpected opportunity to investigate the relationship be-
tween convergence in function (e.g., transmissibility) and convergence of the underlying genetic
and/or structural components of the trait [79].
At the scale of cross-species emergence, the role of virus adaptation is the subject of
longstanding debate: when (if ever) is adaptation required, and where does it occur [78]?
Natural selection can drive the evolution of a trait that is later commandeered for a new func-
tion, and such exaptation of viruses in animal reservoirs can facilitate host jumps. Genetic
analysis has revealed a furin-recognition motif in the SARS-CoV-2 spike protein which facili-
tates binding of human ACE2 and enables cleavage by the furin protease [6]. This motif is
present in a coronavirus found in Malayan pangolins (Manis javanica) but is absent in the co-
ronavirus (RaTG13) most genetically similar to SARS-CoV-2 (found in horseshoe bats;
Rhinolophus afnis)[6]. The acquisition of this motif (via an unclear pathway) may have
functioned as an exaptation that mediated transfer of the SARS-CoV-2 progenitor from wildlife
into humans. Since cellular entry is a key determinant of viral host range, the use of highly con-
served receptors (and hence a generalist life history) may function as an alternative typeof exapta-
tion that provides more opportunities for spillover events [80,81]. For example, ACE2 is highly
conserved among humans, various bat species, and potential intermediate hosts [23]. Indeed,
many host species have proved susceptible to SARS-CoV-2, including ferrets and cats [82]; in
silico analysis identies many other potentially susceptible species, providing insights into the
current or future host range of the virus [80].
Concluding Remarks
Given the increasing frequency of zoonotic emergence events and their potential societal im-
pacts, it is imperative to leverage all applicable tools to better understand, predict, and prevent
the spillover and subsequent spread of novel infectious diseases. Ecological and evolutionary
concepts, which have been crafted and tested for decades, provide key insights into the origin
of emerging viruses, the trajectory of an outbreak or pandemic, and the risk of future spillover.
These concepts, which are already inherently intertwined (Figure 2), prove even more powerful
when integrated with other elds, including microbiology, epidemiology, and medicine. Such in-
terdisciplinary approaches can uncover key insights that are otherwise unattainable, and, in par-
ticular, could answer many unresolved questions about SARS-CoV-2 (see Outstanding
Questions). The insights gained, and new avenues for investigation developed from these ques-
tions, will drive progress in further rening ecological and evolutionary theory, directing future in-
terdisciplinary research, and improving general understanding of the mechanisms that drive the
emergence of infectious diseases.
We thank allparticipants of the seminar where theideas for this manuscriptwere formulated.We apologize to the manyauthors
whose work we were unable to include because of space limitations. All gures were created using
supported by the European Commission Horizon 2020 Marie Skłodowská-Curie Actions (grant no. 707840). C.E.S was
supported by the National Institutes of Health (grant 5T32 GM008185-33). R.V.B. was supported by the La Kretz Center for
CaliforniaConservation Science at the Universityof California Los Angeles.J.O.L.-S. and A.G. were supported by the Defense
Advanced Research Projects Agency DARPA PREEMPT #D18AC00031, the National Science Foundation (DEB-1557022),
and the UCLA AIDSInstitute and Charity Treks. The content of the article does not necessarily reect the position or the policy
of the US government, and no ofcial endorsement should be inferred.
Declaration of Interests
There are no interests to declare.
Outstanding Questions
Can insights from invasion ecology
be harnessed to formulate a new
generation of mechanistic dose
response models? Can we leverage
biomedical ndings to model a viral
exposure event as a population
growth process on a heterogeneous
Does pre-existing immunity to SARS-
CoV-2 arise from prior exposure to
endemic coronaviruses? If so, do
differences in these virus community
interactions explain geographic varia-
tion in pandemic intensity? Can con-
sumerresource models be combined
with patient data to investigate the
impacts of pre-existing immunity on
disease course?
Can animal tracking technologies
reveal the interactions of potential
reservoir and intermediate hosts of
SARS-CoV-2? Does overlaying this
information with human population
data reveal regions and species that
CoV-2 spillover? Can these techniques
identify high-risk areas for future
emerging viruses?
How is SARS-CoV-2 tissue tropism
inuenced by features of the within-host
landscape (e.g., temperature, pH, pro-
tein expression)? Can species distribu-
tion models incorporate this information
to clarify the apparent disparities
between ACE2 expression and SARS-
CoV-2 tissue tropism?
The up versus down conformation in the
spike protein may favor reproduction (via
better receptor binding) versus survival
(via hiding epitopes from antibodies),
respectively. Can the optimal balance of
these states be understood using the
evolutionary theory of life history trade-
How will the virulence of SARS-CoV-2 in
humans change over time, and will this
be governed chiey by population immu-
nity or viral evolution, or both? Did other
endemic human coronaviruses begin as
catastrophic pandemics and evolve into
'common cold' viruses?
How many endemic coronaviruses can
the human population sustain, and
what forces govern this limit? Is there
competition among coronaviruses for
Trends in Microbiology
10 Trends in Microbiology, Month 2021, Vol. xx, No. xx
1. Wolfe, N.D. et al. (2007) Origins of major human infectious
diseases. Nature 447, 279283
2. Schrag, S.J.and Wiener, P. (1995) Emerginginfectious disease:
what arethe relative roles ofecology and evolution?Trends Ecol.
Evol. 10, 319324
3. Wasik, B.R. and Turner, P.E. (2013) On the biological success
of viruses. Annu. Rev. Microbiol. 67, 519541
4. Peak, C.M. et al. (2017) Comparing nonpharmaceutical inter-
ventions for containing emerging epidemics. Proc. Natl.
Acad. Sci. U. S. A. 114, 40234028
5. Ruktanonchai, N.W. et al. (2020) Assessing the impact of
coordinated COVID-19 exit strategies across Europe. Science
369, 14651470
6. Zhang, T. et al. (2020) Probable pangolin origin of SARS-CoV-
2 associated with the COVID-19 outbreak. Curr. Biol. 30,
7. Viboud, C. et al. (2020) Beyond clinical trials: Evolutionary and
epidemiological considerations for development of a universal
inuenza vaccine. PLoS Pathog. 16, e1008583
8. Dearlove, B. et al. (2020 ) A SARS-CoV-2 vaccine candidate
would likely match all currently circulating variants. Proc. Natl.
Acad. Sci. U. S. A. 117, 2365223662
9. Tompkins, D.M. et al. (2011) Wildlife diseases: from individuals
to ecosystems. J. Anim. Ecol. 80, 1938
10. Blackburn, T.M. et al. (2011) A proposed unied framework for
biological invasions. Trends Ecol. Evol. 26, 333339
11. Plowright, R. K. et al. (2017) Pathways to zoonotic spill over.
Nat. Rev. Microbiol. 15, 502510
12. Nuñez, M.A. et al. (2020) Invasion science and the global
spread of SARS-CoV-2. Trends Ecol. Evol. 35, 642645
13. Worobey, M. et al. (2020) The emergence of SARS-Co V-2 in
Europe and North America. Science 370, 564
14. Anderson, R.M. and May, R.M. (1979) Population biologyof in-
fectious diseases: Part I. Nature 280, 361367
15. Lockwood, J.L. et al. (2005) The role of propagule pressure
in explaining species invasions. Trends Ecol. Evol. 20,
16. Hou, Y.J. et al. (2020) SARS-CoV-2 reverse genetics reveals a
variable infection gradient in the respiratory tract. Cell 182,
17. Keane, R.M. and Crawley, M.J. (2002) Exotic plant invasions
and the enemy release hypothesis. Trends Ecol. Evol. 17,
18. Haas, C.N. (2015) Microbial dose response modeling:
past, present, and future. Environ. Sci. Technol. 49,
19. Alquisiras-Burgos, I. et al. (2021) Neurological complications
associated with the bloodbrain barrier damage induced by
the inammatory response during SARS-CoV-2 infection.
