Lotze, T., G. Shmueli, S. Murphy, and H. S. Burkom, "A Wavelet-based Anomaly Detector for Early Detection of Disease Outbreaks", Proceedings of the 23rd International Conference on Machine Learning (ICML), Workshop on Machine Learning Algorithms for Surveillance and Event Detection, Pittsburgh, PA, 2006.

Shmueli, G., T. Lotze, and I. Yahav, "Simulating Multivariate Syndromic Time Series and Outbreak Signatures", Working Paper RHS-06-054: Smith School of Business, University of Maryland, 2007.

Lotze, T., and G. Shmueli, "Ensemble Forecasting for Disease Outbreak Detection", 23rd AAAI Conference on Artificial Intelligence (AAAI-08), Chicago, IL, 2008. AAAI7LotzeT.pdf (61 KB)

Lotze, T., S. Murphy, and G. Shmueli, "Preparing Biosurveillance Data for Classic Monitoring", Advances in Disease Surveillance, vol. 6, 2008. DataPreparation AdvDisSurv.pdf (470.11 KB)

Lotze, T., and G. Shmueli, "On the relationship between forecast accuracy and detection performance: An application to biosurveillance", 2008 IEEE Conference on Technologies for Homeland Security, Boston, MA, 2008. ieee.pdf (243.68 KB)

Lotze, T., and G. Shmueli, "How does improved forecasting benefit detection? An application to biosurveillance", International Journal on Forecasting, vol. 25, pp. 467-483, 2009. IJF2009 ForecastingBiosurveillance.pdf (2.19 MB)

Lotze, T., G. Shmueli, and I. Yahav, "Simulating and Evaluating Biosurveillance Datasets", Biosurveillance: Methods and Case Studies: Chapman and Hall, 2010. Book Chapter (1.93 MB)

Yahav, I., T. Lotze, and G. Shmueli, "Algorithm Combination for Improved Detection in Biosurveillance", Infectious Disease Informatics and Biosurveillance: Integrated Series in Information Systems, 1, vol. 27: Springer, pp. 173-189, 2011. 143493_1_En_8_Chapter_OnlinePDF.pdf (1.55 MB)