Comparing the historical limits method with regression models for weekly monitoring of national notifiable diseases reports

Highlights

• Used data from12 national notifiable diseases and simulated alerting signals.

• Compared 4 detection methods by background alert rate, sensitivity, and timeliness.

• Long-term trend adjustment improved detection performance.

• The modified Historical Limits Method (HLM) outperformed the traditional HLM.

• The Farrington-like method provided the best detection performance overall.

Abstract

To compare the performance of the standard Historical Limits Method (HLM), with a modified HLM (MHLM), the Farrington-like Method (FLM), and the Serfling-like Method (SLM) in detecting simulated outbreak signals. We used weekly time series data from 12 infectious diseases from the U.S. Centers for Disease Control and Prevention’s National Notifiable Diseases Surveillance System (NNDSS). Data from 2006 to 2010 were used as baseline and from 2011 to 2014 were used to test the four detection methods. MHLM outperformed HLM in terms of background alert rate, sensitivity, and alerting delay. On average, SLM and FLM had higher sensitivity than MHLM. Among the four methods, the FLM had the highest sensitivity and lowest background alert rate and alerting delay. Revising or replacing the standard HLM may improve the performance of aberration detection for NNDSS standard weekly reports.