<figure><img src="image_1.png" /></figure>

# Evolutionary consequences of feedbacks between within-host competition and disease control

Megan A. Greischar<sup>1,*</sup> Helen K. Alexander,<sup>2,22</sup> Farrah Bashey,<sup>3</sup> Ana I. Bento,<sup>4,23</sup> Amrita Bhattacharya,<sup>3,24</sup> Mary Bushman,<sup>5</sup> Lauren M. Childs,<sup>6</sup> David R. Daversa,<sup>7,8</sup> Troy Day,<sup>9</sup> Christina L. Faust,<sup>10</sup> Molly E. Gallagher,<sup>5</sup> Sylvain Gandon,<sup>11</sup> Caroline K. Glidden,<sup>12</sup> Fletcher W. Halliday,<sup>13</sup> Kathryn A. Hanley,<sup>14</sup> Tsukushi Kamiya,<sup>1</sup> Andrew F. Read,<sup>15</sup> Philipp Schwabl,<sup>10</sup> Amy R. Sweeny,<sup>2</sup> Ann T. Tate,<sup>16</sup> Robin N. Thompson,<sup>2,17,18</sup> Nina Wale,<sup>19</sup> Helen J. Wearing,<sup>20</sup> Pamela J. Yeh<sup>21</sup> and Nicole Mideo<sup>1</sup>

<footnote n="1"><sup>1</sup>Department of Ecology & Evolutionary Biology, University of Toronto, 25 Willcocks St., Toronto, ON M5S 3B2, Canada; <sup>2</sup>Department of Zoology, University of Oxford, Zoology Research and Administration Building, 11a Mansfield Road, Oxford OX1 3SZ, UK; <sup>3</sup>Department of Biology, Indiana University, 1001 E. 3rd St., Bloomington, IN 47405, USA; <sup>4</sup>Odum School of Ecology and the Center for the Ecology of Infectious Diseases, University of Georgia, 140 E Green St., Athens, GA 30602, USA; <sup>5</sup>Department of Biology, Emory University, Atlanta, GA 30322, USA; <sup>6</sup>Department of Mathematics, McBryde Hall, Virginia Tech, Blacksburg, VA 24061, USA; <sup>7</sup>Institute of Integrative Biology, University of Liverpool, Liverpool, L69 3BX, UK; <sup>8</sup>Institute of Zoology, Zoological Society of London, Regent's Park, NW1 4RY, UK; <sup>9</sup>Departments of Mathematics & Biology, Jeffery Hall, Queen's University, Kingston, ON K7L 3N6, Canada; <sup>10</sup>Institute of Biodiversity, Animal Health & Comparative Medicine, University of Glasgow, Glasgow G12 8QQ, UK; <sup>11</sup>CEFE UMR 5175, CNRS - Université de Montpellier, Université Paul-Valéry Montpellier, EPHE, 1919, Route de Mende, 34293 Montpellier Cedex 5, France; <sup>12</sup>Department of Integrative Biology, Oregon State University, 3029 Cordley Hall Corvallis, OR 97331, USA; <sup>13</sup>Department of Evolutionary Biology and Environmental Studies, University of Zürich, Zürich, 8057, Switzerland; <sup>14</sup>Department of Biology, New Mexico State University, Foster Hall, Las Cruces, NM 88003, USA; <sup>15</sup>Center for Infectious Disease Dynamics, Huck Institutes for the Life Sciences; Departments of Biology and Entomology, Pennsylvania State University, University Park, PA 16802, USA; <sup>16</sup>Department of Biological Sciences, Vanderbilt University, Nashville, TN 37235, USA; <sup>17</sup>Mathematical Institute, University of Oxford, Woodstock Road, Oxford OX2 6GG, UK; <sup>18</sup>Christ Church, University of Oxford, St Aldates, Oxford OX1 1DP, UK; <sup>19</sup>Department of Ecology & Evolutionary Biology, University of Michigan, 1105 North University Ave, Biological Sciences Building, Ann Arbor, MI 48109, USA; <sup>20</sup>Departments of Biology and Mathematics & Statistics, The University of New Mexico, Albuquerque, NM 87131, USA; <sup>21</sup>Department of Ecology & Evolutionary Biology, University of California, Los Angeles, 621 Charles E Young Dr South, Los Angeles, CA 90095, USA; <sup>22</sup>Present address: Institute of Evolutionary Biology, School of Biological Sciences, University of Edinburgh, Charlotte Auerbach Road, Edinburgh EH9 3FL, UK; <sup>23</sup>Present address: Epidemiology & Biostatistics, Indiana University, 1025 E. 7th St., Bloomington, IN 47405, USA and <sup>24</sup>Present address: Department of Biology, Pennsylvania State University, University Park, PA 16802, USA.</footnote>

