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Transmission dynamics: critical questions
and challenges
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Janis Antonovics
Department of Biology, University of Virginia, Charlottesville, VA 22904, USA
JA, 0000-0002-5189-1611
Opinion piece
Cite this article: Antonovics J. 2017
Transmission dynamics: critical questions and
challenges. Phil. Trans. R. Soc. B 372:
20160087.
http://dx.doi.org/10.1098/rstb.2016.0087
Accepted: 25 August 2016
One contribution of 16 to a theme issue
‘Opening the black box: re-examining the
ecology and evolution of parasite transmission’.
Subject Areas:
evolution, ecology, behaviour, microbiology
Keywords:
transmission triangle, vector, perception kernel,
force of infection, contact matrix
Author for correspondence:
Janis Antonovics
e-mail: [email protected]
This article overviews the dynamics of disease transmission in one-host –
one-parasite systems. Transmission is the result of interacting host and
pathogen processes, encapsulated with the environment in a ‘transmission
triangle’. Multiple transmission modes and their epidemiological consequences are often not understood because the direct measurement of
transmission is difficult. However, its different components can be analysed
using nonlinear transmission functions, contact matrices and networks. A
particular challenge is to develop such functions for spatially extended systems. This is illustrated for vector transmission where a ‘perception kernel’
approach is developed that incorporates vector behaviour in response to
host spacing. A major challenge is understanding the relative merits of the
large number of approaches to quantifying transmission. The evolution of
transmission mode itself has been a rather neglected topic, but is important
in the context of understanding disease emergence and genetic variation in
pathogens. Disease impacts many biological processes such as community
stability, the evolution of sex and speciation, yet the importance of different
transmission modes in these processes is not understood. Broader
approaches and ideas to disease transmission are important in the public
health realm for combating newly emerging infections.
This article is part of the themed issue ‘Opening the black box: reexamining the ecology and evolution of parasite transmission’.
1. Introduction
In this article, which is based on my opening talk as a ‘facilitator’ at the British
Ecological Society retreat on Transmission in September 2015, I review some of
the issues that I think are important when considering the subject of disease
transmission. I was specifically asked to provide an overview of aspects of disease transmission that I find interesting, drawing on examples from my own
experience, and ‘sowing the seeds’ for subsequent discussions. My focus
was on one-host–one-parasite systems, with Jo Webster, the other facilitator,
focusing on multispecies systems [1].
Transmissible diseases are caused by infectious agents, and an imperative of
disease control is to determine and interrupt these routes of transmission to
reduce pathogen fitness. In humans, this is the province of the public health
community, while eliminating the pathogen once it enters the individual is
the focus of the medical profession. However, focusing on cure eventually
fails, because evolutionary processes nearly always trump pharmacological
solutions; population level understanding is essential. There are often other
reasons for studying transmission; in biological control and with live vaccines,
the goal may be to increase transmission rather than decrease it. For an ecologist
or evolutionary biologist, to focus on transmission is to allow that dispersal is as
important a fitness component [2] as the oft-lauded life-history features of
reproductive output and survival.
Transmission is more than dispersal and consists of pathogen presentation
by the host (¼departure), movement between infected and healthy hosts
(¼dispersal per se), and entry into the new host (¼establishment; figure 1).
There will always be debates about what to include in each of these steps
& 2017 The Author(s) Published by the Royal Society. All rights reserved.
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transmission (b)
dispersal
infection
the disease cycle
disease
prevention
within host processes
and how to parse them into meaningful components, but
recognizing the steps emphasizes that transmission is a function of both the host and the pathogen. Because the pathogen
stands to gain from increased transmission, it is tempting to
succumb to ‘adaptive’ thinking and put the onus of transmission ecology and evolution solely in the lap of the
pathogen. However, if we parse the transmission process
into pathogen presentation, dispersal and arrival, then it is
clear that both the host and the pathogen have to be involved.
Most disease biologists are familiar with the famous ‘disease triangle’ of the three factors (host, pathogen and
environment) that contribute to disease expression [3].
