TY - JOUR
T1 - Developing epidemic forecasting models to assist disease surveillance for influenza with electronic health records
AU - Tseng, Yi Ju
AU - Shih, Yu Lien
N1 - Publisher Copyright:
© 2019, © 2019 Informa UK Limited, trading as Taylor & Francis Group.
PY - 2020/8/17
Y1 - 2020/8/17
N2 - Seasonal influenza is an important infectious disease monitored by the Centers for Disease Control. Monitoring, early detection, and prediction of influenza outbreaks can accelerate life-saving public health responses. However, ensuring completeness and timeliness of all surveillance systems can be difficult. Current influenza surveillance systems can be supported through the estimation of influenza activity, which we aim to achieve by defining influenza cases through electronic medical records (EMRs) from Chang Gung Memorial Hospital and constructing predictive models for predicting the epidemic trend for influenza. In total, 84,820 influenza cases were matching the case definition in 2010–2015. Using the exponential smoothing model, autoregressive integrated moving average (ARIMA) model, and Prophet model, we trained the models with influenza cases from 2010 to 2014; we then renewed the models at different intervals and compared their capacities. The results show that the ARIMA model performs best in both Linkou and Kaohsiung branches. In conclusion, we successfully constructed an influenza epidemic predictive model based on EMRs, which may be used as a decision support tool for improving influenza surveillance, controlling infection, and managing resources.
AB - Seasonal influenza is an important infectious disease monitored by the Centers for Disease Control. Monitoring, early detection, and prediction of influenza outbreaks can accelerate life-saving public health responses. However, ensuring completeness and timeliness of all surveillance systems can be difficult. Current influenza surveillance systems can be supported through the estimation of influenza activity, which we aim to achieve by defining influenza cases through electronic medical records (EMRs) from Chang Gung Memorial Hospital and constructing predictive models for predicting the epidemic trend for influenza. In total, 84,820 influenza cases were matching the case definition in 2010–2015. Using the exponential smoothing model, autoregressive integrated moving average (ARIMA) model, and Prophet model, we trained the models with influenza cases from 2010 to 2014; we then renewed the models at different intervals and compared their capacities. The results show that the ARIMA model performs best in both Linkou and Kaohsiung branches. In conclusion, we successfully constructed an influenza epidemic predictive model based on EMRs, which may be used as a decision support tool for improving influenza surveillance, controlling infection, and managing resources.
KW - electronic medical records
KW - forecasting model
KW - infectious disease surveillance
KW - Influenza
UR - http://www.scopus.com/inward/record.url?scp=85070505481&partnerID=8YFLogxK
U2 - 10.1080/1206212X.2019.1633762
DO - 10.1080/1206212X.2019.1633762
M3 - 期刊論文
AN - SCOPUS:85070505481
SN - 1206-212X
VL - 42
SP - 616
EP - 621
JO - International Journal of Computers and Applications
JF - International Journal of Computers and Applications
IS - 6
ER -