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Recent evidence suggests that most parasites can
infect multiple host species and that these are primarily
responsible for emerging infectious disease outbreaks in
humans and wildlife. However, the ecologic and evolution-
ary factors that constrain or facilitate such emergences are
poorly understood. We propose a conceptual framework
based on the pathogen’s between- and within-species
transmission rates to describe possible configurations of a
multihost-pathogen community that may lead to disease
emergence. We establish 3 dynamic thresholds separating
4 classes of disease outcomes, spillover, apparent multi-
host, true multihost, and potential emerging infectious dis-
ease; describe possible disease emergence scenarios;
outline the population dynamics of each case; and clarify
existing terminology. We highlight the utility of this frame-
work with examples of disease threats in human and
wildlife populations, showing how it allows us to understand
which ecologic factors affect disease emergence and pre-
dict the impact of host shifts in a range of disease systems.
Models of host-pathogen dynamics have typically
assumed a single-host population infected by a sin-
gle pathogen. However, most pathogens can infect several
host species; >60% of human pathogens, >68% of wild
primate parasites, and >90% of domesticated animal
pathogens infect multiple host species (1–3). An interest in
multihost pathogens is particularly timely, given that many
of the most threatening current pathogens (e.g., HIV, West
Nile virus, influenza virus, Ebola virus) are believed to
have crossed species barriers to infect humans, domesticat-
ed animals, or wildlife populations (1,3–8). However, we
do not know the host and pathogen characteristics that
determine such host shifts and the likely characteristics of
future emerging infectious diseases. To address this issue,
2 theoretical approaches have been adopted. The first,
using dynamic models, focuses on the host’s perspective
and ascertains how a shared pathogen affects the dynamics
of 2 host populations (9–12). The second approach takes
the pathogen’s point of view and considers how combined
host densities affect pathogen persistence within the com-
munity (13–15). However, as the number of studies grows,
so does the terminology. Terms such as multihost
pathogens, dead-end hosts, reservoir hosts, host shifts, and
spillovers are frequently used, but often different phrases
are used to describe the same phenomenon, and possibly
more concerning, the same terminology may be used to
describe strictly different phenomena.
This lack of consolidation makes it unclear how these
different approaches relate in terms of understanding the
mechanisms driving disease emergence. A need exists for
a single, comprehensive framework that characterizes dis-
ease outcomes based on biologically meaningful process-
es. Recently, attempts have been made to reconcile these
concepts, mainly by highlighting the role of reservoir hosts
(13,16). Haydon et al. (13) proposed a conceptual model
that assumed a target host species was exposed to a
pathogen endemic in a second host species (or species
complex). The outcome of infection then depended on the
sizes of the populations and whether they were able to
maintain the pathogen alone. This approach expanded the
naive view that reservoirs are nonpathogenic, single-
species populations and encompassed the complexity of
pathogen-host communities observed in nature. However,
focusing just on host density ignores many key features of
emerging diseases. The likelihood of disease emergence
will depend on highly dynamic processes determined by
both between- and within-species transmission rates.
Therefore, ecologic forces acting on both hosts and
pathogens will influence the contact structure of the com-
munity and affect the likelihood and persistence of an
emerging infectious disease in a new host.
Community Epidemiology
Framework for Classifying
Disease Threats
Andy Fenton* and Amy B. Pedersen†
Emerging Infectious Diseases • www.cdc.gov/eid • Vol. 11, No. 12, December 2005 1815
*Institute of Zoology, London, United Kingdom; and †University of
Virginia, Charlottesville, Virginia, USA
We propose a conceptual framework to describe the
configurations of a host-pathogen community that may lead
to disease emergence in a target host. We develop our
framework from a simple 2-host 1-pathogen model and
establish thresholds for pathogen and host persistence
based on the between- and within-species net transmission
rates. We then consider what ecologic factors determine the
location of various host-pathogen systems within the
framework. Finally, we use a stochastic model to consider
what characteristics of the hosts and pathogen define the
dynamics and likelihood of an emerging infectious disease.
