Quantifying the determinants of outbreak detection performance through simulation and machine learning
We developed a model for quantifying determinants of outbreak detection performance. We used Bayesian networks to model relations between outbreak and algorithm characteristics and detection performance. We used the model to predict detection performance for different outbreak scenarios. The model can provide a quantitative evaluation of new methods and data in biosurveillance systems.
bayesian networks disease outbreak detection surveillance predicting performance public health informatics outbreak simulation