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Interdisciplinarity and Infectious Diseases: An Ebola Case Study

  • Vanessa O. Ezenwa ,

    Affiliation Odum School of Ecology and Department of Infectious Diseases, College of Veterinary Medicine, University of Georgia, Athens, Georgia, United States of America

  • Anne-Helene Prieur-Richard,

    Affiliation DIVERSITAS, Muséum National d'Histoire Naturelle, Paris, France

  • Benjamin Roche,

    Affiliation UMI IRD/UPMC 209 UMMISCO, Bondy, France

  • Xavier Bailly,

    Affiliation INRA, UR346 Épidémiologie Animale, Saint Genès Champanelle, France

  • Pierre Becquart,

    Affiliation UMR 5290 IRD-CNRS-Université de Montpellier, Centre IRD de Montpellier, Montpellier, France

  • Gabriel E. García-Peña,

    Affiliations UMR 5290 IRD-CNRS-Université de Montpellier, Centre IRD de Montpellier, Montpellier, France, CESAB—Centre de Synthèse et d’Analyse sur la Biodiversité, Aix-en-Provence, France

  • Parviez R. Hosseini,

    Affiliation EcoHealth Alliance, New York, New York, United States of America

  • Felicia Keesing,

    Affiliation Biology Program, Bard College, Annandale-on-Hudson, New York, United States of America

  • Annapaola Rizzoli,

    Affiliation Fondazione Edmund Mach, Department of Biodiveristy and Molecular Ecology, San Michele all’Adige (TN), Italy

  • Gerardo Suzán,

    Affiliation Facultad de Medicina Veterinaria Zootecnia, Universidad Nacional Autónoma de México, Ciudad Universitaria, México, Distrito Federal, México

  • Marco Vignuzzi,

    Affiliation Institut Pasteur, Viral Populations and Pathogenesis, CNRS UMR 3569, Paris, France

  • Marion Vittecoq,

    Affiliation Centre de recherche de la Tour du Valat, Le Sambuc, Arles, France

  • James N. Mills,

    Affiliation Population Biology, Ecology, and Evolution Program, Emory University, Atlanta, Georgia, United States of America

  • Jean-François Guégan

    Affiliation UMR 5290 IRD-CNRS-Université de Montpellier, Centre IRD de Montpellier, Montpellier, France

High-profile epidemics such as Ebola, avian influenza, and severe acute respiratory syndrome (SARS) repeatedly thrust infectious diseases into the limelight. Because the emergence of diseases involves so many factors, the need for interdisciplinary approaches to studying emerging infections, particularly those originating from animals (i.e., zoonoses), is frequently discussed [14]. However, effective integration across disciplines is challenging in practice. Ecological ideas, for example, are rarely considered in biomedical research, while insights from biomedicine are often neglected in ecological studies of infectious diseases. One practical reason for this is that researchers in these fields focus on vastly different scales of biological organization (Fig 1), which are difficult to bridge both intellectually and methodologically. Nevertheless, integration across biological scales is increasingly needed for solving the complex problems zoonotic diseases pose to human and animal well-being. Motivated by current events, we use Ebola virus as a case study to highlight fundamental questions about zoonoses that can be addressed by integrating insights and approaches across scales.

Fig 1. In the two leftmost panels, we depict the hierarchy of biological organization, from molecules and genes to ecosystems.

Each level of the hierarchy reflects an increase in organizational complexity, with each level being primarily composed of the previous level’s basic units. Middle panels illustrate how the study of interactions between infectious disease agents and their hosts differs across the biomedical, public health, and ecological sciences. Specifically, biomedical sciences typically focus on lower- and medium-scale levels of biological organization (e.g., molecules, genes, and organs). In contrast, public health and ecological sciences typically focus on medium- and higher-scale levels of organization (individual, population, community, ecosystem, and environment). The filled circles and solid lines connecting the circles illustrate key cross scale biological interactions studied within each field. The right panel shows example knowledge gaps that can emerge from the “typical” segregation of research activities across the three fields. To better integrate our understanding of the causes and consequences of zoonotic infectious diseases, researchers must begin focusing on these types of missing links.

