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Spatiotemporal anomaly detection methods for outbreak detection in case-based syphilis surveillance

Abstract Number:

2608 

Submission Type:

Contributed Abstract 

Contributed Abstract Type:

Poster 

Participants:

John Angles (1), Erika Martin (2), Petko Bogdanov (3), Edward Valachovic (1)

Institutions:

(1) Department of Epidemiology and Biostatistics, University at Albany, Albany, NY, (2) Public Health Accreditation Board, Alexandria, VA, (3) Department of Computer Science, University at Albany, Albany, NY

Co-Author(s):

Erika Martin  
Public Health Accreditation Board
Petko Bogdanov  
Department of Computer Science, University at Albany
Edward Valachovic  
Department of Epidemiology and Biostatistics, University at Albany

First Author:

John Angles  
Department of Epidemiology and Biostatistics, University at Albany

Presenting Author:

John Angles  
N/A

Abstract Text:

Objective: Timely identification of outliers is critical for disease surveillance and public health intervention. However, real-time outbreak detection on live surveillance data is challenging due to issues including thresholding and the handling of secular trends.
Methods: Five anomaly detection methods were applied to monthly syphilis surveillance data for all US counties from 2014-2021. Known syphilis outbreaks were compiled from the Health Alert Network and state health departments. Summary statistics compared known and detected outbreaks for each method.
Results: Methods accounting for both spatial and temporal components of the data outperformed purely spatial or temporal methods. Spatiotemporal methods correctly detected higher percentages of county-months in a known outbreak state and additional county-months as potential outbreaks as compared to temporal and spatial methods separately.
Discussion: While each method had some success in detecting known syphilis outbreaks, all methods have room for improvement. Future extensions include analyzing multiway stratified demographic data to facilitate the identification of outbreaks otherwise masked by population level noise.

Keywords:

Anomaly detection|Spatiotemporal models and time series|Scan statistics|Signal processing|Disease surveillance|Outbreak detection

Sponsors:

Section on Statistics in Epidemiology

Tracks:

Disease Prediction

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