Track 1: Analytics / Research Methodologies

3. Using cross-correlation networks to identify and visualize patterns in disease transmission T-C Lu and N Abernethy 5. Methodology for prediction of outbreaks of diseases of military importance 7. Analytic disease surveillance methodology based on emulation of experienced human monitors H Burkom, JS Coberly, and SL Lewis 8. Forecasting high-priority surveillance regions: a socioeconomic model 14. Incorporating seasonality and other long-term trends improves surveillance for acute respiratory infections H Zheng, WH Woodall, and S DeLisle 16. Surveillance for acute respiratory infections: should we include all outpatient visits or focus on urgent care areas? 24. A model for flu outbreak surveillance that describes the time lag for data reporting from the first presentation of a case to diagnosis RE Gamache and S Grannis 26. Gastrointestinal disease outbreak detection using multiple data streams from electronic medical records 33. The spatial-temporal pattern of excess influenza visits at the (suburban n scale KJ Konty and DR Olson 35. Where are the data? Accuracy of automated EHR reporting 45. Quantifying the potential benefit of early detection for preventing morbidity and mortality: a simulation study of cryptosporidium outbreak 47. Non-parametric intensity bounds for the visualization of disease clusters FLP Oliveira, LH Duczmal, and ALF Cançado 49. Evaluating a standard influenza-like illness syndrome definition across multiple sites in the Distribute project: The 'ILI-s' Pilot 60. Data quality in federated disease surveillance: using variability as an indicator of quality 62. Estimating the number of deaths attributable to nine common infectious pathogens adjusted for seasonality and temperature L 64. Estimating the incidence of influenza cases that present to emergency departments ABSTRACT Using cross-correlation networks to identify and visualize patterns in disease transmission Objective Time series of influenza-like illness (ILI) events are often used to depict case rates in different regions. We explore the suitability of network visualization to highlight geographic patterns in this data on the basis of cross-correlation of the time series data.