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Prioritising Infectious Disease Mapping.

David M Pigott1, Rosalind E Howes1, Antoinette Wiebe1

  • 1Spatial Ecology & Epidemiology Group, Department of Zoology, University of Oxford, Oxford, United Kingdom.

Plos Neglected Tropical Diseases
|June 11, 2015
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Summary
This summary is machine-generated.

This study presents a new quantitative framework to prioritize infectious disease (ID) mapping efforts. It identifies 44 key diseases, including neglected tropical diseases, to guide global health mapping strategies and address knowledge gaps.

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Area of Science:

  • Global Health
  • Epidemiology
  • Disease Mapping

Background:

  • Growing data and computational power enable infectious disease (ID) mapping for public health.
  • Current knowledge of global geographic patterns for many IDs is limited.
  • An objective method is needed to prioritize mapping efforts and address this deficit.

Purpose of the Study:

  • To develop a quantitative, flexible, and updatable framework for prioritizing infectious disease mapping.
  • To identify key diseases for focused global mapping initiatives.
  • To address significant knowledge gaps in the geographic distribution of diseases.

Main Methods:

  • Diseases were clustered into 33 groups based on taxonomy and epidemiology.
  • Disability-adjusted life years (DALYs) from the Global Burden of Disease 2013 study measured disease burden.
  • Disease clusters were ranked by combining burden metrics and stakeholder interest.

Main Results:

  • A framework was developed to prioritize infectious disease mapping efforts.
  • 44 diseases within 15 principal clusters were identified as high priority.
  • Neglected tropical diseases and vector-borne parasites dominated high-priority clusters, alongside major diseases like malaria, HIV, and tuberculosis.

Conclusions:

  • A quantitative, adaptable framework for prioritizing disease mapping is presented.
  • The study outlines a strategy for diseases with knowledge gaps and recognizes progress in others.
  • Exploiting shared epidemiological information across diseases remains a future opportunity.