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PLoS Negl Trop Dis. 2015 Jun 10;9(6):e0003756. doi: 10.1371/journal.pntd.0003756. eCollection 2015.

Prioritising Infectious Disease Mapping.

Author information

1
Spatial Ecology & Epidemiology Group, Department of Zoology, University of Oxford, Oxford, United Kingdom.
2
Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, United Kingdom.
3
Bill & Melinda Gates Foundation, Seattle, Washington, United States of America.
4
London School of Hygiene & Tropical Medicine, London, United Kingdom.
5
Institute for Health Metrics and Evaluation, University of Washington, Seattle, Washington, United States of America.
6
Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, United Kingdom; Institute for Health Metrics and Evaluation, University of Washington, Seattle, Washington, United States of America; Fogarty International Center, National Institutes of Health, Bethesda, Maryland, United States of America.

Abstract

BACKGROUND:

Increasing volumes of data and computational capacity afford unprecedented opportunities to scale up infectious disease (ID) mapping for public health uses. Whilst a large number of IDs show global spatial variation, comprehensive knowledge of these geographic patterns is poor. Here we use an objective method to prioritise mapping efforts to begin to address the large deficit in global disease maps currently available.

METHODOLOGY/PRINCIPAL FINDINGS:

Automation of ID mapping requires bespoke methodological adjustments tailored to the epidemiological characteristics of different types of diseases. Diseases were therefore grouped into 33 clusters based upon taxonomic divisions and shared epidemiological characteristics. Disability-adjusted life years, derived from the Global Burden of Disease 2013 study, were used as a globally consistent metric of disease burden. A review of global health stakeholders, existing literature and national health priorities was undertaken to assess relative interest in the diseases. The clusters were ranked by combining both metrics, which identified 44 diseases of main concern within 15 principle clusters. Whilst malaria, HIV and tuberculosis were the highest priority due to their considerable burden, the high priority clusters were dominated by neglected tropical diseases and vector-borne parasites.

CONCLUSIONS/SIGNIFICANCE:

A quantitative, easily-updated and flexible framework for prioritising diseases is presented here. The study identifies a possible future strategy for those diseases where significant knowledge gaps remain, as well as recognising those where global mapping programs have already made significant progress. For many conditions, potential shared epidemiological information has yet to be exploited.

PMID:
26061527
PMCID:
PMC4464526
DOI:
10.1371/journal.pntd.0003756
[Indexed for MEDLINE]
Free PMC Article

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