Understanding the Interrelationships Between Botanical, Human, and Veterinary Epidemiology: The Ys and Rs of It All

.

©1999 Blackwell Science, Inc.

Understanding the Interrelationships Between Botanical, Human, and Veterinary Epidemiology:

The Ys and Rs of It All

Forrest W. Nutter, Jr.

Department of Plant Pathology, Iowa State University, Ames, Iowa

ABSTRACT

Epidemiology is concerned with disease dynamics withinhost populations. The botanical (plant disease), human(medical), and veterinary fields of epidemiology all havea common lineage in that all three disciplines share ecol-ogy as a common parent, yet all three fields have alsoevolved from a practical need to better manage diseasesin their respective host populations. The analysis of bo-tanical, human, and veterinary epidemics shares six com-mon steps: 1) Defining disease problems in quantitativeterms (attainable production or attainable populationhealth minus the current production or populationhealth management level presently being achieved); 2)Quantifying state and rate variables of pathosystemcomponents (pathosystem analysis) to determine the ef-fect of host, pathogen, and vector populations on therate of pathogen spread (r), and the source and strength

of inoculum reservoirs (y

0

); 3) Identifying the most effec-tive management strategy(ies) (i.e., reduce y

0

, r, and/or t)to achieve effective control; 4) Developing and thenquantifying the impacts of specific and integrated man-

agement tactics on disease dynamics as a means to eval-uate a proposed disease management strategy or strate-gies; 5) Integrating management tactics into managementprograms and reevaluating the epidemiologic impacts ofwhole disease management programs on pathosystemdynamics, and finally; 6) Assessing the economic andenvironmental risks versus the actual benefits achievedby implementing integrated disease management pro-grams. Although the botanical, human, and veterinaryfields of epidemiology share many common concepts,the terminology used to express these concepts withinthe different disciplines has evolved different dialects(operational definitions) as well as different approachesto solve epidemiologic questions. By improving commu-nication among epidemiologists who work mostly withintheir own disciplines (botanical, medical, and veterinarybranches of epidemiology), we can better understandnot only our own pathosystems, but we can vastly im-prove our understanding of disease dynamics at the eco-system and global scales as well.

INTRODUCTION

The world’s population continues to increase ex-ponentially in many parts of the world, with pre-dictions that the human population will pass the10 billion benchmark within the next 50 years.The anticipated increase in the human popula-tion carries with it an increased demand for foodand fiber; it is anticipated that a 40% increase incereal grains and a 60% increase in animal prod-

ucts will be needed by the year 2020. There is asimple but potentially devastating cause and ef-fect relationship between the predicted increasesin human (and animal) populations per unit areaand reductions in the health and productivity ofmany of the earth’s ecosystems (Rapport

et al.

1998a). It goes hand-in-hand that a decline in thehealth of the earth’s ecosystems will likely resultin a decline in the health and productivity of hu-man and animal populations as well (Rapport

etal.

1998b; 1999; Rapport 1999). Pathosystem anal-ysis, which involves breaking a pathosystem hostpopulation: pathogen population: environmentinto its component parts (state and rate variables),

Address correspondence to: Forrest Nutter, Jr., Department of Plant Pathology, 351 Bessey Hall, Iowa State University, Ames, IA 50011; E-mail [email protected].

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Ecosystem Health Vol. 5 No. 3 September 1999

can provide a framework to assess the impact ofmitigation strategies aimed at reducing initial in-oculum, the rate of disease development, and/orthe time that host and pathogen populations in-teract over both time and space (Nutter 1991;Nutter 1997).

