ecology of infectious diseases: an example with …chapter 12 ecology of infectious diseases: an...

◆◆◆◆◆◆◆◆◆◆◆◆◆◆ ◆◆◆◆◆◆◆◆◆◆◆◆◆CHAPTER 12

Ecology Of Infectious Diseases: An Example with TwoVaccine-Preventable Infectious Diseases

H. Broutin,1 N. Mantilla-Beniers,2 and P. Rohani31Unit 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, France2Department of Zoology, University of Cambridge, Downing Street, Cambridge, CB2 3EJ, United Kingdom and Instituto Gulbenkian de

Ciência,Apartado 14, 2781-901 Oeiras, Portuga3Institute of Ecology, University of Georgia,Athens, GA 30602, USA

lent useful concepts to epidemiology, and finds in the latter afruitful test-bed for its theoretical developments. Moreover,changes in social habits (e.g., increasing frequency and range oftravel), demography, habitat structure (e.g., urbanization) andthe environment (e.g., deforestation, temperature) are responsi-ble for recent, unexpected increase in the importance of infec-tious diseases as causes of mortality in an increasingly complexecosystem [12,34]. In this context, humans cannot be consid-ered as “particular case’’ or a “particular host’’ for pathogens.Emerging or re-emerging diseases affect many animal and plantspecies and, as such, humans should not be considered as a spe-cial host; from an evolutionary perspective, humans might evenbe 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 betweenbiodiversity and human health [5,41].Thus, in order to face upto the challenge presently posed to epidemiology, it will benecessary to reinforce its links with ecology, and also seek tounderstand how pathogens evolve.

The ecological study of infectious diseases [22] places theindividual within a population in a given environment, there-by analyzing the spread of diseases over different scales of timeand space. Its goal is a global understanding of the development(emergence, spread, etc.) and persistence of a disease in a hostpopulation that integrates the biology of the host–pathogenassociation (pathogen life history, host demography, socialhabits, etc.) and the influence of environmental factors (e.g.,precipitation, temperature).

189

Encyclopedia of Infectious Diseases: Modern Methodologies, by M.TibayrencCopyright © 2007 John Wiley & Sons, Inc.

12.1 INTRODUCTION

It nowadays often comes as asurprise to the non-specialistthat ecology and infectiousdiseases can be integrated in anarea of study.The irony is thatthey have a common originand only came to exist as sep-arate fields with scientificdevelopment and the advent ofreductionism.

The dissociation of naturalscience into subfields such asimmunology, pathology, molec-ular biology, and genetics has

led to key scientific breakthroughs, some of which make upthe foundations of modern infectious disease epidemiology. Inparticular, scientific evidence of the mechanism of contagionpresented epidemiologists with fundamental answers and led toreformulating central questions in the field. Another conse-quence of our improved understanding of disease transmissionis that mathematics began to be used as an explanatory [27]and, more recently, predictive tool [2,17,31] in the study ofinfectious disease epidemiology.

Interestingly, the host–parasite interactions captured in epi-demic and demographic data are part of the study matter ofpopulation ecologists.As a result (cf. Section 12.1), ecology has

The present wealth of scientific knowledge might make itimpossible for modern scientists to apprehend all what oncewas the realm of natural historians, but interdisciplinary col-laboration can (and should!) be used to bring together areasof knowledge that relate to present problems.

In this chapter, we will first present some of the main con-cepts and methods currently used in the study of infectiousdiseases. In the second and last section, we will illustrate howthese tools are implemented using results obtained by ourgroups in the study of pertussis and measles epidemiology.

12.2 CONCEPTS AND METHODS

12.2.1 Mathematics—ModelingThe first use of mathematics in epidemiology is generallyattributed to the Englishman John Graunt, who in the seven-teenth century proposed that greatest progress would be madein the battle to understand the causes of human mortality byquantifying their rate through time. Although this effort waslargely descriptive [19], it was of paramount importancebecause it laid the basis for quantitative reasoning and the col-lation and comparative analysis of public health data. Moderntechniques of time series analysis and the development ofcomputers have made it possible to capture essential informa-tion that was formerly lost in statistical studies [10,20].

With the development of germ theory came the first math-ematical models that incorporated assumptions on the mecha-nism of contagion.The mechanistic approach was used to try toexplain patterns found in epidemic data [27]. It has producedfundamental theoretical progress and remains highly productive.

