Ecology Disease Ecology
by
Richard S. Ostfeld
  • LAST REVIEWED: 06 June 2017
  • LAST MODIFIED: 22 February 2018
  • DOI: 10.1093/obo/9780199830060-0128

Introduction

Disease ecology is a rapidly developing subdiscipline of ecology concerned with how species interactions and abiotic components of the environment affect patterns and processes of disease. To date, disease ecology has focused largely on infectious disease. The scientific study of infectious disease has a long history dominated by specialists on the taxa of infectious agents (e.g., bacteriologists, virologists), mechanisms of host defense (e.g., immunologists), effects of infection on individual hosts (e.g., pathologists), effects on host populations (epidemiologists), and treatment (e.g., practicing physicians and veterinarians). Disease ecology arose as scientists increasingly recognized that the interactions between pathogen and host could be conceptually united with other interspecific interactions, such as those between predator and prey, competitors, or mutualists. At its simplest, an infectious disease consists of an interaction between one species of pathogen and one species of host. The evolution of disease ecology since the late 20th century has incorporated additional layers of complexity, including recognition that most pathogens infect multiple species of host, that hosts are infected with multiple pathogens, and that abiotic conditions (e.g., temperature, moisture) interact with biotic conditions to affect transmission and disease. As a consequence, a framework broader than the simplest host-pathogen system is often required to understand disease dynamics. Disease ecologists are interested both in the ecological causes of disease patterns (for instance, how the population density of a host influences transmission rates), and the ecological consequences of disease (for instance, how the population dynamics of a host species change as an epidemic progresses). Consequently, disease ecology today often integrates across several levels of biological organization, from molecular mechanisms of pathology and immunity; to individual-organism changes in health, survival, and reproduction; to population dynamics of hosts and pathogens; to community dynamics of hosts and pathogens; to impacts of disease on ecosystem processes; to ecosystem-level effects of climate change and landscape change on disease.

Historical Developments

The emergence of disease ecology has involved the gradual integration of several distinct lines of inquiry. One foundational development was the creation of a mathematical model of malaria shortly after the initial description of the life cycle of the malaria parasite, Plasmodium, in Anopheles mosquitoes by Sir Ronald Ross (Ross 1915). Ross’s model distinguished subpopulations of mosquitoes and humans that were susceptible from those that were infected, and it tracked the latency to infection both in vector and host. The Ross model was later generalized in models in Kermack and McKendrick 1927, which classified individuals in a host population into the following epidemiological compartments: susceptible (S), exposed (E), infectious (I), or recovered (R), and it tracked the rate at which they transitioned from one class to another. These models were tailored for specific types of diseases according to, for example, whether there is latency from exposure to infectiousness, whether hosts recover, or whether recovered hosts regain susceptibility. Tracking the numbers of individuals in each compartment and rates of transition allowed researchers to quantitatively describe and predict epidemics. Another foundational development was the incorporation of parasites into early experimental ecology. Thomas Park’s observation (Park 1948) that infection by a sporozoan parasite converted a dominant competitor into an inferior one, reversing the outcome of competition between two species of flour beetle (Tribolium spp), strongly influenced population and community ecologists. A third key development was recognition of the profound importance of infectious diseases on host populations, communities, and ecosystems. Examples include descriptions of diseases shaping human history in Diamond 1997 and Dobson and Carper 1996, a study of rinderpest shaping African wildlife communities in Plowright 1982, and Daszak, et al. 1999, which is an exploration of the association of the chytrid fungus Batrachochytrium dendrobatidis with amphibian declines worldwide. Together, these developments allowed epidemics to be understood through models and impressed upon ecologists their importance in affecting population and community dynamics.

  • Daszak, P., L. Berger, A. A. Cunningham, A. D. Hyatt, D. E. Green, and R. Speare. 1999. Emerging infectious diseases and amphibian population declines. Emerging Infectious Diseases 5.6: 735–748.

    DOI: 10.3201/eid0506.990601Save Citation »Export Citation »E-mail Citation »

    This paper was among the first to describe the effect of pathogen invasions, particularly the chytrid fungus Batrachochytrium dendrobatidis, on amphibian populations and the conservation consequences of emerging diseases of wildlife.

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  • Diamond, J. 1997. Guns, germs, and steel: The fates of human societies. New York: W. W. Norton.

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    This influential popular book argues that infectious diseases have profoundly affected the course of human civilization. The advent of agriculture in Eurasia caused crowding and the domestication of wild ungulates, which in turn led to zoonotic transmission of animal pathogens that adapted to human hosts (including smallpox, measles, and influenza viruses). Later dispersal of these pathogens with their human hosts transformed the course of history. Revised edition published as recently as 2011.

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  • Dobson, A. P., and E. R. Carper. 1996. Infectious diseases and human population history. BioScience 46.2: 115–126.

    DOI: 10.2307/1312814Save Citation »Export Citation »E-mail Citation »

    These authors describe the effects of human population density, aggregation, age structure, and other demographic variables on disease transmission, linking historical human population dynamics to the history of some major infectious diseases.

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  • Kermack, W. O., and A. G. McKendrick. 1927. Contribution to the mathematical theory of epidemics. Proceedings of the Royal Society of London A 115.772: 700–721.

    DOI: 10.1098/rspa.1927.0118Save Citation »Export Citation »E-mail Citation »

    This foundational paper describes the first models of pathogen transmission in which the host population is divided into an exhaustive set of compartments indicating their infection status: susceptible, exposed, infectious, and recovered. This paper provided the basis for many subsequent models of many infectious diseases.

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  • Park, T. 1948. Experimental studies of interspecies competition 1: Competition between populations of the flour beetles, Tribolium confusum Duval and Tribolium castaneum Herbst. Ecological Monographs 18.2: 265–307.

    DOI: 10.2307/1948641Save Citation »Export Citation »E-mail Citation »

    A classic paper in competition theory, this was highly influential in experimentally demonstrating how parasites could fundamentally alter the outcome of competition between closely related species. Park showed how a competitively dominant species of flour beetle became competitively subordinate when it was infected.

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  • Plowright, W. 1982. The effects of rinderpest and rinderpest control on wildlife in Africa. Symposia of the Zoological Society of London 50:1–28.

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    A foundational description of the history of rinderpest introduction, via cattle, into populations of wild ungulates in sub-Saharan Africa, this paper describes the consequences of wildlife mortality for African ecosystems, and the effects of livestock vaccination programs on the dynamics of this disease.

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  • Ross, R. 1915. Some a priori pathometric equations. British Medical Journal 1.2830: 546–547.

    DOI: 10.1136/bmj.1.2830.546Save Citation »Export Citation »E-mail Citation »

    After having discovered the life cycle of the malaria parasite (Plasmodium) in mosquitoes in the 1890s, Sir Ronald Ross devised a simple model to describe the relationship among mosquito abundance, Plasmodium infection, and human cases of malaria. This foundational model has influenced malaria models throughout the subsequent century.

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Overviews

Probably a consequence of its youth as a distinct subdiscipline, disease ecology currently lacks a textbook. Two edited volumes from the mid-1990s, Scott and Smith 1994 and Grenfell and Dobson 1995, were highly influential in describing basic concepts and modeling approaches and illustrating these with case studies both from wildlife and human diseases. Impacts of diseases on humans, wildlife, and plants, including those of economic and conservation importance, are further emphasized in Hudson, et al. 2001 and Aguirre, et al. 2002. More-recent general overviews of the relationship between epidemiology and community ecology are provided in Collinge and Ray 2006; Hatcher and Dunn 2011; and Ostfeld, et al. 2008. An additional volume that provides useful overviews of more-specific aspects of disease ecology is Poulin and Morand 2004 for descriptions of the diversity of animal parasites.

  • Aguirre, A. A., R. S. Ostfeld, G. M. Tabor, C. House, and M. C. Pearl, eds. 2002. Conservation medicine: Ecological health in practice. New York: Oxford Univ. Press.

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    This edited volume provides a series of overviews and case studies of the ways in which the health of humans, nonhuman animals, and ecosystems are interrelated. Its contributors include ecologists, conservation biologists, veterinarians, and medical doctors, covering a range of topics that are sometimes united under the concepts of “One Health” or “Ecohealth,” as well as conservation medicine.

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  • Collinge, S. K., and C. Ray, eds. 2006. Disease ecology: Community structure and pathogen dynamics. New York: Oxford Univ. Press.

    DOI: 10.1093/acprof:oso/9780198567080.001.0001Save Citation »Export Citation »E-mail Citation »

    This edited volume is devoted to exploring how the dynamics of infectious diseases affect, and are affected by, the community of interacting species within which pathogens and their hosts are embedded. Uniting case studies and theory with common themes, it has helped foster the unification of community ecology and disease ecology.

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  • Grenfell, B. T., and A. P. Dobson, eds. 1995. Ecology of infectious diseases in natural populations. Publications of the Newton Institute 7. Cambridge, UK: Cambridge Univ. Press.

    DOI: 10.1017/CBO9780511629396Save Citation »Export Citation »E-mail Citation »

    This edited volume is a pioneering work that confronts patterns of infectious diseases in wild animals (and occasionally plants) both with general and specific mathematical models. Chapters focus both on broad patterns of disease epidemiology in wild animals and on important ecological and genetic heterogeneities.

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  • Hatcher, M. J., and A. M. Dunn. 2011. Parasites in ecological communities: From interactions to ecosystems. Ecology, Biodiversity, and Conservation. Cambridge, UK: Cambridge Univ. Press.

