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. 2022 Jan 4;42(1):226–236. doi: 10.1007/s11596-021-2493-0

Construction of Influenza Early Warning Model Based on Combinatorial Judgment Classifier: A Case Study of Seasonal Influenza in Hong Kong

Zi-xiao Wang 1,2,3, James Ntambara 4, Yan Lu 1, Wei Dai 1, Rui-jun Meng 1, Dan-min Qian 1,5,✉
PMCID: PMC8727490  PMID: 34985610

Abstract

Objective

The annual influenza epidemic is a heavy burden on the health care system, and has increasingly become a major public health problem in some areas, such as Hong Kong (China). Therefore, based on a variety of machine learning methods, and considering the seasonal influenza in Hong Kong, the study aims to establish a Combinatorial Judgment Classifier (CJC) model to classify the epidemic trend and improve the accuracy of influenza epidemic early warning.

Methods

The characteristic variables were selected using the single-factor statistical method to establish the influencing factor system of an influenza outbreak. On this basis, the CJC model was proposed to provide an early warning for an influenza outbreak. The characteristic variables in the final model included atmospheric pressure, absolute maximum temperature, mean temperature, absolute minimum temperature, mean dew point temperature, the number of positive detections of seasonal influenza viruses, the positive percentage among all respiratory specimens, and the admission rates in public hospitals with a principal diagnosis of influenza.

Results

The accuracy of the CJC model for the influenza outbreak trend reached 96.47%, the sensitivity and specificity change rates of this model were lower than those of other models. Hence, the CJC model has a more stable prediction performance. In the present study, the epidemic situation and meteorological data of Hong Kong in recent years were used as the research objects for the construction of the model index system, and a lag correlation was found between the influencing factors and influenza outbreak. However, some potential risk factors, such as geographical nature and human factors, were not incorporated, which ideally affected the prediction performance to some extent.

Conclusion

In general, the CJC model exhibits a statistically better performance, when compared to some classical early warning algorithms, such as Support Vector Machine, Discriminant Analysis, and Ensemble Classfiers, which improves the performance of the early warning of seasonal influenza.

Key words: influenza prediction, data-driven, Support Vector Machine, Discriminant Analysis, Ensemble Classifier

Conflict of Interest Statement

The authors declare no conflict of interest.

Footnotes

This project was supported by grants from the Ministry of Education Humanities and Social Sciences Research Fund Project (No. 17YJCZH140), the Jiangsu Philosophy and Social Sciences Fund Project (No. 18SHB004), and the Jiangsu University Philosophy and Social Sciences Research Fund Project (No. 2017SJB1211).

Contributor Information

Zi-xiao Wang, Email: zwang62@nyit.edu.

Dan-min Qian, Email: qdm11@163.com.

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