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CHAPTER 12
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Ecology Of Infectious Diseases:An Example with Two
Vaccine-Preventable Infectious Diseases
H. Broutin,1 N. Mantilla-Beniers,2 and P. Rohani3
1
Unit of Research 165 “Genetics and Evolution of Infectious Diseases,’’ UMR CNRS/IRD 2724, Institute of Research for the
Development (IRD), BP 64501 34394 Montpellier Cedex 5, France
2
Department of Zoology, University of Cambridge, Downing Street, Cambridge, CB2 3EJ, United Kingdom and Instituto Gulbenkian de
Ciência, Apartado 14, 2781-901 Oeiras, Portuga
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Institute of Ecology, University of Georgia, Athens, GA 30602, USA
12.1
INTRODUCTION
It nowadays often comes as a
surprise to the non-specialist
that ecology and infectious
diseases can be integrated in an
area of study. The irony is that
they have a common origin
and only came to exist as separate fields with scientific
development and the advent of
reductionism.
The dissociation of natural
science into subfields such as
immunology, pathology, molecular biology, and genetics has
led to key scientific breakthroughs, some of which make up
the foundations of modern infectious disease epidemiology. In
particular, scientific evidence of the mechanism of contagion
presented epidemiologists with fundamental answers and led to
reformulating central questions in the field. Another consequence of our improved understanding of disease transmission
is that mathematics began to be used as an explanatory [27]
and, more recently, predictive tool [2,17,31] in the study of
infectious disease epidemiology.
Interestingly, the host–parasite interactions captured in epidemic and demographic data are part of the study matter of
population ecologists.As a result (cf. Section 12.1), ecology has
lent useful concepts to epidemiology, and finds in the latter a
fruitful test-bed for its theoretical developments. Moreover,
changes in social habits (e.g., increasing frequency and range of
travel), demography, habitat structure (e.g., urbanization) and
the environment (e.g., deforestation, temperature) are responsible for recent, unexpected increase in the importance of infectious diseases as causes of mortality in an increasingly complex
ecosystem [12,34]. In this context, humans cannot be considered as “particular case’’ or a “particular host’’ for pathogens.
Emerging or re-emerging diseases affect many animal and plant
species and, as such, humans should not be considered as a special host; from an evolutionary perspective, humans might even
be a “bad’’ host in many cases.
In fact, the study of human infections must be linked with
“ecosystem health’’ putting in light the strong relation between
biodiversity and human health [5,41]. Thus, in order to face up
to the challenge presently posed to epidemiology, it will be
necessary to reinforce its links with ecology, and also seek to
understand how pathogens evolve.
The ecological study of infectious diseases [22] places the
individual within a population in a given environment, thereby analyzing the spread of diseases over different scales of time
and space. Its goal is a global understanding of the development
(emergence, spread, etc.) and persistence of a disease in a host
population that integrates the biology of the host–pathogen
association (pathogen life history, host demography, social
habits, etc.) and the influence of environmental factors (e.g.,
precipitation, temperature).
Encyclopedia of Infectious Diseases: Modern Methodologies, by M.Tibayrenc
Copyright © 2007 John Wiley & Sons, Inc.
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The present wealth of scientific knowledge might make it
impossible for modern scientists to apprehend all what once
was the realm of natural historians, but interdisciplinary collaboration can (and should!) be used to bring together areas
of knowledge that relate to present problems.
In this chapter, we will first present some of the main concepts and methods currently used in the study of infectious
diseases. In the second and last section, we will illustrate how
these tools are implemented using results obtained by our
groups in the study of pertussis and measles epidemiology.
sets even contain information from various host and
pathogen populations, thus providing the opportunity for
comparative studies. Lastly, the impact of large-scale perturbations, such as sudden demographic changes or the
start of mass-vaccination, is reflected in some data sets.
These perturbations equate to environmental changes
(e.g., habitat fragmentation) that would be questionable
interventions in other ecological systems, therefore providing invaluable information to conservationists.
