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Automatic outbreak detection algorithm versus electronic reporting system.

Straetemans M, Altmann D, Eckmanns T, Krause G - Emerging Infect. Dis. (2008)

Bottom Line: To determine efficacy of automatic outbreak detection algorithms (AODAs), we analyzed 3,582 AODA signals and 4,427 reports of outbreaks caused by Campylobacter spp. or norovirus during 2005-2006 in Germany.Local health departments reported local outbreaks with higher sensitivity and positive predictive value than did AODAs.

View Article: PubMed Central - PubMed

Affiliation: Robert Koch Institute, Berlin, Germany. straetemansm@kncvtbc.nl

ABSTRACT
To determine efficacy of automatic outbreak detection algorithms (AODAs), we analyzed 3,582 AODA signals and 4,427 reports of outbreaks caused by Campylobacter spp. or norovirus during 2005-2006 in Germany. Local health departments reported local outbreaks with higher sensitivity and positive predictive value than did AODAs.

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Example of 1 reported outbreak being detected by 3 signals. In this example, 3 signal outbreaks (S1, S2, S3) can be associated with 1 reported outbreak in same municipality and during the same period.
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Figure 1: Example of 1 reported outbreak being detected by 3 signals. In this example, 3 signal outbreaks (S1, S2, S3) can be associated with 1 reported outbreak in same municipality and during the same period.

Mentions: During the study period, 118 and 4,309 outbreaks with >4 cases, associated with the pathogens Campylobacter spp. and norovirus, respectively, had been reported. The AODA had signaled 52 (44.1%) of the 118 reported Campylobacter spp. outbreaks and 2,538 (58.9%) of the 4,309 reported norovirus outbreaks (Table). The probability that a signal outbreak reflected a reported outbreak (positive predictive value of AODA) was lower for Campylobacter spp. than for norovirus: 50 (6.4%) of 781 Campylobacter spp. signal outbreaks and 2,115 (75.5%) of 2,801 norovirus signal outbreaks were associated with reported outbreaks. The AODA may have triggered multiple signals during the outbreak if the threshold level was reached during several consecutive weeks (Figure 1). Of the Campylobacter spp. outbreaks, 3 (6.0%) were each identified by 2 different signals; of the norovirus outbreaks, 727 (28.6%) were identified by multiple signals (2–20 signals per reported outbreak) (Table). Furthermore, 1 signal outbreak could correspond with different reported outbreaks when these occurred in the same local area and during the same period (Figure 2). For Campylobacter spp., 4 (8.0%) of the outbreak signals could correspond to >1 reported outbreak; for norovirus, 760 (35.9%) of the signal outbreaks could correspond to 2–26 reported outbreaks (Table).


Automatic outbreak detection algorithm versus electronic reporting system.

Straetemans M, Altmann D, Eckmanns T, Krause G - Emerging Infect. Dis. (2008)

Example of 1 reported outbreak being detected by 3 signals. In this example, 3 signal outbreaks (S1, S2, S3) can be associated with 1 reported outbreak in same municipality and during the same period.
© Copyright Policy
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC2609880&req=5

Figure 1: Example of 1 reported outbreak being detected by 3 signals. In this example, 3 signal outbreaks (S1, S2, S3) can be associated with 1 reported outbreak in same municipality and during the same period.
Mentions: During the study period, 118 and 4,309 outbreaks with >4 cases, associated with the pathogens Campylobacter spp. and norovirus, respectively, had been reported. The AODA had signaled 52 (44.1%) of the 118 reported Campylobacter spp. outbreaks and 2,538 (58.9%) of the 4,309 reported norovirus outbreaks (Table). The probability that a signal outbreak reflected a reported outbreak (positive predictive value of AODA) was lower for Campylobacter spp. than for norovirus: 50 (6.4%) of 781 Campylobacter spp. signal outbreaks and 2,115 (75.5%) of 2,801 norovirus signal outbreaks were associated with reported outbreaks. The AODA may have triggered multiple signals during the outbreak if the threshold level was reached during several consecutive weeks (Figure 1). Of the Campylobacter spp. outbreaks, 3 (6.0%) were each identified by 2 different signals; of the norovirus outbreaks, 727 (28.6%) were identified by multiple signals (2–20 signals per reported outbreak) (Table). Furthermore, 1 signal outbreak could correspond with different reported outbreaks when these occurred in the same local area and during the same period (Figure 2). For Campylobacter spp., 4 (8.0%) of the outbreak signals could correspond to >1 reported outbreak; for norovirus, 760 (35.9%) of the signal outbreaks could correspond to 2–26 reported outbreaks (Table).

Bottom Line: To determine efficacy of automatic outbreak detection algorithms (AODAs), we analyzed 3,582 AODA signals and 4,427 reports of outbreaks caused by Campylobacter spp. or norovirus during 2005-2006 in Germany.Local health departments reported local outbreaks with higher sensitivity and positive predictive value than did AODAs.

View Article: PubMed Central - PubMed

Affiliation: Robert Koch Institute, Berlin, Germany. straetemansm@kncvtbc.nl

ABSTRACT
To determine efficacy of automatic outbreak detection algorithms (AODAs), we analyzed 3,582 AODA signals and 4,427 reports of outbreaks caused by Campylobacter spp. or norovirus during 2005-2006 in Germany. Local health departments reported local outbreaks with higher sensitivity and positive predictive value than did AODAs.

Show MeSH