4.7 Review

Assembling evidence for identifying reservoirs of infection

Journal

TRENDS IN ECOLOGY & EVOLUTION
Volume 29, Issue 5, Pages 270-279

Publisher

ELSEVIER SCIENCE LONDON
DOI: 10.1016/j.tree.2014.03.002

Keywords

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Funding

  1. RAPIDD programme of the Science and Technology Directorate of the US Department of Homeland Security
  2. National Institutes of Health Fogarty International Center
  3. Newton International Fellowship from The Royal Society
  4. EPSRC [EP/P505534/1]
  5. NIH [RO1 AI047498]
  6. National Science Foundation [OCE-1335657]
  7. De Logi Chair in Biological Sciences
  8. UK Medical Research Council [G0901135]
  9. BBSRC [BB/H009302/1, BB/J010367/1]
  10. Direct For Biological Sciences
  11. Division Of Environmental Biology [1067129] Funding Source: National Science Foundation
  12. Directorate For Geosciences [1335657] Funding Source: National Science Foundation
  13. Division Of Ocean Sciences [1335657] Funding Source: National Science Foundation
  14. Biotechnology and Biological Sciences Research Council [BB/H009302/1, BB/J010367/1, BB/H009035/1] Funding Source: researchfish
  15. Medical Research Council [G0901135] Funding Source: researchfish
  16. BBSRC [BB/H009302/1, BB/H009035/1, BB/J010367/1] Funding Source: UKRI
  17. MRC [G0901135] Funding Source: UKRI

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Many pathogens persist in multihost systems, making the identification of infection reservoirs crucial for devising effective interventions. Here, we present a conceptual framework for classifying patterns of incidence and prevalence, and review recent scientific advances that allow us to study and manage reservoirs simultaneously. We argue that interventions can have a crucial role in enriching our mechanistic understanding of how reservoirs function and should be embedded as quasi-experimental studies in adaptive management frameworks. Single approaches to the study of reservoirs are unlikely to generate conclusive insights whereas the formal integration of data and methodologies, involving interventions, pathogen genetics, and contemporary surveillance techniques, promises to open up new opportunities to advance understanding of complex multihost systems.

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