    DOI: 10.1017/CBO9780511987359Save Citation »Export Citation »E-mail Citation »

    This book has the breadth and depth of a textbook in disease ecology, although at a somewhat advanced level. It focuses on bridging the gap between epidemiology and community ecology, with special attention to the roles of parasites in ecological communities.

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  • Hudson, P. J., A. Rizzoli, B. T. Grenfell, H. Heesterbeek, and A. P. Dobson, eds. 2001. The ecology of wildlife diseases. Oxford: Oxford Univ. Press.

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    This concise and influential book, based on a brainstorming workshop, provides a series of chapters that together constitute a useful introduction to infectious disease epidemiology as applied to wildlife. The main focus is on the triggers of wildlife epidemics, their control, and how to integrate mathematical models with data.

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  • Ostfeld, R. S., F. Keesing, and V. T. Eviner, eds. 2008. Infectious disease ecology: Effects of ecosystems on disease and of disease on ecosystems. Princeton, NJ: Princeton Univ. Press.

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    This book is the first to expand consideration of the causes and consequences of infectious-disease dynamics to the ecosystem level. It includes chapters addressing the effects of ecosystem properties and processes on infectious diseases, the effects of pathogens and the diseases they cause on ecosystem functions, and the application of disease ecology to the management of ecosystems and their components.

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  • Poulin, R., and S. Morand. 2004. Parasite biodiversity. Washington, DC: Smithsonian.

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    A lively treatise on parasitology, this book examines the diversity of parasites and their life cycles from an ecological and biogeographic point of view, describing the relationship between parasite biodiversity and that of free-living organisms, including hosts.

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  • Scott, M. E., and G. Smith, eds. 1994. Parasitic and infectious diseases: Epidemiology and ecology. San Diego, CA: Academic Press.

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    One of the earliest volumes on disease ecology, the chapters in this book, several written by the editors, cover topics ranging from basic epidemiological concepts and modeling approaches to chemotherapeutic and vaccination strategies to the ecology of specific pathogens and parasitic diseases.

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Journals

Scientific papers on disease ecology are increasingly being published in mainstream journals in ecology, including American Naturalist, Ecological Applications, Ecological Monographs, Ecology, Ecology Letters, Journal of Animal Ecology, Oecologia, Oikos, Proceedings of the Royal Society B, and Trends in Ecology & Evolution. Disease ecology also is increasingly covered in general science journals such as Nature, Proceedings of the National Academy of Sciences of the United States of America, Science, and PLoS ONE. Several other journals are somewhat more specialized in disease ecology, although most also publish papers on other aspects of infectious disease.

Defining Disease Ecology

Infectious-disease ecology can be defined as the scientific study of the ecological causes and consequences of interactions between pathogens and their hosts (see Ostfeld, et al. 2008, cited under Overviews). The interaction between pathogens and hosts can (but doesn’t necessarily) give rise to infectious disease. Pathogens in this context include both microparasites (generally consisting of viruses, bacteria, fungi, protozoans) and macroparasites (typically various helminthes and arthropods). Microparasites reproduce within hosts and typically elicit an immune response, whereas macroparasites do not reproduce within hosts and are less likely to elicit an immune response. Diseases can also be caused by genetic factors, toxic substances, radiation, poor nutrition, and other noninfectious sources, although these have not yet been well integrated into disease ecology. One exception, exemplified in Rohr, et al. 2008, is the exploration of interactions between infectious agents and toxins in causing disease in some wildlife populations. One source of terminological confusion described in Pearce 2000 is the use of the terms “ecological studies” and “ecological fallacy” in the 20th century epidemiology literature. “Ecological studies” were misleadingly defined as broad-scale studies of correlations between disease incidence in a population and some putative environmental cause. Susser 1994a and Susser 1994b point out that such studies were often unable to evaluate causation and therefore were considered inferior to other approaches such as following a cohort through time, or experimentally comparing case and control individuals. The “ecological fallacy” is attributing causation to such correlations. It is now well established that ecological approaches to infectious disease can be experimental, observational, or theoretical, and that a variety of statistical approaches can be used to evaluate correlation and to infer causation.

  • Pearce, N. 2000. The ecological fallacy strikes back. Journal of Epidemiology & Community Health 54.5: 326–327.

    DOI: 10.1136/jech.54.5.326Save Citation »Export Citation »E-mail Citation »

    This brief commentary provides a useful description of basic methods of epidemiological analysis, including pros and cons of the so-called “ecological” approach, and advocates a multilevel, inclusive approach to epidemiological study.

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  • Rohr, J. R., A. M. Schotthoefer, T. R. Raffel, et al. 2008. Agrochemicals increase trematode infections in a declining amphibian species. Nature 455.7217: 1235–1239.

    DOI: 10.1038/nature07281Save Citation »Export Citation »E-mail Citation »

    This paper constitutes an early, influential description of the interactions between noninfectious agents (agrochemical toxins) and infectious agents (parasites) in causing disease and affecting population dynamics of wildlife.

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  • Susser, M. 1994a. The logic in ecological, I: The logic of analysis. American Journal of Public Health 84.5: 825–829.

    DOI: 10.2105/AJPH.84.5.825Save Citation »Export Citation »E-mail Citation »

    This set of companion papers (see Susser 1994b) describes design and analytical issues concerning public health and epidemiological studies. The strengths and weaknesses of different methods (e.g., comparative versus experimental) and units of analysis (e.g., individuals versus populations) are compared and contrasted to underpin recommendations for study design and analysis.

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  • Susser, M. 1994b. The logic in ecological, II: The logic of design. American Journal of Public Health 84.5: 830–835.

    DOI: 10.2105/AJPH.84.5.830Save Citation »Export Citation »E-mail Citation »

    A companion to Susser 1994a, this paper focuses on philosophical and practical issues in the design of public health and epidemiological studies. It describes the epidemiological perspective on the meaning of an “ecological approach” to disease.

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Basic Epidemiological Models

On the basis of work by Sir Ronald Ross (Ross 1915, cited under Historical Developments) and William Kermack and Anderson McKendrick (Kermack and McKendrick 1927, cited under Historical Developments), the authors of Anderson and May 1979 and May and Anderson 1979 developed general epidemiological models to allow researchers to predict and explain the course of epidemics in host populations. To predict or track epidemics, the host population is divided into a set of mutually exclusive categories based on their interaction with the pathogen. These categories can include susceptible, exposed (infected but not yet infectious), infectious, and recovered (and at least temporarily immune to reinfection). According to Anderson and May 1991, diseases can be classified on the basis of whether hosts transition only from susceptible to infectious, remaining so until they die, or also can transition from infectious to recovered or from infectious back to susceptible. The models track rates by which individuals move between categories. These models also identify other parameters that are crucial in predicting disease dynamics, including β, the probability of transmission between an infectious and susceptible host; α, mortality rate caused by infection; and γ, the rate at which infected individuals recover. β, which is one of the most difficult parameters to measure, itself incorporates the probability of contact between an infectious and susceptible host and the probability of transmission given contact. Anderson 1991 and McCallum, et al. 2001 describe how the simplest models apply to systems in which the pathogen is a microparasite, transmission occurs only through direct contact between infectious and susceptible individuals, and individual hosts are randomly distributed in space (i.e., populations are unstructured and therefore well mixed). More-complex models accounting for parasites with multiple life stages and hosts and transmission that involve vectors are explored in Anderson and May 1979; May and Anderson 1979; and Lord, et al. 1996. In addition, Antolin 2008 describes the forms and importance of the transmission parameter β in considerable detail. By tracking the rate of change in numbers of infected individuals in a population, these models can predict the circumstances under which a disease will spread when initially rare (Keeling and Rohani 2007).

  • Anderson, R. M. 1991. Populations and infectious diseases: Ecology or epidemiology? Journal of Animal Ecology 60.1: 1–50.

    DOI: 10.2307/5443Save Citation »Export Citation »E-mail Citation »

    This paper arose from the eighth Tansley lecture from the British Ecological Society. It is a comprehensive treatise on how to meld ecological and epidemiological approaches to understanding infectious diseases, as well as analyzing both philosophical and mechanistic issues.

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  • Anderson, R. M., and R. M. May. 1979. Population biology of infectious diseases: Part I. Nature 280.5721: 361–367.

    DOI: 10.1038/280361a0Save Citation »Export Citation »E-mail Citation »

    This foundational paper and its companion (May and Anderson 1979) were critical in providing the theoretical basis for understanding and predicting the course of epidemics arising from infectious disease. A major innovation was the development of models with dynamically varying host population sizes. Both this paper and its companion describe how evolution alters host-parasite interactions.

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  • Anderson, R. M., and R. M. May. 1991. Infectious diseases of humans: Dynamics and control. Oxford Science Publications. Oxford: Oxford Univ. Press.

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    This book provides a comprehensive, detailed discussion of mathematical models of human infectious diseases caused by viruses, bacteria, protozoa, and various parasitic invertebrates. It is richly illustrated with examples and applies models and data analyses to strategies of disease abatement and control.

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  • Antolin, M. F. 2008. Unpacking β: Within-host dynamics and the evolutionary ecology of pathogen transmission. Annual Review of Ecology, Evolution, and Systematics 39:415–437.