Mol. Neurobiol. 58, 520535
20. Coleman, M.E. et al. (2017) Modeling rabbit responses to
single and multiple aerosol exposures of bacillus anthracis
spores. Risk Anal. 37, 943957
21. Schreiber, S.J. and Lloyd-Smith, J.O. (2009) Invasion dynamics in
spatially heterogeneous environments. Am. Nat. 174, 490505
22. Plowright, R.K. et al. (2021) Land use-induced spillover: a call
to action to safeguard environmental, animal, and human
health. Lancet Planet. Health 2, e237e245
23. Letko, M. et al. (2020) Bat-borne virus diversity, spillover and
emergence. Nat. Rev. Microbiol. 18, 461471
susceptible human hosts? Will SARS-
CoV-2 outcompete and drive another
coronavirus to extinction, as seen in
inuenza pandemics?
Figure 2. The Inherent Connections between Five Ecological and Evolutionary Concepts. Graphical
representation of the connections between the ve presented concepts. A small circle within a larger circle represents an
individual entity within a higher level of biological organization, which is generalizable to: a cell within an organ, an organ
within an individual host, an individual within a population, a population within a meta-population, or a species within a
community. A small gray circle represents a susceptible entity, and colored circles represent infected entities, where color
denotes viral strain.
Trends in Microbiology
Trends in Microbiology, Month 2021, Vol. xx, No. xx 11
24. Wang, N. et al. (2018) Serological evidence of bat SARS-
related coronavirus infection in humans, China. Virol. Sin. 33,
25. Brook, C.E. et al. (2020) Accelerated viral dynamics in bat cell
lines, with implications for zoonotic emergence. eLife 9, e48401
26. Lafferty, K.D. et al. (2015) A general consumer-resource popu-
lation model. Science 349, 854857
27. Bubar, K.M. et al. (2021) Model-informed COVID-19 vaccine
prioritization strategies by age and serostatus. Science 371,
28. Metcalf, C.J.E. et al. (2020) Disentangling the dynamical under-
pinnings of differences in SARS-CoV-2 pathology using within-
host ecological models. PLoS Pathog. 16, e1009105
29. Perelson, A.S. (2002) Modelling viral and immune system dy-
namics. Nat. Rev. Immunol. 2, 2836
30. De Boer, R.J. and Perelson, A.S. (1998) Target cell limited and
immune control models of HIV infection: a comparison.
J. Theor. Biol. 190, 201214
31. Karesh, W.B. et al. ( 2012) Ecology of zoonoses: natural and
unnatural histories. Lancet 380, 19361945
32. Mena, I. et al. (2016) Origins of the 2009 H1N1 inuenza pan-
demic in swine in Mexico. eLife 5, e16777
33. Huang, C. et al. (2020) Clinical features of patients infected with
2019 novel coronavirus in Wuhan, China. Lancet 395,
34. Munnink, B.B.O. et al. (2021) Transmission of SARS-CoV-2 on
mink farms between humans and mink and back to humans.
Science 371, 172177
35. Nelson, M.I. et al. (2012) Global transmission of inuenza
viruses from humans to swine. J. Gen. Virol. 93, 21952203
36. Roe, D. and Lee, T.M. (2021) Possible negative consequences
of a wildlife trade ban. Nat. Sustain. 4, 56
37. Pulliam, H.R. (1988) Sources, sinks, and population regulation.
Am. Nat. 132, 652661
38. Kraemer, M.U.G. et al. (2020) The effect of human mobility and
control measures on the COVID-19 epidemic in China.Science
368, 493497
39. Liebhold, A. et al. (2004) Spatial synchrony in population dy-
namics. Annu. Rev. Ecol. Evol. Syst. 35, 467490
40. Earn, D.J.D. et al. (1998) Persistence, chaos and synchrony in
ecology and epidemiology. Proc.R.Soc.Lond.BBiol.Sci.
265, 710
41. Dennehy, J.J. et al. (2007) Virus population extinction via eco-
logical traps. Ecol. Lett. 10, 230240
42. Shang, J. et al. (2020) Cell entry mechanisms of SARS-CoV-2.
Proc. Natl. Acad. Sci. U. S. A. 117, 1172711734
43. Sungnak, W. et al. (2020) SARS-CoV-2 entry factors are highly
expressed in nasal epithelial cells together with innate immune
genes. Nat. Med. 26, 681687
44. Hoffmann, M.A.G. et al. (2021) In vitro characterization of
engineered red blood cells as viral traps against HIV-1 and
SARS-CoV-2. Mol. Ther. Methods Clin. Dev. 21, 161170
45. Petersen, E. et al. (2020) Comparing SARS-CoV-2 with SARS-
CoV and inuenza pandemics. Lancet Infect. Dis. 20,
46. McKee, C.D. et al. (2021) The ecology of Nipah virus in
Bangladesh: a nexus of land-use change and opportunistic
feeding behavior in bats. Viruses 13, 169
47. Gibb, R. et al. (2020) Zoonotic host diversity increases in
human-dominated ecosystems. Nature 584, 398402
48. Kays, R. et al. (2015) Terrestrial animal tracking as an eye on life
and planet. Science 348, aaa2478
49. Noonan,M.J. et al. (2021) Estimating encounterlocation distribu-
tions from animal tracking data. Methods Ecol. Evol. Published
online March 25, 2021.
50. Lalić, al. (2011) Effect of host species on the distribution of
mutational tness effects for an RNA virus. PLoS Genet. 7,
51. Pachetti, M. et al. (2020) Emerging SARS-CoV-2 mutation
hot spots include a novel RNA-dependent-RNA polym erase
variant. J. Transl. Med. 18, 179
52. Denison, M.R. et al. (2011) Coronaviruses:an RNA proofreading
machine regulates replication delity and diversity. RNA Biol. 8,
53. Gribble, J. et al. (2021) The corona virus proofreading
exoribonuclease mediates extensive viral recombination. PLoS
Pathog. 17, e1009226
54. van Dorp, L. et al. (2020) No evidence for increased transmis-
sibility from recurrent mutations in SARS-CoV-2. Nat. Commun.
11, 5986
55. Avanzato, V.A. et al. (2020) Case study: prolonged infec-
tious SARS-CoV-2 shedding from an asymptomatic immu-
nocompromised individual with cancer. Cell 183,
56. Choi, B. et al. (2020) Persistence and evolution of SARS-CoV-2
in an immunocompromised host. N. Engl. J. Med. 383,
57. Zwart, M.P. and Elena, S.F. (2015) Matters of size: genetic
bottlenecks in virus infectionand their potential impacton evolu-
tion. Annu. Rev. Virol. 2, 161179
58. Desai, S. et al. (2021) Evolving insight s from SARS-CoV-2
genomefrom 200K COVID-19 patients. bioRxivPublished online
January 21, 2021.
59. Rambaut, A. et al. (2020) P reliminary Genomic Charact erisa-
tion of an Emergent SARS-Co V-2 Lineage in the UK Dene d
by a Novel Set of Spike Mutations, ARTIC Network
60. OToole, Á. et al. (2020) Pangolin: lineage assignment in an
emerging pandemic as an epidemiological tool. PANGO line-
61. Kin, N. et al. (2015) Genomic analysis of 15 human
coronaviruses OC43 (hCoV-OC43s) circulating in France
from 2001 to 2013 reveals a high intra-specic diversity with
new recombinant genotypes. Viruses 7, 23582377
62. Boni, M.F. et al. (2020) Evolutionary origins of the SARS-CoV-2
sarbecovirus lineage responsible for the COVID-19 pandemic.
Nat. Microbiol. 5, 14081417
63. Wells, H.L. et al. (2021) The evolutionary history of ACE2 usage
within the coronavirus subgenus Sarbecovirus .Virus Evol. 7,
64. Vijaykrishna, D. et al. (2007) Evolutionary insights into the
ecology of coronaviruses. J. Virol. 81, 40124020
65. Park, M. et al. (2013) Multiple scales of selection inuence the
evolutionary emergence of novel pathogens. Philos. Trans. R.