Conceptual Framework of an Emerging
Infectious Disease
We start by considering the assembly of a 2-host com-
munity infected by a single pathogen (15,17,18) where the
pathogen is endemic within host population H1such that
individuals of H1are either susceptible (S1) or infected (I1).
We then assume a second target host population (H2) enters
the community and can become infected by the pathogen
(Figure 1A). Since the pathogen is well established in H1,
we assume S1and I1are unchanged by H2; thus, our model
most closely resembles the asymmetric model of Dobson
(15). In the terminology of Haydon et al. (12), H1is a
maintenance host species (or species complex) with the
potential to be a disease reservoir for H2. H2may or may
not be a maintenance host (see below). The model is
(model 1)
where ris the reproductive rate, Kthe carrying capacity,
and dthe death rate of the infected hosts. The composite
functions f22 and f12 describe the net within-species (H2 to
H2) and between-species (H1 to H2) transmission rates,
respectively. We assume density-dependent transmission
and so these functions have the form fij = βij IiS2, where βij
is the per capita transmission rate from species ito species
j. Therefore, for example, the net rate of transmission from
H1to H2(f12) depends on the size of the susceptible target
population (S2), the size of the reservoir (I1), and the level
of exposure and susceptibility of H2(β12).
The target host population H2has 4 possible outcomes:
1) uninfected, 2) infected but unable to sustain the
pathogen, 3) infected and able to sustain the pathogen, or
4) infected and driven to extinction by the pathogen
(Figure 1). These 4 outcomes are separated by 3 thresholds
(Figure 1C): i) invasion threshold, ii) persistence thresh-
old, and iii) host extinction threshold. The first 2 thresh-
olds are analogous to established density-based thresholds
in epidemiology; the first allows ecologic invasion of a
pathogen, which subsequently dies out, and the second
allows persistence of the pathogen (19). Here we combine
these density effects with the per capita rates of infection
to express these thresholds in terms of the magnitude of the
net between- and within-species transmission rates (f12 and
f22, respectively).
Community-Epidemiology Continuum
Infection of H2by H1and transmission within H2are 2
separate processes determined by f12 and f22. Different
combinations of these parameters lead to the different out-
comes described above, and all possible scenarios can be
placed within a 2-dimensional continuum (Figure 2), with
f12 on one axis (i.e., can H2get infected from H1?) and f22
on the other (i.e., can H2sustain infection?). We can then
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1816 Emerging Infectious Diseases • www.cdc.gov/eid • Vol. 11, No. 12, December 2005
21222
2
1222
2
2
2
I
I
)()
K
H
1(H
S
dff
dt
d
ffr
dt
d
−+=
+−−=
Figure 1. Emerging infectious disease framework. A) Schematic
diagram of the multihost-pathogen community. B) Possible out-
comes for a novel host, H2, after an initial infection by a pathogen
endemic in an existing host, H1, where (1) the pathogen is unable
to invade H2, (2) the pathogen invades but cannot be sustained
within H2, (3) the pathogen invades and persists in H2, and (4) the
pathogen invades and drives H2to extinction. C) Three thresholds
separating the 4 possible outcomes: (i) the invasion threshold, (ii)
the persistence threshold, and (iii) the host extinction threshold.
divide the f12 – f22 parameter space into regions of different
disease outcomes.
Case 1: Spillover
In this case, the within-H2transmission rate is too low
to sustain the pathogen (f22 →0). The between-species
transmission from H1is also low (f12 →0). Thus, although
infections of H2do occasionally occur, they are transient.
This represents the case in which the pathogen is special-
ized to the endemic host and there is either very low expo-
sure to H2(an ecologic constraint, such as parasite
transmission mode) or H2is resistant to infection (a phys-
iologic constraint). We recommend the term spillover to
describe this form of cross-species infection. Previously,
spillover has been used to describe a wide range of
dynamics (20), but we recommend limiting its use to tran-
sient infections in a target host because of transmission
from a reservoir host that is not self-sustaining in the tar-
get population.