The initial attempts to predict epidemic spread using extantdata were hindered by the lack of detailed epidemiologicaland demographic information, the computing demands ofthe models and gaps in the knowledge of disease transmis-sion. However, recent ventures have concluded with success[2,17,31], highlighting the role that mathematics can play inmodern epidemiology. One of its main applications is theprospective study of alternative control strategies.

More details are given in Chapter 23 (M. Choisy, J.F.Guégan, P. Rohani)

12.2.2 Population EcologyAs mentioned in Section 12.1, the link between populationecology and epidemiology is rather natural, because the formerseeks to understand what causes temporal and spatial changesin population abundance and how these changes relate to envi-ronmental factors and to those intrinsic to ecological interac-tions – and the latter focuses on host–pathogen dynamics.

Population ecologists rarely have access to data of thespatio-temporal span and resolution that can characterizedisease records. Furthermore, host demography, socialhabits, and environment are often well documented in par-allel to pathogen dynamics, particularly in the case ofhuman populations. As a result, important parameters ofthe two central populations are known. Exceptional data

sets even contain information from various host andpathogen populations, thus providing the opportunity forcomparative studies. Lastly, the impact of large-scale per-turbations, such as sudden demographic changes or thestart of mass-vaccination, is reflected in some data sets.These perturbations equate to environmental changes(e.g., habitat fragmentation) that would be questionableinterventions in other ecological systems, therefore provid-ing invaluable information to conservationists.

12.2.2.1 Persistence and spread of infectious diseases—metapopulation concept Population ecology is concernedwith infectious dynamics at spatial and temporal scales thatdiffer from those used in other disciplines studying infec-tious diseases.At the largest scale, it seeks to understand dis-ease behavior in the population of an entire geographicregion.The aim is to answer different basic questions that arerelevant because they reflect what affects disease incidence: Isthe disease predictably periodic? Do epidemics occur everywhere atthe same time? Can the disease persist over time, or does it goextinct? How does disease spread between geographically isolatedhost populations? Is disease transmission the same today as it was50 years ago?

Population ecology looks in particular at the spatial struc-ture of communities and its consequences on the persistence andgeographic distribution of species [42].A concept from populationecology that is now highly developed for the study of infec-tious diseases is the concept of metapopulation [28,33]. Ametapopulation is loosely defined as a population of subpopu-lations interconnected by immigration (see Box 12.1). For a

190 ◆ ENCYCLOPEDIA OF INFECTIOUS DISEASES: MODERN METHODOLOGIES

BOX 12.1: METAPOPULATION CONCEPT

A metapopulation (Fig. A) is defined in ecology as apopulation of subpopulations (gray circles) intercon-nected by immigration (black arrows) [28]. Thedynamics of the entire metapopulation will dependon the extinction and recolonisation of its constituentsubpopulations (or habitat patches). Metapopulationdynamics will depend mainly on (i) local dynamics(which at a fundamental level depend on the size ofthe subpopulation) and (ii) population flux betweenpatches, with the proviso that migration rates are suf-ficiently low so as not to affect local dynamics per se.An interesting metapopulation configuration is the“source–sink’’ structure, where only one direction ofpopulation flux is relevant to local and global popu-lation dynamics (cf. Fig. B). In this case, the dynam-ics of big populations (called “sources,” in black)shapes abundance patterns in small populations(called “sinks,” in gray) and therefore definesmetapopulation dynamics.

given species, we can thus study the global dynamics of thepopulations, taking into account the different community sizesof subpopulations and the flux of individuals between them.

Applied to the study of infectious diseases, subpopulationscorrespond to human communities that can be considered atdifferent spatial scales (e.g., family, city, country).This approachhas been applied to the study of measles and pertussis at acountry level [23,37] and a very fine rural scale [9].

Moreover, in the study of disease persistence, the relationbetween population size and the duration of infection fade-outs (periods when the disease cannot be detected in the hostpopulation) can be used to estimate the infection’s CriticalCommunity Size (CCS) (see Box 12.2). The CCS [6,7] isdefined as the population size below which the disease can-not persist.Vaccination strategies are expected to decrease dis-ease transmission and increase the CCS.