Ebola Severity: A Cell-to-Ecological Community Perspective

Zaire ebolavirus (EBOV), the virus responsible for the 2014 Ebola outbreak in West Africa, causes a deadly haemorrhagic disease in humans with case fatality rates ranging from 60%–88% [5]. Although well-known for its lethality, Ebola severity is variable at the individual level; some people die of infection, some survive, and some never develop symptoms [68]. Asymptomatic infection is poorly understood but may have important implications for how EBOV spreads. After a 1996 outbreak in Gabon, one study found that 45% of household contacts of symptomatic patients never developed disease symptoms despite becoming infected with the virus and mounting EBOV-specific immune responses [7]. Intriguingly, asymptomatic infection might also result from contact between humans and animals. As an example, a 2010 serological survey of over 4,000 people from 220 villages in Gabon found that 15% of people overall, and 19% of those in forested regions, had EBOV-specific immunoglobulin G (IgG) antibodies [9]. Detection of EBOV-specific T cell responses in a subset of IgG+ individuals corroborated that these individuals were exposed to EBOV. Based on the known epidemiology of Ebola in Gabon, the authors ruled out human-to-human transmission as a sufficient explanation for the high antibody prevalence. Instead, they hypothesized that human–animal contact, specifically human contact with noninfectious virus particles in the environment (e.g., by eating or handling fruit contaminated with the saliva of infected bats), may have triggered virus-specific immune responses. If the immune responses detected in Gabon are protective against subsequent EBOV infection, large-scale phenomena occurring at the level of the ecological community might interact with molecular and cellular-level processes to influence the severity of any given Ebola outbreak.

Using an epidemiological model, Bellan et al. [10] showed that accounting for asymptomatic infections that induce protective immunity reduced Ebola incidence projections for Liberia by 50%. Ultimately, the relative frequency of protective asymptomatic infections determines the size of this effect. Although the model was predicated on asymptomatic infection occurring during human-to-human transmission, asymptomatic cases that arise from environmental exposure, as hypothesized by Becquart et al. [9], could have similar dampening effects on epidemic spread. The frequency of such environmental exposure would depend on the animal community in a region. If certain bat species are the natural reservoirs of EBOV [11], their presence, relative abundance, and behaviour could all affect the frequency with which humans come into contact with them and thereby develop “environmentally-induced” immune protection. Of course, human contact with bats also triggers Ebola outbreaks [12], so understanding the context in which human–bat contact is protective (e.g., induces asymptomatic infection and immunity) rather than hazardous (e.g., causes symptomatic infection and epidemic spread) requires investigating phenomena occurring in both humans and bats, from the drivers and frequency of contact between humans, bats, and other relevant species to the characteristics of host cell–virus interactions upon contact.

Ecosystem Dynamics, Viral Evolution, and Human Epidemics

The Ebola outbreak in West Africa and punctuated outbreaks in Central Africa since the 1970s raise fundamental questions about what drives disease spillover to humans. Ebola outbreaks are not limited to human populations. Wildlife die-offs occur routinely before or during human epidemics, indicating that the virus circulates in a range of other mammal species, including great apes and forest antelopes [1316]. Even though these species are not considered natural reservoirs, circulation of EBOV in these animals still has implications for human disease. First, human contact with these species can directly trigger disease outbreaks [17]. Second, these animals might affect spillover risk by influencing rates of virus evolution. Phylogenetic analysis of EBOV in great apes [18] suggests that genetic variation can accumulate rapidly during EBOV transmission in these populations. Importantly, virus evolution in animal hosts may facilitate the emergence of strains that spread more efficiently to humans or that cause more severe disease.

Although unknown for EBOV, the idea that virus circulation in wild species can drive changes that impact human–virus interactions has support for other RNA viruses such as SARS coronavirus and influenza A virus (see Table 1) [19,20]. Given evidence from these other viruses, understanding if and how animal hosts affect EBOV evolution is crucial. Doing this requires studies that connect large-scale environmental and ecosystem processes to small-scale genetic and molecular processes. For example, food web or habitat structure may determine the diets of target wildlife species, and host nutrition could affect rates of infection, virus replication, and shedding. Likewise, contact rates among species determine levels of cross species virus transmission, which may influence virus mutation or recombination rates. These examples, though speculative, highlight how cross scale chains of events might influence disease emergence in humans.

Table 1. Examples of zoonotic disease systems in which cross scale research has contributed to key insights about infection dynamics.

Towards a More Integrative Future

The Ebola outbreak in West Africa reminds us that zoonotic diseases continue to be a major threat. The benefits of cross scale research are evident for several high-profile zoonoses (Table 1). Nevertheless, this type of work is far from the norm, and successful integration of research approaches across vastly different biological scales remains challenging. A first step toward greater integration involves student training. Training programs in infectious disease typically focus on a single or narrow range of biological scales, but more crosscutting approaches are needed. Training grants focused on multiscale literacy in infectious disease research should be a priority for funding agencies, for example. Professional societies could also lead the way by sponsoring workshops, symposia, and other events on integration across disciplines. The involvement of professional societies has the added benefit of allowing infectious disease researchers to expand their perspectives beyond their years of formal training. Updating our collective mind-set in these and other ways will put us in a much better position to tackle the next zoonotic disease threat.