The recent outbreak of West Nile encephalitis in the
United States is such a spillover: the virus moved from
bird populations (H1) to infect humans (H2), which are
unable to transmit the pathogen (β22 = 0) (21).
Nevertheless, spillovers still represent a serious health
concern; increases in the reservoir population may lead to
dramatic increases in disease prevalence in the target host.
Case 2: Apparent Multihost Pathogen
In this case, the within-species transmission rate for the
target host is low, but the between-species transmission
rate exceeds the invasion threshold, resulting in persistent
infections in H2. This case represents apparent multihost
dynamics that differ from spillover dynamics in that the
disease is nontransient in H2, but the pathogen is sustained
because of frequent between-species transmission from the
disease-endemic host. Apparent multihost dynamics exist
because the potentially high prevalence in the target host
would give the appearance of a true multihost pathogen,
but the lack of within-species transmission means the dis-
ease cannot be maintained in the absence of H1. We recom-
mend the term reservoir to describe H1in both cases 1 and
2, in which the pathogen is permanently maintained in H1
and without between-species transmission (β12), the dis-
ease would not persist in the target host.
An example of an apparent multihost pathogen is rabies
in side-striped jackals (H2) in Africa. Until a recent analy-
sis (22), rabies was considered sustainable in the jackal
population (H2), but detailed monitoring showed that
rabies is not self-sustaining because of the density of the
low susceptible jackal population (S2), and epidemics are
frequently seeded from the domestic dog reservoir (high
β12).
Case 3: True Multihost Pathogen
In this case, both the within- and between-species trans-
mission rates are high. Thus, since the pathogen can inde-
pendently persist in either host population in the absence
of the other, following Haydon et al (13), both are consid-
ered maintenance hosts. This case represents a true multi-
host pathogen with substantial within- and between-
species transmission. One example is brucellosis infec-
tions around Yellowstone National Park, where the
pathogen can be endemically maintained in cattle, bison,
and elk populations (23).
Case 4: Potential Emerging Infectious Disease
In this case, the within-H2transmission rate is high, but
the between-species transmission rate is very low (f12 →
0). Thus, the pathogen can persist in the target host (H2),
but the net rate of between-species transmission is so low
that H2is rarely exposed to the disease. This case might
occur when a disease is transmitted through close contact
and thus has little chance of transmission between species.
Similarly, the barrier to infection could be an ecologic fac-
tor, such as geographic isolation, which may be overcome
by an anthropogenic change such as the introduction of
exotic or invasive species. Thus, this case represents a
potential emerging infectious disease in which the
pathogen will become self-sustaining in H2once the initial
barrier to infection has been crossed. This case may be the
region of greatest future concern since a single transmis-
sion event can have devastating consequences because of
Framework for Classifying Disease Threats
Emerging Infectious Diseases • www.cdc.gov/eid • Vol. 11, No. 12, December 2005 1817
Figure 2. Community-epidemiology continuum, determined by the
net between-H1 and -H2transmission rate (f12) and the net within-
H2transmission rate (f22). EID, emerging infectious disease.
the high rate of within-species transmission in the target
host.
Recent examples of potential emerging infectious dis-
eases that were realized include the emergence of HIV-1
and HIV-2 in human populations, in which the close-con-
tact nature of the infection process prevented transmission
of simian immunodeficiency virus (SIV) from primates to
humans (6,24). Another example is severe acute respirato-
ry syndrome–associated coronavirus in humans, in which
the primary transmission event is believed to be the result
of close human contact with civet cats in China. Once the
infection was successful, it spread rapidly throughout the
human population by direct contact (25).
Factors Affecting Location of a
Host-Pathogen Community
The location of a host-pathogen system within the con-
tinuum will be determined by characteristics of both host
populations and the pathogen. For instance, the pathogen’s
transmission mode will greatly determine its likelihood of
encountering new hosts (26). Parasites transmitted by
close contact may have limited exposure to multiple
species and thus transmission modes that decouple host-to-
host contact (i.e., waterborne or soilborne transmission)
will increase the opportunity for between-species trans-
mission. Evidence from wild primates and humans shows
that pathogens with direct contact transmission are associ-
ated with high host specificity (1,3). Therefore, host-
pathogen systems should segregate along the f12 axis
according to their transmission mode.