While this triangle is a tautology (how could these factors
not be essential!), it has been useful in emphasizing that if
we ignore or are unable to measure one of the factors, then
it is erroneous to ascribe the cause of the disease only to
the measured factors [4]; ulcers were happily attributed to
genetics and environment, in the absence of any conviction
that Helicobacter pylori could be a causative infectious agent
[5]. Correspondingly, it is also possible to produce a ‘transmission triangle’ where now the process of transmission is
a function of the pathogen, the host and the environment
(figure 2). Because all hosts and pathogens necessarily consist
of component genotypes, then clearly transmission itself can
evolve, and hosts and pathogens can potentially co-evolve
in the traits determining their transmission.
2. Multiplicity of transmission modes and routes
In considering transmission, the term ‘mode’ is best thought of
as referring to the method that a pathogen uses to move
among hosts, whereas the ‘route’ refers to the specific path
taken, including the point of presentation of the infective
stages, and their point of entry. Investigating the contribution
of different modes and routes of transmission is not straightforward. While genomic markers may trace an infection to an
individual, they cannot in and of themselves always identify
how that infection was transmitted. There is a huge diversity
of transmission processes that lead to different epidemiological
outcomes, alter conditions for disease increase, and affect control policies [6]. Furthermore, any particular host–pathogen
interaction usually involves multiple modes and routes of
transmission [7,8]. This raises additional challenges: how to
characterize those pathways and how to assess their fitness
and dynamical consequences either singly or in combination.
This complexity can be illustrated by our studies on the
transmission process in a plant disease, anther smut,
caused by the fungus Microbotryum infecting wild species
mostly in the carnation family. Anther smut has become a
transmission
mode
host
genotype
environment
dynamics
Figure 2. Transmission is the beating heart of the disease triangle: the idea that
host and pathogen genotype and environment contribute to disease expression
is equally applicable to disease transmission. (Online version in colour.)
model for studying disease dynamics in natural systems
because of its large fitness effects (it completely sterilizes
the host plant) and ease of experimental manipulation
[9,10]. The fungal spores are produced in the anthers of a
flower, and because it is transmitted by pollinators during
sexual reproduction of the host (figure 3), it exhibits features
in common with many other sexually transmitted diseases in
animals [7,11]. The pollinators act as passive vectors, and
spore deposition on flowers does not occur if pollinators
are experimentally excluded. However, several studies have
now shown that this may not be the main mode of transmission [12– 14]. Instead, aerial transmission of spores
falling on nearby plants may be as important. It is well
known that seedlings and plants in the vegetative stage are
susceptible to infection, and they are routinely inoculated
by this route in the laboratory; experimental studies have
also shown that non-flowering plants placed under diseased
individuals can become diseased [12]. As emphasized with
the ‘transmission triangle’, the mode of transmission may be
determined in large measure by the degree to which different
stages and sites of entry into the host are susceptible to fungal
spores rather than by any difference in the pathogen stage
that is involved; obviously the environment, whether this be
the density of plants or the composition of the pollinator
community, will also play a role.
Anther smut spores also land on the soil, but infection via
this environmental route has never been detected. However,
there are other, poorly studied, transmission pathways in this
system. For example, pollinators can carry spores to flowers
of other species [15]. When the spores germinate, they
undergo meiosis and produce a yeast-like haploid stage
that can multiply asexually in nectar, not only of conspecifics,
but also of other species; flowers of these other species can
therefore potentially act as asymptomatic carriers.
I am not sure that there is any great message here for
other host –pathogen systems, other than to say that there is
probably no disease for which all the modes and routes of
transmission have been characterized, let alone quantified
in terms of their contribution to disease spread; perhaps
HIV comes closest. Certainly, even diseases that are quite
host-specific may show multiple transmission modes and
routes. Research into multiple transmission modes is important, because even minority routes may be critical for
determining disease thresholds and dynamics [9,16].
Phil. Trans. R. Soc. B 372: 20160087
Figure 1. ‘The curse of beta’: diagram of the disease cycle illustrating the
complex nature of the transmission process, that often is summarized by
one parameter b. (Online version in colour.)
evolution
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presentation
2
pathogen
genotype
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3
(a)
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(c)
Figure 3. (a) Skipper butterfly (Hesperiidae) visiting Dianthus pavonius diseased with anther smut. (b) Healthy flower. (c) Diseased flower. Photos: Michael E. Hood.