Another issue that can be studied within this frameworkis the detailed spatio-temporal dynamics of the pathogen

CHAPTER 12 ECOLOGY OF INFECTIOUS DISEASES ◆ 191

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 frominfection counted from the moment in which transmission occurred. Only during this time can the patient him/her-self transmit the infection. In the case of measles, for example, this is approximately 2 weeks. Therefore, when nonew cases are reported in 3 weeks or more, it is safe to assume that the chain of transmission is broken and thatthe 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 per-tussis times series in 60 cities in England and Wales during the vaccination era (1957–1974). Graph (A) representsthe mean duration of fade-outs (in weeks) in relation with the population size of each locality. As you can see, thebigger the locality, the shorter the period of disease extinction. To determine the CCS, we focused on the thresholdof 3 weeks for pertussis (if a new case is reported in the locality more than 3 weeks after the last one, it cannot bedue to transmission within the locality and must be the result of an infectious contact with someone from outsidethe 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 dura-tion 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 localitieswith a population size above CCS (see (D) as example).

Applied to infectious diseases, habitat patches arehuman groups that can be defined at many differentspatial scales, from, for example, families or neigh-borhoods (local scale) to countries or continents(global scale).

Sources

Sinks

Figure A Figure B

2220181614121086420

(B)

(A)

(C)

(D)

0 1 2 3Population size (×105)

Mea

n fa

de-o

ut d

urat

ion

(wee

ks)

4 5 6 7 8

metapopulation. One question is as follows: Does the diseasespread between all communities without any direction or does the dis-ease spread in a special direction? This question is important forthe control of infectious diseases, because it can help to iden-tify potential sources of infection. Here, the mainland–islandparadigm of ecology [28] finds a counterpart in the “city–village’’ concept proposed by Anderson et al. [3]. They sug-gested 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 ofecological time series have shown that measles and pertussisindeed spread following a size-hierarchy, both in England andWales 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 infectionare studied using various methods of time series analyses ofdisease cases. A time series is defined as a series of observa-tions ordered in time, for instance, the monthly number ofnew cases for a given disease in a given host population.Different methods can be used to investigate the periodicityof epidemics and the study of the synchrony with whichthey occur in different subpopulations. In ecology, timeseries analyses have been used to determine the degree ofcoherence in oscillations in abundance of separate animal pop-ulations [29,30] and to study the periodicity of these fluctua-tions population in relation to geographical gradients [32].Adirect application of this approach concerns conservationbiology [28]. Indeed, for a given species, synchrony of differ-ent populations in different geographical locations (i.e., pop-ulations in the same dynamical state simultaneously) impliesan increase in the risk of extinction. In contrast, if popula-tion dynamics are not synchronized, then the extinction ofone local population can be balanced by recolonization froma neighboring population. This is termed the rescue effect inecology, and effectively enhances the overall chances of per-sistence of the species.

The same rationale can be applied to pathogen popula-tions. Using infectious disease data of unique temporal spanand spatial resolution, various studies [3,16,20,38] have char-acterized the periodicity and synchrony of measles andwhooping cough epidemics before and after the start of massvaccination.When epidemics are incoherent, disease extinc-tion in one population is only temporary, because disease islater reintroduced through contact with infectious individu-als from other populations.

Thus, this new approach to epidemiological issues is pri-mordial for better understanding the patterns of diseasespread in space and time, and naturally inspects issues that arerelevant to disease control. In the second part of this chapter,we will illustrate this approach with studies of pertussis andmeasles dynamics at two different spatial scales and in differ-ent environments.To obtain an integrated picture of diseasespread, it is crucial to compare different environmental anddemographic conditions.

12.2.3 Comparative Approach—The Search forEmerging Themes?Although major research developments have recently comeabout in other fields of life science, that is, population dynam-ics, community ecology, and macroecology [32], often throughthe use of a comparative research perspective, epidemiologycontinues to suffer from a lack of comparative studies.