<footnote>*Corresponding author. Department of Ecology & Evolutionary Biology, University of Toronto, 25 Willcocks St., Toronto, ON M5S 3B2, Canada. Tel: 1 416 978 1316; Fax: 1 416 978 5878; E-mail: megan.greischar@utoronto.ca
Recevied 10 December 2019; revised version accepted 22 January 2020</footnote>
**Lay Summary:** Competition often occurs among diverse parasites within a single host, but control efforts could change its strength. We examined how the interplay between competition and control could shape the evolution of parasite traits like drug resistance and disease severity.

Parasites often share their hosts with other parasites, meaning that hosts infected by a single, homogenous parasite population represent a rare exception. More commonly, hosts are infected by a variety of pathogenic organisms—any of which we refer to as ‘parasites’—that may themselves comprise multiple genetic variants (‘strains’). Such diversity within the host can lead to competition for resources or for respite from the shared threat of host immune defenses. Within-host competition has long been a focus of theoretical and experimental research in evolutionary ecology, and it is now widely appreciated that competition can alter the evolutionary trajectories of key parasite traits like virulence (reviewed in [1]). Equally widespread is the recognition that medical and public health interventions also drive the evolution of parasite traits; drug resistance, for example, has made its way to the forefront of our common conscience and our newsfeeds. Yet the interactions between these two sources of selection pressure are underexplored despite their inextricable linkage: by limiting transmission between hosts and/or inhibiting replication within hosts, control efforts—when successful—are likely to reduce the frequency and intensity of within-host competition. The evolutionary consequences of such interactions for parasite traits are as yet unmapped, and the implications for host health and disease control remain uncertain.

Seeking to fill this gap and elucidate common principles—or lack thereof—shaping parasite evolution in the presence of competition and disease control, we recently held an interdisciplinary workshop on this topic at Princeton University. The workshop was organized by Nicole Mideo and Megan A. Greischar as part of the NSF-funded Infectious Disease Evolution Across Scales (IDEAS) Research Coordination Network, with a combination of invited speakers and participants selected based on blinded review of applications (see author list). Here, we use the main discussion points of the workshop as a guide to define the level of understanding required to anticipate the evolutionary impact of feedbacks among within-host interactions, epidemiological processes and disease control, and we identify key open challenges for generating this understanding.

## OUTSTANDING QUESTIONS IN EVOLUTION, COMPETITION AND DISEASE CONTROL

### When do single versus multiple infections yield distinct health outcomes?

In evolutionary theory, infections are often categorized into two types: single or multiple infections, with the latter implying two or more different parasite strains (or species). This simple

dichotomy belies both the complexity of many human infectious diseases and the myriad potential outcomes of competitive interactions within the host, but it is nonetheless predicted to have a large impact on host health. Is there any evidence that the ‘precise’ diversity of an infection—the number of strains represented by the ‘multiple’ moniker—needs to be considered to understand clinical outcomes?