Soc. B Biol. Sci. 368, 20120333
66. Thomson, E.C. et al. (2021) Circulating SARS-CoV-2 spike
N439K variants maintain tness while evading antibody-
mediated immunity. Cell 184, 11711187
67. Piccoli, L. et al. (2020) Mapping neutralizing and
immunodominant sites on th e SARS-CoV-2 spike receptor-
binding domain by structure-guided high-resolution serology.
Cell 183, 10241042.e21
68. Korber, B. et al. (2020) Tracking changes in SARS-CoV-2
spike: evidence that D614G increases infectivity of the
COVID-19 virus. Cell 182, 812827.e19
69. Yurkovetskiy, L. et al. (2020) Structural and functional analysis
of the D614G SARS-CoV-2 spike protein variant. Cell 183,
70. Greaney, A.J. et al. (2021) Comprehensive mapping of muta-
tions in the SARS-CoV-2 receptor-binding domain that affect
recognition by polyclonal human plasma antibodies. Cell Host
Microbe 29, 463476.e6
71. Wang, P. et al. (2021) Antibody resistance of SARS-CoV-2var-
iants B.1.351 and B.1.1.7. Natur e Published online March 8,
72. Day, T. et al. (2020) On the evolutionary epidemiology of
SARS-CoV-2. Curr. Biol. 30, R849R857
73. Volz, E. et al. (2021) Evaluating the effects of SARS-CoV-2
spike mutation D614G on transmissibility and pathogenicity.
Cell 184, 6475.e11
74. Plante, J.A. et al. (2021) Spike mutation D614G alters SARS-
CoV-2 tness. Nature 592, 116121
75. Hou, Y.J. et al. (2020) SARS-CoV-2 D614G variant exhibits ef-
cient replication ex vivo and transmission in vivo.Science 370,
76. Sabino, E.C. et al. (2021) Resurgence of COVID-19 in Ma-
naus, Brazil, despite high seroprevalence. Lancet 397,
77. Collier, D.A. et al. (2021) Sensitivity of SARS-CoV-2 B.1.1.7 to
mRNA vaccine-elicited antibodies. Nature Published online
Trends in Microbiology
12 Trends in Microbiology, Month 2021, Vol. xx, No. xx
March 11, 2021.
78. Pepin, K.M. et al. (2010) Identifying genetic markers of adapta-
tion for surveillance of viral host jumps. Nat. Rev. Microbiol. 8,
79. Wainwright, P.C. et al. (2005) Many-to-one mapping of form to
function: a general principle in organismal design? Integr.
Comp. Biol. 45, 256262
80. Damas, J. et al. (2020) Broad host range of SARS-CoV-2 predicted
by comparative and structural analysis of ACE2 in vertebrates. Proc.
Natl.Acad.Sci.U.S.A.117, 2231122322
81. MacLean, O.A. et al. (2021) Natural selection in the evolution of
SARS-CoV-2 in bats created a generalist virus and highly ca-
pable human pathogen. PLoS Biol. 19, e3001115
82. Shi, J. et al. (2020) Susceptibility of ferrets, cats, dogs, and
other domesticated animals to SARS-coronavirus 2. Science
368, 10161020
83. Ferguson, N.M. et al. (2001) The foot-and-mouth epidemic in
Great Britain: pattern of spread and impact of interventions.
Science 292, 11551160
84. Lloyd-Smith, J.O. et al. (2009) Epidemic dynamics at the
human-animal interface. Science 326, 13621367
85. Pepin, K.M. et al. (2017) Inferring infection hazard in wildlife
populations by linking data across individual and population
scales. Ecol. Lett. 20, 275292
86. DiRenzo, G.V. et al. (2019) Disease-structured N-mixture
models: a practical guide to model disease dynamics using
count data. Ecol. Evol. 9, 899909
87. Isaac, N.J.B. et al. (2020) Data integration for large-scale
models of species distributions. Trends Ecol. Evol. 35, 5667
88. Johnson, E.E. et al. (2019) An ecological framework for model-
ing the geography of disease transmission. Trends Ecol. Evol.
34, 655668
89. Carlson, C.J. et al. (2020) Species distribution models are inap-
propriate for COVID-19. Nat. Ecol. Evol. 4, 770771
90. MacKenzie, D.I. et al. (2002) Estimating site occupancy rates
when detection probabilities are less than one. Ecology 83,
91. Knape, J. et al. (2011) On observation distributions for state
space models of population survey data. J. Anim. Ecol. 80,
92. McClintock, B.T. et al. (2010) Seeking a second opinion: un-
certainty in disease ecology. Ecol. Lett. 13, 659674
93. Chaudhary, V. et al. (2020) A multi-state occupancy
modelling framework for robust estimation of disease
prevalence in multi-tissue disease systems. J. Appl.
Ecol. 57, 24632474
94. Sjodin, A.R. et al. (2020) Accounting for imperfect detection re-
veals role of host traits in structuring viral diversity of a wild bat
community. bioRxiv Publis hed online June 30, 2020. https://
95. Balisi, M.A. and Van Valkenburgh, B. (2020) Iterative evolution
of large-bodied hypercarnivory in canids ben ets species but
not clades. Commun. Biol. 3, 19
96. Jablonski, D. (2008) Species selection: theory and data. Annu.
Rev. Ecol. Evol. Syst. 39, 501524
97. Harvey, M.G. et al. (2019) Beyond reproductive isolation: de-
mographic controls on the speciation process. Annu. Rev.
Ecol. Evol. Syst. 50, 7595
98. Benton, M.J. (2009) The Red Queen and the Court Jester:
species diversity and the role of biotic and abiotic factors
through time. Science 323, 728732
99. Simmonds, P. et al. (2017) Virus taxonomy in the age of
metagenomics. Nat. Rev. Microbiol. 15, 161168
100. Volz, E.M. et al. (2013) Viral phylodynamics. PLoS Comput.
Biol. 9, e1002947
Trends in Microbiology
Trends in Microbiology, Month 2021, Vol. xx, No. xx 13
... y÷ka jd §fï f.da ,S h wjYH;djla ;s fnk nj;a [104] ta i|yd wka ;¾cd;s l jYfhka l% s hd;a ul nyq md¾Yúl yd nyq úIhs l iyfhda .S ;djla mj;a jd .; hq ;= nj;a wjOdrKh flf¾' [105] cd;Hka ;r iïuq ;s " m% {ma ;s fyda tlÕ;d u; ffcj úúO;a j ixrla IKh i|yd is ÿ lrk f.da ,S h m% h;a khka ys m% ;s M, fndfyda fihs ka m% cdj lrd ks is f,i .,d fkd hEu ks id m% dfoa YS h uÜgfï mßir moa O;S ka g is ÿ jk n,mEï jeä ù ;s fí' ta wkq j ia jNdúl jdiN+ ñ iy mßir moa O;s iq rla IKh i|yd jk cd;Hka ;r j tlÕ úh yels rduq jla yryd jkúkdYh iy fujeks jix.; w;r jk iyiïnka O;dj ì| fy<s h hq ;= h' [106] tys § mßir moa O;s iS udjka jHdêckl udre ùï jeäfhka is ÿ úh yels WKq iq ï l,dm [98] flfrys n,mEï l< yels ;s ridr mßir hdka ;% K y÷ka jd §u wjYH fõ' [107] fldúâ-19 hkq fulS udkjlD; f.da ,S h ffcj úúO;a j w¾nq ofha iy mdßißl wmrdOj, m% ;s M,hla nj;a jHdêcklhs ka f.a wka ;¾-úfYa I udre ùï ks id is ÿ úh yels fi!LH fÄojdplhg fyd| u ks o¾Ykhla nj;a úoHd{fhda fmka jd fo;s ' [30] [117] ta wkq j jki;a ;a j úúO;a jh iy nyq Odrl úfYa I ;= < jHdêckl iïfma % IKh u; mokï jQ wka ;¾ úfYa I udreùïj,g wod< j jix.;fõ §h wdlD;s hla ks ¾udKh ls Íug ffioa Odka ;s l rduq jla ielis h hq ;= h' [118] óg wjYH jk mßÈ f.da ,S h foa Ymd,ks l" iudc iy wd¾Ól moa O;s iq iudo¾Yh udre ls Ífï yels hdj h<s ;a iudf,da pkh ls Ífuka ia jNdúl mßir moa O;s j, fi!LHh flfrys hï mQ ¾jdrla Il m% fõYhla iys ; j lDIs mdßißl l% s hdjka iudc.; l< yels h' [73][119] [120] i;a ;a jckH udreùï i|yd fya ;= jk mdßißl" jix.;fõ §h iy p¾hd;a ul idOl ks je/Èj y÷kd.ekS u yryd OQ rdj,s .; ndOl we;s ls Íug jvd;a M,odhs " ;s ridr" ms ßueiq ïodhs " iy ueks h yels úi÷ï fhda ckd l< yels h' [64][83] [121] f,da l fi!LH ixúOdkh y÷ka jd ÿka One Health jevigyk foa YS h" cd;s l" l,dmS h iy f.da ,S h uÜgfuka lghq ;= ls Íu i|yd nyq úIhs h m% fõYhla iys ; j jD;a ;s lhs ka ta ldnoa O ls Ífï m% h;a khla jk w;r ta u.s ka fuu i;a ;a jckH frda . ...