    DOI: 10.1146/annurev.ecolsys.37.091305.110119Save Citation »Export Citation »E-mail Citation »

    This paper conducts a comprehensive review and analysis of the conceptual and theoretical richness underlying the transmission parameter, beta, which describes the probability of pathogen transmission between an infectious and a susceptible host. This parameter underlies many models of disease dynamics but has many hidden characteristics that are important to the interpretations of epidemiological models and data.

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  • Keeling, M. J., and P. Rohani. 2007. Modeling infectious diseases in humans and animals. Princeton, NJ: Princeton Univ. Press.

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    This is an early-21st-century textbook that thoroughly describes and assesses various modeling approaches to infectious-disease transmission and dynamics in humans and natural populations of nonhuman animals.

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  • Lord, C. C., M. E. J. Woolhouse, J. A. P. Heesterbeek, and P. S. Mellor. 1996. Vector-borne diseases and the basic reproduction number: A case study of African horse sickness. Medical and Veterinary Entomology 10.1: 19–28.

    DOI: 10.1111/j.1365-2915.1996.tb00077.xSave Citation »Export Citation »E-mail Citation »

    This paper provides one of the first formal treatments of the basic reproductive number, R0, for vector-borne diseases and applies models to a fly-transmitted viral illness of livestock, African horse sickness.

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  • May, R. M., and R. M. Anderson. 1979. Population biology of infectious diseases: Part II. Nature 280.5722: 455–461.

    DOI: 10.1038/280455a0Save Citation »Export Citation »E-mail Citation »

    This paper is a companion to Anderson and May 1979, helping to advance the theoretical basis for understanding and predicting the course of epidemics in host populations that vary in size. These companion papers show how disease can regulate host population size and how host population size determines whether a pathogen can invade.

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  • McCallum, H., N. Barlow, and J. Hone. 2001. How should pathogen transmission be modelled? Trends in Ecology & Evolution 16.6: 295–300.

    DOI: 10.1016/S0169-5347(01)02144-9Save Citation »Export Citation »E-mail Citation »

    This review describes the degree to which models of infectious-disease dynamics have relied on the assumption that individuals within host populations randomly mix with no spatial structure to their distributions and interactions. It then provides discussion of alternative models that more realistically relax that assumption, evaluating applications of the newer models to specific disease systems.

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The Basic Reproductive Number and Population Threshold

The basic reproductive number, R0 (also called the basic reproductive rate and basic reproductive ratio), represents the average number of new infections that arise when an infected host enters an otherwise wholly susceptible population. The importance of R0 for epidemiological models and in parasite evolution was described in early contributions by Roy Anderson and Robert May (May and Anderson 1983 and Anderson and May 1991, the latter cited under Basic Epidemiological Models). An epidemic can proceed only when an infectious host entering a susceptible population infects at least one other host before dying; that is, when R0 is greater than 1. If R0 is much greater than 1, epidemic spread will be rapid. Consequently, much interest in disease ecology has focused on estimating R0 for various diseases, as discussed in McCallum, et al. 2001 (cited under Basic Epidemiological Models) and Dobson 2004. Although epidemic spread correlates positively with R0, Hudson, et al. 2008 shows that the probability of epidemic “burnout” and the extinction of the pathogen also increases with R0. In the simple epidemiological models described under Basic Epidemiological Models, the transmission rate depends on the number of contacts between infectious individuals and susceptible ones, which in turn depends on density of susceptible individuals in the population. These models are therefore called density-dependent models and assume that all members of the population encounter one another randomly. Under these conditions, Anderson and May 1979 (cited under Basic Epidemiological Models) shows that one can use R0 to predict the threshold density in a host population above which an incipient epidemic will proceed and below which it will die out. As shown in Lloyd-Smith, et al. 2005, however, when the predicted density thresholds are sought in the real world, they are rarely found.

  • Dobson, A. 2004. Population dynamics of pathogens with multiple host species. In Special issue: Ecology and evolution of host-pathogen interactions in natural populations. American Naturalist 164.S5: S64–S78.

    DOI: 10.1086/424681Save Citation »Export Citation »E-mail Citation »

    This paper provides a broad theoretical treatment of how to model and understand the dynamics of diseases in which the pathogen infects multiple host species, each of which can act both as a source and a victim of infection. It asks how R0 can be estimated in multihost communities when within-species transmission and between-species transmission differ.

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  • Hudson, P. J., S. E. Perkins, and I. M. Cattadori. 2008. The emergence of wildlife disease and the application of ecology. In Infectious disease ecology: Effects of ecosystems on disease and of disease on ecosystems. Edited by R. Ostfeld, F. Keesing, and V. T. Eviner, 347–367. Princeton, NJ: Princeton Univ. Press.

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    This chapter describes, among other issues, some important limitations concerning the usefulness of the basic reproductive number, R0, in understanding or predicting disease dynamics. It shows how diseases with a high R0 value might fail to persist, and it clarifies some misuses of the R0 concept.

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  • Lloyd-Smith, J. O., P. C. Cross, C. J. Briggs, et al. 2005. Should we expect population thresholds for wildlife disease? Trends in Ecology & Evolution 20.9: 511–519.

    DOI: 10.1016/j.tree.2005.07.004Save Citation »Export Citation »E-mail Citation »

    Although individual variation among hosts in infectiousness has long been known to characterize most disease systems, this paper is among the first to formalize its importance to the basic reproductive number. The authors compare the likelihoods of epidemic outbreaks and die-outs, assuming high variability around mean infectiousness (i.e., superspreaders) or low variability, and find profound differences, with implications for outbreak control.

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  • Lloyd-Smith, J. O., D. George, K. M. Pepin, et al. 2009. Epidemic dynamics at the human-animal interface. Science 326.5958: 1362–1367.

    DOI: 10.1126/science.1177345Save Citation »Export Citation »E-mail Citation »

    A review of models of zoonotic disease transmission and dynamics, this paper argues that new approaches are needed to understand R0 and disease spread, especially for vector-borne pathogens, protozoal pathogens, and pathogens that cause chronic infections. It also describes the many uses of dynamical models in identifying mechanisms, predicting future trends, and comparing potential control strategies.

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  • May, R. M., and R. M. Anderson. 1983. Epidemiology and genetics in the coevolution of parasites and hosts. Proceedings of the Royal Society of London B: Biological Sciences 219.1216: 281–313.

    DOI: 10.1098/rspb.1983.0075Save Citation »Export Citation »E-mail Citation »

    This early treatise integrates genetics with epidemiology to ask about the coevolution of host and parasite traits. One key conclusion from models is that the relationship between pathogen virulence and transmissibility determines whether pathogens evolve toward lower virulence.

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Frequency-Dependent Models, Individual Heterogeneities, and Superspreaders

For many diseases, the number of contacts between infected and susceptible individuals is independent of the density of susceptible individuals in the population, and, as described in Getz and Pickering 1983 and Thrall, et al. 1995, density-dependent models do not apply. One example, explored in Anderson, et al. 1991 and Garnett and Anderson 1996, is sexually transmitted diseases, in which transmission depends on the frequency of sexual contacts between infectious and susceptible individuals. In other words, the expected number of sexual contacts for any infected individual is thought to be determined by factors other than total population density. Another example is vector-borne diseases, in which transmission depends on the frequency of contacts between infected vectors (e.g., insects or ticks) and susceptible hosts. To represent these types of situations, R0 is modeled assuming that transmission depends on the frequency (proportion) of infectious individuals in the population but not their density, as described in Begon, et al. 2002 and McCallum, et al. 2001 (the latter cited under Basic Epidemiological Models). Under these circumstances, Lloyd-Smith, et al. 2005a shows that a threshold density for an epidemic is not expected. Both density-dependent and frequency-dependent models represent extremes, in which population density is either, respectively, the major determinant of transmission dynamics or irrelevant. More realism has been introduced in Grenfell, et al. 2001, which recognizes that disease dynamics are affected by the extent of population structuring, which in turn affects numbers and rates of contacts. The importance of host population structuring for disease dynamics has been shown in Vicente, et al. 2007 for bovine tuberculosis and in Anderson, et al. 1991 for HIV/AIDS, among various other diseases. In addition, pathogen transmission is affected profoundly by differences between individuals in infectiousness or susceptibility, and the presence of “superspreaders,” which are individuals with a disproportionate effect on disease spread owing to their physiology or behavior (Lloyd-Smith, et al. 2005b). A general relationship in which about 20 percent of the host population contributes at least 80 percent of the transmission events has been termed in Woolhouse, et al. 1997 the 20/80 rule and has been found to represent many disease systems, as described in Hudson, et al. 2008, cited under the Basic Reproductive Number and Population Threshold. Unless superspreaders can be identified proactively, however, it is not possible to control epidemics by targeting specific individuals or classes of individuals.

  • Anderson, R. M., R. M. May, M. C. Boily, G. P. Garnett, and J. T. Rowley. 1991. The spread of HIV-1 in Africa: Sexual contact patterns and the predicted demographic impact of AIDS. Nature 352.6336: 581–589.

    DOI: 10.1038/352581a0Save Citation »Export Citation »E-mail Citation »

    A study of HIV/AIDS models as applied to Africa, this paper shows how patterns of sexual behavior and networks of sexual contact are critical in determining epidemiological patterns and demographic impacts of this disease.

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  • Begon, M., M. Bennett, R. G. Bowers, N. P. French, S. M. Hazel, and J. Turner. 2002. A clarification of transmission terms in host-microparasite models: Numbers, densities and areas. Epidemiology & Infection 129.1: 147–153.

