This paper is in the following e-collection/theme issue:

Public Health Data Analytics (72)

Published on in Vol 6, No 1 (2014):

Autoregressive Integrated Moving Average (ARIMA) Modeling of Time Series of Local Telephone Triage Data for Syndromic Surveillance

Autoregressive Integrated Moving Average (ARIMA) Modeling of Time Series of Local Telephone Triage Data for Syndromic Surveillance

Autoregressive Integrated Moving Average (ARIMA) Modeling of Time Series of Local Telephone Triage Data for Syndromic Surveillance

Authors of this article:

Micael Widerström1 ;   Maria Omberg2 ;   Martin Ferm3 ;   Ann-Katrine Pettersson4 ;   Malin Rundvik Eriksson2 ;   Ingela Eckerdal4 ;   Johan Wiström5
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    Weekly aggregated local telephone triage data collected from 2007 to 2012 in eight municipalities in Jämtland County, Sweden, were analyzed by seasonal autoregressive integrated moving average (ARIMA) modeling of time series of the two common syndromes acute gastroenteritis (AGE) and influenza-like illness (ILI). Data analysis was done using the free software R (http://www.r-project.org). The seasonal ARIMA model detected AGE and ILI outbreak signals in the majority of the investigated communities. This forecast model can prove to be an important tool for monitoring local levels of AGE and ILI to enable early detection of outbreaks of these conditions.