Virtual globes and geospatial health: the potential of new tools in the management and control of vector-borne diseases.

The rapidly growing field of three-dimensional software modeling of the Earth holds promise for applications in the geospatial health sciences. Easy-to-use, intuitive virtual globe technologies such as Google Earth enable scientists around the world to share their data and research results in a visually attractive and readily understandable fashion without the need for highly sophisticated geographical information systems (GIS) or much technical assistance. This paper discusses the utility of the rapid and simultaneous visualization of how the agents of parasitic diseases are distributed, as well as that of their vectors and/or intermediate hosts together with other spatially-explicit information. The resulting better understanding of the epidemiology of infectious diseases, and the multidimensional environment in which they occur, are highlighted. In particular, the value of Google Earth, and its web-based pendant Google Maps, are reviewed from a public health view point, combining results from literature searches and experiences gained thus far from a multidisciplinary project aimed at optimizing schistosomiasis control and transmission surveillance in sub-Saharan Africa. Although the basic analytical capabilities of virtual globe applications are limited, we conclude that they have considerable potential in the support and promotion of the geospatial health sciences as a userfriendly, straightforward GIS tool for the improvement of data collation, visualization and exploration. The potential of these systems for data sharing and broad dissemination of scientific research and results is emphasized.

[1]  Theodore B Lefer,et al.  Using Google Earth as an Innovative Tool for Community Mapping , 2008, Public health reports.

[2]  Shawn Mikula,et al.  Internet-enabled high-resolution brain mapping and virtual microscopy , 2007, NeuroImage.

[3]  Kenneth D. Mandl,et al.  HealthMap: Global Infectious Disease Monitoring through Automated Classification and Visualization of Internet Media Reports , 2008, Journal of the American Medical Informatics Association.

[4]  James C Wallace,et al.  Integrating open-source technologies to build low-cost information systems for improved access to public health data , 2008, International journal of health geographics.

[5]  Andrew B. Lawson,et al.  An Introductory Guide to Disease Mapping , 2001 .

[6]  Raoul Kamadjeu,et al.  Tracking the polio virus down the Congo River: a case study on the use of Google Earth™ in public health planning and mapping , 2009, International journal of health geographics.

[7]  Annibale Biggeri,et al.  New insights into the application of geographical information systems and remote sensing in veterinary parasitology. , 2006, Geospatial health.

[8]  E. Sala,et al.  Mangroves in the Gulf of California increase fishery yields , 2008, Proceedings of the National Academy of Sciences.

[9]  E. Luedeling,et al.  Typology of oases in northern Oman based on Landsat and SRTM imagery and geological survey data , 2008 .

[10]  Vincent Herbreteau,et al.  Thirty years of use and improvement of remote sensing, applied to epidemiology: from early promises to lasting frustration. , 2007, Health & place.

[11]  J. Bruno,et al.  Regional Decline of Coral Cover in the Indo-Pacific: Timing, Extent, and Subregional Comparisons , 2007, PloS one.

[12]  Erik Sundvall,et al.  Graphical Overview and Navigation of Electronic Health Records in a Prototyping Environment Using Google Earth and openEHR Archetypes , 2007, MedInfo.

[13]  B. Sharp,et al.  Potential effect of climate change on malaria transmission in Africa , 2003, The Lancet.

[14]  Malone Jb.  Biology-based mapping of vector-borne parasites by Geographic Information Systems and Remote Sensing. , 2005 .

[15]  S. Brooker.  Spatial epidemiology of human schistosomiasis in Africa: risk models, transmission dynamics and control , 2007, Transactions of the Royal Society of Tropical Medicine and Hygiene.

[16]  M. Brauer,et al.  Predicting personal exposure of pregnant women to traffic-related air pollutants. , 2008, The Science of the total environment.

[17]  Sheng Gao,et al.  Online GIS services for mapping and sharing disease information , 2008, International journal of health geographics.

[18]  Declan Butler,et al.  Virtual globes: The web-wide world , 2006, Nature.

[19]  J B Malone.  Biology-based mapping of vector-borne parasites by Geographic Information Systems and Remote Sensing. , 2005, Parassitologia.

[20]  Joshua M. Pearce,et al.  3D-Mapping Optimization of Embodied Energy of Transportation , 2007 .

[21]  Alan D. Lopez,et al.  Comparative quantification of health risks. Global and regional burden of disease attributable to selected major risk factors. Volume 1. , 2004 .