EPIDEMIOLOGY DEFINED

The science of epidemiology is extremely relevantand vital to today’s global ecosystems (Campbell1999). Epidemiology is the science that deals withqualitative and quantitative issues regarding thehealth of populations including, but not limitedto, animal, plant, and human populations. Ecosys-tems, cropping systems, states, regions, and evencountries constitute other potential populationunits that can be studied from a geospatial/epide-miological perspective. According to medical epi-demiologists, epidemiology is defined as “thestudy of the distribution and determination of dis-ease in human populations” (Woodward 1999).Veterinary and plant disease epidemiologists dis-agree with this definition as these disciplines offera more general definition that is not limited tojust human populations. According to one veteri-nary epidemiology textbook (Thrusfield 1995),veterinary epidemiology is the science that is con-cerned with disease in animal populations. Someveterinary epidemiologists, however, advocate theuse of the term “epizootiology” for the study ofdisease in animal populations; moreover, “epori-thology” has been proposed as a term to describethe study of disease in bird populations—and so itgoes—a new term for each epidemiological hostpopulation being studied! Although some plantdisease (botanical) epidemiologists have proposedthe use of the term “epiphytology” when studyingdisease in plant populations (Zadoks & Schein1979), the term epidemiology, which means epi(upon), deme (population), ology (study), is firmlyentrenched throughout the scientific literature ofmany disciplines. In my opinion, I see no advan-tage in the usage of scientific jargon that onlyserves to fragment rather than unite the branchesof science that concern the study of disease dy-namics within host populations. In lieu of Latinterms that are not as readily comprehended bynonscientists or even scientists from other disci-plines, I propose that a descriptive prefix term beused (medical, human, veterinary, plant disease,etc.) to differentiate the different branches of ep-idemiology (e.g., plant disease epidemiology vs.

veterinary epidemiology for animal systems), andthat the term epidemiology be broadly defined toinclude all subdisciplines. The definition that I fa-vor states that epidemiology “is the science thatdeals with the study of the dynamic interaction ofhost and pathogen populations over time andspace as affected by the environment (Nutter, un-published).”

REFERENCE POINTS FOR POPULATION HEALTH

The efficient application of any disease manage-ment tactic or program first requires the collec-tion of accurate and precise disease and pathogenassessment data (Nutter & Gaunt 1996). Reliableestimates of disease dynamics in host populations(human, plant, veterinary, ecosystem) facilitatesthe objective identification of the relative impor-tance of biotic and abiotic pathogens. Conse-quently, limited resources from federal, state, pri-vate industry, health care organizations, etc., canbe assigned on a priority basis to optimize returnsfrom a given investment (Nutter 1991; Wegulo

etal.

1997). Several reference points for the healthof a population must be characterized before pri-oritizing health research/protection programs ac-cording to need (Figure 1).

Maximum Attainable Population Health isthe theoretical health of a population that couldbe achieved if the population (plant, animal, hu-man) was allowed to develop under optimum en-vironmental conditions (i.e., in the absence ofabiotic [air pollutants, water pollutants, contami-nated soil] and biotic diseases and pests [insects,nematodes, microorganisms that cause disease]).The health of the population is constrained onlyby population genetics and not by environmental(abiotic) or biotic stresses (note: genetic disor-ders are considered to be inherent within hostpopulations, and though some are treatable, forthe purposes of this article I will consider them as“disorders” rather than “diseases”).

Attainable Population Health is the healthlevel of a population that is obtained for a specificgeographic sampling unit (field, hospital, county,region, state, country) when all available healthprotection tactics are used to alleviate the diseasescaused by biotic pathogens and pests. Some ex-amples of preventive or proactive tactics and theirepidemiologic effects may include: providing safewater and food supplies (reduces y

0

), immuniza-tions to protect individuals in the population (re-

Nutter: The Ys and Rs of it All

133

duces the proportion of susceptible individuals,1

2

y), the use antibiotics (reduces y

0

in diseasedindividuals and the rate of pathogen spread, r, byshortening the infections period within individu-als) (Goldmann 1999), and the use of quarantineprocedures (reduces r within populations sinceinfected individuals do not contribute to new in-fections as well as reducing y

0

if infected individu-als are not allowed to enter a healthy population)(Donnelly

et al.

1999). Thus, attainable popula-tion health is site-specific (within a geographicallydefined unit) and is the level of population healthachieved when biotic health risk factors are al-leviated, but not environmental (abiotic) factorssuch as soil fertility, soil contamination, air pollut-ants, droughts, floods, automobile accidents, fire-arm deaths, etc., resulting in a Population HealthGap (I) caused by environmental (abiotic) stresses.These factors can be quantitatively assessed andprioritized for any given geographically definedunit.

Actual Population Health is the health levelcurrently achieved by using the health care pro-grams and facilities (or plant disease manage-ment programs) that are currently recommendedand available (not all available technologies aredeployed, just those currently recommended).Thus, the difference between Attainable and Ac-tual Population Health is the Population Health

Gap (II) that is caused by one or more biotic fac-tors in a host population within a geographicallydefined unit. This second “Population HealthGap (II)” represents the measurable constraintsto population health due to as yet unavoidable orunmanageable biotic factors. If the impact ofthese biotic factors were alleviated, the actuallevel of population health would be closer to at-tainable population health levels. This health gapis analogous to the Food and Agriculture defini-tion for crop losses in agricultural systems, i.e.,the difference between attainable yields and theactual yields obtained by farmers due to diseasesand pests that are not fully controlled (Nutter &Munkvold 1998). This “yield gap” provides a quan-titative measure of crop loss (Nutter

et al.