12.2.2.1 Persistence and spread of infectious diseases—
metapopulation concept Population ecology is concerned
12.2
12.2.1
CONCEPTS AND METHODS
Mathematics—Modeling
The first use of mathematics in epidemiology is generally
attributed to the Englishman John Graunt, who in the seventeenth century proposed that greatest progress would be made
in the battle to understand the causes of human mortality by
quantifying their rate through time. Although this effort was
largely descriptive [19], it was of paramount importance
because it laid the basis for quantitative reasoning and the collation and comparative analysis of public health data. Modern
techniques of time series analysis and the development of
computers have made it possible to capture essential information that was formerly lost in statistical studies [10,20].
With the development of germ theory came the first mathematical models that incorporated assumptions on the mechanism of contagion.The mechanistic approach was used to try to
explain patterns found in epidemic data [27]. It has produced
fundamental theoretical progress and remains highly productive.
The initial attempts to predict epidemic spread using extant
data were hindered by the lack of detailed epidemiological
and demographic information, the computing demands of
the models and gaps in the knowledge of disease transmission. However, recent ventures have concluded with success
[2,17,31], highlighting the role that mathematics can play in
modern epidemiology. One of its main applications is the
prospective study of alternative control strategies.
More details are given in Chapter 23 (M. Choisy, J.F.
Guégan, P. Rohani)
12.2.2
Population Ecology
As mentioned in Section 12.1, the link between population
ecology and epidemiology is rather natural, because the former
seeks to understand what causes temporal and spatial changes
in population abundance and how these changes relate to environmental factors and to those intrinsic to ecological interactions – and the latter focuses on host–pathogen dynamics.
Population ecologists rarely have access to data of the
spatio-temporal span and resolution that can characterize
disease records. Furthermore, host demography, social
habits, and environment are often well documented in parallel to pathogen dynamics, particularly in the case of
human populations. As a result, important parameters of
the two central populations are known. Exceptional data
with infectious dynamics at spatial and temporal scales that
differ from those used in other disciplines studying infectious diseases. At the largest scale, it seeks to understand disease behavior in the population of an entire geographic
region.The aim is to answer different basic questions that are
relevant because they reflect what affects disease incidence: Is
the disease predictably periodic? Do epidemics occur everywhere at
the same time? Can the disease persist over time, or does it go
extinct? How does disease spread between geographically isolated
host populations? Is disease transmission the same today as it was
50 years ago?
Population ecology looks in particular at the spatial structure of communities and its consequences on the persistence and
geographic distribution of species [42].A concept from population
ecology that is now highly developed for the study of infectious diseases is the concept of metapopulation [28,33]. A
metapopulation is loosely defined as a population of subpopulations interconnected by immigration (see Box 12.1). For a
BOX 12.1: METAPOPULATION CONCEPT
A metapopulation (Fig. A) is defined in ecology as a
population of subpopulations (gray circles) interconnected by immigration (black arrows) [28]. The
dynamics of the entire metapopulation will depend
on the extinction and recolonisation of its constituent
subpopulations (or habitat patches). Metapopulation
dynamics will depend mainly on (i) local dynamics
(which at a fundamental level depend on the size of
the subpopulation) and (ii) population flux between
patches, with the proviso that migration rates are sufficiently low so as not to affect local dynamics per se.
An interesting metapopulation configuration is the
“source–sink’’ structure, where only one direction of
population flux is relevant to local and global population dynamics (cf. Fig. B). In this case, the dynamics of big populations (called “sources,” in black)
shapes abundance patterns in small populations
(called “sinks,” in gray) and therefore defines
metapopulation dynamics.
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given species, we can thus study the global dynamics of the
populations, taking into account the different community sizes
of subpopulations and the flux of individuals between them.
Applied to the study of infectious diseases, subpopulations
correspond to human communities that can be considered at
different spatial scales (e.g., family, city, country).This approach
has been applied to the study of measles and pertussis at a
country level [23,37] and a very fine rural scale [9].