... u yryd OQ rdj,s .; ndOl we;s ls Íug jvd;a M,odhs " ;s ridr" ms ßueiq ïodhs " iy ueks h yels úi÷ï fhda ckd l< yels h' [64][83] [121] f,da l fi!LH ixúOdkh y÷ka jd ÿka One Health jevigyk foa YS h" cd;s l" l,dmS h iy f.da ,S h uÜgfuka lghq ;= ls Íu i|yd nyq úIhs h m% fõYhla iys ; j jD;a ;s lhs ka ta ldnoa O ls Ífï m% h;a khla jk w;r ta u.s ka fuu i;a ;a jckH frda . jHdma ;s h wju ls Íug wjYH fndfyda ie,iq ï bÈßm;a flf¾' [120][122] [123] [124] [125] tu.s ka mdr-úIhs l m% fõYhka yryd fujeks fi!LH wk;= rej,g l,a mj;a kd úi÷ï fhda ckd ls Íug fkdfhl= ;a wxYhka ys úoa j;= ka f.a mq ¿,a iyNd.s ;a jhla wfma la Id lrk w;r m¾fha IK Èßu;a ls Ífuka Ydia ;% S h ys vei ms rùug W;a idy lrhs ' [125] [126] tys § ffcj 501 fmf¾rd" wdhq ¾fõfoda la ; ckmfoda èjxih fldúâ frda . ...
... iïNjfha ks odkh f,i úúO;a j w¾nq oh yd ne÷Kq fi!LH wjodku wju ls Ífï ,d f.da ,S h foa Ymd,ks l ueÈy;a ùï úêu;a f,i fufyhùug [101] iy wkd.; wk;= re je<ela ùug One Health jevigyk wkq j l% s hd;a ul úh hq ;= h' [73][103] [110] [127] tys § mdrúIhs l ks ¾jpk u; mokï jQ iudcmdßißl moa O;s rduq (SESF) yryd One Health jevigyfka iynoa O;d m% fõYhka fhdod .ks ñka wkd.; fi!LH iy mdßißl wjodkï je<ela ùu l< yels h' [119] N+ ñ mßyrKhg wod<j úoHd;a ul m% fõYhka u; mokï jQ m% ;s m;a ;s iy l<ukdlrK úi÷ï ,nd §u yryd fujeks jix.;j,g fya ;= ldrl jk i;a ;a j iïNj jHdêckl udre ùï wju ls Íug wjYH nyq úIhs h iyfhda .S ;d iy hdka ;% K y÷ka jdÈh yels h' [120] [127] úfYa Ifhka u fuys § f.da ,S h m% fõYhla iys ; j nyq úIhs l mßir l<ukdlrK l% s hdoduhka id¾:l lr .ekS ug wjYH úoHd;a ul m¾fha IK i|yd m% uq L;dj ,nd Èh hq ;= h' [42] prl ixys ;d úudkia :dk 3 jeks wOHdhh ckmfoda oa èjxihg fya ;= jk m% Odk idOl 4 la fmka jd fohs ' ta jd kï ú.= K;a jhg m;a jQ jd;h" c,h" N+ ñh yd ld,h hk idOlhka fõ' ta jdfha uQ , fya ;= j jk m% {dmrdOh ks id wO¾uh ìys ù Yia ;% m% Nj ckmfoda oa èjxih fyda wNs Ydmm% Nj ckmfoda oa èjxih we;s fõ' ta wkq j m¾fha IK u.s ka fy<s ù we;s mßÈ fun÷ jix.; jHdma ;s hg fya ;= jk udkjlD; ffcj úúO;a j ydhkh" mßir ¥IKh" foa Y.= ‚l úm¾hdi yd úmÍ; Ôjkrgdj hk idOl wdhq ¾fõo ixys ;d .% ...
Bleeding disorders occur due to problem with the clotting mechanism associated with liver disorders. According to the Ayurveda concepts, this ailment is known as Raktapitta, which is a serious disease and it afflicts the patient at a great speed. This study was done to assess the ability to manage non-traumatic ear-bleeding according to the Ayurveda concepts of Urdhvagataraktapitta Chikitsa. The case study was done for five weeks at National Ayurveda Teaching Hospital and literary data were reviewed from Vriddhatraya and web sources from the internet. A male, 47 years old, patient presented to the hospital with a complaint of acute bleeding from the left ear with moderate dizziness since two days. Laboratory tests revealed an full blood count (FBC), with red blood cells (RBC) count as 5.94 x 10 6/L, packed cells volume (PCV) as 51.9%, and an increased clotting time (over 10 minutes). The disease was diagnosed as Urdhvagataraktapitta and the patient was treated for 5 weeks with a decoction of Mudgasalajadi, a decoction of Amrita Nagaran Dhanyaka, a decoction of Duralabha, Virecana (purgative) Karma, and leech therapy for the head. According to the treatments, he was completely cured with a normal full blood count (FBC). Decoction of Mudgasalajadi cured acute Raktapitta in this patient by its Shita Guna, Pitta, and Rakta Shamaka properties. Decoction of Duralabha (Fagonia cretica) with ghee pacifies the dizziness. It was concluded that acute ear-bleeding can be managed according to the Urdhvagataraktapitta Chikitsa mentioned in Ayurveda texts.