With recent evidence of the impact of large-scale phe-nomena, for example, climatic change, on infectious diseasepatterns [34], the recognition of the importance of regionalor even global processes interacting with microbe populationdynamics in local human communities has become evident.Modern epidemiology is now confronted with the problemof how to identify the spatio-temporal and organizationalscales that might be relevant in explaining disease patternsand processes. Many investigations on childhood diseaseshave provided clear evidence of how large-scale studies are ofsubstantial interest for public health [4,21,38].There is now agrowing scientific tendency, under the impetus of populationbiologists, to provide a broader perspective on epidemiolog-ical systems in order that only the important disease general-ities or patterns remain. This approach is called comparativeanalysis, and it consists of the comparison, on a broad spatialscale, of long-term data for a given disease across differentlocalities.The main focus of comparative analysis in general isto contrast data acquired at a smaller spatial scale and to con-sider that emerging patterns may exist at a larger scaleencompassing the total data set under study.The basic role ofcomparative analyses in epidemiology is to describe the dif-ferent spatio-temporal patterns that may be at work on thedifferent hierarchical scales under scrutiny, and then toexplore the corresponding processes responsible for theobserved patterns.

Comparative studies of pathogen population dynamics arethus a promising way to explore public health issues, offeringa much broader perspective on health and a more quantitativeapproach with which similarities and specificities in thebehavior of infectious diseases can be distinguished. Suchstudies, based on an ecological understanding of infectiousdiseases, should help us to improve and adapt the means forcontrolling these infections (using vaccination, for example)on a global scale.

12.3 AN EXAMPLE WITH TWO DIRECTLYTRANSMITTED DISEASES: MEASLES ANDPERTUSSIS DYNAMICS

12.3.1 Pertussis and Measles: Two VaccinePreventable DiseasesPertussis and measles are two ubiquitous vaccine-preventable diseasesof humans. Both are highly infectious and are transmitted inaerosol droplets following contact between infected and sus-ceptible individuals. Pertussis (also called “whooping cough”) isa respiratory disease caused by Gram-negative bacteria of the



Fig. 12.1. World maps showing vaccination coverage against measles (left panel) and pertussis (rightpanel).These maps were extracted from the WHO website http://www.who.int.

1The Institute of Research for the Development (IRD) performed thedemographic (since 1963) and epidemiological (since 1983) survey in theNiakhar population, a small rural area in Senegal.

species Bordetella pertussis. Individuals infected with pertussisbecome infectious after an incubation period of approximately8 days, during which the bacterium spreads and proliferates inthe host.They are then infectious for approximately 14–21 days.Measles is due to a paramyxovirus (see chapter 9 for classifica-tion). In this case, an incubation period of 8 days on average isfollowed by approximately 5 days during which patients remaininfectious.Active immunity results from either recovery to nat-ural infection, or vaccination.

In developed countries, large-scale vaccination programsagainst both infections started between 40 and 60 years ago. Inmany developing countries, however, systematic vaccinationwas initiated only recently (1974) via the ExpandedProgramme on Immunisation, which has been implemented inAfrica since the mid-1980s (Fig.12.1).Global incidence of bothinfections has been dramatically reduced as a result of vaccination,but measles and pertussis remain an important public health problemin developing countries [43,44]. Furthermore, in several developedcountries a resurgence in whooping cough has been detected in the lastdecades [13,14], despite high vaccine coverage [15,26].

The unrelenting toll of life taken by these infections hasmotivated the collection of records of morbidity and mortal-ity in numerous human populations around the globe. In par-ticular, disease reporting has rendered detailed incidencereports that date back to the years preceding the start of vac-cination campaigns from regions in two countries of disparatesocial, demographic, and economic conditions: the Niakhar inSenegal1 [8,9].[c1] (Fig. 12.2), and England and Wales in theUnited Kingdom [37]. Each of these data sets is made up byweekly reports from geographically separate human popula-tions that are linked epidemiologically by human travel.

Niakhar is a small rural area located around 150 km east ofDakar in Senegal. It is constituted by 30 localities with popu-

lation sizes ranging from 50 to 3000 inhabitants (see Figs. 12.3and 12.4). Pertussis and measles cases have been reported since1983, and vaccination started at the end of 1986. Data forEngland and Wales originate from the largest 60 towns andcities in the area. Sizes range from 20,000 inhabitants inTeignmouth to over 3 million in London. British data spanfrom 1944 to 1994, and mass vaccination started in 1957.

In what follows, we present studies of the spatio-temporaldynamics and persistence of pertussis in each area. Theseanalyses draw parallels between the two regions, comparingthe manner in which immunization programs altered diseasedynamics in each case. Study methods have a strong base inecological studies. We hope they make apparent the fruitfulinterchange of ideas between ecology and epidemiology that wasoutlined in the first part of this chapter.

