Forecasting respiratory infectious outbreaks using ED-based syndromic surveillance for febrile ED visits in a Metropolitan City - 07/08/18

Doi : 10.1016/j.ajem.2018.05.007

Tae Han Kim, MD ^a, Ki Jeong Hong, MD, PhD ^b,^{^⁎} , Sang Do Shin, MD, PhD ^c , Gwan Jin Park, MD ^d, Sungwan Kim, PhD ^e , Nhayoung Hong ^f
^a Department of Emergency Medicine, Seoul National University Boramae Medical Center, Republic of Korea
^b Department of Emergency Medicine, Seoul National University Hospital, Republic of Korea
^c Department of Emergency Medicine, Seoul National University College of Medicine, Republic of Korea
^d Department of Emergency Medicine, Chungbuk National University Hospital, Republic of Korea
^e Institute of Medical and Biological Engineering, Seoul National University, Republic of Korea
^f Interdisciplinary Program for Bioengineering, Graduate School, Seoul National University, Republic of Korea

^⁎Corresponding author at: Department of Emergency Medicine, Seoul National University Hospital, 101, Daehak-Ro, Jongno-Gu, 03080, Seoul, Republic of Korea.Department of Emergency MedicineSeoul National University Hospital101, Daehak-Ro, Jongno-Gu, 03080SeoulRepublic of Korea

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Abstract

Background

Monitoring and detecting sudden outbreaks of respiratory infectious disease is important. Emergency Department (ED)-based syndromic surveillance systems have been introduced for early detection of infectious outbreaks. The aim of this study was to develop and validate a forecasting model of respiratory infectious disease outbreaks based on a nationwide ED syndromic surveillance using daily number of emergency department visits with fever.

Methods

We measured the number of daily ED visits with body temperature ≥ 38.0 °C and daily number of patients diagnosed as respiratory illness by the ICD-10 codes from the National Emergency Department Information System (NEDIS) database of Seoul, Korea. We developed a forecast model according to the Autoregressive Integrated Moving Average (ARIMA) method using the NEDIS data from 2013 to 2014 and validated it using the data from 2015. We defined alarming criteria for extreme numbers of ED febrile visits that exceed the forecasted number. Finally, the predictive performance of the alarm generated by the forecast model was estimated.

Results

From 2013 to 2015, data of 4,080,766 ED visits were collected. 303,469 (7.4%) were ED visits with fever, and 388,943 patients (9.5%) were diagnosed with respiratory infectious disease. The ARIMA (7.0.7) model was the most suitable model for predicting febrile ED visits the next day. The number of patients with respiratory infectious disease spiked concurrently with the alarms generated by the forecast model.

Conclusions

A forecast model using syndromic surveillance based on the number of ED visits was feasible for early detection of ED respiratory infectious disease outbreak.

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Keywords : Syndromic surveillance, Forecast, ARIMA, Respiratory infectious disease, Fever