Moreover, in the study of disease persistence, the relation
between population size and the duration of infection fadeouts (periods when the disease cannot be detected in the host
population) can be used to estimate the infection’s Critical
Community Size (CCS) (see Box 12.2). The CCS [6,7] is
defined as the population size below which the disease cannot persist.Vaccination strategies are expected to decrease disease transmission and increase the CCS.
Another issue that can be studied within this framework
is the detailed spatio-temporal dynamics of the pathogen
Sources
Sinks
Figure A
ECOLOGY OF INFECTIOUS DISEASES
Figure B
Applied to infectious diseases, habitat patches are
human groups that can be defined at many different
spatial scales, from, for example, families or neighborhoods (local scale) to countries or continents
(global scale).
BOX 12.2: CRITICAL COMMUNITY SIZE (CCS)
One way to estimate the CCS for a given disease is to plot the mean duration of disease extinction (i.e., fade-out)
in relation to population size. A fade-out is defined based on the average time that a patient takes to recover from
infection counted from the moment in which transmission occurred. Only during this time can the patient him/herself transmit the infection. In the case of measles, for example, this is approximately 2 weeks. Therefore, when no
new cases are reported in 3 weeks or more, it is safe to assume that the chain of transmission is broken and that
the pathogen has gone extinct in that host population.
Let us illustrate the CCS with an example (see figure below) extracted from Rohani et al. [38], who studied pertussis times series in 60 cities in England and Wales during the vaccination era (1957–1974). Graph (A) represents
the mean duration of fade-outs (in weeks) in relation with the population size of each locality. As you can see, the
bigger the locality, the shorter the period of disease extinction. To determine the CCS, we focused on the threshold
of 3 weeks for pertussis (if a new case is reported in the locality more than 3 weeks after the last one, it cannot be
due to transmission within the locality and must be the result of an infectious contact with someone from outside
the locality) represented by the blue dot line. Below this threshold, the disease persists and above it, goes extinct.
The CCS will correspond to the population size at the intersection between this threshold and the fade-out duration curve (see dot arrow), here around 250,000 inhabitants. For localities with a population size below this CCS,
the disease goes extinct (see (B) and (C) as examples), whereas there is no extinction of the disease in the localities
with a population size above CCS (see (D) as example).
(B)
(C)
Mean fade-out duration (weeks)
(A)
(D)
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metapopulation. One question is as follows: Does the disease
spread between all communities without any direction or does the disease spread in a special direction? This question is important for
the control of infectious diseases, because it can help to identify potential sources of infection. Here, the mainland–island
paradigm of ecology [28] finds a counterpart in the “city–
village’’ concept proposed by Anderson et al. [3]. They suggested that diseases spread from big cities to small villages,
which corresponds to a particular metapopulation structure,
namely the “source–sink’’ paradigm (see Box 1). Analyses of
ecological time series have shown that measles and pertussis
indeed spread following a size-hierarchy, both in England and
Wales and in a small rural area of Senegal (cf. Section 12.3)
[8,21,25].
12.2.2.2 Periodicity and synchrony of epidemics—
times series analyses The temporal dynamics of infection
are studied using various methods of time series analyses of
disease cases. A time series is defined as a series of observations ordered in time, for instance, the monthly number of
new cases for a given disease in a given host population.
Different methods can be used to investigate the periodicity
of epidemics and the study of the synchrony with which
they occur in different subpopulations. In ecology, time
series analyses have been used to determine the degree of
coherence in oscillations in abundance of separate animal populations [29,30] and to study the periodicity of these fluctuations population in relation to geographical gradients [32].A
direct application of this approach concerns conservation
biology [28]. Indeed, for a given species, synchrony of different populations in different geographical locations (i.e., populations in the same dynamical state simultaneously) implies
an increase in the risk of extinction. In contrast, if population dynamics are not synchronized, then the extinction of
one local population can be balanced by recolonization from
a neighboring population. This is termed the rescue effect in
ecology, and effectively enhances the overall chances of persistence of the species.