12.3.2 Persistence—CCS and Impact ofVaccinationAs detailed before (cf. Box 2), the CCS of an infection can beestimated by counting the number of weeks in which nocases are reported in a given host population and noting howthe duration of disease fade-outs relates to population size.Figure 12.5 shows results obtained for pertussis in Englandand Wales [38] and in Niakhar, Senegal [9].

Visual inspection of these figures shows two importantsimilarities between British and Senegalese data.The first ofthem concerns the general shape of the relation. In bothcases, the larger the host population, the shorter the durationof its disease fade-outs. Indeed, the disease persists better in largepopulations, where the number of individuals susceptible toinfection is large enough to maintain the chain of transmis-sion.A second point that is evident from these figures is thatfade-outs are longer after the start of immunisation cam-paigns (black vs. gray curves).Thus, vaccination effectively reduceddisease persistence in both England and Wales and in Niakhar.

It is important to remark that the largest towns in Niakharare roughly an order of magnitude smaller than the smallest


Fig. 12.4. Picture of inhabitants of a big compound in Niakhar,Senegal (photo: H. Broutin).

Fig. 12.3. Picture of the Niakhar area, Senegal, in dry season. Thearea is constituted by 30 villages. Each village is divided into ham-lets, themselves composed of “compounds.”The compound, repre-senting the smallest structure of the zone, corresponds to a group ofhouses where extended families live, occupying one, or several,households (photo: H. Broutin).

Fig. 12.2. Location and map of the Niakhar area in Senegal. Gray areas correspond to backwaters dur-ing the rainy season.Villages are delimited by the black lines. Black dots represent compounds – groupsof houses – and thus exhibit the geographical distribution of human populations within Niakhar.

populations found in the British records analyzed here.Interestingly, fade-out duration in populations in Niakharappears to extend the results obtained for England and Wales.The range of population sizes represented in British dataincludes places where the disease does not fade-out and soallows for a direct estimate of the CCS of pertussis before andafter the start of immunization [38]. In contrast, the muchsmaller population of Niakhar means that there are periods inwhich pertussis is absent from the entire region (Fig. 12.6).Thus, its recurrence after a fade-out depends on contactswith infected individuals from outside Niakhar.

Thus, the reduction in transmission brought about by vaccinationcan be observed at very different spatial scales and in communities ofvery different socioeconomic and demographic characteristics.

Epidemiological information from all the communities ina geographical region (Niakhar) permitted an unprecedentedstudy of the spatial spread of pertussis [8,9].This is presentedin the next section, along with an analogous study of measlesdynamics in England and Wales [21].

12.3.3 “CITY–VILLAGE” SPREADBased on theoretical studies,Anderson et al. [3] first proposedthat infections spread following a size hierarchy from cities to

villages (the “city–village” paradigm). This was later con-firmed by different empirical studies of measles cases in theBritish Isles and the United States [11,20,21], which showedthat infection progressively diffuses from urban centers to thesurrounding rural areas, and at regional and large scales.Thephenomenon of infectious disease diffusion from big cities tosmaller localities could be quite important in terms of infec-tious disease control. Indeed, the identification of “sources” ofinfection could be used to target vaccination efforts. For thisreason, it is very important to study the spatio-temporaldynamics of infectious diseases in different contexts.

Measles diffusion in England and Wales was analyzed usingmorbidity reports from 845 towns and cities and 457 ruraldistricts [21]. In this analysis, only the 60 largest cities areclassed as “urban” populations.The remaining time series arethen aggregated in a “rural total.” The proportion of totalcases reported in each “urban” population was then com-pared to the proportion of total cases found in the rural totaltime series.Their relation was characterized by their Pearsoncorrelation coefficient, and negative correlations correspondto populations in which epidemics take-off before they do inthe rural total.

