Application of a Bayesian Spatiotemporal Surveillance Method to NYC Syndromic Data

Authors

  • Alison Alison NYC Dept of Health and Mental Hygiene, Queens, NY
  • Ana Corberán-Vallet University of Valencia, Valencia
  • Andrew B. Lawson Medical University of South Carolina, Charleston, SC
  • Ramona Lall NYC Dept of Health and Mental Hygiene, Queens, NY
  • Robert Mathes NYC Dept of Health and Mental Hygiene, Queens, NY

DOI:

https://doi.org/10.5210/ojphi.v6i1.5040

Abstract

Incorporating prior knowledge (e.g., the spatial distribution of zip codes and background population effects) into a model using Bayesian methods could potentially improve outbreak detection. We adapted a previously described Bayesian model-based spatiotemporal surveillance technique to daily respiratory syndrome counts in NYC Emergency Department data in 2009, the year of the H1N1 influenza pandemic. Citywide, 56 alarms were produced across 15 zip codes, all during days of elevated respiratory visits. Future work includes evaluating our choice of baseline length, considering other alarm thresholds, and conducting a formal evaluation of the method across five syndromes in NYC.

Author Biography

Alison Alison, NYC Dept of Health and Mental Hygiene, Queens, NY

Alison Levin-Rector is a member of the Reportable Disease Data, Informatics, and Analysis unit and the Syndromic Surveillance team in the Bureau of Communicable Disease at the NYC Department of Health and Mental Hygiene. She has worked on a variety of public health problems in her career and is currently enjoying the challenge of maintaining and improving health surveillance in a diverse and ever-changing city.

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Published

2014-03-03

How to Cite

Alison, A., Corberán-Vallet, A., Lawson, A. B., Lall, R., & Mathes, R. (2014). Application of a Bayesian Spatiotemporal Surveillance Method to NYC Syndromic Data. Online Journal of Public Health Informatics, 6(1). https://doi.org/10.5210/ojphi.v6i1.5040

Issue

Section

Poster Presentations