    DOI: 10.1017/S0950268802007148Save Citation »Export Citation »E-mail Citation »

    A detailed exploration of the concepts embedded within the transmission term of infectious-disease models, this paper explores the ways in which assumptions concerning the way host populations are structured affect the suitability of models and their applications. The paper covers differences between density- and frequency-dependent models, as well as the distinct roles of spatial structuring of hosts and the scales at which individuals interact.

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  • Garnett, G. P., and R. M. Anderson. 1996. Sexually transmitted diseases and sexual behavior: Insights from mathematical models. Journal of Infectious Diseases 174.S2: S150–S161.

    DOI: 10.1093/infdis/174.Supplement_2.S150Save Citation »Export Citation »E-mail Citation »

    This essay advocates for a key role of mathematical models in understanding the effects of various sources of heterogeneity on transmission of HIV/AIDS. It compares assortative mixing versus random mixing and heterogeneity versus homogeneity in numbers of sexual partners in affecting epidemic outcomes.

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  • Getz, W. M., and J. Pickering. 1983. Epidemic models: Thresholds and population regulation. American Naturalist 121.6: 892–898.

    DOI: 10.1086/284112Save Citation »Export Citation »E-mail Citation »

    This note provides a key early theoretical investigation of the role of frequency-dependent transmission in epidemic models and is considered a seminal contribution describing how diseases with frequency dependence will typically fail to regulate host populations.

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  • Grenfell, B. T., O. N. Bjørnstad, and J. Kappey. 2001. Travelling waves and spatial hierarchies in measles epidemics. Nature 414.6865: 716–723.

    DOI: 10.1038/414716aSave Citation »Export Citation »E-mail Citation »

    An assessment of the geographic spread of measles in England and Wales before widespread vaccination, this paper provides strong support for the importance of spatial structuring of host populations in determining rates and geographic patterns of spread.

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  • Lloyd-Smith, J. O., P. C. Cross, C. J. Briggs, et al. 2005a. Should we expect population thresholds for wildlife disease? Trends in Ecology & Evolution 20.9: 511–519.

    DOI: 10.1016/j.tree.2005.07.004Save Citation »Export Citation »E-mail Citation »

    This review describes the theoretical underpinnings behind the expectation that diseases with density-dependent transmission should exist only in populations that exceed a threshold density. It provides considerable evidence that such threshold densities do not prevail in the real world and why modeling approaches should incorporate greater realism.

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  • Lloyd-Smith, J. O., S. J. Schreiber, P. E. Kopp, and W. M. Getz. 2005b. Superspreading and the effect of individual variation on disease emergence. Nature 438.7066: 355–359.

    DOI: 10.1038/nature04153Save Citation »Export Citation »E-mail Citation »

    This review describes the theoretical underpinnings behind the expectation that diseases with density-dependent transmission should exist only in populations that exceed a threshold density. It provides considerable evidence that such threshold densities do not prevail in the real world and why modeling approaches should incorporate greater realism.

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  • Thrall, P. H., A. Biere, and M. K. Uyenoyama. 1995. Frequency-dependent disease transmission and the dynamics of the Silene-Ustilago host-pathogen system. American Naturalist 145.1: 43–62.

    DOI: 10.1086/285727Save Citation »Export Citation »E-mail Citation »

    This paper represents one of the earliest detailed explorations of the importance of frequency-dependent transmission in disease models, in this case applied to a plant-fungal pathogen system that has since become a model for fruitful synthesis of empirical and theoretical approaches.

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  • Vicente, J., R. J. Delahay, N. J. Walker, and C. L. Cheeseman. 2007. Social organization and movement influence the incidence of bovine tuberculosis in an undisturbed high-density badger Meles meles population. Journal of Animal Ecology 76.2: 348–360.

    DOI: 10.1111/j.1365-2656.2006.01199.xSave Citation »Export Citation »E-mail Citation »

    An intensive, long-term study of badger populations that can transmit bovine tuberculosis to livestock, this paper shows how social and spatial structuring of badgers affects the stability of group composition, which in turn can mitigate against the spread of the pathogen among badger populations and to cattle.

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  • Woolhouse, M. E. J., C. Dye, J.-F. Etard, et al. 1997. Heterogeneities in the transmission of infectious agents: Implications for the design of control programs. Proceedings of the National Academy of Sciences of the United States of America 94.1: 338–342.

    DOI: 10.1073/pnas.94.1.338Save Citation »Export Citation »E-mail Citation »

    A highly influential paper showing how knowledge that a small fraction of host populations are responsible for the majority of transmission events (the 20/80 rule) can influence the effectiveness of disease control strategies. In particular, the paper demonstrates that mitigation directed at the “core 20%” is much more effective than less targeted interventions.

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Extending to Multihost Pathogens

Traditional epidemiological models apply to diseases in which a pathogen infects only one host species (i.e., it is a host specialist), and the host is not infected with any other pathogens. Many of the important developments in disease ecology since the turn of the 21st century have relaxed these assumptions to examine disease systems in which pathogens can infect multiple hosts (the topic of this section; modeled in Dobson 2004, cited under the Basic Reproductive Number and Population Threshold, and in Rudolf and Antonovics 2005) and in which host species are infected with multiple pathogens (the topic of Expanding to Multiple Pathogens in Hosts). Although some pathogens are strictly limited to one host species—for example, measles virus and human immunodeficiency virus (HIV) in humans—Cleaveland, et al. 2001 shows how most can and do infect more than one host species. When a pathogen encounters and potentially replicates within more than one host, Holt, et al. 2003 shows that the potential exists for transmission rates and disease to be affected strongly by a community of hosts rather than by just one population. For example, as described in Cleaveland, et al. 2001, all zoonoses (diseases in which the pathogen is transmitted from a nonhuman vertebrate to humans) are by definition multihost pathogens. For multihost pathogens, some hosts can increase pathogen abundance or transmission (called “amplification hosts” in Keesing, et al. 2006) whereas others can decrease it (“dilution hosts”). Early interest focused on single amplification hosts, or “reservoir hosts,” such as European badgers for bovine tuberculosis, or on single dilution hosts, such as the use of cattle for “zooprophylaxis” in the case of human malaria, as reviewed in Keesing, et al. 2006. Later efforts, including Holt, et al. 2003, have focused on determining how communities of multiple, interacting species both of amplification and dilution hosts affect disease dynamics. Many theoretical and empirical studies, reviewed in Keesing, et al. 2010, show that reduced pathogen transmission accompanies increased diversity in the host community as the effects of dilution hosts increase, a phenomenon called the “dilution effect.” For many multihost diseases of humans, wildlife, and plants, Johnson, et al. 2013 and Keesing, et al. 2010 describe how amplification hosts tend to predominate in low-diversity communities, and dilution hosts tend to predominate when diversity is high. Some studies, such as Cronin, et al. 2010, suggest that tolerance or susceptibility to multihost pathogens is more likely in species that have fast life histories, which in turn promote resilience to disturbances that otherwise reduce biodiversity. How generally the dilution effect applies to diseases in the real world, and the conditions under which a buffering versus amplifying effect of diversity is expected, are subjects of considerable controversy, but a meta-analysis by Civitello, et al. 2015 confirms the widespread occurrence of dilution effects.

  • Civitello, D. J., J. Cohen, H. Fatima, et al. 2015. Biodiversity inhibits parasites: Broad evidence for the dilution effect. Proceedings of the National Academy of Sciences of the United States of America 112:8667–8671.

    DOI: 10.1073/pnas.1506279112Save Citation »Export Citation »E-mail Citation »

    Responding to disagreement about how frequently biodiversity reduces disease risk, this paper conducted a formal meta-analysis, finding widespread support for the dilution effect. Reduced parasite abundance with increased diversity was found irrespective of the type of host, type of parasite, or nature of the study (experimental vs. observational).

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  • Cleaveland, S., M. K. Laurenson, and L. H. Taylor. 2001. Diseases of humans and their domestic mammals: Pathogen characteristics, host range and the risk of emergence. Philosophical Transactions of the Royal Society B: Biological Sciences 356.1411: 991–999.

    DOI: 10.1098/rstb.2001.0889Save Citation »Export Citation »E-mail Citation »

    This is an early, influential review of the animal sources and ecological and socioeconomic drivers of zoonotic-disease emergence. It has stimulated many follow-up studies searching for patterns in and causes of emerging infectious diseases.

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  • Cronin, J. P., M. E. Welsh, M. G. Dekkers, S. T. Abercrombie, and C. E. Mitchell. 2010. Host physiological phenotype explains pathogen reservoir potential. Ecology Letters 13.10: 1221–1232.

    DOI: 10.1111/j.1461-0248.2010.01513.xSave Citation »Export Citation »E-mail Citation »

    This study was among the first to describe the relationship between life-history features, especially the pace of life, of host organisms and their ability to support the proliferation of pathogens. The focal system was aphid-transmitted viruses of herbaceous plants. The authors show how hosts with a “fast-return” life history are typically better “reservoirs” for generalist pathogens.

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  • Holt, R. D., A. P. Dobson, M. Begon, R. G. Bowers, and E. M. Schauber. 2003. Parasite establishment in host communities. Ecology Letters 6.9: 837–842.