Correlation coefficients are plotted in relation to popula-tion size in Figure 12.3A. Correlation coefficients betweenthe rural total and time series from large populations (asproportions of the total cases) are usually negative, showingthat measles spreads from the largest cities (e.g., London orBirmingham, which correspond to the two largest greendisks in Fig. 12.7) to the surrounding area. Populations abovethe CCS serve as “reservoirs” from which measles spreads torural 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 finerspatial scale.As before, we defined a rural total, made up of allbut the two largest cities in Niakhar (Toukar and Diohine).Our study suggested that disease progresses from the largest


Fig. 12.5. Mean duration of fade-out (in weeks) in relation to community size for pertussis inEngland 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 (ingray) and in vaccine era, that is, 1987–2000 (in black). It is important to notice differences in scalesbetween the two graphs.

Population size (x105) Population size

Mea

n fa

de-o

ut d

urat

ion

(wee

ks)

Mea

n fa

de-o

ut d

urat

ion

(wee

ks)

1 2 3 4 5 6 7 0 500 1000 1500 2000 2500 3000 350080

2220181614121086420

160

140

120

100

80

60

40

20

0

Extracted from Rohini et al (2000) Extracted from Broutin et al (2004)

England and Wales Niakhar, Senegal

PERTUSSIS

Fig. 12.6. Weekly cases of pertussis in all of Niakhar (Senegal,1984–1999). Black dots at the top show weeks in which no caseswere recorded.

town (Toukar) to the rural surroundings (see Fig. 12.7B).Indeed, negative correlations between “urban”populations andthe rural aggregate are indicative of an urban–rural hierarchyin pertussis epidemics in Niakhar after vaccination. Epidemicsin “urban” populations (which have markets, bus stations, andhealth centers) begin and reach their peak between 10 and 15weeks before “rural” epidemics, in conformity with resultsobtained for measles in England and Wales [21].

As noted before (Section 12.3.2), whooping cough cannotpersist in Niakhar without external input of new cases (seeFig. 12.5 the CCS is not reached in Niakhar).We can nowadd a new fact to this mechanism: cases arrive initially in thebiggest villages,Toukar, before spreading to the surroundingareas. Even though this pattern of disease spread can explainthe spatio-temporal dynamics of pertussis in the studied area,it must be noted that other mechanisms (e.g., pertussis arriv-ing directly in the small villages from an external source) can-not be ruled out. Thus, a size hierarchy could potentiallydetermine the spatio-temporal dynamics of an infection evenin effectively rural areas from which the infection fades outafter epidemics.

This type of approach needs to be completed by other stud-ies, because it could be helpful for adapting vaccination strate-gies. If spread of disease from urban centers to rural countiescan be generalized, then this mechanism could imply newstrategies for vaccinations, with less expanded but more preciseprograms that would be more realistic in the field, particularlyin developing countries.This new type of research should leadto better control of disease using targeted vaccination.

12.4 CONCLUSION

The example in Section 12.3 illustrates how the interchangeof ideas between ecologists and epidemiologists has con-tributed to our understanding of pathogen populationdynamics. Some of the theoretical developments derivedfrom this approach have in fact led to concrete suggestionsaimed at improving vaccination strategies [1,35]. Other stud-ies with a similar perspective have shown that ecologicalinteractions between pathogens might also have consequenceson disease dynamics [36,39].This suggests that the interaction


Fig. 12.7. Illustration of the comparison of infectious disease spread at two different spatial scales.Theleft part (A) describes the spread of measles in England and Wales (cf. [21]).The right part (B) showspertussis 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) andmeasles in a “rural” aggregate (see text for details). For each part, the top graph provides on the y-axisthe correlation between the “rural” aggregate and the time series in individual populations (845 local-ities in A and 30 in B), in relation with the population size (on the x-axis). A negative correlationdescribes a delay between rural cases and epidemic increase in the study population.The bottom mapsare a spatial representation of the upper graphs. More analyses (not shown here) confirm that casesappeared first in the biggest localities and then in rural populations (black arrows symbolize diseasediffusion). See color plates.

between a pathogen and its host need not be the only onerelevant to epidemiologists.