1993)and represents measurable constraints to produc-tion.

Certainly there is the potential for interac-tions between abiotic and biotic factors. For ex-ample, exposure to a factor contributing to healthgap I could predispose a host population to dis-ease and pest organisms listed under populationhealth gap II. Moreover, populations geneticallyat risk to environmental factors that trigger dis-ease (cancer) or cause a lowering of the immunesystem (AIDS, diabetes), could result in greaterhealth risks from biotic factors listed under Popu-lation Health Gap II.

FIGURE 1. Reference points for population health.

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The cost of deploying all available tactics to al-leviate (minimize) either Population Health Gap Ior II may be higher than individuals, federal andstate governments, and health care systems arewilling to pay. The return on investment (bene-fits/risks) may be such that the population at riskis willing to reduce health care (or plant protec-tion) costs in exchange for “an acceptable level ofrisk” to individuals within a plant, animal, or hu-man population. This is the Economic PopulationHealth level. The Economic Population Healthlevel is usually located above the Actual Popula-tion Health level but below the Attainable Pop-ulation Health level. The Economic PopulationHealth concept, although not new, is one thatevokes a great deal of emotion and discussion be-cause what is acceptable risk to some individualsin a population is not acceptable to others. Theconcept is more readily applied by farmers. If bi-otic pathogens (mostly fungi) reduce corn yieldsby five bushels per acre and the price of the cornis $3.00 per bushel, it would not be cost effec-tive for a farmer to apply a fungicide that costs$20.00 per acre per application to protect the cropfrom a five bushel loss ($15 damage). Therefore,some disease (risk) in the corn population can beeconomically tolerated (Wegulo

et al.

1997). Thisconcept is difficult to accept in human and animalpopulations when a family member or the familypet is stricken with a disease. I know this from per-sonal experience as my oldest son recently under-went a bone marrow transplant for Hodgkin’sLymphoma. Although early detection is critical tothe effective treatment of this disease, it would beunrealistic to expect a health care system to bearthe financial burden of screening every individualfor this disease (other than a physical examina-tion) when the diagnostic/detection tests (CTscans, gallium scans, etc.) are not only cost prohib-itive, but such tests also carry a degree of healthrisk in themselves.

Balancing the risks versus benefits of screen-ing for plant pathogens in seeds and plants hasmuch in common with medical testing for theearly detection of diseased individuals withinlarge populations. Phytosanitary plant and seedinspections are often performed by regulatoryagencies to prevent the movement and dispersalof pathogen-infected (or infested) seed and nurs-ery stock. Several plant pathogens, such as thecausal agent of bacterial ring rot of potato, have azero tolerance in that seed potatoes cannot besold if even one plant in a field or one tuber ina seed lot is found to be infected (Campbell &

Madden 1990; Schumann 1991). The strategy isto reduce initial inoculum (y

0

) to zero (at thesource) and to prevent the introduction of bothnon-native pathogen species (e.g., karnel bunt ofwheat from Mexico), or the introduction of morevirulent or aggressive pathogen biotypes such asthe A2 mating strain of the potato late blight fun-gus,

Phytophthora infestans

, into North Americaand Europe (Miller

et al.

1998). The coexistenceof both strains allows for sexual reproduction andthe probable production of new variants that mayovercome host resistance or the development ofnew strains resistant to present-day fungicides(Kadish

et al.

1990; Peng

et al.

1999).The development and implementation of

seed certification programs is not always practical.If a pathogen is already endemic (e.g., pea seed-borne mosaic virus in pea in Australia) and thepathogen causes minimal damage in terms of re-ducing yield and quality, then the development ofa phytosanitary seed certification program to testand detect the presence of the pathogen in peaseed lots may not provide a benefit to pea growersin terms of reducing disease risk and increasingnet returns. The cost of such a program wouldgreatly outweigh the disease management bene-fits. In this example, it would be more beneficialto spend research dollars on the development ofpea varieties with improved resistance to pea seed-borne mosaic virus than to spend resource dollarson phytosanitary programs that provide little orno economic return.