    DOI: 10.1046/j.1461-0248.2003.00501.xSave Citation »Export Citation »E-mail Citation »

    This paper used a theoretical approach to ask how the structure of host communities affects the ability of multihost pathogens to invade and establish. Using isocline analysis, the authors explored a set of scenarios in which two hosts interact or not, and whether their interactions are complementary, substitutable, or inhibitory, demonstrating how host-host and host-pathogen interactions determine establishment in these communities.

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  • Johnson, P. T. J., D. L. Preston, J. T. Hoverman, and K. L. D. Richgels. 2013. Biodiversity decreases disease through predictable changes in host community competence. Nature 494.7436: 230–233.

    DOI: 10.1038/nature11883Save Citation »Export Citation »E-mail Citation »

    This elegant study describes mechanistically how high diversity in communities of amphibians reduces parasite transmission, providing an important example of the dilution effect. The authors show how the species that remain in species-poor communities tend to be those most likely to host and transmit parasites.

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  • Keesing, F., L. K. Belden, P. Daszak, et al. 2010. Impacts of biodiversity on the emergence and transmission of infectious diseases. Nature 468.7324: 647–652.

    DOI: 10.1038/nature09575Save Citation »Export Citation »E-mail Citation »

    In this review, the authors confront the expectations of the dilution effect (reduced disease transmission with increasing diversity) with then-recent empirical studies, finding widespread although not universal support. The authors apply the dilution effect to observed patterns of disease when diversity in microbial communities (e.g., microbiomes) varies.

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  • Keesing, F., R. D. Holt, and R. S. Ostfeld. 2006. Effects of species diversity on disease risk. Ecology Letters 9.4: 485–498.

    DOI: 10.1111/j.1461-0248.2006.00885.xSave Citation »Export Citation »E-mail Citation »

    This important synthesis describes and reviews the history of the dilution effect concept and uses epidemiological models to erect a set of mechanisms that underlie the general pattern whereby high diversity reduces pathogen transmission and disease risk.

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  • Rudolf, V. H. W., and J. Antonovics. 2005. Species coexistence and pathogens with frequency-dependent transmission. American Naturalist 166.1: 112–118.

    DOI: 10.1086/430674Save Citation »Export Citation »E-mail Citation »

    This modeling paper explores the interactions between a pathogen and multiple hosts when transmission is frequency dependent versus density dependent. It asks about the conditions under which host species coexistence is affected by the pathogen and about how host diversity affects pathogen persistence.

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  • Taylor, L. H., S. M. Latham, and M. E. J. Woolhouse. 2001. Risk factors for human disease emergence. Philosophical Transactions of the Royal Society B: Biological Sciences 356.1411: 983–989.

    DOI: 10.1098/rstb.2001.0888Save Citation »Export Citation »E-mail Citation »

    One of the earliest quantitative studies of the origins and determinants of emerging infectious diseases, this paper argues that 61 percent of all human pathogens and 75 percent of ones that are “emerging” are zoonotic in origin. Bacterial pathogens, which the authors argue dominate the list of human pathogens, are less likely than viruses and protozoans to be among the emerging pathogens.

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Expanding to Multiple Pathogens in Hosts

Although traditional models track the dynamics of single pathogens in hosts, assuming that other pathogens are absent or unimportant, early-21st-century research suggests that this approach is inadequate. Many examples from mollusks, insects, fish, amphibians, birds, and mammals, described in Hatcher and Dunn 2011 (cited under Overviews) and Johnson and Hoverman 2012, indicate that parasites and pathogens compete with one another for resources, or facilitate one another, within hosts. Consequently, the dynamics of a specific pathogen-host interaction often cannot be understood without knowing the identities, abundances, and infection histories of other pathogens (Telfer, et al. 2010). In addition to direct interactions between pathogens inside hosts (e.g., competition for host resources), pathogens can interact indirectly when they affect host immune function. Jolles, et al. 2008 shows that macroparasite (helminth) infection in African buffalo increases host susceptibility to a microparasite (bovine tuberculosis bacterium, Mycobacterium bovis), because the immunological responses elicited by helminth parasites reduce immunological response to intracellular pathogens such as M. bovis. Due to its potent and general immunosuppressive effects in humans, HIV/AIDS provides a well-known case of indirect facilitation of various secondary infections, most especially tuberculosis, by a preexisting infection. In other situations, however, exposure to a specific pathogen can suppress infection by other, closely related pathogens, for instance when the initial infection with an influenza virus or dengue virus causes cross-protective immunity to other strains. Graham 2008 explores the ecological rules that might determine when coinfection of a host with a helminth and microparasite facilitates, inhibits, or is irrelevant to disease manifestations. Pathogens are also known to reverse dominance between host species that compete (see Historical Developments) and to increase species richness in host communities (Bradley, et al. 2008). Taken together, recognition that pathogens often infect multiple hosts, and hosts are typically infected with multiple pathogens, suggests that pathogen-host interactions are distributed throughout ecological communities. Research in Dobson, et al. 2008 suggests that there are more species of parasites than of free-living organisms, implying that parasites are an underappreciated aspect of biological diversity.

  • Bradley, D. J., G. S. Gilbert, and J. B. H. Martiny. 2008. Pathogens promote plant diversity through a compensatory response. Ecology Letters 11.5: 461–469.

    DOI: 10.1111/j.1461-0248.2008.01162.xSave Citation »Export Citation »E-mail Citation »

    This study experimentally manipulated bacterial and fungal pathogens of plants, demonstrating that pathogens consistently increased plant diversity. Unlike other observations that pathogens increase host diversity by preferentially affecting community dominants, this study demonstrates a different mechanism—namely, that pathogens can cause compensatory increases in reproduction in the rarer plants.

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  • Dobson, A., K. D. Lafferty, A. M. Kuris, R. F. Hechinger, and W. Jetz. 2008. Homage to Linnaeus: How many parasites? How many hosts? Proceedings of the National Academy of Sciences of the United States of America 105.S1: 11482–11489.

    DOI: 10.1073/pnas.0803232105Save Citation »Export Citation »E-mail Citation »

    One of the most rigorous assessments of the global species richness of helminth parasites, this paper addresses the relationships between biodiversity of parasites and that of free-living organisms. It argues that biodiversity in the former is poorly understood but is important both to ecological interactions and conservation.

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  • Graham, A. L. 2008. Ecological rules governing helminth–microparasite coinfection. Proceedings of the National Academy of Sciences of the United States of America 105.2: 566–570.

    DOI: 10.1073/pnas.0707221105Save Citation »Export Citation »E-mail Citation »

    This paper presents a meta-analysis of studies on laboratory mice and shows that helminth infection tends to exacerbate microparasite infection when the former suppresses certain immune pathways but suppresses microparasite infection when the helminth competes for resources.

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  • Johnson, P. T. J., and J. T. Hoverman. 2012. Parasite diversity and coinfection determine pathogen infection success and host fitness. Proceedings of the National Academy of Sciences of the United States of America 109.23: 9006–9011.

    DOI: 10.1073/pnas.1201790109Save Citation »Export Citation »E-mail Citation »

    This study quantifies the effects of variable diversity both of amphibian hosts and helminth parasites in determining the pattern of infection and severity of disease. The authors find that high diversity both of hosts and parasites synergistically reduces the occurrence and severity of disease, thus providing the first hierarchical study of diversity at multiple levels.

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  • Jolles, A. E., V. O. Ezenwa, R. S. Etienne, W. C. Turner, and H. Olff. 2008. Interactions between macroparasites and microparasites drive infection patterns in free-ranging African buffalo. Ecology 89.8: 2239–2250.

    DOI: 10.1890/07-0995.1Save Citation »Export Citation »E-mail Citation »

    By analyzing patterns of survival and infection of African buffalo both with bovine tuberculosis bacteria and intestinal helminthes, this study addresses how different parasites within one host species affect each other. The authors found that these pathogens interact indirectly by downregulating immune response to the other pathogen, and also by affecting survival of coinfected hosts.

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  • Telfer, S., X. Lambin, R. Birtles, et al. 2010. Species interactions in a parasite community drive infection risk in a wildlife population. Science 330.6001: 243–246.

    DOI: 10.1126/science.1190333Save Citation »Export Citation »E-mail Citation »

    In this study, the authors use time-series analysis of pathogens in wild rodent populations to ask whether preexisting infections affect subsequent ones, and they find strong inhibitory and facilitative effects, depending on the pathogen and host. The authors argue that conclusions concerning the causes of pathogen-host interactions can be misleading if pathogens are treated in isolation, and they advocate a community approach to pathogen infection.

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Impacts of Pathogens on Ecosystems

Until the early 21st century, parasites were not incorporated together with free-living species in analyses of food web structure. Studies such as Kuris, et al. 2008 argue that excluding parasites and pathogens can distort food web analyses. Lafferty, et al. 2006 argues that when parasites are included in analyses of food web structure, the number of trophic levels, the density of interspecific linkages, and the degree of connectance tend to increase, and the nestedness of the community (i.e., the tendency for less diverse communities to contain a nested subset of more-diverse communities) can either decrease or increase. The community studied by Kevin Lafferty and colleagues was dominated by snails and their helminth parasitic castrators (parasites that consume and inactivate reproductive tissues), which can comprise up to one-third of the host’s mass. Whether such high ratios of parasite-to-host biomass are common in food webs, and therefore whether parasites generally affect food web structure, remains controversial. Evidence compiled in Hatcher and Dunn 2011 (cited under Overviews) suggests that, by increasing the number and importance of weak interactions in food webs, parasitism might stabilize community dynamics. Pathogens can also cause profound state changes in ecosystems. Holdo, et al. 2009 describes how the eradication of rinderpest from East Africa caused wildebeest populations to irrupt, reducing standing-plant biomass and thus the frequency and extent of wildfires. This in turn allowed woody plants to reinvade, profoundly altering carbon storage dynamics both above and below ground. Other examples of pathogens affecting ecosystem processes in terrestrial and aquatic environments are given in Ostfeld, et al. 2008 (cited under Overviews).