  1. 1. Morens DM, Folkers GK, Fauci AS (2004) The challenge of emerging and re-emerging infectious diseases. Nature 430: 242–249. pmid:15241422
  2. 2. Holmes EC (2013) What can we predict about viral evolution and emergence? Curr Opin Virol 3: 180–184. pmid:23273851
  3. 3. Zinsstag J, Schelling E, Waltner-Toews D, Tanner M (2011) From "one medicine" to "one health" and systemic approaches to health and well-being. Prev Vet Med 101: 148–156. pmid:20832879
  4. 4. Parkes MW, Bienen L, Breilh J, Hsu L-N, McDonald M, et al. (2005) All Hands on Deck: Transdisciplinary Approaches to Emerging Infectious Disease. EcoHealth 2: 258–272.
  5. 5. Lefebvre A, Fiet C, Belpois-Duchamp C, Tiv M, Astruc K, et al. (2014) Case fatality rates of Ebola virus diseases: a meta-analysis of World Health Organization data. Med Mal Infect 44: 412–416. pmid:25193630
  6. 6. Gonzalez JP, Nakoune E, Slenczka W, Vidal P, Morvan JM (2000) Ebola and Marburg virus antibody prevalence in selected populations of the Central African Republic. Microbes Infect 2: 39–44. pmid:10717539
  7. 7. Leroy EM, Baize S, Volchkov VE, Fisher-Hoch SP, Georges-Courbot MC, et al. (2000) Human asymptomatic Ebola infection and strong inflammatory response. Lancet 355: 2210–2215. pmid:10881895
  8. 8. Goeijenbier M, van Kampen JJ, Reusken CB, Koopmans MP, van Gorp EC (2014) Ebola virus disease: a review on epidemiology, symptoms, treatment and pathogenesis. Neth J Med 72: 442–448. pmid:25387613
  9. 9. Becquart P, Wauquier N, Mahlakoiv T, Nkoghe D, Padilla C, et al. (2010) High prevalence of both humoral and cellular immunity to Zaire ebolavirus among rural populations in Gabon. PLoS One 5: e9126. pmid:20161740
  10. 10. Bellan SE, Pulliam JR, Dushoff J, Meyers LA (2014) Ebola control: effect of asymptomatic infection and acquired immunity. Lancet 384: 1499–1500. pmid:25390569
  11. 11. Leroy EM, Kumulungui B, Pourrut X, Rouquet P, Hassanin A, et al. (2005) Fruit bats as reservoirs of Ebola virus. Nature 438: 575–576. pmid:16319873
  12. 12. Leroy EM, Epelboin A, Mondonge V, Pourrut X, Gonzalez JP, et al. (2009) Human Ebola outbreak resulting from direct exposure to fruit bats in Luebo, Democratic Republic of Congo, 2007. Vector Borne Zoonotic Dis 9: 723–728. pmid:19323614
  13. 13. Leroy EM, Rouquet P, Formenty P, Souquiere S, Kilbourne A, et al. (2004) Multiple Ebola virus transmission events and rapid decline of central African wildlife. Science 303: 387–390. pmid:14726594
  14. 14. Rouquet P, Froment JM, Bermejo M, Kilbourn A, Karesh W, et al. (2005) Wild animal mortality monitoring and human Ebola outbreaks, Gabon and Republic of Congo, 2001–2003. Emerg Infect Dis 11: 283–290. pmid:15752448
  15. 15. Bermejo M, Rodriguez-Teijeiro JD, Illera G, Barroso A, Vila C, et al. (2006) Ebola outbreak killed 5000 gorillas. Science 314: 1564. pmid:17158318
  16. 16. Lahm SA, Kombila M, Swanepoel R, Barnes RF (2007) Morbidity and mortality of wild animals in relation to outbreaks of Ebola haemorrhagic fever in Gabon, 1994–2003. Trans R Soc Trop Med Hyg 101: 64–78. pmid:17010400
  17. 17. Pourrut X, Kumulungui B, Wittmann T, Moussavou G, Delicat A, et al. (2005) The natural history of Ebola virus in Africa. Microbes Infect 7: 1005–1014. pmid:16002313
  18. 18. Wittmann TJ, Biek R, Hassanin A, Rouquet P, Reed P, et al. (2007) Isolates of Zaire ebolavirus from wild apes reveal genetic lineage and recombinants. Proc Natl Acad Sci USA 104: 17123–17127. pmid:17942693
  19. 19. Graham RL, Baric RS (2010) Recombination, reservoirs, and the modular spike: mechanisms of coronavirus cross-species transmission. J Virol 84: 3134–3146. pmid:19906932
  20. 20. Worobey M, Han GZ, Rambaut A (2014) A synchronized global sweep of the internal genes of modern avian influenza virus. Nature 508: 254–257. pmid:24531761