PATHOSYSTEM ANALYSIS

A pathosystem is a subsystem of an ecological sys-tem involving the interaction of host and patho-gen populations in the environment over timeand space (Campbell & Madden 1990). Pathosys-tem analysis involves breaking down a pathosys-tem into its component state and rate variables inorder to better understand disease/host/vectordynamics. Quantitative comparisons of state andrate variables allow researchers to evaluate the ef-fects of various disease management strategiesand tactics (Nutter 1997). Strategies are effortsaimed primarily at reducing either the initial in-oculum (y

0

), the rate of pathogen spread withrespect to time (r), or the amount of time thathost and pathogen populations interact (t) (Nut-ter & Gaunt 1996; Nutter 1997). Tactics are thespecific methods, procedures, or efforts used to


135

achieve strategic goals (e.g., crop rotation to re-duce initial inoculum in a farmer’s field or the useof sanitation to eradicate disease organisms onsurgical instruments) (Nutter & Gaunt 1996).

The selection of a temporal model that bestdescribes the change in disease intensity (y) withrespect to time (t) involves considerable subjectiveand objective criteria (Nutter 1997). There areseveral population growth models available thatcan be used to describe temporal disease dynam-ics. The most commonly used models include thelinear, monomolecular, exponential, logistic, andGompertz population growth models (Campbell& Madden 1990; Nutter 1997). The steps requiredto select the most appropriate model have beenpreviously described (Nutter 1997).

In plant disease, human, and veterinary epi-demiology, the logistic population growth modelis probably the model most frequently used todescribe disease dynamics with respect to time(Nutter 1997; Wegulo

et al.

1998). Disease (untrans-formed) progress curves typically have a character-istic sigmoid (S-shaped) form, with an inflection

point at the time disease incidence or severityreaches a proportion of 0.5 (50%). The absoluterate curve (dy/dt) versus time is symmetrical withthe highest rate occurring when disease incidence(or severity)

5

0.5. A biological interpretation ofthis model is that, early in the epidemic, the ab-solute rate of disease increase (dy/dt) acceleratesas disease intensity (y) approaches 0.5 because in-creasing numbers of newly diseased individuals be-come infectious and contribute to more diseasedindividuals. At later stages, the diminishing inci-dence (proportion) of healthy plants remaining(1

2

y) limits the rate of increase. This relationshipcan be expressed in a differential equation for theabsolute rate of disease increase as: dy/dt

5

r

l

y(1

2

y). Thus, the absolute rate of disease increaseis proportional to the incidence of noninfected in-dividuals late in the epidemic (1

2

y), the level ofdisease incidence early in the epidemic (y), andthe logistic rate of disease increase (r

l

). Based onthe incidence data (medical definition) for thenumber of deaths caused by the bubonic plague inLondon during the year 1665–1666 (Figure 2A), a

FIGURE 2. (A) Number of new deaths/week caused by the bubonic plague epidemic in London during the period 1665–1666, (B) cumulative incidence of deaths caused by bubonic plague over time, (C) the rate curve (dy/dt vs. t) for the 1665–1666 bubonic plague epidemic in London, and (D) the rate of death (0.45 logits/week) calculated using the logistic model.

136


typical S-shaped death progress curve can be con-structed by graphing cumulative deaths over time(Figure 2B). The rate curve (dy/dt vs. t) for thisepidemic is bell shaped and symmetrical, indicat-ing that the logistic model would likely be the bestmodel to describe the temporal dynamics of hu-man deaths caused by the bubonic plague (Fig-ure 2C). The rate of death for this epidemic wasextremely fast (0.45 logits/week), and time (inweeks) explained 98% of the variation in thechange of logit y (

R

2

) (Figure 2D). The rate ofindividuals “removed” from the epidemic dueto human deaths probably exceeded the rate ofnewly diseased individuals which is a major reasonwhy the epidemic declined after reaching a peak ofnearly 8000 deaths/week (Figure 2A and 2C).

A typical S-shaped disease progress curve isalso demonstrated in Figure 3A for the disease“Yellow Crinkle” in papaya caused by a phyto-plasma. This epidemic was monitored on a pa-paya plantation located near Katherine, NorthernTerritory, Australia, during the years 1996–1999(Nutter

et al.

