- Lotze, T., "Anomaly Detection in Time Series: Theoretical and Practical Improvements for Disease Outbreak Detection" (Ph.D. Dissertation)
- Lotze, T, Shmueli, G. and Yahav, I., "Simulating and Evaluating Biosurveillance Datasets", Biosurveillance: A Health Protection Priority, Kass-Hout, T. & Zhang, X. (ed.), Chapman and Hall, Forthcoming. (link is to an earlier working paper)
- Yahav, I., Lotze, T. and Shmueli, G., "Algorithm Combination for Improved Detection in Biosurveillance", Infectious Disease Informatics and Biosurveillance: Research, Systems, and Case Studies, Springer, Forthcoming.
- Lotze, T. and Shmueli, G., "How does improved forecasting benefit detection? An application to biosurveillance", International Journal of Forecasting, 2008, 25(3), 467-483.
- Lotze, T. and Shmueli, G. "Ensemble Forecasting for Disease Outbreak Detection", Proceedings of the 23rd AAAI Conference on Artificial Intelligence (AAAI-08), Chicago, IL, 2008.
- Lotze, T., Murphy, S. and Shmueli, G., "Preparing Biosurveillance Data for Classic Monitoring", Advances in Disease Surveillance, 2008, 6, 1-20.
- Lotze, T. and Shmueli, G. "On the relationship between forecast accuracy and detection performance: An application to biosurveillance", Proceedings of the 2008 IEEE Conference on Technologies for Homeland Security, Boston, MA, 2008.
- Lotze, T., Shmueli, G., Murphy, S. and Burkom, H. (2006) "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.