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Spatially explicit multi-criteria decision analysis for managing vector-borne diseases

Valerie Hongoh1*, Anne Gatewood Hoen2, Cécile Aenishaenslin1, Jean-Philippe Waaub3, Denise Bélanger1, Pascal Michel14 and The Lyme-MCDA Consortium

Author Affiliations

1 Groupe de Recherche en Épidémiologie des Zoonoses et Santé Publique (GREZOSP), Pavillon de la santé publique, Faculté de médecine vétérinaire, Université de Montréal, Case postale 5000, Saint-Hyacinthe, Québec, J2S 7C6, Canada

2 Department of Community and Family Medicine, Dartmouth Medical School, HB 7937, One Medical Center Drive, Dartmouth-Hitchcock Medical Center, Lebanon, New Hampshire, 03756, USA

3 Département de Géographie, Université du Québec à Montréal, Case postale 8888, Succursale Centre-ville, Montréal, Québec, H3C 3P8, Canada

4 Laboratory for Foodborne Zoonoses, Public Health Agency of Canada, CP 5000, St-Hyacinthe, Québec, H2S 7C6, Canada

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International Journal of Health Geographics 2011, 10:70  doi:10.1186/1476-072X-10-70

Published: 29 December 2011


The complex epidemiology of vector-borne diseases creates significant challenges in the design and delivery of prevention and control strategies, especially in light of rapid social and environmental changes. Spatial models for predicting disease risk based on environmental factors such as climate and landscape have been developed for a number of important vector-borne diseases. The resulting risk maps have proven value for highlighting areas for targeting public health programs. However, these methods generally only offer technical information on the spatial distribution of disease risk itself, which may be incomplete for making decisions in a complex situation. In prioritizing surveillance and intervention strategies, decision-makers often also need to consider spatially explicit information on other important dimensions, such as the regional specificity of public acceptance, population vulnerability, resource availability, intervention effectiveness, and land use. There is a need for a unified strategy for supporting public health decision making that integrates available data for assessing spatially explicit disease risk, with other criteria, to implement effective prevention and control strategies. Multi-criteria decision analysis (MCDA) is a decision support tool that allows for the consideration of diverse quantitative and qualitative criteria using both data-driven and qualitative indicators for evaluating alternative strategies with transparency and stakeholder participation. Here we propose a MCDA-based approach to the development of geospatial models and spatially explicit decision support tools for the management of vector-borne diseases. We describe the conceptual framework that MCDA offers as well as technical considerations, approaches to implementation and expected outcomes. We conclude that MCDA is a powerful tool that offers tremendous potential for use in public health decision-making in general and vector-borne disease management in particular.

spatial multi-criteria decision analysis; vector-borne disease; risk modeling