The same rationale can be applied to pathogen populations. Using infectious disease data of unique temporal span
and spatial resolution, various studies [3,16,20,38] have characterized the periodicity and synchrony of measles and
whooping cough epidemics before and after the start of mass
vaccination. When epidemics are incoherent, disease extinction in one population is only temporary, because disease is
later reintroduced through contact with infectious individuals from other populations.
Thus, this new approach to epidemiological issues is primordial for better understanding the patterns of disease
spread in space and time, and naturally inspects issues that are
relevant to disease control. In the second part of this chapter,
we will illustrate this approach with studies of pertussis and
measles dynamics at two different spatial scales and in different environments. To obtain an integrated picture of disease
spread, it is crucial to compare different environmental and
demographic conditions.
12.2.3 Comparative Approach—The Search for
Emerging Themes?
Although major research developments have recently come
about in other fields of life science, that is, population dynamics, community ecology, and macroecology [32], often through
the use of a comparative research perspective, epidemiology
continues to suffer from a lack of comparative studies.
With recent evidence of the impact of large-scale phenomena, for example, climatic change, on infectious disease
patterns [34], the recognition of the importance of regional
or even global processes interacting with microbe population
dynamics in local human communities has become evident.
Modern epidemiology is now confronted with the problem
of how to identify the spatio-temporal and organizational
scales that might be relevant in explaining disease patterns
and processes. Many investigations on childhood diseases
have provided clear evidence of how large-scale studies are of
substantial interest for public health [4,21,38].There is now a
growing scientific tendency, under the impetus of population
biologists, to provide a broader perspective on epidemiological systems in order that only the important disease generalities or patterns remain. This approach is called comparative
analysis, and it consists of the comparison, on a broad spatial
scale, of long-term data for a given disease across different
localities.The main focus of comparative analysis in general is
to contrast data acquired at a smaller spatial scale and to consider that emerging patterns may exist at a larger scale
encompassing the total data set under study.The basic role of
comparative analyses in epidemiology is to describe the different spatio-temporal patterns that may be at work on the
different hierarchical scales under scrutiny, and then to
explore the corresponding processes responsible for the
observed patterns.
Comparative studies of pathogen population dynamics are
thus a promising way to explore public health issues, offering
a much broader perspective on health and a more quantitative
approach with which similarities and specificities in the
behavior of infectious diseases can be distinguished. Such
studies, based on an ecological understanding of infectious
diseases, should help us to improve and adapt the means for
controlling these infections (using vaccination, for example)
on a global scale.
12.3 AN EXAMPLE WITH TWO DIRECTLY
TRANSMITTED DISEASES: MEASLES AND
PERTUSSIS DYNAMICS
12.3.1 Pertussis and Measles: Two Vaccine
Preventable Diseases
Pertussis and measles are two ubiquitous vaccine-preventable diseases
of humans. Both are highly infectious and are transmitted in
aerosol droplets following contact between infected and susceptible individuals. Pertussis (also called “whooping cough”) is
a respiratory disease caused by Gram-negative bacteria of the
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ECOLOGY OF INFECTIOUS DISEASES
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Fig. 12.1. World maps showing vaccination coverage against measles (left panel) and pertussis (right
panel).These maps were extracted from the WHO website http://www.who.int.
species Bordetella pertussis. Individuals infected with pertussis
become infectious after an incubation period of approximately
8 days, during which the bacterium spreads and proliferates in
the host.They are then infectious for approximately 14–21 days.
Measles is due to a paramyxovirus (see chapter 9 for classification). In this case, an incubation period of 8 days on average is
followed by approximately 5 days during which patients remain
infectious.Active immunity results from either recovery to natural infection, or vaccination.