  • Holdo, R. M., A. R. E. Sinclair, A. P. Dobson, et al. 2009. A disease-mediated trophic cascade in the Serengeti and its implications for ecosystem C. PLoS Biology 7.9: e1000210.

    DOI: 10.1371/journal.pbio.1000210Save Citation »Export Citation »E-mail Citation »

    This study uses a Bayesian state space model to explore how the eradication of rinderpest, by releasing wildebeest populations from control by disease, increased herbivory, reduced fire frequency and distribution, increased woody plant biomass, and profoundly affected carbon storage in an iconic savanna ecosystem.

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  • Kuris, A. M., R. F. Hechinger, J. C. Shaw, et al. 2008. Ecosystem energetic implications of parasite and free-living biomass in three estuaries. Nature 454.7203: 515–518.

    DOI: 10.1038/nature06970Save Citation »Export Citation »E-mail Citation »

    Analyzing salt marsh communities of California and Baja California, the authors show that the biomass of parasites rivaled that of some components of the free-living animal community. They argue that the biomass and productivity of parasites are underappreciated in terms both of magnitude and importance.

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  • Lafferty, K. D., A. P. Dobson, and A. M. Kuris. 2006. Parasites dominate food web links. Proceedings of the National Academy of Sciences of the United States of America 103.30: 11211–11216.

    DOI: 10.1073/pnas.0604755103Save Citation »Export Citation »E-mail Citation »

    This paper formally analyzes food webs in which parasites are included, and compares food web attributes to those that exclude parasites. The authors find that including parasites can either increase or decrease estimates of connectedness and nestedness but typically increases food chain length and numbers of linkages.

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Heterogeneity in Space/Landscape Epidemiology

Ever since John Snow (Snow 1855) inferred the spatial spread of cholera from a contaminated London well through the surrounding neighborhood, epidemiologists have been aware that pathogen transmission is often highly spatially structured. More-modern studies of spatial aspects of pathogen transmission were influenced by the publication of Pavlovsky 1966, which mapped the distributions of pathogens, vectors, and reservoir hosts, focusing on areas of overlap as presenting high risk. The production of “risk maps” of disease has dominated spatial epidemiology ever since (references in Ostfeld, et al. 2005). Risk maps can be useful for managing disease if scientists can infer the determinants of spatial distributions (Peterson and Shaw 2003). The increasing availability of remote-sensing data in the late 20th century, combined with enhanced ability of computer software (geographic information systems, or GIS) to create and analyze maps, allowed researchers to seek spatial correlations between abiotic conditions such as temperature and humidity and various metrics of disease (reviewed in Ostfeld, et al. 2005). Such correlations are often assumed to indicate causation, even though causation is rarely established. Despite their limitations, risk maps are used to describe spatial variation at scales from global to local. Of course, the distributions of diseases are not static, and much effort has been devoted to describing patterns of spread or compression and inferring their causes. For example, high-quality time-series data on measles have allowed researchers to describe how spatial structuring and sizes of human populations in towns and cities affect traveling waves of epidemics (see Grenfell, et al. 2001, cited under Frequency-Dependent Models, Individual Heterogeneities, and Superspreaders). The organization of hosts into metapopulations, which are groups of somewhat distinct populations connected by dispersal, can either increase or decrease the susceptibility to infection and disease. Some modeling studies, such as Hess 1996, suggest that disease incidence in hosts tends to increase with increased dispersal among subpopulations, whereas others, such as McCallum and Dobson 2002, suggest that dispersal corridors promote persistence of hosts even when pathogens can use them. Patz, et al. 2004 argues that in real landscapes, habitat fragmentation is often expected to increase pathogen transmission and disease, apparently because fragmentation often promotes dominance by species that amplify transmission. However, rigorous explorations of this expectation are few, and the effects of habitat fragmentation on disease dynamics remain poorly explored.

  • Hess, G. 1996. Disease in metapopulation models: Implications for conservation. Ecology 77.5: 1617–1632.

    DOI: 10.2307/2265556Save Citation »Export Citation »E-mail Citation »

    This modeling exercise asked whether the degree of connectedness of subpopulations in a metapopulation can affect the likelihood of disease spread and metapopulation extinction due to disease. The paper served as a cautionary tale for conservationists who advocated for dispersal corridors, by showing that pathogen use of such corridors can increase the probability of host extinction.

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  • McCallum, H., and A. Dobson. 2002. Disease, habitat fragmentation and conservation. Proceedings of the Royal Society B: Biological Sciences 269.1504: 2041–2049.

    DOI: 10.1098/rspb.2002.2079Save Citation »Export Citation »E-mail Citation »

    This modeling paper suggests, in contrast to Hess 1996, that corridors generally are of strong conservation benefit to imperiled species, even when corridors are used by pathogens to disperse to new host populations.

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  • Ostfeld, R. S., G. E. Glass, and F. Keesing. 2005. Spatial epidemiology: An emerging (or re-emerging) discipline. Trends in Ecology & Evolution 20.6: 328–336.

    DOI: 10.1016/j.tree.2005.03.009Save Citation »Export Citation »E-mail Citation »

    In a review of spatial, or landscape, epidemiology, the authors argue that spatial patterns of infectious diseases are well described, but underlying processes are often poorly understood. They point out opportunities for increasing the intellectual cross-fertilization between landscape ecology and landscape epidemiology.

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  • Patz, J. A., P. Daszak, G. M. Tabor, et al. 2004. Unhealthy landscapes: Policy recommendations on land use change and infectious disease emergence. Environmental Health Perspectives 112.10: 1092–1098.

    DOI: 10.1289/ehp.6877Save Citation »Export Citation »E-mail Citation »

    This essay describes many ways that anthropogenic environmental changes, including agricultural encroachment, deforestation, road construction, dam building, irrigation, mining, urbanization, and degradation of coastal zones, exacerbate the transmission of infectious diseases. It calls for new partnerships in education, research, and policy to respond to these threats.

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  • Pavlovsky, E. N. 1966. Natural nidality of transmissible diseases: With special reference to the landscape epidemiology of zooanthroponoses. Translated by F. K. Plous Jr. Edited by N. D. Levine. Urbana: Univ. of Illinois Press.

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    This book is a treatise on what Pavlovsky called the “natural nidality” of disease, arguing that, because several distinct species are required for transmission of zoonotic pathogens to humans—including the pathogen, wildlife reservoir, arthropod vector, and human—that researchers should focus effort in the areas of overlapping distributions of all these species.

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  • Peterson, A. T., and J. Shaw. 2003. Lutzomyia vectors for cutaneous leishmaniasis in Southern Brazil: Ecological niche models, predicted geographic distributions, and climate change effects. International Journal for Parasitology 33.9: 919–931.

    DOI: 10.1016/S0020-7519(03)00094-8Save Citation »Export Citation »E-mail Citation »

    This study focuses on ecological-niche modeling of sand flies (genus Lutzomyia), which are the vectors of leishmaniasis in much of the New World. The paper demonstrates the utility of this tool for describing the geographic distribution of species important to disease transmission, inferring ecological factors responsible for those distributions and projecting future distributional changes if ecological factors change.

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  • Snow, J. 1855. On the mode of communication of cholera. 2d ed. London: John Churchill.

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    In this remarkable book, first published in 1849, John Snow conducts perhaps the first spatial analysis of disease spread, tracing cases of cholera as they spread throughout London from the Broad Street pump after it became contaminated with sewage, resulting in more than five hundred deaths within ten days. Republished in 1965 (New Delhi: US Agency for International Development).

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Impacts on Hosts

Much early-21st-century interest has focused on emerging infectious diseases (EIDs) and the process of emergence. As described in Daszak, et al. 2000, EIDs are those in which the pathogen (or disease) has newly evolved, jumped to a new host, entered a new geographic region, or dramatically increased in incidence. The rates at which infectious diseases of humans and wildlife have emerged appear to have increased in the late 20th and possibly the early 21st centuries (Daszak, et al. 2000; Jones, et al. 2008). According to Taylor, et al. 2001, cited under Extending to Multihost Pathogens, between 60 and 75 percent of the EIDs of humans are zoonotic, and the causes of emergence include human population growth, land use change, antibiotic use, global trade and travel, and some agricultural practices. Rodents, carnivores, and ungulates are the sources of most emerging zoonotic pathogens (Han, et al. 2016). Emerging pathogens are increasingly causing population crashes and biodiversity loss in plant and animal hosts, particularly when the hosts have not previously experienced the pathogen. Some of the highest-profile examples are the demise of the American chestnut due to chestnut blight, mass mortality of oaks and tanoaks due to Phytophthora ramorum (sudden oak death pathogen), widespread and profound declines in wild ungulate populations from rinderpest in Africa, worldwide amphibian population declines and extinctions due to the fungus Batrachochytrium dendrobatidis, and precipitous population declines in Myotis and other bats owing to white-nose syndrome. Extensive documentation of host declines caused by novel pathogens has been provided for Panamanian amphibians in Lips, et al. 2006 and for North American birds in LaDeau, et al. 2007. In other cases, such as red grouse in Britain, parasites are responsible for dramatic population cycles, and when parasites are removed, host populations stabilize (Hudson, et al. 1998). More generally, basic ecological theory suggests that parasites and infectious disease can have a strong impact on the ability of host species to coexist, as shown in Holt and Pickering 1985.