For HIV infections, the answer seems to be no. A lot can be predicted about clinical outcomes from knowing simply whether an infection was founded by one viral strain or more. Infections founded by more than one strain tend to be more virulent as they yield higher viral loads, which in turn hasten progression to AIDS; in reality, most new infections are founded by single strains [2 and references therein; NB: we use ‘strain’ here for consistency rather than ‘variant’ as favored in that literature]. The preponderance of infections founded by single strains arises in part from the fact that transmission of multiple viral particles is a prerequisite for inoculation with multiple strains. Transmission of many viral particles is particularly likely early on in HIV infections, before viral populations have diversified, and so relatively few strains are available for transmission [2]. Widespread drug treatment should reduce still further the odds of transmission during later stages of infection, and hence inoculations with more than one viral strain [2]. Thus, relying on the simple one-versus-many dichotomy, we may predict synergistic effects of within-host ecology and drug treatment in reducing virulence in HIV infections.

A similar ‘one-versus-many’ distinction emerges from studying transmission of drug-resistant malaria strains. In untreated rodent malaria infections, drug-resistant strains are competitively suppressed by drug-sensitive competitors and fail to transmit, regardless of whether competitors comprise one or more strains [3]. Unlike our optimistic view for virulence in HIV infections, as control efforts succeed in reducing the prevalence of malaria infections, and coincidentally increasing the frequency of single infections, drug-resistant strains may transmit more efficiently (Fig. 1A). In a further contrast with HIV infections, the number of ‘distinct’ competitors seems crucial for understanding disease severity in this system. In experimental infections, increasing the number of strains elevates the total parasite burden and exacerbates infection-induced anemia [3, and references therein]. Thus, projecting how control efforts will alter virulence in malaria requires understanding the distribution of strains within hosts, resolution that is not required to predict consequences for the spread of drug resistance. Altogether, evidence suggests that comparing single versus multiple infections is sometimes useful (and, indeed, sufficient)
for predicting health outcomes, but the patterns vary consider-
ably across and even within systems.

Do mechanisms underlying competition need to be
understood to make public health gains?

There is increasing interest in harnessing competition to
achieve particular public health outcomes, for example, delaying
or suppressing the emergence of drug resistance [4]. But com-
petition can take many forms, depending on the mechanistic
details of within-host interactions (e.g. direct resource competi-
tion, immune-mediated apparent competition and interference
competition; [1]) and it is not always clear what form (or forms)
are at play in any given system. Does this lack of mechanistic
understanding preclude exploiting within-host competition for
public health gain?

We suggest that the answer to this question is not necessar-
ily. If interventions can be found that mimic the beneficial
effects of competition and improve clinical outcomes, then—
whatever the underlying mechanisms—those interventions
should probably be deployed. However, an understanding of
mechanisms is likely to prove crucial for predicting the longer-
term consequences of those interventions, and in particular,
the parasite evolutionary responses that may erode or bolster
their direct effects. For example, intensifying resource competi-
tion may select for faster proliferating strains that cause more
harm to hosts. In contrast, enhancing interference competition
could select for slower-proliferating strains that produce more
energetically expensive compounds (e.g. bacteriocins) and
cause less harm to hosts [5]. Thus, the same control efforts that
reduce transmission, and the prevalence of multiple infections,
could have divergent evolutionary consequences for virulence
(Fig. 1B), which can only be predicted if mechanisms are
known. For public health, an important open challenge is identi-
fying the mechanistic basis for competitive interactions within
human hosts, as nearly all current data come from *in vitro*
experiments and model disease systems.

The mechanisms underlying competition also determine any
priority effects, where an initial infection alters the within-host
environment—positively or negatively—for subsequently colo-
nizing strains. An initial infection could make a host more vul-
nerable to later colonizing strains, for example by exhausting
host defenses. Alternately, a combination of resource and
immune-mediated apparent competition could reduce the suc-
cess of parasites that subsequently colonize a host. These prior-
ity effects can influence individual health outcomes as well as
evolutionary patterns, like the emergence of drug resistance. In
projecting the spread of drug resistance, it is useful to recog-
nize that drug-resistant strains are likely to appear at low dens-
ity in hosts already occupied by drug-sensitive strains. Applied
to malaria infections, models show that this numerical

disadvantage makes resistant strains more extinction-prone, a
disadvantage compounded by priority effects arising from
immune-mediated competition [6]. Although the numerical dis-
advantages (i.e. greater probability of extinction) are likely ubi-
quitous across systems when resistance arises de novo within a
host, those risks could be exaggerated or minimized depending
on the ecology governing how strains interact. In sum, the bulk
of evidence suggests that mechanistic understanding of compe-
tition is key to evaluating the long-term efficacy of public health
interventions.

