Earlier Prediction of Influenza Epidemic by Hospital-based Data in Taiwan

Abstract

In Taiwan, a pandemic with human-transmissible influenza severely affected the allocation of acute care resources. Using hospitals’ clinical data may offer a unique opportunity to predict of influenza epidemic. The aim of this study was to make a use of Taipei Medical University (TMU) clinical database, which can routinely collect clinical data from the three TMU affiliated hospitals, for early predicting of influenza epidemic. We first identified the influenza-related factors through literature validation. Then we extracted the outpatient medical records (2015.06 ~ 2016.10), and through the Pearson correlation coefficient calculated the best combination of influenza detection factors. In addition, through the CUSUM control chart to establish influenza detection model. Finally, we compare the influenza surveillance model with the Taiwan Centers for Disease Control (TWCDC) to monitor the consistency of the model. During the study period, the optimal combination of influenza detection factors had the highest correlation with TWCDC ILI data and virologic surveillance data (r = 0.95086 and 0.87312, respectively; p, 0.0001). The results showed that TMU EDMF and TWCDC epidemic information trends consistent. We automatically collect clinical data to ensure the timeliness of information and 1 to 2 weeks earlier than traditional monitoring methods.