Knowledge discovery in open data for epidemic disease prediction

Objective: The research reveals the determinants associated with the epidemic diseases (dengue, influenza, and enterovirus) in Taiwan. It demonstrates the value of open data in prediction model development to support policymaking in the domain of public health care. Method: A knowledge discovery technique was employed to extract determinants from open data on epidemic diseases. The open dataset collected and integrated from Taiwan's Center for Disease Control, the Center Weather Bureau, and Google Trends includes 70,915 dengue, 34,062 enterovirus, and 52,908 influenza cases. A prediction model using the classification-oriented extraction mechanism was applied to open epidemic data, climate data, and Google Trends data. Prediction models that either included or did not include Google Trends data were compared. Prediction accuracy and simplicity of the decision rules are presented. Results: Prediction accuracy and simplicity of three diseases is acceptable when Google Trends is excluded but is slightly different when Google Trends is considered. Location (county) holds the main predictor of the three epidemic diseases. Time (month) presents the second-highest determinant for dengue, and age shows remarkable determinant for enterovirus and influenza. Mean temperature exhibits the highest entropy for dengue, time for enterovirus, and humidity for influenza. Conclusions: The number of confirmed cases for all three epidemic diseases cannot be predicted by a single variable. Knowledge extraction using the classification-oriented technique can be successfully applied in prediction model development. Google Trends data reveal a remarkable but inconsistent role in predicting three epidemic diseases with respect to prediction accuracy and simplicity of the generated decision tree. (c) 2021 Fellowship of Postgraduate Medicine. Published by Elsevier Ltd. All rights reserved.