Title: A monitoring and early warning of respiratory infectious disease symptoms based on multi-source information data fusion

Authors: Shengcong Tao; Yirong Guo

Addresses: Health Statistics Centre of Lanzhou City, Lanzhou, Gansu Province, China ' School of Information Engineering, Longdong University, Qingyang, Gansu Province, China

Abstract: An oversight and alert methodology grounded in multi-source information data amalgamation is proposed to address the issues of elevated root mean square error and suboptimal alert efficacy in respiratory infectious disease symptom monitoring. First, manifestation data characteristics are delineated through time series analysis, and Support Vector Machines (SVM) are employed for feature extraction. Wavelet transformation technology is utilised to eliminate noise and rectify missing data. Subsequently, data level, feature level and decision level are progressively integrated to consolidate multi-source data characteristics, while Markov chain models are amalgamated to determine alert zones. The experimental results demonstrate that the proposed method achieves optimal performance in the root mean square error test of multi-source respiratory infectious disease symptom data fusion, with a minimum error of 0.11%. In the absolute accuracy value test for symptom monitoring and warning, the highest accuracy is observed to approach 100%.

Keywords: data fusion; time series definition; SVM; decision level fusion; Markov chain.

DOI: 10.1504/IJCAT.2026.153102

International Journal of Computer Applications in Technology, 2026 Vol.78 No.3, pp.226 - 235

Received: 30 Oct 2024
Accepted: 24 Apr 2025
Published online: 22 Apr 2026 *

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