TY - GEN
T1 - Visualizing Malaria Spread Under Climate Variability
AU - Liang, X.
AU - Aggarwal, R.
AU - Cherif, A.
AU - Gumel, A.
AU - Mascaro, G.
AU - Maciejewski, R.
N1 - Funding Information:
Some of the material presented here was supported by the NSF under Grant No. 1350573, in part by the U.S. Department of Homeland Security VACCINE Center under Award Number 2009-ST-061-CI0001, and by the Global Security Initiative at Arizona State University. One of authors (ABG) is grateful to National Institute for Mathematical and Biological Synthesis (NIMBioS) for funding the Working Group on Climate Change and Vector-borne Diseases. NIMBioS is an Institute sponsored by the National Science Foundation, the U.S. Department of Homeland Security, and the U.S. Department of Agriculture through NSF Award #EF-0832858, with additional support from The University of Tennessee, Knoxville.
Publisher Copyright:
© 2016 The Author(s) Eurographics Proceedings © 2016 The Eurographics Association.
PY - 2016
Y1 - 2016
N2 - In order to better control and prevent the infectious diseases, measures of vulnerability and risk to increased infectious disease outbreaks have been explored. Research investigating possible links between variations in climate and transmission of infectious diseases has led to a variety of predictive models for estimating the future impact of infectious disease under projected climate change. Underlying all of these approaches is the connection of multiple data sources and the need for computational models that can capture the spatio-temporal dynamics of emerging infectious diseases and climate variability, especially as the impact of climate variability on the land surface is becoming increasingly critical in predicting the geo-temporal evolution of infectious disease outbreaks. This paper presents an initial visualization prototype that combines data from population and climate simulations as inputs to a patch-based mosquito spread model for analyzing potential disease spread vectors and their relationship to climate variability.
AB - In order to better control and prevent the infectious diseases, measures of vulnerability and risk to increased infectious disease outbreaks have been explored. Research investigating possible links between variations in climate and transmission of infectious diseases has led to a variety of predictive models for estimating the future impact of infectious disease under projected climate change. Underlying all of these approaches is the connection of multiple data sources and the need for computational models that can capture the spatio-temporal dynamics of emerging infectious diseases and climate variability, especially as the impact of climate variability on the land surface is becoming increasingly critical in predicting the geo-temporal evolution of infectious disease outbreaks. This paper presents an initial visualization prototype that combines data from population and climate simulations as inputs to a patch-based mosquito spread model for analyzing potential disease spread vectors and their relationship to climate variability.
UR - http://www.scopus.com/inward/record.url?scp=85123834227&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85123834227&partnerID=8YFLogxK
U2 - 10.2312/envirvis.20161104
DO - 10.2312/envirvis.20161104
M3 - Conference contribution
AN - SCOPUS:85123834227
T3 - EnvirVis 2016 - Workshop on Visualisation in Environmental Sciences
SP - 29
EP - 33
BT - EnvirVis 2016 - Workshop on Visualisation in Environmental Sciences
A2 - Fellner, Dieter
PB - The Eurographics Association
T2 - 4th Workshop on Visualisation in Environmental Sciences, EnvirVis 2016 at EuroVis 2016
Y2 - 6 June 2016 through 7 June 2016
ER -