3. Transmission dynamics
Even though transmission is a complex process, in simple
models, it is often encapsulated using one parameter, b, the
transmission coefficient (figure 1). We are reminded of
Anderson and May’s [17, p. 63] warning that ‘direct measurement of b is essentially impossible for most infections’. It is
true that transmission is to all intents and purposes invisible:
we cannot place a camera on every microorganism in every
individual at every location and watch a virus or even tapeworm move between hosts. However, it is perhaps better to
see Anderson and May’s ‘impossible’ more as a challenge
than a fact: the goal is to face this multifaceted complexity
of transmission, and distil from it useful understanding that
can be applied to diseases in nature, agriculture and
humans [18].
One way of distilling the complexity of the transmission
coefficient, b, is to consider how the per capita rate at which
healthy individuals become infected F, or ‘the force of infection’, is a function of the number of susceptible and infected
individuals, S and I, respectively. If F ¼ bI, the force of infection increases linearly with number of contacts, and
transmission is said to be ‘density-dependent’. The simplest
alternative is to assume that transmission depends on the fraction of individuals that are diseased, in which case the force of
infection, F ¼ bI/(S þ I ), and transmission is said to be ‘frequency-dependent’ (table 1; [19]). The latter scenario is
considered to be likely in sexually transmitted infections
because the per-individual rate of sexual contacts does not
usually increase as population density increases. Frequencydependent transmission is also considered to occur in vector
transmitted diseases, provided that vectors actively move
further if their hosts are further apart [20]. This is likely to be
the case for flying vectors such as mosquitoes, but perhaps
less likely for fleas or ticks. Another circumstance engendering
frequency-dependent transmission would be if the population
was subdivided into groups of similar size, regardless of overall population size, and within which most of the transmission
occurred. Transmission functions may therefore subsume
several transmission modes.
Frequency- versus density-dependent transmission predicts contrasting thresholds and dynamics, and such
functions are useful for making predictions about disease
spread and disease characteristics (table 1). For example,
while in practice, the distinction may not be clear cut or may
be scale dependent, the recognition of the differences between
them has huge policy implications. If the transmission of a disease (such as a sexually transmitted infection) is indeed purely
frequency dependent, then herd immunity is not operative,
and can no longer be proposed as a tenable reason for policies
of compulsory vaccination. A number of other transmission
functions have been proposed which take into account decreasing per capita rates of contact with increasing density either
Phil. Trans. R. Soc. B 372: 20160087
(b)
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Table 1. Contrasting features of the dynamics of aerial and sexually transmitted diseases (for further details, see Lockhart et al. [7]). b, transmission
coefficient; d , death rate; I, number infectious; S, number susceptible; N ¼ S þ I, total population size.
sexual transmission
force of infection
threshold for disease spread
density-dependent (bI)
yes (b . d/S)
frequency-dependent (bI/N)
no (b . d)
host – pathogen dynamics
mostly stable
host or pathogen wins, stabilized by differential
density effects on S and I
that the relationship between population density and disease
transmission (spore deposition in the case of anther smut) follows theoretical expectations [20,28] for a vector transmitted
disease (figure 4), thus supporting the ‘perception model’ concept. At low densities, hosts are far apart and not found/
perceived by the vector; at intermediate densities, pollinators
adjust their flight distances to compensate for changes in host
density, whereas at very high host densities the dilution effect
becomes important such that the per host visitation rate again
falls. All of this assumes that vector numerical dynamics is
independent of host density.
In addition, the results show that measuring only vector
movement from diseased to healthy individuals cannot accurately assess vector impacts, as many vectors simply do not
visit hosts in areas of low density. As an example, when density
was 0.1 hosts m22 (average spacing 3.16 m), vectors visited
hosts only 24.3% of the time, whereas when it was 2 m22 (spacing 0.71 m) they visited 99.7% of the time. However, the
difference in the overall dispersal distances was much less
(mean: 1.56 versus 1.02, median: 0.82 versus 0.45, for 10 000
visits) even though there was an increase in the number of
short distance visits at the higher density (figure 5). Importantly, this shows that observations of vector movement
between extant hosts, in and of itself, does not provide a complete picture, because ‘non-contacts’ are invisible; thus, studies
of the actual transmission process (pick-up, transport and
deposition) and not just vector dispersal distances are required
to understand spatial dynamics of vector transmission.
