Track 1: Analytics / Research Methodologies 3. Using cross-correlation networks to identify and v... more 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 AL Buczak, PT Koshute, SM Babin, BH Feighner, and SH Lewis 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 E Chan, T Choden, T Brewer, L Madoff, M Pollack, and J Brownstein 10. Sample size and spatial cluster detection power JF Conley 12. Spatial cluster detection in schools using school catchment information S Day, KJ Konty, and C Goranson 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? H Zheng, T Siddiqui, and S DeLisle 18. A Voronoi based scan for space-time cluster detection in point event data LH Duczmal, GJP Moreira, D Burgarelli, RHC Takahashi, FCO Magalhães, and EC Bodevan 20. Evaluating the performance of two alternative geographic surveillance schemes G Fairchild, A Segre, P Polgreen, and G Rushton 22. Weighted non-connectivity for detection of irregular clusters AR Duarte, SB Silva, L Duczmal, SJ Ferreira, and ALF Cancado 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 SK Greene, J Huang, AM Abrams, M Reed, R Platt, and M Kulldorff 27. Impact of including physician's prescribing directions on calculations of medication possession ratios CL Hayden, BC Sauer, and GW Cannon 28. A spatio-temporal absorbing state model for disease and syndromic surveillance
Background: Use of robust and broadly applicable statistical alerting methods is essential for a ... more Background: Use of robust and broadly applicable statistical alerting methods is essential for a public health syndromic surveillance system, such as CDC BioSense program. Methods: Excess visit counts (at least two standard deviations above calculated baseline expectations) were artificially injected into gastrointestinal (GI) syndrome-related daily aggregate time series data from the BioSense Syndromic Surveillance system. Algorithm sensitivity was calculated as the ratio of days with injects for which the algorithm value exceeded a computed threshold. We compared these sensitivities among the three adaptive methods: a CUSUM (CS) chart and two Shewhart chart variations based on sums and ratios (C2a, C2b) adjusted for total visits. We also examined overall sensitivities before and after stratification by weekday versus weekend and holiday with different background ratios to total visits that are GI-related. Results: At a daily background alert rate of 5% and 1%, the sensitivities ra...
Track 1: Analytics / Research Methodologies 3. Using cross-correlation networks to identify and v... more 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 AL Buczak, PT Koshute, SM Babin, BH Feighner, and SH Lewis 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 E Chan, T Choden, T Brewer, L Madoff, M Pollack, and J Brownstein 10. Sample size and spatial cluster detection power JF Conley 12. Spatial cluster detection in schools using school catchment information S Day, KJ Konty, and C Goranson 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? H Zheng, T Siddiqui, and S DeLisle 18. A Voronoi based scan for space-time cluster detection in point event data LH Duczmal, GJP Moreira, D Burgarelli, RHC Takahashi, FCO Magalhães, and EC Bodevan 20. Evaluating the performance of two alternative geographic surveillance schemes G Fairchild, A Segre, P Polgreen, and G Rushton 22. Weighted non-connectivity for detection of irregular clusters AR Duarte, SB Silva, L Duczmal, SJ Ferreira, and ALF Cancado 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 SK Greene, J Huang, AM Abrams, M Reed, R Platt, and M Kulldorff 27. Impact of including physician's prescribing directions on calculations of medication possession ratios CL Hayden, BC Sauer, and GW Cannon 28. A spatio-temporal absorbing state model for disease and syndromic surveillance
Background: Use of robust and broadly applicable statistical alerting methods is essential for a ... more Background: Use of robust and broadly applicable statistical alerting methods is essential for a public health syndromic surveillance system, such as CDC BioSense program. Methods: Excess visit counts (at least two standard deviations above calculated baseline expectations) were artificially injected into gastrointestinal (GI) syndrome-related daily aggregate time series data from the BioSense Syndromic Surveillance system. Algorithm sensitivity was calculated as the ratio of days with injects for which the algorithm value exceeded a computed threshold. We compared these sensitivities among the three adaptive methods: a CUSUM (CS) chart and two Shewhart chart variations based on sums and ratios (C2a, C2b) adjusted for total visits. We also examined overall sensitivities before and after stratification by weekday versus weekend and holiday with different background ratios to total visits that are GI-related. Results: At a daily background alert rate of 5% and 1%, the sensitivities ra...
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Papers by Loren Akaka