Evaluating a Seasonal ARIMA Model for Event Detection in New York City

Authors

  • Jessica Sell New York City Department of Health and Mental Hygiene, Queens, NY, United States
  • Robert Mathes New York City Department of Health and Mental Hygiene, Queens, NY, United States

DOI:

https://doi.org/10.5210/ojphi.v6i1.5092

Abstract

Seasonal autoregressive integrated moving average (ARIMA) models can generate future forecasts, making it a potential method for modeling syndromic data for aberration detection. We built ARIMA models for five routinely monitored syndromes in New York City and tested the models' ability to prospectively detect outbreaks using datasets spiked with simulated outbreaks. Less than 10% of all outbreaks were detected at a fixed alert threshold of 1 signal per 100 days. These models did not perform well in detecting outbreaks and may require frequent monitoring and readjustment of model parameters.

Author Biography

Jessica Sell, New York City Department of Health and Mental Hygiene, Queens, NY, United States

Jessica Sell is an analyst in the Syndromic Surveillance Unit within the Bureau of Communicable Disease at the New York City Department of Health and Mental Hygiene.

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Published

2014-03-09

How to Cite

Sell, J., & Mathes, R. (2014). Evaluating a Seasonal ARIMA Model for Event Detection in New York City. Online Journal of Public Health Informatics, 6(1). https://doi.org/10.5210/ojphi.v6i1.5092

Issue

Section

Poster Presentations