4. Heterogeneity in contact rates and the
transmission process
Every contact is not the same. In a real-world situation,
healthy individuals making multiple contacts with a diseased
individual may already have become infected by an earlier
contact; in that case the force of infection is best represented
by F ¼ 1 2 (1 2 d)c I/N, or 1 minus the probability of not
getting diseased after c contacts, where d is the per contact
infection probability. If contacts increase linearly with host
density, with a proportionality constant k, i.e. c ¼ kN, then
F reduces to 1 2 (1 2 d)kI. This immediately introduces a nonlinearity in the transmission function. If d is small, this can be
further approximated by 1 2 exp (2 lb’), where now b’ ¼ dk.
The beta in this function can be termed the ‘exponential
transmission coefficient’ [20] and is more appropriate
for difference equation models where there are multiple contacts (cf. the Nicholson– Bailey model of host–parasitoid
interactions [29]).
Phil. Trans. R. Soc. B 372: 20160087
with other hosts and vectors [20] or with environmentally
transmitted infectious stages [21].
A particular challenge is to develop such functions for
spatially extended systems. Individuals will likely have more
contacts with their neighbours, and the spread of a disease
will itself change the relative positions of susceptible and infectious individuals. For example, Best et al. [22] examined the
dynamics that result from three different local transmission
mechanisms: density-dependent, frequency-dependent and
active host searching. They showed that when frequencydependent transmission is local, extinction is less likely than
when it is global. However, when hosts actively search for contacts, the conditions for population extinction approach those
of global frequency-dependence.
Much effort has been put into estimating ‘vectorial
capacity’, essentially a measure of rate of pathogen increase
where vectors both take up and deliver infectious stages
[23,24], but such a measure cannot be used to study vector
transmission in a spatial context. A standard way to model
and study spatial spread of disease is to estimate and use
dispersal distributions (or kernels), which effectively show
how transmission is expected to decline with distance [25].
However, such kernels are inappropriate for modelling disease spread by actively flying vectors because they are likely
to adjust their flight behaviour depending on the spacing
among hosts, moving and searching further when hosts are
further apart. It is difficult to observe individual vectors,
especially over large distances and over substantial periods
of time; even when they can be observed, their transmission
efficiency is hard to estimate [26]. I therefore developed a
simple agent-based model capturing essential features of
vector behaviour, and which can be applied to field data
and used to predict disease dynamics in new spatial contexts.
This model uses a ‘perception kernel’, rather than a fixed
dispersal kernel, to model vector behaviour. For another
approach that takes animal movement processes into account
see Fofana & Hurford [27].
The model in its simplest form is as follows. The vector
arrives at a point and ‘perceives’ the hosts around it; hosts
further from the point of arrival are given less weight according to a simple power function, where the perceived density
P
d ¼ k (xi)2p, where k and p are constants, and xi is distance.
Whether the vector remains or leaves is dependent on this
perceived host density; the vector stays with probability 1 2
exp (2 d). The number of vector visits, v, can also be varied.
If the vector stays then it moves to the host with a probability
proportional to its contribution to perceived density, and the
process is iterated. Using randomly generated spatial arrays at
different densities, and parameter values close to those
observed by fitting the model to field data, it can be shown
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aerial transmission
4
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density
dependent
frequency
dependent
dilution
effect
60
5
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50
0.2
40
0
0
50
100
density
150
200
Nonlinearities arise in many other ways. It has long been
appreciated that there is often extreme heterogeneity in the
contribution of different individuals to disease spread
[17,30]. A major challenge in the field of disease epidemiology is to decide on an approach for assessing transmission
while accounting for heterogeneity in contact rates among
individuals; these can arise from differences in age, spatial
position, genetics, or social position. Several types of
approaches have been used, and their merits may depend
on the specific disease and the question being asked. One
approach is to take simple measures of the heterogeneity
and re-calibrate the transmission parameter accordingly. For
example, Anderson & May [17, p. 233] showed that in sexually transmitted diseases with individual variation in the
number of partners, the ‘effective contact rate’ for a population was equal to the mean plus the coefficient of
variation in the number of partners. This has led to a focus
on the importance of super-spreaders and mechanisms of
identifying them to increase the efficacy of disease control
[31]. Another approach is to use ‘contact matrices’ which
assign contact rates between different subpopulations
(social classes, sexes, ages), and whose implementation into
models (analytically or by simulation) can be used to predict
disease dynamics [32].
