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

Description: 

ARIMA models use past values (autoregressive terms) and past forecasting errors (moving average terms) to generate future forecasts, making it a potential candidate method for modeling citywide time series of syndromic data [1]. While past research supports the use of ARIMA modeling as a detection algorithm in syndromic surveillance [2], there has been little evaluation of an ARIMA model's prospective outbreak detection capabilities. We built an ARIMA model to prospectively detect simulated outbreaks in ED syndromic data. This method is one of eight being formally evaluated as part of a grant from the Alfred P. Sloan Foundation.

Objective

To evaluate seasonal autoregressive integrated moving average (ARIMA) models for prospective analysis of New York City (NYC) emergency department (ED) syndromic data.

Primary Topic Areas: 
Original Publication Year: 
2013
Event/Publication Date: 
December, 2013

August 22, 2018

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This Knowledge Repository is made possible through the activities of the Centers for Disease Control and Prevention Cooperative Agreement/Grant #1 NU500E000098-01, National Surveillance Program Community of Practice (NSSP-CoP): Strengthening Health Surveillance Capabilities Nationwide, which is in the interest of public health.

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