... y÷ka jd §fï f.da ,S h wjYH;djla ;s fnk nj;a [104] ta i|yd wka ;¾cd;s l jYfhka l% s hd;a ul nyq md¾Yúl yd nyq úIhs l iyfhda .S ;djla mj;a jd .; hq ;= nj;a wjOdrKh flf¾' [105] cd;Hka ;r iïuq ;s " m% {ma ;s fyda tlÕ;d u; ffcj úúO;a j ixrla IKh i|yd is ÿ lrk f.da ,S h m% h;a khka ys m% ;s M, fndfyda fihs ka m% cdj lrd ks is f,i .,d fkd hEu ks id m% dfoa YS h uÜgfï mßir moa O;S ka g is ÿ jk n,mEï jeä ù ;s fí' ta wkq j ia jNdúl jdiN+ ñ iy mßir moa O;s iq rla IKh i|yd jk cd;Hka ;r j tlÕ úh yels rduq jla yryd jkúkdYh iy fujeks jix.; w;r jk iyiïnka O;dj ì| fy<s h hq ;= h' [106] tys § mßir moa O;s iS udjka jHdêckl udre ùï jeäfhka is ÿ úh yels WKq iq ï l,dm [98] flfrys n,mEï l< yels ;s ridr mßir hdka ;% K y÷ka jd §u wjYH fõ' [107] fldúâ-19 hkq fulS udkjlD; f.da ,S h ffcj úúO;a j w¾nq ofha iy mdßißl wmrdOj, m% ;s M,hla nj;a jHdêcklhs ka f.a wka ;¾-úfYa I udre ùï ks id is ÿ úh yels fi!LH fÄojdplhg fyd| u ks o¾Ykhla nj;a úoHd{fhda fmka jd fo;s ' [30] [117] ta wkq j jki;a ;a j úúO;a jh iy nyq Odrl úfYa I ;= < jHdêckl iïfma % IKh u; mokï jQ wka ;¾ úfYa I udreùïj,g wod< j jix.;fõ §h wdlD;s hla ks ¾udKh ls Íug ffioa Odka ;s l rduq jla ielis h hq ;= h' [118] óg wjYH jk mßÈ f.da ,S h foa Ymd,ks l" iudc iy wd¾Ól moa O;s iq iudo¾Yh udre ls Ífï yels hdj h<s ;a iudf,da pkh ls Ífuka ia jNdúl mßir moa O;s j, fi!LHh flfrys hï mQ ¾jdrla Il m% fõYhla iys ; j lDIs mdßißl l% s hdjka iudc.; l< yels h' [73][119] [120] i;a ;a jckH udreùï i|yd fya ;= jk mdßißl" jix.;fõ §h iy p¾hd;a ul idOl ks je/Èj y÷kd.ekS u yryd OQ rdj,s .; ndOl we;s ls Íug jvd;a M,odhs " ;s ridr" ms ßueiq ïodhs " iy ueks h yels úi÷ï fhda ckd l< yels h' [64][83] [121] f,da l fi!LH ixúOdkh y÷ka jd ÿka One Health jevigyk foa YS h" cd;s l" l,dmS h iy f.da ,S h uÜgfuka lghq ;= ls Íu i|yd nyq úIhs h m% fõYhla iys ; j jD;a ;s lhs ka ta ldnoa O ls Ífï m% h;a khla jk w;r ta u.s ka fuu i;a ;a jckH frda . ...
... u yryd OQ rdj,s .; ndOl we;s ls Íug jvd;a M,odhs " ;s ridr" ms ßueiq ïodhs " iy ueks h yels úi÷ï fhda ckd l< yels h' [64][83] [121] f,da l fi!LH ixúOdkh y÷ka jd ÿka One Health jevigyk foa YS h" cd;s l" l,dmS h iy f.da ,S h uÜgfuka lghq ;= ls Íu i|yd nyq úIhs h m% fõYhla iys ; j jD;a ;s lhs ka ta ldnoa O ls Ífï m% h;a khla jk w;r ta u.s ka fuu i;a ;a jckH frda . jHdma ;s h wju ls Íug wjYH fndfyda ie,iq ï bÈßm;a flf¾' [120][122] [123] [124] [125] tu.s ka mdr-úIhs l m% fõYhka yryd fujeks fi!LH wk;= rej,g l,a mj;a kd úi÷ï fhda ckd ls Íug fkdfhl= ;a wxYhka ys úoa j;= ka f.a mq ¿,a iyNd.s ;a jhla wfma la Id lrk w;r m¾fha IK Èßu;a ls Ífuka Ydia ;% S h ys vei ms rùug W;a idy lrhs ' [125] [126] tys § ffcj 501 fmf¾rd" wdhq ¾fõfoda la ; ckmfoda èjxih fldúâ frda . ...
... iïNjfha ks odkh f,i úúO;a j w¾nq oh yd ne÷Kq fi!LH wjodku wju ls Ífï ,d f.da ,S h foa Ymd,ks l ueÈy;a ùï úêu;a f,i fufyhùug [101] iy wkd.; wk;= re je<ela ùug One Health jevigyk wkq j l% s hd;a ul úh hq ;= h' [73][103] [110] [127] tys § mdrúIhs l ks ¾jpk u; mokï jQ iudcmdßißl moa O;s rduq (SESF) yryd One Health jevigyfka iynoa O;d m% fõYhka fhdod .ks ñka wkd.; fi!LH iy mdßißl wjodkï je<ela ùu l< yels h' [119] N+ ñ mßyrKhg wod<j úoHd;a ul m% fõYhka u; mokï jQ m% ;s m;a ;s iy l<ukdlrK úi÷ï ,nd §u yryd fujeks jix.;j,g fya ;= ldrl jk i;a ;a j iïNj jHdêckl udre ùï wju ls Íug wjYH nyq úIhs h iyfhda .S ;d iy hdka ;% K y÷ka jdÈh yels h' [120] [127] úfYa Ifhka u fuys § f.da ,S h m% fõYhla iys ; j nyq úIhs l mßir l<ukdlrK l% s hdoduhka id¾:l lr .ekS ug wjYH úoHd;a ul m¾fha IK i|yd m% uq L;dj ,nd Èh hq ;= h' [42] prl ixys ;d úudkia :dk 3 jeks wOHdhh ckmfoda oa èjxihg fya ;= jk m% Odk idOl 4 la fmka jd fohs ' ta jd kï ú.= K;a jhg m;a jQ jd;h" c,h" N+ ñh yd ld,h hk idOlhka fõ' ta jdfha uQ , fya ;= j jk m% {dmrdOh ks id wO¾uh ìys ù Yia ;% m% Nj ckmfoda oa èjxih fyda wNs Ydmm% Nj ckmfoda oa èjxih we;s fõ' ta wkq j m¾fha IK u.s ka fy<s ù we;s mßÈ fun÷ jix.; jHdma ;s hg fya ;= jk udkjlD; ffcj úúO;a j ydhkh" mßir ¥IKh" foa Y.= ‚l úm¾hdi yd úmÍ; Ôjkrgdj hk idOl wdhq ¾fõo ixys ;d .% ...
Full-text available
Garbhini Paricarya includes Garbhopaghatakara Bhava which is contraindicated during pregnancy for the wellbeing of the fetus. Food is an essential factor for survival. Though it is essential, in some instances, it may act adversely on the developing fetus during pregnancy. The objective of this research is to review the data on Garbhopaghatakara Bhava and analyze it with the support of modern research findings. This aims to review the Garbhopaghatakara Bhava related to food, demonstrate its importance in Garbhini Paricarya supported with current scientific findings. This research was conducted as a conceptual study based on available literature in Ayurveda classics, including Brihatryi, and scientific evidence provided by previously published research papers. Excessive consumption of all the six tastes, unwholesome food articles including alcohol, proscribed incompatible foods, neglected nutritional conditions, tap water treated with chemicals, modern diets full of free-radicals and artificial additives, prenatal exposure to heavy metals, persistent organic pollutants, polychlorinated biphenyls, bisphenol A, phytochemicals, and nutritional impairments are negative factors that affect the fetus adversely, and result in fetal anomalies, birth complications etc. Hence, it is important to draw attention to the Garbhini Paricarya related to the contraindications during pregnancy, which is termed as Garbhopaghatakara Bhava. Diet is very important throughout life, but unwholesome dietary habits and food consumption may act as an etiology for fetal anomalies, still births, and congenital anomalies. According to Ayurvedic teachings and modern research findings also, avoiding Garbhopaghatakara Bhava and following Garbhini Paricarya properly may be effective in good health outcomes of the offspring. Keywords: Garbhopaghatakara Bhava, Garbhini Paricarya, Diet, Anomalies, Contraindications 1 UG Scholar, Faculty of Indigenous Medicine, Gampaha Wi
... There is indirect bat-to-human spillover involving intermediate hosts, including the Hendra virus in 1994 involving horses [15] and the Nipah virus in Malaysia in 1997 and 1998 related to the infection of pigs [16]. More recently, the SARS-CoV 2002-2003 outbreak in southern China and the 2019 SARS-CoV-2 emergence in central China were retrospectively connected to bat populations and appeared to involve intermediate hosts [17,18]. There is also the possibility that some viruses (arboviruses) are vectored by hematophagous insects from bats to humans and other species [19]. ...
