1.
Introduction
Pathogen spillover is the process by which a pathogen is transmitted from a reservoir
host species to a recipient host species [1,2]. The term is sometimes used more broadly,
particularly in public discourse, blending in elements of onward transmission in the
novel host species or even pathogen adaptation to the novel host [3,4]. This theme
issue focuses on pathogen spillover sensu stricto, except where explicitly noted.
Many of the examples considered pertain to zoonotic spillover (i.e. from wildlife
or domestic animals to humans), given recent epidemics (e.g. Ebola virus [5]) and
pandemics (e.g. H1N1 influenza virus [6]); however, we emphasize the general methods
and mechanisms involved in understanding spillover between any two species, such as
those that threaten wildlife conservation (e.g. Mycoplasma ovipneumoniae from domestic
sheep to bighorn sheep [7]) and the agricultural sector (e.g. Brucella abortus from
elk to cattle [8]).
Spillover requires the spatial and temporal alignment of several hierarchical factors
that must occur for a pathogen to be transmitted from a reservoir or source host to
a recipient host of a different species [9]. These factors include reservoir host
distribution and abundance, pathogen prevalence and shedding from reservoir hosts;
pathogen survival in the environment or arthropod vector; recipient host contact with
the infectious agent, reservoir host or arthropod vector; and susceptibility of the
recipient host. Following spillover, another suite of factors determines whether a
pathogen is transmitted within the recipient host population (e.g. [10]). Research
on pathogen spillover is often focused on a single component of this process through
the lens of a particular discipline. For example, the distribution of reservoir hosts
is often studied through ecology, contact between reservoirs and humans is often studied
via epidemiology or anthropology, and the pathogenesis of zoonoses in humans is often
studied with medical microbiology and immunology. While each factor must be studied
and quantified, spillover is the emergent property of these collective processes.
Studying each factor in isolation fails to account for the hierarchical and often
nonlinear dynamics of the spillover system [9]. Pathogen surveillance, epidemic preparedness
and management interventions would all benefit from integrative approaches that consider
multiple components of pathogen spillover [1].
This theme issue stemmed from a 2018 workshop on cross-species transmission of pathogens,
where participants from interlinked fields including ecology, mathematical modelling,
epidemiology, virology and immunology discussed how to better understand and predict
pathogen spillover. Here, we bring together a diverse set of perspectives—including
empirical research, theory and synthetic reviews—to highlight cutting-edge research
and to provide a roadmap for quantifying and integrating host–pathogen dynamics at
each step in the spillover process. Manuscripts are organized around three approaches.
The first set of manuscripts focuses on integrating data streams to understand spillover
dynamics and predict risk. The second set of manuscripts focuses on in-depth analysis
of each of the factors affecting cross-species transmission: infection dynamics in
reservoir hosts, pathogen survival in the environment, recipient host exposure, dose–response
relationships and establishment of infection in recipient hosts. The final set of
manuscripts focuses on applied perspectives, with an emphasis on surveillance and
interventions. Here, we summarize these contributions to highlight key insights, methodologies
and future directions to improve our understanding of pathogen spillover.
2.
Integrating data streams to understand spillover dynamics
Because spillover is the outcome of multiple ecological, epidemiological and immunological
factors aligning in space and time [9,10], predictive frameworks aim to integrate
data pertinent to these factors to quantify the relative importance of these processes
and to estimate risk. Cross et al. [12] review approaches for estimating spatio-temporal
variation in spillover risk, focusing on the wildlife–livestock interface. The authors
highlight the challenges inherent in either correlating observed spillover events
with relevant covariates or integrating data on host density, distribution and pathogen
prevalence using mechanistic models. They highlight that mechanistic approaches may
be especially useful in systems where spillovers are infrequent, rarely observed or
hard to differentiate from within-species transmission; however, linking datasets
on different factors in the spillover pathway requires that such datasets be related
to a common spatial and temporal resolution. The authors use case studies of brucellosis
in the Greater Yellowstone Ecosystem [13] and of avian influenza virus in China and
North America [14] to emphasize potential solutions to these challenges for estimating
spillover risk.