Contact matrices can also be presented as networks where
each element in the matrix is a function or quantity describing the properties of the ‘edge’, or line representing the
connections between the ‘nodes’ (i.e. the row– column identities); depending on the situation, the nodes can be
individuals or groups, such as social categories or cities,
characterized by their interactions or distances from other
groups. The edges can be differentially weighted to take
into account host behaviour, directional transmission,
distance and other factors affecting the interactions between
each pair of nodes. For example, in anther smut disease, the
spatial arrangement of plants can be represented as a network, with lines representing all possible combinations,
nearest neighbour combinations or some intermediate level
of connectedness [33]. The application of network theory to
epidemiology is a rapidly developing field [34– 36]. Most of
the focus of this approach has been on human diseases
where social contact patterns are often known or can be
30
20
10
0
0
5
10
distance
Figure 5. Dispersal kernel for a vector visiting a high-density population
(N ¼ 200, red) and low-density population (N ¼ 10, blue). Parameters
for the simulation, as in figure 1. Units for distance in the anther-smut
system are metres.
inferred from airline schedules, phone calls, proximity sensors [37] or even (in the case of Hanta virus) from the sales
statistics for mouse traps (Kurt Vandegrift 2016, personal
communication).
There have been some notable insights from this approach,
as for example in predicting efficient vaccination strategies
especially where vaccine supply may be limited [38,39]. Directed
networks (where transmission is asymmetrical depending on
the characteristics of the individuals involved in the contact)
were also useful for predicting the impact on healthcare workers
of control of hospital infections [40].
I see the major challenge here as integrating these different approaches to contact structures and assessing their
relative merits. It is difficult to evaluate any one such
model against another especially with regard to what could
be termed ‘eco-statistical reality’; detailed models that break
processes into their many component parts may actually be
worse at prediction than more phenomenological approaches
that allow for better estimation of fewer parameters [41,42].
Network approaches are more reductionist, but are they
always therefore better, especially when judged against
alternatives and especially in situations where the connectedness measures themselves are subject to error? It is also a
question of deciding what predictions one wants to make:
for example, a mean field model may be reasonably adequate
at predicting changes in overall disease prevalence, but it
cannot predict the likelihood that a particular individual
will get infected in relation to its connectedness to other
individuals in the population.
5. Evolution of transmission mode
Evolutionary studies related to transmission mode have
largely focused on the consequences of transmission modes
for the evolution of virulence [43 –45] and evolution of disease characteristics [8,46] rather than on the evolution of
transmission mode itself. However, except in the case of transitions between horizontal and vertical transmission [9,47]
and transitions between sexual and non-sexual transmission
Phil. Trans. R. Soc. B 372: 20160087
Figure 4. Transmission (fraction of healthy hosts with spores) as a function of
density for a vector transmitted disease. Points are each means of 20 runs from
a simulation of the ‘perception model’ using parameters approximating spore
deposition in the anther-smut system (p ¼ 3, k ¼ 4.93, v ¼ 100, with
disease prevalence of 40% as is seen in the field). Units for density represent
numbers per 100 m2. Line is best fit of theoretical expectations based on
Antonovics et al. [20] for vector transmission y ¼ aN/(1 þ bN)2, where
N ¼ density, a ¼ 0.0128, b ¼ 0.113.
percentage
transmission
0.4
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As mentioned above, there has been substantial research on
the adaptations of both hosts and pathogens to different
transmission modes. For example, the co-transmission ‘interests’ of genes and endosymbionts should be expected to lead
to a low virulence of vertically transmitted pathogens; and
parasite manipulation of the behaviour and appearance of
their intermediate hosts is readily interpretable as an adaptation of the pathogen to increase its trophic transmission to
definitive hosts [59]. In this context, it is often difficult to
distinguish features that are ‘adaptive’ (i.e. the product of
natural selection for traits increasing fitness) from traits that
promote persistence in a community and determine ‘assembly rules’ [60–63]. Thus, in the case of sexually transmitted
diseases, sterility has been interpreted as a pathogen trait
that has evolved to increase opportunities for transmission
by preventing pregnancy and encouraging repeated mating
[64]. Similarly, the low virulence of such diseases could be
7. Conclusion
The investigation of pathways and mechanisms of transmission is a major undertaking in the public health realm,
and takes on especial urgency in the case of newly emerging
infections. As with the discovery of the Ebola virus in seminal
fluid of ‘cured’ patients [71], new potential routes of transmission bring new concerns. In the more academic realm of
disease ecology and evolution, transmission has been rather
‘taken for granted’ perhaps, because in ecological and evolutionary thinking, dispersal as a fitness trait has played
second fiddle to death and reproduction. I have argued
here that there are still major areas where both theoretical
and empirical studies of transmission mode are needed,
and many problems wait to be resolved. This article has
only illustrated some of these, and then with a bias towards
my own experiences and interests. Many others are illustrated
in the series of papers that follow.