Full-text available
Chaphamaparvovirus (CHPV) is a recently characterized genus of the Parvoviridae family whose members can infect different hosts, including bats, which constitute the second most diverse order of mammals and are described worldwide as important transmitters of zoonotic diseases. In this study, we identified a new CHPV in bat samples from the municipality of Santarém (Pará state, North Brazil). A total of 18 Molossus molossus bats were analyzed using viral metagenomics. In five animals, we identified CHPVs. These CHPV sequences presented the genome with a size ranging from 3797 to 4284 bp. Phylogenetic analysis-based nucleotide and amino acid sequences of the VP1 and NS1 regions showed that all CHPV sequences are monophyletic. They are also closely related to CHPV sequences previously identified in bats in southern and southeast Brazil. According to the International Committee on Taxonomy of Viruses (ICTV) classification criteria for this species (the CHPV NS1 gene region must have 85% identity to be classified in the same species), our sequences are likely a new specie within the genus Chaphamaparvovirus, since they have less than 80% identity with other CHPV described earlier in bats. We also make some phylogenetic considerations about the interaction between CHPV and their host. We suggest a high level of specificity of CPHV and its hosts. Thus, the findings contribute to improving information about the viral diversity of parvoviruses and show the importance of better investigating bats, considering that they harbor a variety of viruses that may favor zoonotic events. Citation: Ramos, E.d.S.F.; Abreu, W.U.; Rodrigues, L.R.R.; Marinho, L.F.; Morais, V.d.S.; Villanova, F.; Pandey, R.P.; Araújo, E.L.L.; Deng, X.; Delwart, E.; et al. Novel Chaphamaparvovirus in Insectivorous Molossus molossus Bats, from the Brazilian Amazon Region. Viruses 2023, 15, 606. https://doi.
... The first important aspect we found was that the decline of the FFRNT 50 value for the different SARS-CoV-2 Omicron sublineages in recipients of four doses of monovalent mRNA-based COVID-19 vaccines progressed in accordance with the time of emergence of each of the five sublineages of the Omicron family included in our analysis. This aspect was easily predictable, since all living biological species are subjected to a constant ecologic pressure [22]. Human pathogens, including SARS-CoV-2, make no exception to this rule, since they gradually accumulate mutations that may arise and spread by chance (e.g., encompassing drift and founder effects) or through natural selection [23], and which have the ultimate scope of ameliorating their adaptation to the external habitat (e.g., improving resistance to environmental conditions) or by increasing their efficiency to colonize and reproduce within the host (e.g., developing higher affinity for human cell receptors, reducing intracellular antiviral defenses, improving replication efficiency, etc.). ...
Full-text available
This study is aimed at developing a simple epidemiologic model that could help predict the impaired neutralization of new SARS-CoV-2 variants. We explored the potential association between neutralization of recent and more prevalent SARS-CoV-2 sublineages belonging to the Omicron family (i.e., BA.4/5, BA.4.6, BA.2.75.2, BQ.1.1 and XBB.1) expressed as FFRNT50 (>50% suppression of fluorescent foci fluorescent focus reduction neutralization test) in recipients of four doses of monovalent mRNA-based coronavirus disease 2019 (COVID-19) vaccines, with epidemiologic variables like emergence date and number of spike protein mutations of these sublineages, cumulative worldwide COVID-19 cases and cumulative number of COVID-19 vaccine doses administered worldwide at the time of SARS-CoV-2 Omicron sublineage emergence. In the univariate analysis, the FFRNT50 value for the different SARS-CoV-2 Omicron sublineages was significantly associated with all such variables except with the number of spike protein mutations. Such associations were confirmed in the multivariate analysis, which enabled the construction of the equation: “−0.3917 × [Emergence (date)] + 1.403 × [COVID-19 cases (million)] − 121.8 × [COVID-19 Vaccine doses (billion)] + 18,250”, predicting the FFRNT50 value of the five SARS-CoV-2 Omicron sublineages with 0.996 accuracy (p = 0.013). We have shown in this work that a simple mathematical approach, encompassing a limited number of widely available epidemiologic variables, such as emergence date of new variants and number of COVID-19 cases and vaccinations, could help identifying the emergence and surge of future lineages with major propensity to impair humoral immunity.
... These interactions create varied land-use patterns that invariably provide host structure for virus mutation such as COVID-19 (A. Olusola et al., 2020;Syal, 2021;Snedden et al., 2021;Lloyd-Smith, 2013). The ongoing pattern, mutation and distribution of the COVID-19 has renewed interest in the factors that help perpetuates the survival of the host and the replica of the pathogen itself. ...
Full-text available
Several approaches have been used in the race against time to mitigate the spread and impact of COVID-19. In this study, we investigated the role of temperature, relative humidity, and particulate matter in the spread of COVID-19 cases within two densely populated cities of South Africa - Pretoria and Cape Town. The role of different levels of COVID-19 restrictions in the air pollution levels, obtained from the Purple Air Network, of the two cities were also considered. Our results suggest that 26.73% and 43.66% reduction in PM2.5 levels were observed in Cape Town and Pretoria respectively for no lockdown (Level 0) to the strictest lockdown level (Level 5). Furthermore, our results showed a significant relationship between particulate matter and COVID-19 in the two cities. Particulate matter was found to be a good predictor, based on the significance of causality test, of COVID-19 cases in Pretoria with a lag of 7 days and more. This suggests that the effect of particulate matter on the number of cases can be felt after 7 days and beyond in Pretoria.
... While we limit ourselves to the theoretical population genetic literature, evolutionary aspects of pandemics overlap with many academic disciplines, and we recommend readers also see other excellent reviews in this broad area (for instance, [3][4][5]). We additionally limit our scope to prospective, forward-in-time theoretical population genetic models, thus excluding retrospective approaches such as genealogy-based and phylogeographic models for which existing reviews are available (see [6,7], respectively). ...
Full-text available
Theoretical population genetics has long studied the arrival and geographic spread of adaptive variants through the analysis of mathematical models of dispersal and natural selection. These models take on a renewed interest in the context of the COVID-19 pandemic, especially given the consequences that novel adaptive variants have had on the course of the pandemic as they have spread through global populations. Here, we review theoretical models for the spatial spread of adaptive variants and identify areas to be improved in future work, toward a better understanding of variants of concern in Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) evolution and other contemporary applications. As we describe, characteristics of pandemics such as COVID-19—such as the impact of long-distance travel patterns and the overdispersion of lineages due to superspreading events—suggest new directions for improving upon existing population genetic models.
... It is not subject to copyright under 17 USC with caution. Similarly, viral load should not be treated as a single quantity that rises and falls synchronously throughout the host; spatial models of infection may be required to identify the best correlates of infectiousness [42]. Crucially, there is a period early in infection (around 24 h postinfection in inoculated hamsters) when oral swabs show high viral titers, but air samples exhibit low or undetectable levels of virus. ...
Full-text available
It remains poorly understood how SARS-CoV-2 infection influences the physiological host factors important for aerosol transmission. We assessed breathing pattern, exhaled droplets, and infectious virus after infection with Alpha and Delta variants of concern (VOC) in the Syrian hamster. Both VOCs displayed a confined window of detectable airborne virus (24h - 48h), shorter than compared to oropharyngeal swabs. The loss of airborne shedding was linked to airway constriction resulting in a decrease of fine aerosols produced. Male sex was associated with increased viral replication and virus shedding in the air, including a VOC-independent particle-profile shift towards smaller droplets. Transmission efficiency varied among donors, including a superspreading event. Co-infection with VOCs only occurred when both viruses were shed by the same donor during an increased exposure timeframe. This highlights that assessment of host and virus factors resulting in a differential exhaled particle profile is critical for understanding airborne transmission.
... Thus, the selection pressures these pathogens experience from public health interventions, and nonpharmaceutical interventions in particular, have the potential to be very different from the selection pressures operating on endemic pathogens. Third, several novel variants of SARS-CoV-2 with distinct epidemiological profiles have readily emerged [49,93,113,115]. This speaks to the considerable evolvability of the virus, making it an attractive case study for evolutionary epidemiology. ...