Definitively, ecology and epidemiology need to develop strongerlinks, both with each other and with more, relevant, disciplines suchimmunology and microbiology. Ecology is based on the popula-tion-level studies of ecosystem dynamics and interactions.Infectious diseases or pathogen populations suffer the sameecological and evolutionary laws experienced by other livingbeings.Whatever the name (“Medical ecology,”“Eco-epidemi-ology,” or “Ecology of health”), the coupling of both ecologyand epidemiology is now the necessary way of research to bet-ter understand and control infectious diseases in this fast evolv-ing word. In fact, much remains to be done to control pre-existing infections, and emerging and re-emerging infectionspresent novel challenges to epidemiologists.The magnitude ofthe challenge is enormous. It is therefore essential to maintaina close collaboration between epidemiologists and ecologists.Yet it is similarly important to expand interdisciplinary linksand strengthen the relationship with, for example, populationgeneticists that can potentially lead to predicting pathogenevolution and emergence (18,24,40).

The task ahead will also require a coordinated effort togather information at different biological levels of organiza-tion (from molecular scale to ecosystem). Indeed, the lack ofreliable time series constitutes a major brake to the under-standing of population dynamics in epidemiology. For that,standardized health surveys that reflect pathogen dynamics andkeep track of host habits and demography will be indispensable.Population genetics will be a necessary complement.Additionally, data on environmental changes and their effect onspecies diversity and habitat composition will be needed tocomplete the picture. In fact, we need first to observe and under-stand 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 effortsto 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, theNational Science Foundation, and the Ellison MedicalFoundation.

REFERENCES

1. Agur Z, Cojocaru L, Mazor G,Anderson RM, Danon YL. Pulsemass measles vaccination across age cohorts. Proc Natl Acad SciUSA 1993;90(24):11698–702.

2. Anderson RM, Donnelly CA, Ferguson NM, et al.Transmissiondynamics and epidemiology of BSE in British cattle. Nature1996;382(6594):779–88.

3. Anderson RM, Grenfell BT, May RM. Oscillatory fluctuationsin the incidence of infectious disease and the impact of vacci-nation: time series analysis. J Hyg (Lond) 1984;93(3):587–608.

4. Anderson RM,May RM. Infectious Diseases of Humans:Dynamicsand Control. Oxford University Press, Oxford, 1991.

5. Aron JL, Patz JA. Ecosystem Change and Public Health.The JohnsHopkins University Press, Baltimore, 2001.

6. Bartlett MS. Measles periodicity and community size. J R StatSoc A 1957;120:48–70.

7. Bartlett MS.The critical community size for measles in UnitedStates. J R Stat Soc, Ser A 1960;123:37–44.

8. Broutin H, Elguero E, Simondon F, Guégan JF. Spatial dynam-ics of pertussis in a small region of Senegal. Proc R Soc Lond SerB Biol Sci 2004;271(1553):2091–8.

9. Broutin H, Simondon F, Guégan JF.Whooping cough metapop-ulation dynamics in tropical conditions: disease persistence andimpact of vaccination. Proc R Soc Lon Ser B Bio Sci 2004;271(Suppl):S302–5.

10. Cazelles B, Chavez M, McMichael AJ, Hales S. Nonstationaryinfluence of El Nino on the Synchronous Dengue Epidemics inThailand. PloS Med 2005;2(4):e106.

11. Cliff A, Haggett P, Smallman-Raynor M. Measles: An HistoricalGeography of a Major Human Viral Disease from Global Expansionto Local Retreat. Blackwell Scientific Publication, Oxford, 1993,pp. 1840–990.

12. Cohen ML. Changing patterns of infectious disease. Nature2000;406(6797):762–7.

13. Crowcroft NS, Britto J.Whooping cough – a continuing prob-lem. Br Med J 2002;324(7353):1537–8.

14. Das P. Whooping cough makes global comeback. Lancet InfectDis 2002;2(6):322.

15. de Melker HE, Schellekens JF, Neppelenbroek SE, Mooi FR,Rumke HC, Conyn-van Spaendonck MA. Reemergence ofpertussis in the highly vaccinated population of theNetherlands: observations on surveillance data. Emerg Infect Dis2000;6(4):348–57.

16. Duncan CJ,Duncan SR,Scott S.Oscillatory dynamics of smallpoxand the impact of vaccination. J Theor Biol 1996;183(4): 447–54.

17. Ferguson NM, Donnelly CA, Anderson RM. The foot-and-mouth epidemic in Great Britain: pattern of spread and impactof interventions. Science 2001;292(5519):1155–60.

18. Ferguson NM, Galvani AP, Bush RM. Ecological and immuno-logical determinants of influenza evolution. Nature 2003;422(6930):428–33.