Furthermore, the evolutionary potential of a pathogen
will affect its ability to infect a new host (2,27). Pathogens
in taxa with high mutation rates, antigenic diversity, and
short generation times may rapidly adapt to new hosts
(28,29), and recent evidence suggests that RNA viruses are
the most likely group to emerge in humans (26,30), possi-
bly because of their high mutation rate (31). Thus, host-
parasite systems may segregate along the f22 axis according
to taxonomy. Similarly, the phylogenetic relationship
between the reservoir and target host will have conse-
quences for disease emergence; viruses are less likely to
jump to new hosts as the phylogenetic distance between
hosts increases (32).
However, host-pathogen systems are not static, and a
community may move across the continuum either
because of ecologic or evolutionary shifts of the host or
pathogen (27). In particular, anthropogenic changes, such
as environmental exploitation and the introduction of
domestic animals into previously uninhabited areas, may
increase exposure to the pathogen and drive such transi-
tions. For instance, although transmission of SIV from
chimpanzees to humans may have occurred on a number of
distinct occasions (6), these spillovers remained isolated.
Only through various anthropogenic changes, including
urbanization (increasing S2) and increased global travel
(increasing β22) did the HIV pandemic take off in the 20th
century.
In addition, pathogen evolution may greatly affect the
likelihood of disease emergence by increasing the
pathogen’s basic reproductive ratio (R0) (18,26). For exam-
ple, avian influenza has emerged several times in human
populations since 1997. Typically, limited human-to-
human transmission exists (β22 ≈ 0), so that although the
avian reservoir (I1) and susceptible human populations (S2)
are high, outbreaks are rare and isolated (i.e., occupying
region 1 of the continuum). Only through recombination
between strains and acquisition of human-specific respira-
tory epithelium receptors (thereby increasing β22) could
the virus evolve sufficient transmissibility to be sustained
in the human population, which poses the greatest risk for
pandemics (33). These genetic changes could shift avian
flu from being a spillover to becoming a true multihost
parasite, which would have serious implications for human
health.
Stochastic Dynamics and Consequences for
Vulnerable Host Populations
Theoretical and empiric evidence suggest that
pathogens harbored by reservoir host populations are of
particular concern because they can drive target hosts to
extinction (34). Therefore, we must investigate population
dynamic properties of different regions of the continuum
and regions that pose the greatest risk for a target host. In a
deterministic model, the invasion and persistence thresh-
olds are the same and are determined by the pathogen’s
basic reproductive ratio (R0); if R0>1, an initial infection
can both become established and persist. As shown by
Dobson (15), R0for a pathogen in an asymmetric host com-
munity (with no back-transmission from the target host to
the reservoir) is dominated by the largest within-species
transmission term, which implies that infection dynamics in
the 2 host populations are largely independent; once
between-species transmission has occurred, infection in H2
is driven solely by within-H2transmission. However, in the
stochastic reality of the natural world, an established infec-
tion may fade out, and reinfection from H1could occur in
the future (19). Therefore, we developed a stochastic ana-
log of the above deterministic model to explore dynamics
of the community-epidemiology continuum. The model
was a discrete-time Monte Carlo simulation model, in
which each event in model 1 (births, deaths, between- and
within-species transmission) occurred probabilistically, and
the next event was chosen at random based on those prob-
abilities. The model was run 100 times for different combi-
nations of within- and between-species transmission rates,
and the infection status of the target host (H2) was measured
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1818 Emerging Infectious Diseases • www.cdc.gov/eid • Vol. 11, No. 12, December 2005
as the mean prevalence over time, the proportion of time
the pathogen was absent from H2(the proportion of time
the pathogen faded out), and the proportion of runs in
which the pathogen drove the host to extinction. This sto-
chastic model is appropriate for exploring the dynamics of
emerging infectious diseases not captured by continuous-
time deterministic models, in particular when exposure of a
target host to a pathogen from a reservoir is likely to occur
at discrete intervals (27).