Full-text available
Controlling many infectious diseases, including SARS-Coronavirus-2 (SARS-CoV-2), requires surveillance followed by isolation, contact-tracing and quarantining. These interventions often begin by identifying symptomatic individuals. However, actively removing pathogen strains causing symptomatic infections may inadvertently select for strains less likely to cause symptomatic infections. Moreover, a pathogen’s fitness landscape is structured around a heterogeneous host pool; uneven surveillance efforts and distinct transmission risks across host classes can meaningfully alter selection pressures. Here, we explore this interplay between evolution caused by disease control efforts and the evolutionary consequences of host heterogeneity. Using an evolutionary epidemiology model parameterized for coronaviruses, we show that intense symptoms-driven disease control selects for asymptomatic strains, particularly when these efforts are applied unevenly across host groups. Under these conditions, increasing quarantine efforts have diverging effects. If isolation alone cannot eradicate, intensive quarantine efforts combined with uneven detections of asymptomatic infections (e.g., via neglect of some host classes) can favor the evolution of asymptomatic strains. We further show how, when intervention intensity depends on the prevalence of symptomatic infections, higher removal efforts (and isolating symptomatic cases in particular) more readily select for asymptomatic strains than when these efforts do not depend on prevalence. The selection pressures on pathogens caused by isolation and quarantining likely lie between the extremes of no intervention and thoroughly successful eradication. Thus, analyzing how different public health responses can select for asymptomatic pathogen strains is critical for identifying disease suppression efforts that can effectively manage emerging infectious diseases.
... The last decades have been characterized by viral epidemics that led to the coronavirus pandemic in 2020 and 2021 [1,2]. This places as the main objective of numerous research groups the development of protocols and predictive studies to understand evolutionary ecology and the emergence of new virus variants [3,4]. On the other hand, both epidemiological and patient-based approaches are often late in avoiding pandemic events due to the ability of viruses to quickly gain new infective features during pandemic advancement [5]. ...
Full-text available
The helical geometry of virus capsid allows simple self-assembly of identical protein subunits with a low request of free energy and a similar spiral path to virus nucleic acid. Consequently, small variations in protein subunits can affect the stability of the entire phage particle. Previously, we observed that rearrangement in the capsid structure of M13 engineered phages affected the resistance to UV-C exposure, while that to H2O2 was mainly ascribable to the amino acids’ sequence of the foreign peptide. Based on these findings, in this work, the resistance to accelerated proton beam exposure (5.0 MeV energy) of the same phage clones was determined at different absorbed doses and dose rates. Then, the number of viral particles able to infect and replicate in the natural host, Escherichia coli F+, was evaluated. By comparing the results with the M13 wild-type vector (pC89), we observed that 12III1 phage clones, with the foreign peptide containing amino acids favorable to carbonylation, exhibited the highest reduction in phage titer associated with a radiation damage (RD) of 35 × 10−3/Gy at 50 dose Gy. On the other hand, P9b phage clones, containing amino acids unfavorable to carbonylation, showed the lowest reduction with an RD of 4.83 × 10−3/Gy at 500 dose Gy. These findings could improve the understanding of the molecular mechanisms underlying the radiation resistance of viruses
Full-text available
The emergence of SARS-like coronaviruses is a multi-stage process from wildlife reservoirs to people. Here we characterize multivariate indicators associated with the risk of zoonotic spillover of SARS-like coronaviruses in different areas to help inform surveillance and mitigation activities. We consider direct and indirect transmission pathways by modeling four scenarios with livestock and mammalian wildlife as potential and known reservoirs, before examining how access to healthcare varies within areas. We found 19 multivariate clusters that had differing risk factor contributions. High-risk areas were mostly close (11-20%) rather than far (<1%) from healthcare. With the presented framework, areas with the highest estimated risk can be priority intervention targets in which risk management strategies can be implemented, such as land use planning and preventive measures to reduce contact between people and potential hosts. Teaser Multivariate clusters of stressors associated with SARS-like coronavirus spillover risk.
Full-text available
The response of the global virus genomics community to the SARS-CoV-2 pandemic has been unprecedented, with significant advances made towards the ‘real-time’ generation and sharing of SARS-CoV-2 genomic data. The rapid growth in virus genome data production has necessitated the development of new analytical methods that can deal with orders of magnitude more genomes than previously available. Here we present and describe pangolin (Phylogenetic Assignment of Named Global Outbreak Lineages), a computational tool that has been developed to assign the most likely lineage to a given SARS-CoV-2 genome sequence according to the Pango dynamic nomenclature scheme described in Rambaut et al. (2020). To date, nearly two million virus genomes have been submitted to the web-application implementation of pangolin, which has facilitated the SARS-CoV-2 genomic epidemiology and provided researchers with access to actionable information about the pandemic’s transmission lineages.
Full-text available
Ecologists have long been interested in linking individual behaviour with higher level processes. For motile species, this ‘upscaling’ is governed by how well any given movement strategy maximizes encounters with positive factors and minimizes encounters with negative factors. Despite the importance of encounter events for a broad range of ecological processes, encounter theory has not kept pace with developments in animal tracking or movement modelling. Furthermore, existing work has focused primarily on the relationship between animal movement and encounter rates while the relationship between individual movement and the spatial locations of encounter events in the environment has remained conspicuously understudied. Here, we bridge this gap by introducing a method for describing the long‐term encounter location probabilities for movement within home ranges, termed the conditional distribution of encounters (CDE). We then derive this distribution, as well as confidence intervals, implement its statistical estimator into open‐source software and demonstrate the broad ecological relevance of this distribution. We first use simulated data to show how our estimator provides asymptotically consistent estimates. We then demonstrate the general utility of this method for three simulation‐based scenarios that occur routinely in biological systems: (a) a population of individuals with home ranges that overlap with neighbours; (b) a pair of individuals with a hard territorial border between their home ranges; and (c) a predator with a large home range that encompassed the home ranges of multiple prey individuals. Using GPS data from white‐faced capuchins Cebus capucinus , tracked on Barro Colorado Island, Panama, and sleepy lizards Tiliqua rugosa, tracked in Bundey, South Australia, we then show how the CDE can be used to estimate the locations of territorial borders, identify key resources, quantify the potential for competitive or predatory interactions and/or identify any changes in behaviour that directly result from location‐specific encounter probability. The CDE enables researchers to better understand the dynamics of populations of interacting individuals. Notably, the general estimation framework developed in this work builds straightforwardly off of home range estimation and requires no specialized data collection protocols. This method is now openly available via the ctmm R package.
Full-text available
Virus host shifts are generally associated with novel adaptations to exploit the cells of the new host species optimally. Surprisingly, Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) has apparently required little to no significant adaptation to humans since the start of the Coronavirus Disease 2019 (COVID-19) pandemic and to October 2020. Here we assess the types of natural selection taking place in Sarbecoviruses in horseshoe bats versus the early SARS-CoV-2 evolution in humans. While there is moderate evidence of diversifying positive selection in SARS-CoV-2 in humans, it is limited to the early phase of the pandemic, and purifying selection is much weaker in SARS-CoV-2 than in related bat Sarbecoviruses . In contrast, our analysis detects evidence for significant positive episodic diversifying selection acting at the base of the bat virus lineage SARS-CoV-2 emerged from, accompanied by an adaptive depletion in CpG composition presumed to be linked to the action of antiviral mechanisms in these ancestral bat hosts. The closest bat virus to SARS-CoV-2, RmYN02 (sharing an ancestor about 1976), is a recombinant with a structure that includes differential CpG content in Spike; clear evidence of coinfection and evolution in bats without involvement of other species. While an undiscovered “facilitating” intermediate species cannot be discounted, collectively, our results support the progenitor of SARS-CoV-2 being capable of efficient human–human transmission as a consequence of its adaptive evolutionary history in bats, not humans, which created a relatively generalist virus.
