Decision Support

Arthropod-borne diseases such as malaria, dengue, Chagas disease, filariasis, leishmaniasis, and trypanosomiasis place tremendous public health burdens upon developing countries. The operational value of Decision Support Systems for management of these and other arthropod-borne diseases is enhanced by a Geographic Information System (GIS) spatial backbone allowing for visualization of spatiotemporal arthropod vector and disease patterns. However, resource-poor environments in desperate need of GIS-based solutions to more effectively manage arthropod-borne diseases can be faced with the reality that even the most basic GIS data are lacking and that investment in the infrastructure (high end computers, sophisticated GIS software, technical personnel) needed to develop such data is costprohibitive. This problem was addressed by use of Google Earth which freely provides access to both satellite imagery and mapping tools capable of generating polygons, lines and placemarks.

Objective

As part of a Dengue Decision Support System project funded by the Innovative Vector Control Consortium, we used satellite imagery and mapping tools freely available through Google Earth to: 1) generate data for basic city structure that could be imported into a GIS; and 2) serve as the spatial underpinning of a Decision Support System for arthropod-borne disease management.

A Bayesian Network (BN) is a probabilistic graphical model representing dependencies and relationships. The structure of the network and conditional probabilities capture an expert’s view of a system. BN have been applied to the public health domain for research purposes, but have not been used directly by the end users of public health systems. As BN technology becomes more and more accepted in the public health domain, the data fusion visualization becomes a critical component of the overall system design. The tools developed utilize computer assisted analysis on BN in the public health domain, provide a concise view of the data for better decision support, and shorten the decision making phase allowing rapid dissemination of information to public health.

OBJECTIVE

This paper describes the use and visualization of BNs to better assists public health users. The Data Fusion Visualization (DFV) provides an intuitive graphical interface that supports users in three ways. The first is by providing a seamless drill down interpretation of a dataset. The second is by providing an intuitive interpretation of BN. Finally, by abstracting the visualization from the underlying model, the DFV is capable of masking inter-operating BNs into a single visualization. The DFV provides a graphical representation of BN Network Data Fusion.