[22]  J. Blamont.  We the people: Consequences of the revolution in the management of space applications , 2008 .

[23]  Robert P Guralnick,et al.  Towards a collaborative, global infrastructure for biodiversity assessment , 2007, Ecology letters.

[24]  G. Veletsianos,et al.  An Investigation of the Use of Real-Time, Authentic Geospatial Data in the K–12 Classroom , 2008 .

[25]  Nathan Chan,et al.  Climate Suitability: For Stable Malaria Transmission in Zimbabwe Under Different Climate Change Scenarios , 2002 .

[26]  Lars Eisen,et al.  Use of Google Earth TM to strengthen public health capacity and facilitate management of vector-borne diseases in resource-poor environments , 2008 .

[27]  Jürg Utzinger,et al.  Demographic and health surveillance of mobile pastoralists in Chad: integration of biometric fingerprint identification into a geographical information system. , 2008, Geospatial health.

[28]  Simon Brooker,et al.  Spatial heterogeneity of parasite co-infection: Determinants and geostatistical prediction at regional scales , 2009, International journal for parasitology.

[29]  F. Doumani,et al.  Environmental Health: Bridging the Gaps , 2001 .

[30]  C. Corvalan,et al.  How Much Disease Burden can be Prevented by Environmental Interventions? , 2007, Epidemiology.

[31]  P. Thenkabail,et al.  Sub-pixel Area Calculation Methods for Estimating Irrigated Areas , 2007, Sensors.

[32]  Aaron Addison,et al.  Google Earth, GIS, and the Great Divide: a new and simple method for sharing paleontological data. , 2008, Journal of human evolution.

[33]  M. Tanner,et al.  A review of geographic information system and remote sensing with applications to the epidemiology and control of schistosomiasis in China. , 2005, Acta tropica.

[34]  A. Curtis,et al.  Spatial confidentiality and GIS: re-engineering mortality locations from published maps about Hurricane Katrina , 2006, International journal of health geographics.

[35]  C. Dye,et al.  Heterogeneities in the transmission of infectious agents: implications for the design of control programs. , 1997, Proceedings of the National Academy of Sciences of the United States of America.

[36]  Kei-Hoi Cheung,et al.  Web GIS in practice VI: a demo playlist of geo-mashups for public health neogeographers , 2008, International journal of health geographics.

[37]  S. Brooker,et al.  Towards an atlas of human helminth infection in sub-Saharan Africa: the use of geographical information systems (GIS). , 2000, Parasitology today.

[38]  Petr Cizek,et al.  The ethics of Google Earth: crossing thresholds from spatial data to landscape visualisation. , 2009, Journal of environmental management.

[39]  C. Vionnet.  [Environmental health]. , 1983, Krankenpflege. Soins infirmiers.

[40]  C. Cheung,et al.  Atlas of the global distribution of schistosomiasis. , 1987 .

[41]  G. Garner,et al.  Identifying areas of Australia at risk of H5N1 avian influenza infection from exposure to migratory birds: a spatial analysis. , 2008, Geospatial health.

[42]  R. Guralnick,et al.  BioGeomancer: Automated Georeferencing to Map the World's Biodiversity Data , 2006, PLoS biology.

[43]  S. Hales,et al.  Potential effect of population and climate changes on global distribution of dengue fever: an empirical model , 2002, The Lancet.

[44]  Maged N Kamel Boulos,et al.  Web GIS in practice III: creating a simple interactive map of England's Strategic Health Authorities using Google Maps API, Google Earth KML, and MSN Virtual Earth Map Control , 2005, International journal of health geographics.

[45]  Xiaoru Yuan,et al.  An Efficient System for Creating Synthetic InSAR Images from Simulations , 2008 .

[46]  Declan Butler,et al.  Mashups mix data into global service , 2006, Nature.

[47]  Roger Bivand,et al.  Implementing Spatial Data Analysis Software Tools in R , 2006 .

[48]  Keith C. Clarke,et al.  Interactive Visual Exploration of a Large Spatio-temporal Dataset: Reflections on a Geovisualization Mashup. , 2007, IEEE Transactions on Visualization and Computer Graphics.

[49]  Todd C. Patterson.  Google Earth as a (Not Just) Geography Education Tool , 2007 .

[50]  Paavo Monkkonen,et al.  Using Online Satellite Imagery as a Research Tool , 2008 .