Full-text available
SARS-CoV-2 transmission is uncontrolled in many parts of the world, compounded in some areas by higher transmission potential of the B1.1.7 variant¹ now reported in 94 countries. It is unclear whether responses to SARS-CoV-2 vaccines based on the prototypic strain will be impacted by mutations found in B.1.1.7. Here we assessed immune responses following vaccination with mRNA-based vaccine BNT162b2². We measured neutralising antibody responses following first and second immunisations using pseudoviruses expressing the wild-type Spike protein or the 8 amino acid mutations found in the B.1.1.7 spike protein. The vaccine sera exhibited a broad range of neutralising titres against the wild-type pseudoviruses that were modestly reduced against B.1.1.7 variant. This reduction was also evident in sera from some convalescent patients. Decreased B.1.1.7 neutralisation was also observed with monoclonal antibodies targeting the N-terminal domain (9 out of 10), the RBM (5 out of 31), but not in RBD neutralising mAbs binding outside the RBM. Introduction of the E484K mutation in a B.1.1.7 background to reflect a newly emergent Variant of Concern (VOC 202102/02) led to a more substantial loss of neutralising activity by vaccine-elicited antibodies and mAbs (19 out of 31) over that conferred by the B.1.1.7 mutations alone. E484K emergence on a B.1.1.7 background represents a threat to the vaccine BNT162b.
Full-text available
The COVID-19 pandemic has ravaged the globe, and its causative agent, SARS-CoV-2, continues to rage. The prospects of ending this pandemic rest on the development of effective interventions. Single and combination monoclonal antibody (mAb) therapeutics have received emergency use authorization1–3, with more in the pipeline4–7. Furthermore, multiple vaccine constructs have shown promise8, including two with ~95% protective efficacy against COVID-199,10. However, these interventions were directed toward the initial SARS-CoV-2 that emerged in 2019. The recent emergence of new SARS-CoV-2 variants B.1.1.7 in the UK11 and B.1.351 in South Africa12 is of concern because of their purported ease of transmission and extensive mutations in the spike protein. We now report that B.1.1.7 is refractory to neutralization by most mAbs to the N-terminal domain (NTD) of the spike and relatively resistant to a few mAbs to the receptor-binding domain (RBD). It is not more resistant to convalescent plasma or vaccinee sera. Findings on B.1.351 are more worrisome in that this variant is not only refractory to neutralization by most NTD mAbs but also by multiple individual mAbs to the receptor-binding motif on RBD, largely owing to an E484K mutation. Moreover, B.1.351 is markedly more resistant to neutralization by convalescent plasma (9.4 fold) and vaccinee sera (10.3-12.4 fold). B.1.351 and emergent variants13,14 with similar spike mutations present new challenges for mAb therapy and threaten the protective efficacy of current vaccines.
Full-text available
The rapid global spread and human health impacts of SARS-CoV-2, the virus that causes COVID-19, show humanity's vulnerability to zoonotic disease pandemics. Although anthropogenic land use change is known to be the major driver of zoonotic pathogen spillover from wildlife to human populations, the scientific underpinnings of land use-induced zoonotic spillover have rarely been investigated from the landscape perspective. We call for interdisciplinary collaborations to advance knowledge on land use implications for zoonotic disease emergence with a view toward informing the decisions needed to protect human health. In particular, we urge a mechanistic focus on the zoonotic pathogen infect–shed–spill–spread cascade to enable protection of landscape immunity—the ecological conditions that reduce the risk of pathogen spillover from reservoir hosts—as a conservation and biosecurity priority. Results are urgently needed to formulate an integrated, holistic set of science-based policy and management measures that effectively and cost-efficiently minimise zoonotic disease risk. We consider opportunities to better institute the necessary scientific collaboration, address primary technical challenges, and advance policy and management issues that warrant particular attention to effectively address health security from local to global scales.
Full-text available
Global dispersal and increasing frequency of the SARS-CoV-2 spike protein variant D614G are suggestive of a selective advantage but may also be due to a random founder effect. We investigate the hypothesis for positive selection of spike D614G in the United Kingdom using more than 25,000 whole genome SARS-CoV-2 sequences. Despite the availability of a large dataset, well represented by both spike 614 variants, not all approaches showed a conclusive signal of positive selection. Population genetic analysis indicates that 614G increases in frequency relative to 614D in a manner consistent with a selective advantage. We do not find any indication that patients infected with the spike 614G variant have higher COVID-19 mortality or clinical severity, but 614G is associated with higher viral load and younger age of patients. Significant differences in growth and size of 614G phylogenetic clusters indicate a need for continued study of this variant.
Full-text available
SARS-CoV-1 and SARS-CoV-2 are not phylogenetically closely related; however, both use the ACE2 receptor in humans for cell entry. This is not a universal sarbecovirus trait; for example, many known sarbecoviruses related to SARS-CoV-1 have two deletions in the receptor binding domain of the spike protein that render them incapable of using human ACE2. Here, we report three sequences of a novel sarbecovirus from Rwanda and Uganda which are phylogenetically intermediate to SARS-CoV-1 and SARS-CoV-2 and demonstrate via in vitro studies that they are also unable to utilize human ACE2. Furthermore, we show that the observed pattern of ACE2 usage among sarbecoviruses is best explained by recombination not of SARS-CoV-2, but of SARS-CoV-1 and its relatives. We show that the lineage that includes SARS-CoV-2 is most likely the ancestral ACE2-using lineage, and that recombination with at least one virus from this group conferred ACE2 usage to the lineage including SARS-CoV-1 at some time in the past. We argue that alternative scenarios such as convergent evolution are much less parsimonious; we show that biogeography and patterns of host tropism support the plausibility of a recombination scenario; and we propose a competitive release hypothesis to explain how this recombination event could have occurred and why it is evolutionarily advantageous. The findings provide important insights into the natural history of ACE2 usage for both SARS-CoV-1 and SARS-CoV-2, and a greater understanding of the evolutionary mechanisms that shape zoonotic potential of coronaviruses. This study also underscores the need for increased surveillance for sarbecoviruses in southwestern China, where most ACE2-using viruses have been found to date, as well as other regions such as Africa, where these viruses have only recently been discovered.
Full-text available
SARS-CoV-2 can mutate and evade immunity, with consequences for efficacy of emerging vaccines and antibody therapeutics. Herein we demonstrate that the immunodominant SARS-CoV-2 spike (S) receptor binding motif (RBM) is a highly variable region of S, and provide epidemiological, clinical, and molecular characterization of a prevalent, sentinel RBM mutation, N439K. We demonstrate N439K S protein has enhanced binding affinity to the hACE2 receptor, and N439K viruses have similar in vitro replication fitness and cause infections with similar clinical outcomes as compared to wild-type. We show the N439K mutation confers resistance against several neutralizing monoclonal antibodies, including one authorized for emergency use by the FDA, and reduces the activity of some polyclonal sera from persons recovered from infection. Immune evasion mutations that maintain virulence and fitness such as N439K can emerge within SARS-CoV-2 S, highlighting the need for ongoing molecular surveillance to guide development and usage of vaccines and therapeutics.
The evolution of SARS-CoV-2 could impair recognition of the virus by human antibody-mediated immunity. To facilitate prospective surveillance for such evolution, we map how convalescent plasma antibodies are impacted by all mutations to the spike’s receptor-binding domain (RBD), the main target of plasma neutralizing activity. Binding by polyclonal plasma antibodies is affected by mutations in three main epitopes in the RBD, but longitudinal samples reveal the impact of these mutations on antibody binding varies substantially both among individuals and within the same individual over time. Despite this inter- and intra-person heterogeneity, the mutations that most reduce antibody binding usually occur at just a few sites in the RBD’s receptor binding motif. The most important site is E484, where neutralization by some plasma is reduced >10-fold by several mutations, including one in the emerging 20H/501Y.V2 and 20J/501Y.V3 SARS-CoV-2 lineages. Going forward, these plasma escape maps can inform surveillance of SARS-CoV-2 evolution.