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Objective: To develop a probabilistic model for discovering and quantifying determinants of outbreak detection and to use the model to predict detection performance for new outbreaks., Materials and Methods: We used an existing software platform to simulate waterborne disease outbreaks of varying duration and magnitude. The simulated data were overlaid on real data from visits to emergency department in Montreal for gastroenteritis. We analyzed the combined data using biosurveillance algorithms, varying their parameters over a wide range. We then applied structure and parameter learning algorithms to the resulting data set to build a Bayesian network model for predicting detection performance as a function of outbreak characteristics and surveillance system parameters. We evaluated the predictions of this model through 5-fold cross-validation., Results: The model predicted performance metrics of commonly used outbreak detection methods with an accuracy greater than 0.80. The model also quantified the influence of different outbreak characteristics and parameters of biosurveillance algorithms on detection performance in practically relevant surveillance scenarios. In addition to identifying characteristics expected a priori to have a strong influence on detection performance, such as the alerting threshold and the peak size of the outbreak, the model suggested an important role for other algorithm features, such as adjustment for weekly patterns., Conclusion: We developed a model that accurately predicts how characteristics of disease outbreaks and detection methods will influence on detection. This model can be used to compare the performance of detection methods under different surveillance scenarios, to gain insight into which characteristics of outbreaks and biosurveillance algorithms drive detection performance, and to guide the configuration of surveillance systems., (Copyright © 2014 Elsevier Inc. All rights reserved.)

A wide variety of disease outbreak detection methods has been developed in automated public health surveillance systems. The choice of outbreak detection method results in large changes in performance under different circumstances. In this paper, we investigate how outbreak detection methods can be combined in order to improve the overall detection performance. We used Hierarchical Mixture of Experts, which is a probabilistic model for combining classification methods, for fusion of detection methods. Simulated surveillance data for waterborne disease outbreaks are used in this research to train and evaluate a Hierarchical Mixture of Experts model. Performance evaluation of our approach with respect to sensitivity-specificity trade-off and detection timeliness is provided in comparison with several other detection methods.

Worldwide developments concerning infectious diseases and bioterrorism are driving forces for improving aberrancy detection in public health surveillance. The performance of an aberrancy detection algorithm can be measured in terms of sensitivity, specificity and timeliness. However, these metrics are probabilistically dependent variables and there is always a trade-off between them. This situation raises the question of how to quantify this tradeoff. The answer to this question depends on the characteristics of the specific disease under surveillance, the characteristics of data used for surveillance, and the algorithmic properties of detection methods. In practice, the evidence describing the relative performance of different algorithms remains fragmented and mainly qualitative. In this paper, we consider the development and evaluation of a Bayesian network framework for analysis of performance measures of aberrancy detection algorithms. This framework enables principled comparison of algorithms and identification of suitable algorithms for use in specific public health surveillance settings.

The potentially catastrophic impact of an epidemic specially these due to bioterrorist attack, makes developing effective detection methods essential for public health. Current detection methods trade off reliability of alarms for early detection of outbreaks. The performance of these methods can be improved by disease-specific modeling techniques that take into account the potential costs and effects of an attack to provide optimal warnings and the cost and effectiveness of interventions. We study this optimization problem in the framework of sequential decision making under uncertainty. Our approach relies on estimating the future benefit of true alarms and the costs of false alarms. Using these quantities it identifies optimal decisions regarding the credibility of outputs from a traditional detection method at each point in time. The key contribution of this paper is to apply Partially Observable Markov Decision Processes (POMDPs) on outbreak detection methods for improving alarm function in the case of anthrax. We present empirical evidence illustrating that at a fixed specificity, the performance of detection methods with respect to sensitivity and timeliness is improved significantly by utilizing POMDPs in detection of anthrax attacks.