In developed countries, large-scale vaccination programs
against both infections started between 40 and 60 years ago. In
many developing countries, however, systematic vaccination
was initiated only recently (1974) via the Expanded
Programme on Immunisation, which has been implemented in
Africa since the mid-1980s (Fig. 12.1). Global incidence of both
infections has been dramatically reduced as a result of vaccination,
but measles and pertussis remain an important public health problem
in developing countries [43,44]. Furthermore, in several developed
countries a resurgence in whooping cough has been detected in the last
decades [13,14], despite high vaccine coverage [15,26].
The unrelenting toll of life taken by these infections has
motivated the collection of records of morbidity and mortality in numerous human populations around the globe. In particular, disease reporting has rendered detailed incidence
reports that date back to the years preceding the start of vaccination campaigns from regions in two countries of disparate
social, demographic, and economic conditions: the Niakhar in
Senegal1 [8,9].[c1] (Fig. 12.2), and England and Wales in the
United Kingdom [37]. Each of these data sets is made up by
weekly reports from geographically separate human populations that are linked epidemiologically by human travel.
Niakhar is a small rural area located around 150 km east of
Dakar in Senegal. It is constituted by 30 localities with popu1
The Institute of Research for the Development (IRD) performed the
demographic (since 1963) and epidemiological (since 1983) survey in the
Niakhar population, a small rural area in Senegal.
lation sizes ranging from 50 to 3000 inhabitants (see Figs. 12.3
and 12.4). Pertussis and measles cases have been reported since
1983, and vaccination started at the end of 1986. Data for
England and Wales originate from the largest 60 towns and
cities in the area. Sizes range from 20,000 inhabitants in
Teignmouth to over 3 million in London. British data span
from 1944 to 1994, and mass vaccination started in 1957.
In what follows, we present studies of the spatio-temporal
dynamics and persistence of pertussis in each area. These
analyses draw parallels between the two regions, comparing
the manner in which immunization programs altered disease
dynamics in each case. Study methods have a strong base in
ecological studies. We hope they make apparent the fruitful
interchange of ideas between ecology and epidemiology that was
outlined in the first part of this chapter.
12.3.2 Persistence—CCS and Impact of
Vaccination
As detailed before (cf. Box 2), the CCS of an infection can be
estimated by counting the number of weeks in which no
cases are reported in a given host population and noting how
the duration of disease fade-outs relates to population size.
Figure 12.5 shows results obtained for pertussis in England
and Wales [38] and in Niakhar, Senegal [9].
Visual inspection of these figures shows two important
similarities between British and Senegalese data. The first of
them concerns the general shape of the relation. In both
cases, the larger the host population, the shorter the duration
of its disease fade-outs. Indeed, the disease persists better in large
populations, where the number of individuals susceptible to
infection is large enough to maintain the chain of transmission. A second point that is evident from these figures is that
fade-outs are longer after the start of immunisation campaigns (black vs. gray curves).Thus, vaccination effectively reduced
disease persistence in both England and Wales and in Niakhar.
It is important to remark that the largest towns in Niakhar
are roughly an order of magnitude smaller than the smallest
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Fig. 12.2. Location and map of the Niakhar area in Senegal. Gray areas correspond to backwaters during the rainy season.Villages are delimited by the black lines. Black dots represent compounds – groups
of houses – and thus exhibit the geographical distribution of human populations within Niakhar.
Fig. 12.3. Picture of the Niakhar area, Senegal, in dry season. The
area is constituted by 30 villages. Each village is divided into hamlets, themselves composed of “compounds.” The compound, representing the smallest structure of the zone, corresponds to a group of
houses where extended families live, occupying one, or several,
households (photo: H. Broutin).
Fig. 12.4. Picture of inhabitants of a big compound in Niakhar,
Senegal (photo: H. Broutin).
