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Curr Environ Health Rep. 2018 Oct 22. doi: 10.1007/s40572-018-0221-0. [Epub ahead of print]

Unexplored Opportunities: Use of Climate- and Weather-Driven Early Warning Systems to Reduce the Burden of Infectious Diseases.

Author information

1
University of Washington, 4225 Roosevelt Way NE # 100, Seattle, WA, 98105, USA. cwmorin@uw.edu.
2
European Centre for Disease Prevention and Control, Solna, Sweden.
3
National Oceanic and Atmospheric Administration, Silver Spring, MD, USA.
4
University of Florida, Gainesville, FL, USA.
5
University of Washington, 4225 Roosevelt Way NE # 100, Seattle, WA, 98105, USA.

Abstract

PURPOSE OF REVIEW:

Weather and climate influence multiple aspects of infectious disease ecology. Creating and applying early warning systems based on temperature, precipitation, and other environmental data can identify where and when outbreaks of climate-sensitive infectious diseases could occur and can be used by decision makers to allocate resources. Whether an outbreak actually occurs depends heavily on other social, political, and institutional factors.

RECENT FINDINGS:

Improving the timing and confidence of seasonal climate forecasting, coupled with knowledge of exposure-response relationships, can identify prior conditions conducive to disease outbreaks weeks to months in advance of outbreaks. This information could then be used by public health professionals to improve surveillance in the most likely areas for threats. Early warning systems are well established for drought and famine. And while weather- and climate-driven early warning systems for certain diseases, such as dengue fever and cholera, are employed in some regions, this area of research is underdeveloped. Early warning systems based on temperature, precipitation, and other environmental data provide an opportunity for early detection leading to early action and response to potential pathogen threats, thereby reducing the burden of disease when compared with passive health indicator-based surveillance systems.

KEYWORDS:

Climate; Early warning systems; Forecasting; Infectious disease

PMID:
30350265
DOI:
10.1007/s40572-018-0221-0

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