As in the deterministic case, low between- and within-
species transmission prevents the pathogen from persisting
in the target host (prevalence ≈0, Figure 3A; proportion of
time pathogen was absent ≈100%, Figure 3B). Increasing
the exposure of H2to the pathogen (i.e., increasing β12)
leads to a gradual increase in both the prevalence of infec-
tion and the proportion of time the pathogen is present in
H2. This increase applies even if within-H2transmission is
negligible (β22 →0). Therefore, regular, high exposure to
the pathogen from the reservoir can give the appearance of
endemic infection, even if the pathogen cannot be sus-
tained within the population (case 2: apparent multihost
dynamics). Increasing the within-H2transmission rate
(β22) from very low levels has little impact on the preva-
lence of infection or the proportion of time H2is infected.
Eventually, however, a point is reached at which increas-
ing β22 suddenly allows the long-term persistence of the
pathogen in H2. At this point, the persistence threshold is
reached and the pathogen becomes endemic in H2, regard-
less of input from H1. This threshold can be approximated
from the deterministic model by setting β12 = 0 and solv-
ing for R0= 1, which shows that β22 must be > (d+ r)/Kfor
the pathogen to persist in the absence of input from H1(the
horizontal line in Figure 3).
Increasing either between- or within-species transmis-
sion rates (β12 or β22) leads to a point when the host is driv-
en to extinction (Figure 3C), which highlights the danger
of an emerging infectious disease; even if H2is a poor
transmitter of the disease (β22 →0), repeated exposure
from H1may be sufficient to drive the population to
extinction. Analysis of the equivalent deterministic model
(model 1) suggests that this threshold should be in the
between-species transmission rate (β12) only (host extinc-
tion is not affected by β22) and is given by β12 > dr/(d – r)
for H2extinction to occur (shown by the vertical line in
Figure 3). Thus, even if the probability that H2will con-
tract the pathogen is very low (β12 →0), a single transmis-
sion event may spark an epidemic that completely
decimates the population (region 3).
Implications for Disease Control
The correct classification of the different regions of the
community-epidemiology continuum are of more than just
semantic importance; quantifying the between- and with-
in-species transmission rates and the location of a host-
pathogen system within the continuum are vital to
determine the appropriate control strategy. Haydon et al.
(13) proposed 3 means of controlling infection in a target-
reservoir system: 1) target control, which is aimed at con-
trolling infection within the target population; 2) blocking
tactics, to prevent transmission between the reservoir and
target host population; and 3) reservoir control, which sup-
presses infection within the reservoir. These 3 control
strategies correspond to reducing the within- and between-
species transmission rates (β22, β12, and β11, respectively).
The benefits of each approach will vary according to the
relative contributions different transmission processes
Framework for Classifying Disease Threats
Emerging Infectious Diseases • www.cdc.gov/eid • Vol. 11, No. 12, December 2005 1819
Figure 3. Stochastic model predictions of system behavior in β12–β22 parameter space. Each square represents the average of 100 sim-
ulation runs. Two measures of pathogen persistence are shown: A) Mean prevalence of infection in H2, where black represents zero
prevalence and white represents 100% prevalence, and B) Proportion of time in which the pathogen is absent (i.e., has faded out) from
H2, where white represents zero fade-outs (i.e., the pathogen is always present in H2) and black represents 100% fade-outs (i.e., the
pathogen never infects H2). C) Probability of pathogen-driven host extinction, where black represents the case in which all runs resulted
in host extinction and white the case in which none of the runs resulted in host extinction. The horizontal dashed lines are the determin-
istic approximation threshold. The points marked A and B in panel A and the associated arrows represent different control scenarios for
2-host pathogen systems located at different points within the continuum (see text for details).
make to the overall prevalence in the new host (H2). Our
stochastic model showed that high exposure to the
pathogen from the reservoir host can give the appearance
of endemic infection in the target host, even if it cannot
sustain the pathogen alone. In this case, the optimal control
strategy is completely different from that used against a
true multihost pathogen endemic in the target host. For a
host-pathogen system in region 2 of the continuum (appar-
ent multihost dynamics), where between-species transmis-
sion rates are high but within-H2transmission rates are low
(point A in Figure 3A), the prevalence of infection in H2
may be very high, but mounting a target control program
aimed at reducing within-H2transmission is unlikely to be
effective (the vertical arrow from point A in Figure 3A).
