14th ICID - Poster Abstracts - International Society for Infectious ...
14th ICID - Poster Abstracts - International Society for Infectious ...
14th ICID - Poster Abstracts - International Society for Infectious ...
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When citing these abstracts please use the following reference:<br />
Author(s) of abstract. Title of abstract [abstract]. Int J Infect Dis 2010;14S1: Abstract number.<br />
Please note that the official publication of the <strong>International</strong> Journal of <strong>Infectious</strong> Diseases 2010, Volume 14, Supplement 1<br />
is available electronically on http://www.sciencedirect.com<br />
Final Abstract Number: 76.020<br />
Session: Emerging <strong>Infectious</strong> Diseases<br />
Date: Friday, March 12, 2010<br />
Time: 12:30-13:30<br />
Room: <strong>Poster</strong> & Exhibition Area/Ground Level<br />
Type: <strong>Poster</strong> Presentation<br />
Prioritizing US Dengue Fever interventions utilizing remote sensing and predictive modeling<br />
F. Grant<br />
Northrop Grumman Corporation, Atlanta, GA, USA<br />
Background: Dengue Fever is a mosquito-borne, acute febrile syndrome prevalent in tropical<br />
and subtropical areas. Based upon disease surveillance and an examination of remote sensing<br />
data from climate satellites, Dengue Fever should be considered a significant emerging global<br />
threat and a threat <strong>for</strong> the continental United States. Planning effective public health<br />
interventions around mosquito-borne diseases such as Dengue Fever can be a slow and highly<br />
manual process, requiring many hours of time, requiring specialized tools and knowledge not<br />
always readily available to local public health organizations. Additionally, it is difficult <strong>for</strong> low<br />
resource environments to identify and prioritize the use of limited public health resources.<br />
Methods: An interdisciplinary team of climate and public health scientists, explored the use of<br />
remote sensing data, climate modeling downscaling, GIS, and predictive modeling as adjunctive<br />
tools <strong>for</strong> effective public health planning. Ensemble modeling – multiple runs of regional<br />
downscaled climate models – combined with probabilistic climate data interpretation and<br />
population health data were utilized as variables <strong>for</strong> identifying potential public health intervension<br />
priorities.<br />
Results: The outcome was a GIS visualization which suggested priorities <strong>for</strong> public health<br />
intervention planning. The visualization suggested climate and age-stratified population health<br />
priorities to make best use of limited public health resources in vector monitoring, case<br />
surveillance and reporting, and <strong>for</strong> public in<strong>for</strong>mation alerts regarding Dengue Fever disease<br />
hazards.<br />
Conclusion: Although many fundamental aspects of public health action have been identified,<br />
there is a gap in the ability to translate the global in<strong>for</strong>mation developed from remote sensing data<br />
into specific, effective, locally actionable knowledge. Addressing the connections between global<br />
in<strong>for</strong>mation and local public health intervention can be accelerated by integrating remote sensing<br />
in<strong>for</strong>mation, climate downscaling data, and models. This investigation supported the theoretical<br />
value of interdisciplinary collaboration of Regional Climate Knowledge Integration Centers<br />
(CKICs) as an adjunctive resource to local public health. Additional prototypes and decision aids<br />
are needed to begin exploring how to best integrate and use remote sensing data to benefit local<br />
public health practice needs.
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