Emphasizing that statistical modelling efforts may struggle to detect nonlinear and
stochastic relationships inherent in pathogen spillover, Childs et al. [15] provide
a strong test of theory governing how hierarchical barriers control cross-species
transmission [9]. The authors focus their case study on yellow fever, a mosquito-borne
viral disease of historical importance in South America that persists in the region
largely in sylvatic cycles that occasionally spill over to infect humans [16,17].
Specifically, they use mechanistic models that incorporate spatial ecological and
immunological data from Brazil across 16 years to predict yellow fever spillover in
humans. The authors show that a mechanistic model of spillover risk, based on the
ecology of mosquito vectors and non-human primate reservoirs, best predicts spillover
events compared with models that also include human population size and immunity.
This result arises because spillover occurs even in areas with low human population
density and high vaccination coverage (e.g. parts of the Amazon), so population density
and vaccination coverage tend to inflate the predicted risk in locations with low
ecological suitability. This integrated approach also highlights a key research gap—cyclical
dynamics of susceptible primate populations—that could further improve prediction.
This work illustrates that mechanistically modelling the interactions among the environment,
viruses, vectors, non-human primates and humans can predict rare and seemingly stochastic
spillover events with high accuracy.
Washburne et al. [17] study the general statistical problems that can arise when aiming
to forecast spillover risk. The authors highlight that any such statistical efforts
will compile a dataset of explanatory variables expected to relate to pre-spillover
processes (e.g., infection prevalence in reservoirs, human vaccination coverage) that
are aligned with one of two response variables: the presence and absence of spillover
or the number of spillover events at some spatial and temporal resolution (e.g., spatio-temporal
counts of yellow fever spillovers [15]). The authors show how modelling cross-species
transmission as a percolation process, in which pathogens move from infected reservoirs
to recipient hosts along a graph representing various spillover pathways [18,19],
reveals first principles for how such datasets will behave and how common statistical
tools can produce misleading inferences and poor predictions. For example, percolation
theory reveals an inherent nonlinearity in modelling spillover counts, in which statistical
inferences are driven by the dominant reservoir sources of infection and the most
limiting barriers to cross-species transmission; this nonlinearity can mask the influence
of alternative reservoir species or barriers, both of which could be modified through
interventions but whose sensitivity as a management tool will appear reduced under
linear models. Percolation models provide a conceptual framework to connect statistical
and mechanistic models with applications to limit risk by illuminating unexpected
statistical principles governing pathogen spillover and the nonlinear impacts of management
actions.
3.
Individual factors affecting pathogen spillover
The theme issue's second section uses empirical research, theory and synthetic reviews
to understand the processes operating at each stage of pathogen spillover, from infection
dynamics within the reservoir hosts to susceptibility and establishment of infection
in the recipient host. For the former, the distribution and intensity of infection
in reservoir hosts over space and time is the first determinant of spillover risk
[9]. Data on these spatio-temporal dynamics help elucidate how pathogens circulate
in reservoir hosts and when and where to expect pathogen excretion to be greatest
[21]. However, such field data can be expensive and difficult to collect, and researchers
inevitably must tradeoff between the extent and intensity of spatial versus temporal
sampling. Sampling is thus often opportunistic and fails to adequately describe spillover.
Plowright et al. [22] review factors that influence spatial and temporal variation
in infection in reservoirs and describe sampling designs that can increase the quality
and quantity of this information. Although the standard prescription from sampling
theory is to sample randomly in space and time [23], probabilistic sampling designs
are rare in the study of wildlife disease, given logistical challenges and non-random
distributions of hosts. The authors highlight how stratified random sampling designs
or adaptive sampling designs can help capture spatio-temporal pulses of infection
when researchers have little a priori data on concentrations of infection or spillover
events in space and time. These sampling designs can be integrated into modelling
approaches and used to better quantify pathogen shedding from reservoirs. Accordingly,
Glennon et al. [24] present a case study for how to use mechanistic models to differentiate
among transmission processes for henipaviruses in straw-coloured fruit bats (Eidolon
helvum). Using this virulent zoonosis as a case study, the authors generalize standard
frameworks common in epidemiological modelling [25]. Given that henipavirus infection
dynamics in bats are poorly understood, the authors study all possible transitions
among infection states in bats to produce 46 potential models. Using likelihood-based
methods, they fit these models to longitudinal data from captive bats to show strong
support for reinfection after virus clearance and cycles of recurrent latent infection:
key areas for future empirical work. This inclusive approach to confronting epidemiological
models with longitudinal data in poorly understood reservoir host systems holds promise
for elucidating spatio-temporal risk of pathogen spillover.