1998). All papaya plants (census) inthis plantation (approximately 65 rows

3

55plants per row,

n

5

3575) were assessed eachmonth for symptoms typical of those caused bythe phytoplasma. The first diseased plants wereobserved in May 1996 and cumulative yellow crin-kle disease incidence (botanical epidemiology def-inition) was 22.2% by March 1999. Two popu-lations were monitored in this study. First, theincrease in diseased papaya plants (disease inci-dence) was plotted with respect to time, andsecond, the increase in the incidence of deadplants was plotted over time (which is analogousto plotting the cumulative number of deaths dueto bubonic plague in human populations). Theslope of the regression line relating logit diseaseincidence (y) to time (x) was 0.15 logits/month(the slope parameter is taken as a measure of therate of transformed disease progress with respectto time), while the logistic rate of the incidence ofdead plants was 0.13 logits/month. Thus, the rateof increase in diseased plants was slightly fasterthan the rate of plant death, indicating that thisepidemic is likely to continue to produce newlydiseased individuals at a rate that is faster thanthe rate that individuals are “removed” from theepidemic due to plant death (Figure 3B). Usingthe logistic model, time (x) explained 91% (

R

2

)of the variation in logit disease incidence and94% (

R

2

) of the variation in the logit incidencefor dead plants for this epidemic.

DISEASE ASSESSMENT TERMS AND CONCEPTS

The efficient application of any disease manage-ment program requires accurate and precise infor-mation concerning the quantitative management ofdisease intensity (y). To help others better under-stand how disease intensity is operationally definedand measured among the different scientific disci-plines, it is important that we use terms that clearlyconvey what “y” is and how it can be measuredamong the various branches of epidemiology. Un-fortunately, there is some disagreement regardingthe operational terms and concepts used by the dif-ferent disciplines (branches) of epidemiology.

FIGURE 3. (A) Progress curves for cumulative disease in-cidence of papaya plants with yellow crinkle disease and cumulative incidence of papaya plants killed by yellow crinkle disease in Katherine, Northern Territory, Australia during the period May 1996 through March 1999 and (B) cumulative disease incidence of diseased minus dead plants affected by yellow crinkle disease.


137

Disease intensity is a general (generic) termfor the amount of disease in a population (Camp-bell & Madden 1990; Nutter

et al.

1991). Diseaseassessment is the process of measuring disease in-tensity quantitatively (Nutter & Gaunt 1996). Inplant disease epidemiology, the three most com-mon measures of disease intensity are: (i) inci-dence, (ii) prevalence, and (iii) severity (Nutter

etal.

1991). These terms, however, are definedsomewhat differently by medical and veterinaryepidemiologists, and therefore, it is important forepidemiologists in all disciplines to operationallydefine how these terms are being used by provid-ing the dimensions for state and rate variables(Figure 4).

The most notable difference in the defini-tions for disease incidence is in the dimensions.In botanical (plant disease) epidemiology, diseaseincidence is clearly a state variable with the di-mensions n/N

3

100, whereas both the medicaland veterinary definitions include the dimensiontime (t). The addition of time to the definitioncreates some confusion in that this variable (dis-ease incidence) is sometimes defined as a statevariable and other times as a rate variable. The di-mensions (n/t) indicate that this is a state vari-able rather than a rate variable (which would bedy/dt, the change in incidence with the change intime). Using the yellow crinkle disease of papayaexample, the (cumulative) disease incidence (bo-

tanical epidemiology definition) in January 1999was 21.8%. The number of new cases (diseasedpapaya plants) between December 1998 and Janu-ary 1999 (medical definition) was 20 for this onemonth (31-day period). The rate of disease inci-dence would be the change in disease incidencewith the change in time (dy/dt). Using the logis-tic model to obtain a linear relationship betweenthe change in disease (logit y) with the change intime, the rate of yellow crinkle disease incidenceover the course of the epidemic was 0.15/logitsper month (a moderately fast epidemic).

Another term that varies somewhat amongdisciplines is disease prevalence (Figure 5). In bo-tanical (plant disease) epidemiology, disease prev-alence is often defined as the number of fields (orcounties) where a disease is present divided bythe total number of fields (or counties) assessed(Nutter

et al.

1991). Prevalence data can be veryuseful in helping to prioritize research. Table 1provides quantitative information concerning therelative prevalence of 13 diseases of corn inIowa in 1992 (700 fields) compared to 1998 (1125fields). Notice that one disease, gray leaf spot,caused by the fungus

Cercospora zeae-maydis

, in-creased in prevalence (field basis) from 12% in1992 to 90% in 1998. Between 1981 and 1998,gray leaf spot increased in prevalence exponen-tially at the rate of 0.16 in units per year (

R

2

5

59%) (Figure 6A and 6B) (Ward

et al.