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PERTUSSIS
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Fig. 12.5. Mean duration of fade-out (in weeks) in relation to community size for pertussis in
England and Wales (left graph) before vaccination (1944–1957, in gray), and after vaccination
(1957–1974, in black) and in Niakhar, Senegal (right graph), before vaccination, that is, 1983–1986 (in
gray) and in vaccine era, that is, 1987–2000 (in black). It is important to notice differences in scales
between the two graphs.
populations found in the British records analyzed here.
Interestingly, fade-out duration in populations in Niakhar
appears to extend the results obtained for England and Wales.
The range of population sizes represented in British data
includes places where the disease does not fade-out and so
allows for a direct estimate of the CCS of pertussis before and
after the start of immunization [38]. In contrast, the much
smaller population of Niakhar means that there are periods in
which pertussis is absent from the entire region (Fig. 12.6).
Thus, its recurrence after a fade-out depends on contacts
with infected individuals from outside Niakhar.
Thus, the reduction in transmission brought about by vaccination
can be observed at very different spatial scales and in communities of
very different socioeconomic and demographic characteristics.
Epidemiological information from all the communities in
a geographical region (Niakhar) permitted an unprecedented
study of the spatial spread of pertussis [8,9].This is presented
in the next section, along with an analogous study of measles
dynamics in England and Wales [21].
12.3.3
“CITY–VILLAGE” SPREAD
Based on theoretical studies,Anderson et al. [3] first proposed
that infections spread following a size hierarchy from cities to
Fig. 12.6. Weekly cases of pertussis in all of Niakhar (Senegal,
1984–1999). Black dots at the top show weeks in which no cases
were recorded.
villages (the “city–village” paradigm). This was later confirmed by different empirical studies of measles cases in the
British Isles and the United States [11,20,21], which showed
that infection progressively diffuses from urban centers to the
surrounding rural areas, and at regional and large scales. The
phenomenon of infectious disease diffusion from big cities to
smaller localities could be quite important in terms of infectious disease control. Indeed, the identification of “sources” of
infection could be used to target vaccination efforts. For this
reason, it is very important to study the spatio-temporal
dynamics of infectious diseases in different contexts.
Measles diffusion in England and Wales was analyzed using
morbidity reports from 845 towns and cities and 457 rural
districts [21]. In this analysis, only the 60 largest cities are
classed as “urban” populations.The remaining time series are
then aggregated in a “rural total.” The proportion of total
cases reported in each “urban” population was then compared to the proportion of total cases found in the rural total
time series.Their relation was characterized by their Pearson
correlation coefficient, and negative correlations correspond
to populations in which epidemics take-off before they do in
the rural total.
Correlation coefficients are plotted in relation to population size in Figure 12.3A. Correlation coefficients between
the rural total and time series from large populations (as
proportions of the total cases) are usually negative, showing
that measles spreads from the largest cities (e.g., London or
Birmingham, which correspond to the two largest green
disks in Fig. 12.7) to the surrounding area. Populations above
the CCS serve as “reservoirs” from which measles spreads to
rural settlements.
Similar analyses were performed in the small area (220 km2)
of Niakhar in Senegal for pertussis [8] to test the idea that the
“cities and villages” model might also be relevant on a finer
spatial scale. As before, we defined a rural total, made up of all
but the two largest cities in Niakhar (Toukar and Diohine).
Our study suggested that disease progresses from the largest
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Fig. 12.7. Illustration of the comparison of infectious disease spread at two different spatial scales.The
left part (A) describes the spread of measles in England and Wales (cf. [21]). The right part (B) shows
pertussis spread in the rural area of Niakhar, in Senegal (cf. [8,9]). In the latter, orange (respectively,
green) denotes negative correlations between measles in a population (as proportion of total cases) and
measles in a “rural” aggregate (see text for details). For each part, the top graph provides on the y-axis
the correlation between the “rural” aggregate and the time series in individual populations (845 localities in A and 30 in B), in relation with the population size (on the x-axis). A negative correlation
describes a delay between rural cases and epidemic increase in the study population.The bottom maps
are a spatial representation of the upper graphs. More analyses (not shown here) confirm that cases
appeared first in the biggest localities and then in rural populations (black arrows symbolize disease
diffusion). See color plates.
town (Toukar) to the rural surroundings (see Fig. 12.7B).
