Visualizing Malaria Spread Under Climate Variability

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Scopus citations

Abstract

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.

Original languageEnglish (US)
Title of host publicationEnvirVis 2016 - Workshop on Visualisation in Environmental Sciences
EditorsDieter Fellner
PublisherThe Eurographics Association
Pages29-33
Number of pages5
ISBN (Electronic)9783038680185
DOIs
StatePublished - 2016
Event4th Workshop on Visualisation in Environmental Sciences, EnvirVis 2016 at EuroVis 2016 - Groningen, Netherlands
Duration: Jun 6 2016Jun 7 2016

Publication series

NameEnvirVis 2016 - Workshop on Visualisation in Environmental Sciences

Conference

Conference4th Workshop on Visualisation in Environmental Sciences, EnvirVis 2016 at EuroVis 2016
Country/TerritoryNetherlands
CityGroningen
Period6/6/166/7/16

ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition
  • Computer Graphics and Computer-Aided Design
  • Environmental Science(all)

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