  • Daszak, P., A. A. Cunningham, and A. D. Hyatt. 2000. Emerging infectious diseases of wildlife—threats to biodiversity and human health. Science 287.5452: 443–449.

    DOI: 10.1126/science.287.5452.443Save Citation »Export Citation »E-mail Citation »

    This seminal paper is among the first to describe the modes by which infectious diseases of wildlife emerge, and to link these emerging diseases both to wildlife conservation and threats to human health.

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  • Han, B. A., A. M. Kramer, and J. M. Drake. 2016. Global patterns of zoonotic disease in mammals. Trends in Parasitology 32:565–577.

    DOI: 10.1016/j.pt.2016.04.007Save Citation »Export Citation »E-mail Citation »

    This paper provides a global analysis of zoonotic diseases transmitted by mammals, identifying the most frequent hosts and global hotspots. In contrast to prior analyses, it shows that zoonoses are equally frequent at temperate, compared to tropical, latitudes.

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  • Holt, R. D., and J. Pickering. 1985. Infectious disease and species coexistence: A model of Lotka-Volterra form. American Naturalist 126.2: 196–211.

    DOI: 10.1086/284409Save Citation »Export Citation »E-mail Citation »

    This is a foundational modeling paper, using Lotka-Volterra equations, to ask how parasites can influence competition between two host species. It stimulated considerable further research, both theoretical and empirical, on diseases and host competition.

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  • Hudson, P. J., A. P. Dobson, and D. Newborn. 1998. Prevention of population cycles by parasite removal. Science 282.5397: 2256–2258.

    DOI: 10.1126/science.282.5397.2256Save Citation »Export Citation »E-mail Citation »

    This pioneering paper used an experimental approach to understand the impacts of helminth parasites on population dynamics of red grouse. By administering antihelminthic drugs to wild grouse, the authors stabilized otherwise wildly fluctuating host populations.

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  • Jones, K. E., N. G. Patel, M. A. Levy, et al. 2008. Global trends in emerging infectious diseases. Nature 451.7181: 990–994.

    DOI: 10.1038/nature06536Save Citation »Export Citation »E-mail Citation »

    One of the most influential early-21st-century papers in disease ecology, this study describes spatial patterns and possible causes of the emergence of human infectious diseases over the past several decades. The authors explore the ecological and other drivers of emergence for different types of diseases and the underlying geographic patterns.

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  • LaDeau, S. L., A. M. Kilpatrick, and P. P. Marra. 2007. West Nile virus emergence and large-scale declines of North American bird populations. Nature 447.7145: 710–713.

    DOI: 10.1038/nature05829Save Citation »Export Citation »E-mail Citation »

    In this paper, the authors use Bayesian statistical analyses of spatially extensive, long-term data on bird abundances to ask whether the North American invasion of West Nile virus has caused population declines. The analyses show that certain species of passerine birds have declined dramatically with the arrival of West Nile virus, whereas others remain unaffected.

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  • Lips, K. R., F. Brem, R. Brenes, et al. 2006. Emerging infectious disease and the loss of biodiversity in a Neotropical amphibian community. Proceedings of the National Academy of Sciences of the United States of America 103.9: 3165–3170.

    DOI: 10.1073/pnas.0506889103Save Citation »Export Citation »E-mail Citation »

    This seminal study provides strong evidence that the sudden arrival of the chytrid fungus Batrachochytrium dendrobatidis at a site in Panama caused rapid mass mortality and biodiversity loss in amphibians from eight taxonomic families. Further evidence indicated that a traveling wave of amphibian mortality starting in northern Panama in the 1980s and proceeding southeastward was caused by dispersal of the chytrid fungus.

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  • Woolhouse, M. E. J., and S. Gowtage-Sequeria. 2005. Host range and emerging and reemerging pathogens. Emerging Infectious Diseases 11.12: 1842–1847.

    DOI: 10.3201/eid1112.050997Save Citation »Export Citation »E-mail Citation »

    This survey of the literature demonstrates the predilection for emerging infectious diseases to be zoonotic and to have broad host range, as compared with non-zoonotic diseases of humans. The review shows that land use change, especially for agriculture, and human behavioral changes are often associated with emergence events.

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Impacts of Climate and Climate Change

The species involved in infectious-disease transmission are affected by various abiotic conditions such as temperature, precipitation, humidity, and vapor pressure. For example, for mosquito-borne diseases such as malaria and dengue, many life history features both of the vector and the pathogen are sensitive to temperature and the presence of aquatic habitat for mosquito breeding. In general, rates of larval mosquito survival and development, of biting by adults, and of replication by the pathogen increase with increasing temperature up to an optimum high temperature and then decrease rapidly as lethal temperatures are approached, as reviewed in Harvell, et al. 2002 and Altizer, et al. 2013. In contrast, survival of adult mosquitoes tends to decline with increasing temperature (Mordecai, et al. 2013). Because some determinants of disease transmission increase with temperature while others decrease, researchers have sought to integrate across temperature-dependent vital rates by asking how temperature affects conglomerate metrics of disease risk, such as R0, or the entomological inoculation rate (the rate at which infected vectors encounter susceptible hosts), or the vectorial capacity (the rate at which future inoculations arise from a currently infectious vector). Models such as in Parham and Michael 2010 suggest that curves relating these metrics of disease risk to temperature are unimodal, typically with a long, shallow left-hand tail and a shorter, steeper right-hand tail (i.e., they are left-skewed). Models differ regarding the expected optimum temperature for transmission. A diversity of approaches and underlying philosophies have contributed to considerable controversy regarding the extent to which anthropogenic climate change has affected and will affect the distribution and intensity of infectious diseases. For example, the well-established sensitivity of mosquitoes and mosquito-borne pathogens to abiotic conditions led researchers to create process-based models based on empirical relationships between specific variables (e.g., temperature, relative humidity) and vector and pathogen vital rates. Some process-based malaria models projected alarming geographic expansions in disease risk under changing climatic conditions (e.g., Martens, et al. 1995). These models have been criticized, such as in Rogers and Randolph 2000, as providing overly liberal projections based on model structure, parameterization, and the risk measures used. Rogers and Randolph 2000 includes statistical models of the climatic boundary conditions, describing the current distribution of falciparum malaria and predicts future distributions based on projected changes in those boundary conditions. As described in Altizer, et al. 2013, such climate envelope models can be highly conservative when the assumption that climate, and only climate, determines the present-day distribution of malaria is false. Clearly, nonclimatic factors such as the destruction of mosquito-breeding habitat, application of insecticides, and use of antimalarial drugs have affected the global and regional distribution of malaria, decoupling global malaria incidence from the effects of climate warming on mosquitoes and Plasmodium parasites. Limited resources in developing countries where malaria incidence is highest, restrictions on insecticide applications, and the evolution of resistance to antimalarial drugs and insecticides would appear to increase the sensitivity of malaria to climatic factors in the 21st century (Béguin, et al. 2011; Siraj, et al. 2014).

  • Altizer, S., R. S. Ostfeld, P. T. J. Johnson, S. Kutz, and C. D. Harvell. 2013. Climate change and infectious diseases: From evidence to a predictive framework. Science 341.6145: 514–519.

    DOI: 10.1126/science.1239401Save Citation »Export Citation »E-mail Citation »

    An up-to-date review of the effects of climate change on infectious disease, this paper integrates metabolic, immunological, phenological, demographic, and community responses to climate change to provide a more predictive framework for understanding impacts of anthropogenic climate change on human, animal, and plant diseases, in terrestrial and aquatic systems.

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  • Béguin, A., S. Hales, J. Rocklöv, C. Åström, V. R. Louis, and R. Sauerborn. 2011. The opposing effects of climate change and socio-economic development on the global distribution of malaria. Global Environmental Change: Human and Policy Dimensions 21.4: 1209–1214.

    DOI: 10.1016/j.gloenvcha.2011.06.001Save Citation »Export Citation »E-mail Citation »

    This paper was the first attempt to predict the independent and combined effects of projected future changes in climate and socioeconomic factors on the global distribution of malaria. The authors use statistical modeling to explore this contentious issue more rigorously and dispassionately than prior efforts, finding that, while growing per capita gross domestic product was predicted to mitigate malaria incidence, a warming climate would reduce the effectiveness of such mitigations.

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  • Harvell, C. D., C. E. Mitchell, J. R. Ward, et al. 2002. Climate warming and disease risks for terrestrial and marine biota. Science 296.5576: 2158–2162.

    DOI: 10.1126/science.1063699Save Citation »Export Citation »E-mail Citation »

    This paper provides the first rigorous review of the evidence for climate change effects on human, nonhuman animal, and plant diseases worldwide. It describes numerous examples both in terrestrial and marine systems of diseases spreading or increasing in incidence or impact as the climate has warmed.

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  • Martens, W. J. M., L. W. Niessen, J. Rotmans, T. H. Jetten, and A. J. McMichael. 1995. Potential impact of global climate change on malaria risk. Environmental Health Perspectives 103.5: 458–464.