Following pathogen shedding from reservoir hosts, spillover risk is influenced by
the duration of pathogen survival and possible reproduction outside the host in the
environment [9]. For pathogens such as avian influenza virus, persistence in the environment
(e.g. ponds) can also facilitate viral reassortment when strains co-occur, promoting
co-infections during environmental exposure [26,27]. Pepin et al. [28] review and
discuss how genomics, experimental ecology and epidemiological modelling can be leveraged
to understand viral reassortment in environmental reservoirs. Although no gold standard
for capturing, isolating and identifying avian influenza virus diversity from the
environment exists, environmental metagenomics and field-based viral diagnostics (e.g.
field-based nucleic acid extraction, PCR and sequencing) hold promise for characterizing
this context of viral reassortment [29,30]. The authors note how standardizing such
field protocols and coupling these data streams with quantitative disease models and
natural transmission studies should dramatically improve our understanding of viral
co-occurrence and reassortment and thus, this additional process in the pathway to
spillover.
Exposure of recipient hosts to pathogens (e.g. those persisting in the environment)
can take a variety of forms; however, in a more general sense, exposure often occurs
at elevated rates near boundaries between ecosystems [31]. Borremans et al. [32] review
how ecosystem boundaries can promote spillover by applying ecological theory to understand
landscape permeability across ecosystems. The authors highlight that the traits of
hosts and pathogens are critical for determining effects of ecosystem boundaries on
cross-species transmission. Properties of ecosystem boundaries can also promote or
inhibit exposure; for example, edge effects can affect species composition, diversity
and population size between ecosystems, as can features of landscape configuration
such as patch size and perimeter-to-area ratio [33]. By considering the analogy between
parasite flow and resource flow and by applying concepts from movement ecology, Borremans
et al. [32] connect contact rates and spillover risk across ecosystem boundaries to
generalize between pathogens and integrate into broader ecological theory.
Following the complex interactions between reservoir hosts, vectors, pathogens, the
environment and recipient hosts, a crucial juncture in any potential spillover event
is the point when a recipient host is challenged with a given dose of pathogen (through
a particular route and sometimes over a particular duration) and a successful infection
does or does not ensue [9]. Lunn et al. [34] describe how the dose–response relationship,
which quantifies the probability of successful infection in the recipient host as
a function of challenge dose, can act as a filter on the aforementioned upstream dynamics
to shape pathogen spillover risk. The authors integrate recent developments in the
dose–response literature, as well as re-analysing data from animal challenge experiments
with Nipah virus and Middle East respiratory syndrome coronavirus [35,36], to highlight
challenges and opportunities arising at the intersection of infectious disease ecology,
microbial risk assessment and virology. Lunn et al. [34] call for closer interactions
between these fields and for a new generation of pathogen transmission models that
link dose–response data to epidemiological dynamics. Gostic et al. [37] next provide
an example of the epidemiological insights such an approach can yield. They present
a modelling analysis of dose–response experiments for Leptospira interrogans, a globally
important bacterial zoonosis for which environmental exposure to soil or water contaminated
by urine of infected reservoir hosts is the primary transmission route [38]. By conducting
well-designed challenge experiments across a range of exposure routes, and then developing
a mechanistic model to identify and quantify the key barriers to infection, Gostic
et al. [37] show that intact skin is the crucial defence against leptospiral infection
and that skin abrasions or wounds can increase recipient host infection risk by at
least a million-fold. This close integration of experimental and modelling approaches
isolates a potent and well-defined risk factor for infection with Leptospira, opening
the door to targeted interventions to reduce spillover risk.