Ethics. This work involved no human subjects.
Data accessibility. No unpublished data are presented.
Competing interests. I have no competing interests.
Funding. NSF grant no. DEB1115895 and NIH R01GM122061.
Acknowledgments. I thank Mike Boots, Emme Bruns and Michael Hood
for extensive discussions that contributed to the ideas presented here.
6
Phil. Trans. R. Soc. B 372: 20160087
6. The disease and non-disease consequences
of transmission mode
interpreted as a pathogen trait that allows repeated mating
over successive seasons. However, an analysis of a suite of
population dynamics models [8] showed that in sexually
transmitted diseases, host and pathogen coexistence is also
more likely if the pathogen is sterilizing and causes low mortality; sexually transmitted pathogens causing only mortality
would simply be less likely to stably persist with their
hosts. Distinguishing ‘assembly rules’ from adaptations is
important in understanding disease emergence.
A subject that I think has received little attention is how
transmission mode, as distinct from disease per se, may
impact on the multiplicity of ecological and evolutionary processes affected by pathogens and parasites. These processes
include aspects such as maintenance of community diversity
[65], the evolution of sex [66] or mating systems [67], and speciation [68]. Pathogen risk has also been implemented in
determining traits such as sleep [69] and sexual dimorphism
[70]. The usual presumption, in both verbal and theoretical
arguments, has been that the pathogen is directly transmitted
in a density-dependent manner and the disease causes mortality. A question rarely raised in all these discussions is
what happens if such ‘canonical’ assumptions are relaxed:
does transmission mode matter to the generalizations that
emerge, or is it irrelevant? It has been shown [66] that
arguments about the evolution of sex hinge critically on epidemiological feedbacks and virulence, features also likely to
be affected by transmission mode. Are some transmission
modes more likely than others to result in genetic isolation
and speciation (of the host and/or the pathogen)? If transmission is age specific, do different transmission modes lead
to different host and pathogen life histories? Do our ideas
about disease and the evolution of sex need to take transmission mode into account? How do different transmission
modes likely affect the genetic structure of host and pathogen
populations? There are many questions of this kind.
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[48], the study of the evolution of different horizontal
transmission modes is a surprisingly neglected topic [49].
There has also been very little work examining what
factors select for particular transmission modes. Theoretical
studies on the evolution of sexual transmission [50,51] confirmed that low-density populations should favour sexual
transmission; here, evolution of transmission was under
‘pathogen control’ (i.e. genetic variation in the pathogen
affected transmission mode), but I know of no studies of evolution of transmission mode when it is under ‘host control’.
For example, transmission mode may evolve by selection
on genetic variation in host resistance to pathogen entry at
different sites or at different ages. In addition, there has
been no examination of selection on transmission mode in a
spatial context, even though spatial extensions of evolutionary models show that spatial structure greatly affects the
evolution of infectivity and virulence [52,53].
Concern about the emergence of ‘new’ diseases has
focused on pathogen transmission from wildlife reservoirs
or increases in their virulence [54,55] but changes in transmission mode may be equally important in driving disease
emergence. For example, dourine in domesticated horses is
caused by a trypanosome that has shifted from vector to
sexual transmission [56]. HIV is another potential example,
even though the transmission mode of simian immunodeficiency virus in primates remains uncertain [57]. However, it
is not known whether such emergence resulted from altered
contact structure (suggested for early spread of HIV [58]) or
was contingent on the presence of pathogen genetic variants.
Multiple modes of transmission also present a challenge.
Are multiple modes directly adaptive to the pathogen, or are
some routes dead-ends (e.g. maternal transmission of HIV)?
How are different routes of transmission affected by host or
pathogen genotype, and can different transmission routes
in one population be the result of genetic variants each
with their own singular dynamics and virulence? Do such
genetic variants ‘presage’ an evolutionary shift to an alternative mode? These and other questions are more fully
addressed in the article on evolution of transmission mode
in this special issue [49].
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