Journal
PLOS ONE
Volume 8, Issue 8, Pages -Publisher
PUBLIC LIBRARY SCIENCE
DOI: 10.1371/journal.pone.0069951
Keywords
-
Categories
Funding
- Fogarty International Center [2R01-TW005869]
- NSF [EF-0914866, DEB -1115895]
- National Institute of Health [1R01AI090159]
- National Science Foundation Human and Social Dynamics 'Agents of Change' award [BCS-0826779]
- MRC [MR/K021680/1] Funding Source: UKRI
- NERC [NE/J000507/2] Funding Source: UKRI
- Medical Research Council [MR/K021680/1] Funding Source: researchfish
- Natural Environment Research Council [NE/J000507/2] Funding Source: researchfish
- Direct For Biological Sciences
- Division Of Environmental Biology [0914866] Funding Source: National Science Foundation
Ask authors/readers for more resources
The proper allocation of public health resources for research and control requires quantification of both a disease's current burden and the trend in its impact. Infectious diseases that have been labeled as emerging infectious diseases'' (EIDs) have received heightened scientific and public attention and resources. However, the label 'emerging' is rarely backed by quantitative analysis and is often used subjectively. This can lead to over-allocation of resources to diseases that are incorrectly labelled emerging,'' and insufficient allocation of resources to diseases for which evidence of an increasing or high sustained impact is strong. We suggest a simple quantitative approach, segmented regression, to characterize the trends and emergence of diseases. Segmented regression identifies one or more trends in a time series and determines the most statistically parsimonious split(s) (or joinpoints) in the time series. These joinpoints in the time series indicate time points when a change in trend occurred and may identify periods in which drivers of disease impact change. We illustrate the method by analyzing temporal patterns in incidence data for twelve diseases. This approach provides a way to classify a disease as currently emerging, re-emerging, receding, or stable based on temporal trends, as well as to pinpoint the time when the change in these trends happened. We argue that quantitative approaches to defining emergence based on the trend in impact of a disease can, with appropriate context, be used to prioritize resources for research and control. Implementing this more rigorous definition of an EID will require buy-in and enforcement from scientists, policy makers, peer reviewers and journal editors, but has the potential to improve resource allocation for global health.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available