Once a pathogen has crossed these within-host barriers to replicate and disseminate
in the recipient host, the outcome of infection may range from subclinical illness
to death and from dead-end spillover to sustained onward transmission [9]. Bonneaud
et al. [39] focus on the conditions favouring pathogen emergence, from the initial
jump into the recipient host to adaption in the novel host environment [40]. The authors
highlight that our current understanding of host shifts stems primarily from viral
infections, limiting generalizations to other pathogen taxa, given substantial differences
in ecology and life history [41]. They propose several non-mutually exclusive hypotheses
to explain why novel bacterial pathogens may be less likely to specialize on their
novel hosts and then test these with a mathematical model. The authors demonstrate
that high levels of phenotypic plasticity, low rates of evolution and the ability
to recombine should reduce propensity to specialize, suggesting that novel bacterial
infections may be more likely to result in transient spillovers or increased host
ranges than in host shifts.
Wasik et al. [42] in turn describe the within-host barriers that pathogens, and viruses
in particular, must overcome to replicate and spread in new host populations to cause
onward transmission. They present three well-documented examples of viruses that have
crossed these barriers to cause epidemics or pandemics in the new host species: influenza
A viruses [43], human immunodeficiency virus [44] and canine parvovirus [45]. The
authors emphasize the role of integrated models that consider all the steps required
to go from exposure to spillover to epidemic or pandemic. Guth et al. [46] expand
upon these ideas through a comparative study of host and viral traits that predict
virulence and the capacity for onward transmission in recipient hosts (i.e. humans).
By expanding a previous global dataset of viral zoonoses [47], the authors show that
increasing reservoir host phylogenetic distance from humans positively correlates
with human mortality but negatively correlates with human-to-human transmissibility,
suggesting that the virulence induced by reservoirs at high phylogenetic distance
may limit viral capacity for onward transmission [48]. In particular, distantly related
reservoirs, such as bats, harbour highly virulent zoonotic viruses with a lower capacity
for onward transmission in recipient human hosts, building upon prior work describing
bats as special reservoirs [49].
4.
Applications for management of spillover
The theme issue's final section focuses on applied perspectives to detect early spillover
events (i.e. surveillance) and the role of interventions focused upstream in the spillover
pathway. In particular, early detection is critical for minimizing the spread of zoonotic
pathogens following an initial spillover event [50]. A first series of manuscripts
emphasize different approaches to the surveillance of zoonoses. Schmidt et al. [51]
use machine learning tools (e.g. boosted regression trees [52]) to predict which mammal
species are more likely to play roles in Ebola virus spillover events. The authors
show that large-bodied, frugivorous mammals with slow life histories are likely host
species, implicating some insectivorous bats, Old World monkeys and forest antelopes
as possible Ebola virus reservoirs. Predictions such as these can help prioritize
future wildlife surveillance efforts (e.g. [53]). Kuisma et al. [54] in turn describe
a community-based surveillance effort focused on wildlife mortality reporting and
oriented to early detection of Ebola virus disease outbreaks. Spanning over a decade
and covering 50 000 km2 of challenging terrain in the Congo basin, this programme
has reached hundreds of villages and thousands of hunters and forest gatherers. The
programme has educated community members in wildlife carcass reporting and behavioral
risk reduction as well as built capacity for safe carcass sampling by trained local
responders. This region was not confronted with an Ebola virus outbreak during the
period described here, and all reported carcasses tested negative. Nevertheless, given
the well-recognized fact that early intervention can avert massive human and economic
costs of widespread epidemics, the low-cost and scalable surveillance programme described
by the authors could provide key early detection capability more generally.
Two other contributions focus on zoonotic pathogen surveillance efforts in domestic
animals and human populations. Mwangi et al. [55] present a real-time surveillance
system that leverages the existing mobile phone network and shows immense potential
to improve adaptive management of spillover. This surveillance system has been implemented
in 1500 households across rural Kenya, where participants are asked to report symptom
syndromes in their livestock. Zoonotic diseases such as Rift Valley fever present
with severe clinical signs in domestic animal populations, but lack of active surveillance
can miss these sentinels [56]. The authors demonstrate that illnesses were more likely
to be reported on mobile phones compared with standard routine household animal surveys.
They also show that more severe symptoms are likely to be reported, highlighting the
utility of this surveillance method for diseases such as Rift Valley fever. Das et
al. [57] similarly describe the implementation of a surveillance system in hospitals
in Bangladesh that screens symptomatic patients for potential zoonoses. Most patients
did not have a laboratory diagnosis for their illness, indicating that unidentified
pathogens are likely spilling over in human populations. Broad-scale, sustainable
human surveillance programmes such as outlined by the authors can play a critical
role in early detection of zoonotic spillovers.