19. Graunt J. Observations on the Bills of Mortality. The Raycraft,London, 1962.

20. Grenfell BT, Bjornstad ON, Kappey J.Travelling waves and spatialhierarchies in measles epidemics. Nature 2001;414(6865): 716–23.

21. Grenfell BT, Bolker BM. Cities and villages: infection hierar-chies in a measles metapopulation. Ecol Lett 1998;1:63–70.

22. Grenfell BT, Dobson AP. Ecology of Infectious Diseases in NaturalPopulations. Cambridge University Press, Cambridge, 1998.

23. Grenfell BT, Harwood J. (Meta)population dynamics of infec-tious diseases. Tree 1997;12(10):395–99.

24. Grenfell BT, Pybus OG, Gog JR, et al. Unifying the epidemio-logical and evolutionary dynamics of pathogens. Science 2004;303(5656):327-32.


25. Grimprel E, Baron S, Levy-Bruhl D, et al. Influence of vaccina-tion coverage on pertussis transmission in France. Lancet 1999;354(9191):1699–700.

26. Guris D, Strebel PM, Bardenheier B, et al. Changing epidemiolo-gy of pertussis in the United States: increasing reported incidenceamong adolescents and adults, 1990–1996. Clin Infect Dis1999;28(6):1230–7.

27. Hamer W. Epidemic disease in England. Lancet 1906;1:733–9.28. Hanski IA, Gilpin ME. Metapopulation Biology: Ecology, Genetics,

and Evolution.Academic Press, San Diego, CA, 1997.29. Haydon DT, Stenseth NC, Boyce MS, Greenwood PE. Phase

coupling and synchrony in the spatiotemporal dynamics ofmuskrat and mink populations across Canada. Proc Natl Acad SciUSA 2001;98(23):13149–54.

30. Ims RA,Andreassen HP. Spatial synchronization of vole popula-tion dynamics by predatory birds. Nature 2000;408(6809):194–6.

31. Keeling MJ,Woolhouse ME, May RM, Davies G, Grenfell BT.Modelling vaccination strategies against foot-and-mouth dis-ease. Nature 2003;421(6919):136–42.

32. Kendall BE, Prendergast J, Bjørnstad ON.The macroecology ofpopulation dynamics: taxonomic and biogeographic patterns inpopulation cycles. Ecol Lett 1998;1(3):160–4.

33. Levins R. Some demograpphic and genetic consequences ofenvironmental heterogeneity for biological control. BullEntomol Soc Am 1969;15:237–40.

34. Morens DM, Folkers GK, Fauci AS.The challenge of emergingand re-emerging infectious diseases. Nature 2004;430(6996):242–9.

35. Nokes DJ, Swinton J.Vaccination in pulses: a strategy for globaleradication of measles and polio? Trends Microbiol 1997;5(1):14–9.

36. Rohani P, Earn DJ, Finkenstadt B, Grenfell BT. Populationdynamic interference among childhood diseases. Proc R SocLond Ser B Biol Sci 1998;265(1410):2033–41.

37. Rohani P, Earn DJ, Grenfell BT. Opposite patterns of synchronyin sympatric disease metapopulations. Science 1999;286(5441):968–71.

38. Rohani P, Earn DJ, Grenfell BT. Impact of immunisation onpertussis transmission in England and Wales. Lancet 2000;355(9200):285–6.

39. Rohani P, Green CJ, Mantilla-Beniers NB, Grenfell BT.Ecological interference between fatal diseases. Nature 2003;422(6934):885–8.

40. Smith JM, Feil EJ, Smith NH. Population structure and evolu-tionary dynamics of pathogenic bacteria. Bioessays 2000;22(12):1115–22.

41. Thomas F, Renaud F, Guégan JF. Parasitism and Ecosystems.Oxford University Press, New York, 2005.

42. Tilman D, Kareiva PM. Spatial Ecology: The Role of Space inPopulation Dynamics and Interspecific Interactions. PrincetonUniversity Press, Princeton, NJ, 1997.

43. WHO. Progress towards global control and regional elimination1990–1998. Wkly Epidemiol Rep 1995;70(1):389–94.

44. WHO. Wkly Epidemiol Rep 1999;74(10):137–44.


ecology of infectious diseases: an example with …chapter 12 ecology of infectious diseases: an...

Documents