The Mosquito Online Advanced Analytic Service: a case study for school research projects in Thailand.

The Mosquito Online Advanced Analytic Service (MOAAS) provides an essential tool for querying, analyzing, and visualizing patterns of mosquito larval distribution in Thailand. The MOAAS was developed using Structured Query Language (SQL) technology as a web-based tool for data entry and data access, webMathematica technology for data analysis and data visualization, and Google Earth and Google Maps for Geographic Information System (GIS) visualization. Fifteen selected schools in Thailand provided test data for MOAAS. Users performed data entry using the web-service, data analysis, and data visualization tools with webMathematica, data visualization with bar charts, mosquito larval indices, and three-dimensional (3D) bar charts overlaying on the Google Earth and Google Maps. The 3D bar charts of the number of mosquito larvae were displayed along with spatial information. The mosquito larvae information may be useful for dengue control efforts and health service communities for planning and operational activities.

[1]  Peter White,et al.  Development of a web database portfolio system with PACS connectivity for undergraduate health education and continuing professional development , 2009, Comput. Methods Programs Biomed..

[2]  S I Hay,et al.  Etiology of interepidemic periods of mosquito-borne disease. , 2000, Proceedings of the National Academy of Sciences of the United States of America.

[3]  I. Mueller,et al.  Effectiveness of dengue control practices in household water containers in Northeast Thailand , 2005, Tropical medicine & international health : TM & IH.

[4]  Oliveira,et al.  [Integrated control of the filariasis vector with community participation in an urban area of Recife, Pernambuco, Brazil] , 1996, Cadernos de saude publica.

[5]  N. Komalamisra,et al.  Larval occurrence, oviposition behavior and biting activity of potential mosquito vectors of dengue on Samui Island, Thailand. , 2001, Journal of vector ecology : journal of the Society for Vector Ecology.

[6]  K. Jaroensutasinee,et al.  Development sites of Aedes aegypti and Ae. albopictus in Nakhon Si Thammarat, Thailand , 2007 .

[7]  Ana Simão,et al.  Web-based Gis for Collaborative Planning and Public Participation: an Application to the Strategic Planning of Wind Farm Sites Keywords: Spatial Planning Collaborative Planning Wind Energy Multi-criteria Spatial Decision Support System (mc-sdss) Argumentation Map Learning Environment World Wide Web , 2022 .

[8]  Suzhen Wang,et al.  Realization of simulations for blinded internal pilot study based on web , 2009, J. Biomed. Informatics.

[9]  Martin J. O'Connor,et al.  Knowledge-data integration for temporal reasoning in a clinical trial system , 2009, Int. J. Medical Informatics.

[10]  N. Madeira,et al.  Education in primary school as a strategy to control dengue. , 2002, Revista da Sociedade Brasileira de Medicina Tropical.

[11]  Vipul Kashyap,et al.  Creating and sharing clinical decision support content with Web 2.0: Issues and examples , 2009, J. Biomed. Informatics.

[12]  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.

[13]  P. Harris,et al.  Research electronic data capture (REDCap) - A metadata-driven methodology and workflow process for providing translational research informatics support , 2009, J. Biomed. Informatics.

[14]  Dominikus Herzberg,et al.  Specifying computer-based counseling systems in health care: A new approach to user-interface and interaction design , 2009, J. Biomed. Informatics.

[15]  Masashi Inoue,et al.  Automated graphic image generation system for effective representation of infectious disease surveillance data , 2003, Comput. Methods Programs Biomed..

[16]  Nenad Rogulj,et al.  Identification of sport talents using a web-oriented expert system with a fuzzy module , 2009, Expert Syst. Appl..

[17]  C. Chansang,et al.  Climatic and social risk factors for Aedes infestation in rural Thailand , 2003, Tropical medicine & international health : TM & IH.

[18]  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 .

[19]  K. Jaroensutasinee,et al.  Larval Infestations of Aedes aegypti and Ae. albopictus in Nakhonsrithammarat, Thailand , 2005 .