How do within-host interactions scale up to influence
epidemiology?

Within-host interactions have the potential to alter critical epi-
demiological rates such as transmission, virulence and recov-
ery. But are there predictable patterns across, or even within,
host-parasite systems? Recovery rates are challenging to quan-
tify, especially for human infections where the timing of inocula-
tion is often unknown. However, times series of human malaria
infections—crucially, with known inoculation dates—provide a
basis for understanding the drivers of infection length. Childs
and Buckee [7] used those data to model transmission and in-
fection duration in single versus coinfections, finding that the
addition of a second strain can truncate or extend infection,
and may (or may not) make the host more infectious.
Outcomes in these cases depended on the timing of the infec-
tion and details about host immunity and prior exposure, bely-
ing the existence of simple rules for adjusting epidemiological
rates in the context of multiple infections.

A related challenge is that when diverse parasites generate
similar symptoms, it is difficult to even identify multiple infec-
tions, much less estimate their influence on epidemiological
processes. For example, Zika, dengue and chikungunya viruses
frequently co-circulate and present with similar, dengue-like
symptoms, so that infections composed of more than one of
these viruses may go undetected [reviewed in 8]. Passive case
detection relies on patients presenting at clinics and so cannot
reveal whether infections with multiple viruses are more (or
less) severe; that determination requires data that are currently
lacking on the prevalence of single versus multiple infections
among hosts with subclinical infections, i.e. cases where acute
symptoms are mild or absent [8]. Importantly, simultaneous in-
fection with two of these viruses can actually inhibit the devel-
opment of protective immunity against one of them [8],
rendering individuals potentially more susceptible to that virus
than if they had been exposed sequentially. Thus, predicting the
epidemiological consequences of co-circulation, including the
impact of coinfection on the progression of future epidemics,
depends critically on timing (akin to the priority effects noted
earlier). Common principles may become apparent as more
<figure><img src="image_2.png" /><figcaption>**Figure 1.** Empirical insights on epidemiological feedbacks driving parasite evolution. (A) Reducing transmission could lower multiple infection prevalence and thereby facilitate faster spread of resistance. Competition suppresses the transmission of drug resistance in rodent malaria parasites [3]. (B) Reducing transmission decreases multiple infection prevalence, increasing (i) or decreasing (ii) virulence, depending on the mechanism of competition. (i) Entomopathogenic nematodes release mutualistic bacteria (colored shapes) into host caterpillars after invading. Bacteria and nematodes grow separately until host death, when nematodes reacquire bacteria and exit the cadaver. Bacteria strains interfere with one another—delaying</figcaption></figure>

data, generated through active surveillance, is brought to bear on the question of individual health outcomes of coinfected hosts and broader patterns of circulation. For now, existing data show that multiple infections alter key epidemiological parameters in hugely varied ways, defying straightforward generalizations.

## What is the evidence for indirect evolutionary effects of disease control?

Direct evolutionary responses to interventions, like drug resistance, are a predictable consequence of disease control, but control efforts may also exert unexpected, indirect evolutionary pressures on parasite traits. Feedbacks between competition and control represent a potentially crucial source of indirect selection; e.g. if control efforts reduce the frequency of coinfections (e.g. Fig. 1A and B), parasites may evolve in response to that altered competitive landscape. Devising experiments to detect those outcomes requires clear theoretical predictions about how parasite traits will evolve, but the complexity and idiosyncratic nature of competitive interactions (as outlined above) makes constructing those models a substantial, ongoing challenge (for an example of such a model, see [6]). Nonetheless, existing theoretical and empirical studies show intriguing possibilities for other unexpected, indirect evolutionary consequences.