However, blocking control, which would reduce transmis-
sion from the reservoir to the target host, may drastically
reduce prevalence (the horizontal arrow from point A in
Figure 3A). Conversely, similar levels of prevalence in H2
may be observed for a host-pathogen system located in
region 4 of the continuum (point B in Figure 3A) but
because of fundamentally different processes. In this case,
blocking tactics aimed at preventing transmission from the
reservoir to the target host will be ineffectual (horizontal
arrow from point B in Figure 3A), but target control may
prove highly effective (vertical arrow from point B in
Figure 3A). Therefore, establishing the initial location of a
novel host-pathogen system within the community-epi-
demiology continuum and understanding the within- and
between-species transmission rates are essential for opti-
mizing vaccination and culling strategies to lessen the
impact of disease.
Conclusions
This report provides a conceptual framework to under-
stand the ecologic characteristics of disease emergence
based on between- and within-species transmission rates
involving a potential disease reservoir population and a tar-
get host population. Using this framework, we outlined 4
possible cases of long-term disease dynamics in the target
host and showed that these outcomes occupy different
regions of a 2-dimensional continuum described by the net
between- and within-species transmission rates. Further-
more, the development of the community-epidemiology
framework allows us to clarify the wealth of terminology
currently used to describe disease occurrence in host com-
munities, based on an understanding of the underlying eco-
logic and epidemiologic processes. In particular, the
much-overused terms reservoir and spillover can be seen to
have explicit definitions, depending on whether the
pathogen can be sustained within the target host population.
By explicitly considering how the ecologic and evolu-
tionary characteristics of hosts and pathogens combine to
affect the between- and within-species transmission rates,
and the subsequent consequences for disease occurrence in
a novel host, this framework highlights that current human
diseases, domestic and wild animal diseases, and the
threats of emerging infectious diseases can be understood
by a quantitative framework of the underlying transmis-
sion processes. Given that most parasites can infect multi-
ple host species and the recent surge of emerging
infectious diseases in wildlife and human populations,
understanding the dynamics of disease persistence in novel
hosts has never been more important.
Acknowledgments
We thank S. Altizer for her enthusiasm about undertaking
this project and helpful comments on the manuscript. We also
thank J. Antonovics and M. Hood for comments on earlier drafts.
This work was initiated at a workshop at Penn State University
on the ecology and evolution of infectious diseases (Hudfest ’03).
A.F. was funded by a fellowship from the National
Environment Research Council.
Dr Fenton is a National Environment Research Council
research fellow at the University of Liverpool. His research inter-
ests include the dynamics of host-parasite systems, emerging
infectious diseases, and the evolution of parasite life-history
strategies.
Dr Pedersen is a postdoctoral researcher in the Department
of Biology at the University of Virginia. Her research interests
include the ecology of wildlife diseases and multihost, multipar-
asite community dynamics.
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Address for correspondence: Andy Fenton, School of Biological
Sciences, Biosciences Building, Crown St, University of Liverpool,
Liverpool L69 7ZB, UK; fax: 44-151-795-4408; email: a.fenton@
liverpool.ac.uk
Framework for Classifying Disease Threats
Emerging Infectious Diseases • www.cdc.gov/eid • Vol. 11, No. 12, December 2005 1821
International Symposium
on Emerging Zoonoses
Medical and Veterinary
Partnerships To Address Global
Challenges
March 22-24, 2006
Marriott Marquis Hotel
Atlanta Georgia USA
For Symposium agenda and registration
information visit www.isezconference.org
Late-breaker Abstracts Submissions
Due February 1
See www.iceid.org
Which infectious diseases are emerging?
Whom are they affecting?
Why are they emerging now?
What can we do to prevent and control them?