Following these approaches to surveillance, interventions can accordingly focus upstream
or downstream in the pathway to spillover, given available data and resources, to
limit cross-species transmission. At the wildlife–livestock interface, managing pathogen
spillover is a main goal for animal husbandry, conservation and food security [58].
Yet, managers are often forced to make control decisions on the basis of limited evidence
about intervention efficacy. Manlove et al. [59] develop a spatially explicit, stochastic
model of pathogen transmission within and between wildlife reservoirs and livestock
recipient hosts to improve evidence-based decision-making. By varying host movement
patterns and epidemic growth rates, the authors show that biosecurity, containment
and retroactive vaccination of the reservoir are the most effective for limiting the
spatial spread and magnitude of spillover risk for fast-moving epidemics in mobile
hosts. By contrast, prophylactic vaccination and depopulation of the reservoir host
were more successful for fast-moving epidemics with low rates of host movement. This
framework provides general intuition for how to manage different pathogens at the
wildlife–livestock interface, and a flexible platform for more rigorously investigating
disease control strategies.
Ultimately, one of the primary goals of research focused on pathogen spillover is
to design interventions that can reduce or eliminate disease burden in recipient hosts.
Sokolow et al. [60] explore how ecological interventions, which target the ecological
context in which cross-species transmission occurs, can complement more traditional
biomedical and veterinary interventions (e.g. vaccination, culling). The authors provide
case studies to illustrate the potential for ecological interventions that target
the reservoir host (sometimes indirectly, such as through the restoration of natural
enemy populations [61]), pathogen survival in the environment, contact between reservoir
and recipient hosts, or other aspects of risk in the recipient species. The authors
also present a simple mechanistic model, parameterized for two example systems, that
shows how nonlinear effects can produce counterintuitive results when comparing potential
intervention strategies and highlights the importance of a detailed understanding
of underlying ecological dynamics when designing and assessing interventions. Lastly,
the authors draw attention to the importance of social, economic and political considerations
to intervention success, as these can derail even the most efficient or cost-effective
intervention. In particular, aligning the benefits of an intervention with the costs
incurred is crucial to motivate ecological interventions and may require working across
sectors for successful implementation.
5.
Future directions and conclusion
Pathogen spillover is the result of a complex series of events that result in the
successful establishment of infection in a recipient host [9]. As highlighted in the
final paper of this theme issue, developing actionable forecasts of risk is further
complicated by the various phylogenetic, spatial and temporal scales over which we
study and predict spillover [62]. The authors here contextualize a diverse range of
approaches to pathogen spillover within these scales to illustrate critical areas
of pragmatic overlap. By focusing on an ecological perspective, the authors outline
a research pipeline that connects pathogen discovery and macroecological analyses
with spatio-temporal surveillance in reservoir and recipient hosts. Through several
case studies (e.g. Lyme disease [63], Hendra virus [64], Plasmodium knowlesi [65]),
the authors further demonstrate how ecologically focused research has facilitated
predicting spillover of particular pathogens in space and time and facilitated design
of intervention strategies. This synthesis shows how greater integration of macroecology,
pathogen discovery and surveillance could ultimately generate more actionable predictions
and interventions to limit spillover risks.
Recent epidemics, pandemics and disease emergence events all underscore the need to
improve approaches to predict and prevent pathogen spillover. This theme issue highlights
a range of methods and their commonalities through diverse host–pathogen systems for
which researchers are assessing factors driving spillover risk across varying phylogenetic,
spatial and temporal scales. Contributing manuscripts further emphasize how developing
a mechanistic understanding of the hierarchical factors affecting spillover can facilitate
quantifying the drivers of cross-species transmission, deriving generalizable theory
and making robust predictions, even for seemingly rare and idiosyncratic spillover
events. Importantly, such insights can improve our ability to deploy surveillance
efforts, design interventions at early stages of the pathway to spillover and manage
disease cases in recipient hosts, thereby limiting or preventing further outbreaks.
Continued study of pathogen spillover as a repeated and hierarchical phenomenon will
only improve our ability to predict, prevent and manage cross-species transmission
risks.