    DOI: 10.1289/ehp.95103458Save Citation »Export Citation »E-mail Citation »

    This pioneering study used process-based models of temperature-dependent mosquito and parasite demography to project future changes in human risk of malaria worldwide. It provided the first quantitative predictions concerning changing global burdens of malaria under specific climate change scenarios. It is controversial because predicted increases in risk might not exceed the threshold for malaria to establish in many high-latitude areas.

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  • Mordecai, E. A., K. P. Paaijmans, L. R. Johnson, et al. 2013. Optimal temperature for malaria transmission is dramatically lower than previously predicted. Ecology Letters 16.1: 22–30.

    DOI: 10.1111/ele.12015Save Citation »Export Citation »E-mail Citation »

    This modeling study reanalyzed data linking average temperatures to mosquito demographic processes to recalculate the optimal temperature for malaria transmission. The authors found that the optimal temperature was several degrees lower than had been concluded by prior studies, although the relevance of these findings for future distributions of malaria with a warming climate is unclear.

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  • Parham, P. E., and E. Michael. 2010. Modeling the effects of weather and climate change on malaria transmission. Environmental Health Perspectives 118.5: 620–626.

    DOI: 10.1289/ehp.0901256Save Citation »Export Citation »E-mail Citation »

    This paper uses a dynamic process-based model of malaria transmission to ask how temperature and precipitation affect important mosquito and parasite vital rates and the basic reproductive number of malaria. The authors find that transmission and spread are optimized at about 33 degrees C and that precipitation and seasonality are underappreciated drivers of risk.

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  • Rogers, D. J., and S. E. Randolph. 2000. The global spread of malaria in a future, warmer world. Science 289.5485: 1763–1766.

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    Reacting to prior predictions that malaria risk would increase with future climate warming, this paper used a climate envelope approach to statistically describe the boundary conditions for falciparum malaria, assuming that climate is the sole determinant of malaria distribution. The model suggested that the distribution of malaria would shift over the subsequent fifty years, but without a strong net increase in numbers of people affected.

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  • Siraj, A. S., M. Santos-Vega, M. J. Bouma, D. Yadeta, D. Ruiz Carrascal, and M. Pascual. 2014. Altitudinal changes in malaria incidence in highlands of Ethiopia and Colombia. Science 343.6175: 1154–1158.

    DOI: 10.1126/science.1244325Save Citation »Export Citation »E-mail Citation »

    This paper analyzes data from more than 250 localities over a roughly 15-year period to ask whether interannual changes in temperature explain interannual changes in malaria incidence with altitude. The authors find clear evidence from two countries that malaria moves up in elevation in warmer years, supporting mechanistic links between climate warming and malaria incidence in highlands.

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Applications

Scientific understanding arising from disease ecology has been applied to many issues in health, conservation, and environmental management, as shown in Aguirre, et al. 2012. One early-21st-century example is the use of disease-ecology models in Galvani, et al. 2007 to question the public health efficacy of governmental policy prioritizing vaccination of elderly people against influenza. Medlock and Galvani 2009 finds instead that vaccination of schoolchildren and their parents (adults aged thirty to thirty-nine years) would be far more effective at inhibiting epidemics because children are disproportionately important in transmission, and their parents serve as bridges between children and older adults. A second example pertains to the management of foot and mouth disease (FMD) in cattle. An epidemic in the United Kingdom in 2001 resulted in 2,000 cases in livestock, with control expenditures exceeding 5 billion British pounds (Keeling, et al. 2003). Prior epidemics in the United Kingdom were controlled using traditional methods consisting of slaughtering all infected or at-risk animals on infected premises and banning livestock movement in affected regions (to curtail spread). In contrast, the initial, traditional response to the 2001 epidemic was perceived to be failing, and therefore a new response based on mathematical models produced by disease ecologists (e.g., Keeling, et al. 2003) was applied. These models indicated that greater efficacy would be achieved by culling livestock on all premises, even uninfected ones, surrounding infected ones, combined with strict movement restrictions. The new control methods were generally considered successful in curtailing an epidemic that might otherwise been difficult to control, although they resulted in more than 6.5 million cattle and sheep being slaughtered. Of course, without an experimental design to allow comparison of alternative control methods, it is difficult to draw strong inferences regarding the efficacy of the model-based method. A third example concerns bovine tuberculosis (bTB), caused by the bacterium Mycobacterium bovis, which is transmitted between cattle horizontally through respiratory secretions and vertically through milk. bTB can be transmitted to humans when the (unsterilized) milk from infected cattle is consumed. Control of bTB has been attempted through the culling of infected cattle, through reduction of herd size, and, when European badgers (Meles meles) in the United Kingdom were discovered to be a wildlife reservoir of M. bovis, through the culling of badgers. However, Donnelly, et al. 2003 described how badger culls caused an increase rather than decrease prevalence of bTB infection in cattle. Disease ecologists, including the authors of Donnelly, et al. 2003 and Woodroffe, et al. 2006, determined that the culling of badgers disrupted the social cohesion of badger groups and increased group home ranges and individual dispersal rates, resulting in greater encounter rates between badgers and cattle. Simplistic conceptual models that consider only the population density of wildlife reservoirs needed to be replaced by more-complex models that included social behavior, nonrandom movements, and alternatives to strict density dependence. The strength of evidence regarding the efficacy of various bTB control efforts, including badger culling, has been thoroughly and objectively assessed in Godfray, et al. 2013, although political pressure remains to continue badger culls.

  • Aguirre, A. A., R. S. Ostfeld, and P. Daszak, eds. 2012. New directions in conservation medicine: Applied cases of ecological health. New York: Oxford Univ. Press.

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    In some respects a sequel to the 2002 volume by A. Alonso Aguirre and colleagues (see Aguirre, et al. 2002, cited under Overviews), this edited volume expands the coverage of topics at the intersection among conservation biology, animal health, and human health and exemplifies the early-21st-century trend toward more thoroughly collaborative efforts among ecologists, veterinarians, and physicians.

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  • Donnelly, C. A., R. Woodroffe, D. R. Cox, et al. 2003. Impact of localized badger culling on tuberculosis incidence in British cattle. Nature 426.6968: 834–837.

    DOI: 10.1038/nature02192Save Citation »Export Citation »E-mail Citation »

    The authors of this paper used an extensive data set to address the effectiveness of massive culling of badgers in reducing transmission of bovine tuberculosis to cattle. They found that not only is badger culling ineffective, it may also increase tuberculosis incidence in cattle.

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  • Galvani, A. P., T. C. Reluga, and G. B. Chapman. 2007. Long-standing influenza vaccination policy is in accord with individual self-interest but not with the utilitarian optimum. Proceedings of the National Academy of Sciences of the United States of America 104.13: 5692–5697.

    DOI: 10.1073/pnas.0606774104Save Citation »Export Citation »E-mail Citation »

    In this paper, the authors used a modeling approach to compare the outcomes of two different vaccination strategies against influenza: one based on individual self-interest (prevention of serious illness and death in at-risk individuals), and the other based on “utilitarian optimum,” achieved when community-wide transmission is reduced. The model shows that the two strategies are in conflict, but it suggests ways of bringing the two outcomes closer to alignment.

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  • Godfray, H. C. J., C. A. Donnelly, R. R. Kao, et al. 2013. A restatement of the natural science evidence base relevant to the control of bovine tuberculosis in Great Britain. Proceedings of the Royal Society B: Biological Sciences 280.1768: 20131634.

    DOI: 10.1098/rspb.2013.1634Save Citation »Export Citation »E-mail Citation »

    This paper provides an impartial, evidenced-based appraisal of virtually all possible policy options concerning the control of bovine tuberculosis in the United Kingdom, including evaluations of the strength of evidence. It found little support for the efficacy of culling badgers, but stronger support for several other options.

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  • Keeling, M. J., M. E. J. Woolhouse, R. M. May, G. Davies, and B. T. Grenfell. 2003. Modelling vaccination strategies against foot-and-mouth disease. Nature 421.6919: 136–142.

    DOI: 10.1038/nature01343Save Citation »Export Citation »E-mail Citation »

    This paper provides a model strategy for comparing various approaches to controlling outbreaks of foot-and-mouth disease in particular and livestock diseases more generally. Using a model based on individual farms in the United Kingdom, the authors were able to compare proactive vaccination strategies to reactive vaccination and culling strategies at different spatial scales.

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  • Medlock, J., and A. P. Galvani. 2009. Optimizing influenza vaccine distribution. Science 325.5948: 1705–1708.

    DOI: 10.1126/science.1175570Save Citation »Export Citation »E-mail Citation »

    This paper presents a model parameterized with empirical data to assess influenza vaccination strategies, particularly the targeting of specific age groups. The model indicates that schoolchildren and adults aged thirty to thirty-nine should be prioritized for vaccination, in marked contrast to prioritization strategies implemented by the US Centers for Disease Control and Prevention.

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  • Woodroffe, R., C. A. Donnelly, D. R. Cox, et al. 2006. Effects of culling on badger Meles meles spatial organization: Implications for the control of bovine tuberculosis. Journal of Applied Ecology 43.1: 1–10.

    DOI: 10.1111/j.1365-2664.2005.01144.xSave Citation »Export Citation »E-mail Citation »

    This field study assessed the impacts of badger culling on space use and population density of badgers in the United Kingdom, finding that culling dramatically increased movement distances and home range sizes of badgers while reducing their overall abundance. The authors discussed the implications of culling for the control of badger-cattle contacts and the spread of bovine tuberculosis.

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