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

Tae Han Kim, Ki Jeong Hong, Sang Do Shin, Gwan Jin Park, Sungwan Kim, Nhayoung Hong

Research output: Contribution to journalArticle

2 Citations (Scopus)

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.

Original languageEnglish
Pages (from-to)183-188
Number of pages6
JournalAmerican Journal of Emergency Medicine
Volume37
Issue number2
DOIs
StatePublished - Feb 2019

Fingerprint

Disease Outbreaks
Hospital Emergency Service
Fever
International Classification of Diseases
Information Systems
Communicable Diseases
Korea
Body Temperature
Databases

Keywords

  • ARIMA
  • Fever
  • Forecast
  • Respiratory infectious disease
  • Syndromic surveillance

Cite this

@article{1610603311a54d1f803a47d9a7b6fe42,
title = "Forecasting respiratory infectious outbreaks using ED-based syndromic surveillance for febrile ED visits in a Metropolitan City",
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.",
keywords = "ARIMA, Fever, Forecast, Respiratory infectious disease, Syndromic surveillance",
author = "Kim, {Tae Han} and Hong, {Ki Jeong} and Shin, {Sang Do} and Park, {Gwan Jin} and Sungwan Kim and Nhayoung Hong",
year = "2019",
month = "2",
doi = "10.1016/j.ajem.2018.05.007",
language = "English",
volume = "37",
pages = "183--188",
journal = "American Journal of Emergency Medicine",
issn = "0735-6757",
publisher = "W.B. Saunders Ltd",
number = "2",

}

TY - JOUR

T1 - Forecasting respiratory infectious outbreaks using ED-based syndromic surveillance for febrile ED visits in a Metropolitan City

AU - Kim, Tae Han

AU - Hong, Ki Jeong

AU - Shin, Sang Do

AU - Park, Gwan Jin

AU - Kim, Sungwan

AU - Hong, Nhayoung

PY - 2019/2

Y1 - 2019/2

N2 - 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.

AB - 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.

KW - ARIMA

KW - Fever

KW - Forecast

KW - Respiratory infectious disease

KW - Syndromic surveillance

UR - http://www.scopus.com/inward/record.url?scp=85047225658&partnerID=8YFLogxK

U2 - 10.1016/j.ajem.2018.05.007

DO - 10.1016/j.ajem.2018.05.007

M3 - Article

C2 - 29779674

AN - SCOPUS:85047225658

VL - 37

SP - 183

EP - 188

JO - American Journal of Emergency Medicine

JF - American Journal of Emergency Medicine

SN - 0735-6757

IS - 2

ER -