Observational studyThe American journal of emergency medicine2019
Forecasting respiratory infectious outbreaks using ED-based syndromic surveillance for febrile ED visits in a Metropolitan City.
Observational study in The American journal of emergency medicine, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Monitoring OTC drug sales for early detection of respiratory infectious disease outbreaks.Frontiers in public health · 2025Article
- Real-time surveillance of severe acute respiratory infections in Scottish hospitals: an electronic register-based approach, 2017-2022.Public health · 2022Observational
- Forecasting Hospital Visits Due to Influenza Based on Emergency Department Visits for Fever: A Feasibility Study on Emergency Department-Based Syndromic Surveillance.International journal of environmental research and public health · 2022Article
- A Hybrid Model Based on Improved Transformer and Graph Convolutional Network for COVID-19 Forecasting.International journal of environmental research and public health · 2022Article
- Machine learning-based prediction of critical illness in children visiting the emergency department.PloS one · 2022Observational
- Approaching precision public health by automated syndromic surveillance in communities.PloS one · 2021Article
- Medical service demand forecasting using a hybrid model based on ARIMA and self-adaptive filtering method.BMC medical informatics and decision making · 2020Article
- Prediction of daily COVID-19 cases in European countries using automatic ARIMA model.Journal of public health research · 2020Article
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundMonitoring 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.
methodsWe 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.
resultsFrom 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.
conclusionsA 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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