4.2 Article

Tracking the distribution and impacts of diseases with biological records and distribution modelling

Journal

BIOLOGICAL JOURNAL OF THE LINNEAN SOCIETY
Volume 115, Issue 3, Pages 664-677

Publisher

OXFORD UNIV PRESS
DOI: 10.1111/bij.12567

Keywords

biotic interactions; environmental change; niche concept; risk maps; species

Funding

  1. Biotechnology and Biological Sciences Research Council (BBSRC)
  2. Scottish Government
  3. Department for International Development [BB/H009167/1]
  4. Natural Environment Research Council
  5. Bill & Melinda Gates Foundation [OPP1053338, OPP1093011]
  6. Biotechnology and Biological Sciences Research Council [BB/H009167/1] Funding Source: researchfish
  7. Natural Environment Research Council [ceh020002] Funding Source: researchfish
  8. BBSRC [BB/H009167/1] Funding Source: UKRI
  9. NERC [ceh020002] Funding Source: UKRI

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Species distribution modelling is widely used in epidemiology for mapping spatial patterns and the risk of introduction of diseases and vectors and also for predicting how exposure may alter given future environmental change, motivated by the high societal impact and the multiple environmental drivers of disease outbreaks. Although pathogens and vectors have historically been sparsely recorded, monitoring systems and media sources are generating novel, online data sources on occurrence. Moreover, increasing ecological realism is being incorporated into distribution modelling techniques, focussing on dispersal, biotic interactions and evolutionary constraints that shape species distributions alongside abiotic factors and biases in recording effort, common to pathogens and vectors and wildlife species. Considering pathogens and arthropod vector systems with high impact on plant, animal and human health, the present review describes how biological records for vectors and pathogens arise, introduces the concepts behind distribution models and illustrates the potential for ecologically realistic distribution models to yield insight into the establishment and spread of pathogens. Because distribution modellers aim to provide policy makers with evidence and maps for planning and evaluation of disease mitigation measures, we highlight factors that currently constrain direct translation of models to policy. Disease distributions will be better understood and mapped in the future given improved occurrence data access and integration and combined (correlative and mechanistic) modelling approaches that are developed iteratively in concert with stakeholders.

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