Indeed, negative correlations between “urban” populations and
the rural aggregate are indicative of an urban–rural hierarchy
in pertussis epidemics in Niakhar after vaccination. Epidemics
in “urban” populations (which have markets, bus stations, and
health centers) begin and reach their peak between 10 and 15
weeks before “rural” epidemics, in conformity with results
obtained for measles in England and Wales [21].
As noted before (Section 12.3.2), whooping cough cannot
persist in Niakhar without external input of new cases (see
Fig. 12.5 the CCS is not reached in Niakhar). We can now
add a new fact to this mechanism: cases arrive initially in the
biggest villages, Toukar, before spreading to the surrounding
areas. Even though this pattern of disease spread can explain
the spatio-temporal dynamics of pertussis in the studied area,
it must be noted that other mechanisms (e.g., pertussis arriving directly in the small villages from an external source) cannot be ruled out. Thus, a size hierarchy could potentially
determine the spatio-temporal dynamics of an infection even
in effectively rural areas from which the infection fades out
after epidemics.
This type of approach needs to be completed by other studies, because it could be helpful for adapting vaccination strategies. If spread of disease from urban centers to rural counties
can be generalized, then this mechanism could imply new
strategies for vaccinations, with less expanded but more precise
programs that would be more realistic in the field, particularly
in developing countries.This new type of research should lead
to better control of disease using targeted vaccination.
12.4
CONCLUSION
The example in Section 12.3 illustrates how the interchange
of ideas between ecologists and epidemiologists has contributed to our understanding of pathogen population
dynamics. Some of the theoretical developments derived
from this approach have in fact led to concrete suggestions
aimed at improving vaccination strategies [1,35]. Other studies with a similar perspective have shown that ecological
interactions between pathogens might also have consequences
on disease dynamics [36,39].This suggests that the interaction
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between a pathogen and its host need not be the only one
relevant to epidemiologists.
Definitively, ecology and epidemiology need to develop stronger
links, both with each other and with more, relevant, disciplines such
immunology and microbiology. Ecology is based on the population-level studies of ecosystem dynamics and interactions.
Infectious diseases or pathogen populations suffer the same
ecological and evolutionary laws experienced by other living
beings.Whatever the name (“Medical ecology,”“Eco-epidemiology,” or “Ecology of health”), the coupling of both ecology
and epidemiology is now the necessary way of research to better understand and control infectious diseases in this fast evolving word. In fact, much remains to be done to control preexisting infections, and emerging and re-emerging infections
present novel challenges to epidemiologists.The magnitude of
the challenge is enormous. It is therefore essential to maintain
a close collaboration between epidemiologists and ecologists.
Yet it is similarly important to expand interdisciplinary links
and strengthen the relationship with, for example, population
geneticists that can potentially lead to predicting pathogen
evolution and emergence (18,24,40).
The task ahead will also require a coordinated effort to
gather information at different biological levels of organization (from molecular scale to ecosystem). Indeed, the lack of
reliable time series constitutes a major brake to the understanding of population dynamics in epidemiology. For that,
standardized health surveys that reflect pathogen dynamics and
keep track of host habits and demography will be indispensable.
Population genetics will be a necessary complement.
Additionally, data on environmental changes and their effect on
species diversity and habitat composition will be needed to
complete the picture. In fact, we need first to observe and understand patterns in order to then discover the processes that shape them.
In the light of the challenges presented by the modern world,
it is of fundamental importance to bring together our efforts
to understand it.
ACKNOWLEDGMENTS
HB was funded by Aventis Pasteur, Fondation des Treilles,
CNRS and IRD. NMB was funded by the Wellcome Trust.
PR is funded by the National Institutes of Health, the
National Science Foundation, and the Ellison Medical
Foundation.
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