  21. 21. Shaman J, Goldstein E, Lipsitch M (2011) Absolute humidity and pandemic versus epidemic influenza. Am J Epidemiol 173: 127–135. pmid:21081646
  22. 22. Roche B, Drake JM, Brown J, Stallknecht DE, Bedford T, et al. (2014) Adaptive evolution and environmental durability jointly structure phylodynamic patterns in avian influenza viruses. PLoS Biol 12: e1001931. pmid:25116957
  23. 23. Imai M, Watanabe T, Hatta M, Das SC, Ozawa M, et al. (2012) Experimental adaptation of an influenza H5 HA confers respiratory droplet transmission to a reassortant H5 HA/H1N1 virus in ferrets. Nature 486: 420–428. pmid:22722205
  24. 24. Smith GJ, Vijaykrishna D, Bahl J, Lycett SJ, Worobey M, et al. (2009) Origins and evolutionary genomics of the 2009 swine-origin H1N1 influenza A epidemic. Nature 459: 1122–1125. pmid:19516283
  25. 25. Lau SK, Woo PC, Li KS, Huang Y, Tsoi HW, et al. (2005) Severe acute respiratory syndrome coronavirus-like virus in Chinese horseshoe bats. Proc Natl Acad Sci USA 102: 14040–14045. pmid:16169905
  26. 26. Li W, Shi Z, Yu M, Ren W, Smith C, et al. (2005) Bats are natural reservoirs of SARS-like coronaviruses. Science 310: 676–679. pmid:16195424
  27. 27. Lloyd-Smith JO, Schreiber SJ, Kopp PE, Getz WM (2005) Superspreading and the effect of individual variation on disease emergence. Nature 438: 355–359.5. pmid:16292310
  28. 28. Qu XX, Hao P, Song XJ, Jiang SM, Liu YX, et al. (2005) Identification of two critical amino acid residues of the severe acute respiratory syndrome coronavirus spike protein for its variation in zoonotic tropism transition via a double substitution strategy. J Biol Chem 280: 29588–29595. pmid:15980414
  29. 29. Kan B, Wang M, Jing H, Xu H, Jiang X, et al. (2005) Molecular evolution analysis and geographic investigation of severe acute respiratory syndrome coronavirus-like virus in palm civets at an animal market and on farms. J Virol 79: 11892–11900. pmid:16140765
  30. 30. Song HD, Tu CC, Zhang GW, Wang SY, Zheng K, et al. (2005) Cross-host evolution of severe acute respiratory syndrome coronavirus in palm civet and human. Proc Natl Acad Sci U S A 102: 2430–2435. pmid:15695582
  31. 31. Wang LF, Eaton BT (2007) Bats, civets and the emergence of SARS. Curr Top Microbiol Immunol 315: 325–344. pmid:17848070
  32. 32. Plowright RK, Eby P, Hudson PJ, Smith IL, Westcott D, et al. (2015) Ecological dynamics of emerging bat virus spillover. Proc Biol Sci 282: 20142124. pmid:25392474
  33. 33. Yates TL, Mills JN, Parmenter CA, Ksiazek TG, Parmenter RR, et al. (2002) The ecology and evolutionary history of an emergent disease: hantavirus pulmonary syndrome. Bioscience 52: 989–998.
  34. 34. Mills JN (2005) Regulation of rodent-borne viruses in the natural host: implications for human disease. Arch Virol Suppl: 45–57. pmid:16355867
  35. 35. Ostfeld RS, Canham CD, Oggenfuss K, Winchcombe RJ, Keesing F (2006) Climate, deer, rodents, and acorns as determinants of variation in lyme-disease risk. PLoS Biol 4: e145. pmid:16669698
  36. 36. LoGiudice K, Ostfeld RS, Schmidt KA, Keesing F (2003) The ecology of infectious disease: effects of host diversity and community composition on Lyme disease risk. Proc Natl Acad Sci USA 100: 567–571. pmid:12525705
  37. 37. Schwanz LE, Voordouw MJ, Brisson D, Ostfeld RS (2011) Borrelia burgdorferi has minimal impact on the Lyme disease reservoir host Peromyscus leucopus. Vector Borne Zoonotic Dis 11: 117–124. pmid:20569016
  38. 38. Hersh MH, LaDeau SL, Previtali MA, Ostfeld RS (2014) When is a parasite not a parasite? Effects of larval tick burdens on white-footed mouse survival. Ecology 95:1360–1369. pmid:25000767
  39. 39. Previtali MA., Ostfeld RS, Keesing F, Jolles AE, Hanselmann R, et al. (2012) Relationship between pace of life and immune responses in wild rodents. Oikos 121:1483–1492.