1999). The

FIGURE 4. Disease incidence as defined by botanical, medical, and veterinary epidemiologists.

138


FIGURE 5. Disease prevalence as defined by botanical, medical, and veterinary epidemiologists.

TABLE 1

Prevalence of diseases affecting seed corn fields in Iowa in 1992 and 1998

Prevalence (%)

Disease Pathogen 1992

1

1998

2

Common rust

Puccinia sorghi

96 74Common smut

Ustilago zeae

88 76Northern leaf blight

Exserohilum turcicum

73 26Helminthosporium leaf spot

Bipolaris zeicola

33 44Stewart’s wilt

Pantoea stewartii

13 25Gray leaf spot

Cercospora zeae-maydis

12 90Eyespot

Aureobasidium zeae

10 28Yellow Leaf Blight

Phyllosticta maydis

2 —Anthracnose

Colletotrichum graminicola

0.6 18Physoderma brown spot

Physoderma maydis

0.6 2Holcus leaf spot

Pseudomonas syringae

0.4 4Southern leaf blight

Bipolaris maydis

— 6Head smut

Sphacelotheca reiliana

— 0.2

1

Based on a total of 700 fields that were inspected.

2

Based on a total of 1,101 fields that were inspected.

medical (Anderson & May 1995) and veterinary(Thrusfield 1995) fields of epidemiology both de-fine prevalence as the cumulative number ofcases in a population (n) at a particular point in

time (date, month, year). Although this defini-tion is close to the definition for disease inci-dence as used by botanical epidemiologists, themedical/veterinary definition for prevalence is


139

actually a measure of disease frequency (n) attime t (note that total number of individuals inthe population, N, is not a part of the definitionas it is in the botanical definition for both inci-dence and prevalence).

There is good agreement among the differ-ent branches of epidemiology with regards to thedefinition of the term disease severity (the amountof disease per individual sampling unit). In bo-tanical epidemiology, this could be operationallydefined as the diseased leaf area (l

2

) divided bythe total leaf area of a leaf (L

2

)

3

100 (l

2

/L

2

3

100). In medical or veterinary epidemiology, dis-ease severity could be the size of a lesion (l

3

w)or the volume of a tumor (equation and dimen-sions for a cylinder or a sphere, etc.). Disease se-verity is often defined as a particular (descriptive)stage of disease development for an individual(Nutter

et al

. 1991; Woodward 1999).In summary, the various branches of epidemi-

ology have evolved different dialects to describevery similar epidemiological concepts. Epidemiol-

ogists among the different epidemiological disci-plines should therefore operationally define howdisease assessment terms and concepts are usedin their studies by providing not only a descrip-tive definition, but also an operational definitionthat includes the dimensions that were used todefine state and rate variables. Greater careshould also be given by journal reviewers and edi-tors to ensure that rate variables are not actuallystate variables (and vice versa).

CONCLUSION

The health and productivity of animal, botanical,and human populations are all interrelated. Anepidemic of ergot (

Claviceps purpurea

) of rye cansubsequently cause an epidemic of ergot poison-ing in humans and animals when infected ryegrain is ingested by mammals. A fungal infectionof corn ears by a fungus (

Fusarium

) produces car-cinogenic metabolites that increase the incidenceof certain cancers in human populations (Mara-sas 1995; Munkvold & Desjardins 1997). Misuse ofantibiotics in one pathosystem (animals) maylead to the increased risk of the development ofantimicrobial-resistant pathogen populations thatwill adversely impact on human health (Anony-mous 1999; Goldmann 1999). Introducing rice cul-tivation into Africa to help alleviate food deficits re-sults in an increase in malaria cases because ofthe creation of a more sustainable habitat formosquitoes (pathogen vectors). There are manyepidemiologic concepts, methods, and modelingapproaches that could be shared among the epide-miologists who represent the different branches ofepidemiology to help us better understand ourown pathosystems as well as disease dynamics oper-ating at the ecosystem, country, international, andglobal scales.

ACKNOWLEDGMENTS

Journal Paper No. J-18590 of the Iowa Agricultureand Home Economics Experiment Station, Ames,Iowa 50011. Project no. 3394.

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FIGURE 6. (A) Increase in gray leaf spot prevalence in lowa during the period 1972–1998, and (B) rate of in-crease in gray leaf spot prevalence with respect to time (0.16 in units/year, R2 5 58%).

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Understanding the Interrelationships Between Botanical, Human, and Veterinary Epidemiology: The Ys and Rs of It All