Even in the absence of competition, theory demonstrates the potential for indirect selection via epidemiological feedbacks. For example, epidemic expansion can select for earlier transmission from malaria infections and more aggressive parasite proliferation, to the detriment of host health [9]. That outcome arises from the fact that while an epidemic is expanding, most infections are in the early stages. Parasites therefore pay little cost for traits, like aggressive proliferation, that jeopardize transmission late in the course of infection [9 and references therein]. By limiting the frequency of infections in early stages, theory predicts that slowing epidemic expansion should yield public health benefits beyond reducing prevalence [9]. For malaria, those benefits could be even greater if control efforts also reduce the frequency of coinfections thought to be costly to health [Fig. 1B(ii)]. Although multiple sources of indirect selection may align to drive parasite evolution in ways beneficial to

host death—by producing bacteriocins [reviewed in 1]. (ii) Coinfecting rodent malaria strains (colored shapes) compete for resources, and diverse infections generate greater anemia [3]. (C) Isolating superspreaders generates selection on per-parasite transmissibility. Artificial selection for production of many nematodes (black squiggles) emerging from caterpillar cadavers (i.e. superspreading) resulted in smaller nematodes—expected to reduce transmissibility—compared with selection for the production of few nematodes [10]
public health, other systems may yield more nuanced out-
comes; such synergism would not be expected when a reduc-
tion in competition hastens host mortality [Fig. 1B(i)]. With
further theory across a range of systems, generalities may
emerge about when indirect selection is likely to reinforce the
public health gains of limiting transmission.

Detecting such long-term, evolutionary consequences of con-
trol—especially indirect effects—is a further challenge, but lab
studies can nonetheless highlight possible outcomes. For ex-
ample, artificial evolution experiments suggest that targeting
control to superspreaders could have unintended consequences
for the evolution of parasite traits underlying transmission.
Bashey and Lively [10] artificially selected for entomopathogenic
nematodes that produced many or few juveniles after invading
their insect hosts. The nematodes selected to produce few juve-
niles also produced larger ones, expected to be more transmis-
sible to new hosts [10]. Extrapolating from these experimental
results, public health interventions that isolate superspreaders
(e.g. quarantining hosts with high rates of shedding) could se-
lect for increased per-propagule infectivity to compensate for
lower shedding rates among the hosts who can still contribute
to transmission (Fig. 1C). Depending on the particular tradeoffs
at play, such parasite evolution could undercut the long-term
benefits of control.

Whether the effect is to enhance or undercut control efforts,
theory and experimental data demonstrate convincingly that
interventions can impose indirect selection on clinically and epi-
demiologically relevant parasite traits. A major outstanding
challenge is translating this work to human parasites and deter-
mining how to detect these subtler evolutionary consequences
of intervention efforts. Only then can we evaluate if they repre-
sent an important consideration in designing intervention
strategies.

CONCLUSIONS

Within-host competition and public health interventions are
both highly potent sources of selection on parasite traits.
Failing to account for the potential interactions between these
sources of selection can result in over- (or, sometimes, under-)
estimating the long-term efficacy of interventions, including re-
silience in the face of parasite evolution. Comparing across
case studies, we find a lack of consistent patterns in how com-
petition alters epidemiology and vice versa, precluding robust,
general predictions about parasite evolution. Indeed, current
evidence (Fig. 1) suggests a range of potential evolutionary out-
comes following control, both positive and negative from a pub-
lic health perspective. A critical challenge lies in translating
theory and experiments in model systems to expectations for
evolutionary responses in parasites of global health concern,

especially those subject to large-scale control efforts. Guiding
principles may emerge from a better understanding of the
mechanisms that govern within-host interactions in these
cases, narrowing the range of possible evolutionary outcomes.
Tracking the knock-on consequences of interventions requires
long-term data, ideally obtained through active surveillance
efforts that can detect how coinfections contribute to transmis-
sion and health burdens. In the meantime, existing data show
that the epidemiological feedbacks of altering competition can
drive parasite evolution in subtle, but important ways that de-
serve more attention.

FUNDING

The workshop was organized by Megan A. Greischar and Nicole Mideo, and funded by the National Science Foundation's Infectious Disease Evolution across Scales (IDEAS) Research Coordination Network, hosted at Princeton University.

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