Development of the Dengue Decision Support System DDSS

The Innovative Vector Control Consortium (IVCC) is helping to address this challenge for dengue and malaria by funding projects to develop new tools and approaches to enhance vector and disease surveillance and control (Hemingway et al. 2006). Computer-based Decision Support Systems are widely used in many disciplines and offer great potential for improving prevention, surveillance, and control of dengue and for management of dengue control programs. Decision Support Systems provide improved logistical capacity for data management and analysis and an emphasis on evidence-based and rational decision-making leading to implementation of effective control program strategies, methodologies, and management (Eisen and Beaty 2008).

The DDSS, which is nearing completion, will take into account data related to vector, pathogen, and disease surveillance as well as vector control, pathogen control, clinical information, diagnostic testing, behavior and education of the human population, and demographic and socioeconomic conditions (Eisen and Beaty 2008). The DDSS will support local and regional vector control programs, will promote the creation of a community of vector control people, which will share best practices and knowledge, and will be rationally designed to promote information flow between local, regional, national, and even international stakeholders. The computer-based DDSS will aid and systematize the process of gathering and analyzing information, gaining new insights, generating alternatives and, ultimately, making evidence-based decisions regarding vector and disease surveillance and control (Fig. 4).

Fig. 4 Flow scheme for a dengue decision support system (adapted from a figure previously published in Eisen and Beaty 2008)

The potential for developing the DDSS has been enhanced by the emergence of GIS technology and the rapidly increasing availability of cartographic, demographic, socioeconomic, and environmental GIS-based data. GIS provides capacity for: (1) presentation of spatial and spatiotemporal patterns of risk of exposure to Ae. aegypti and dengue based on location-specific information (e.g., Morrison et al. 1998, 2004; Teng 2001; Rosa-Freitas et al. 2003; Getis et al. 2003; Chadee et al. 2005; Moreno-Sanchez, et al. 2006); and (2) development of predictive spatial risk models based on GIS-derived data and vector or disease measures (Rotela et al. 2007). Free mapping software (e.g., Google Earth, Google, Mountain View, California, U.S.A.; Microsoft® Virtual Earth, Microsoft Corporation, Redmond, Washington, U.S.A.) that provides access to high-quality satellite imagery and basic tools allowing the user to create and label features (place marks, lines, and polygons) are emerging as a powerful complement to GIS software for presentation of information overlaid on an image showing the physical environment. Indeed, we used freely available Google Earth imagery (see example of image quality in Fig. 5) to quickly and efficiently develop cartographic information to support DDSS implementation in Merida, Yucatan, and Chetumal, Quintana Roo, Mexico (Lozano-Fuentes et al., 2008). The developed DDSS will be used to monitor the efficacy of the Casa Segura approach to control Ae. aegypti in Merida.

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