Application of Big Data Analysis in Epidemic Infectious Disease Early Warning: Technical Path, Standardization System and Global Practice

DOI: https://doi.org/10.62517/jbdc.202601116

Author(s)

Junhao Sun

Affiliation(s)

Medical Information Engineering, Wannan Medical College, Wuhu, Anhui, China

Abstract

The frequent occurrence of new infectious diseases worldwide has exposed inherent flaws in traditional monitoring systems. This study systematically analyzes the evolution of big data technology in infectious disease early warning from 2008 to 2024, integrating 127 empirical studies (total sample size exceeding 2.3 billion data points) through meta-analysis, revealing core patterns: web search data (Baidu/Google Index) can achieve 7-14 days of advanced prediction (average r=0.81), while social media data (Twitter/Weibo) enhances early warning sensitivity to 82.6% through NLP sentiment analysis; multi-source data fusion reduces error rates of ARIMA, LSTM, and other models by 18-32%, with the federated learning architecture maintaining 94% accuracy while ensuring privacy protection; Chinas health code system shortened the epidemic response cycle by 3.2 days (95%CI: 2.7-3.8), and the 2022 Shanghai outbreak reduced economic losses by approximately 127 billion yuan. This paper innovatively proposes a three-tier standardized early warning framework: Level I (low risk): web search increase <10% → public opinion guidance; Level II (medium risk): Weibo symptom keywords>50/hour → focused epidemiological investigation; Level III (high risk): multi-source joint probability>80% → regional control. Simultaneously, a cross-departmental federated learning platform is designed to break down data barriers between health, transportation, and communication sectors, enabling secure processing of 170 million data points daily. The study confirms that the big data early warning system increases early detection rates by 47% (OR=3.15, p<0.001), but further breakthroughs are needed.Key challenges include weak model generalization (cross-regional transfer error>25%) and ethical concerns (privacy breach score 6.8/10). To address these, WHO should establish a Global Public Health Data Protocol (GPHN v1.0) to transform early warning systems from passive response to proactive defense [7]. This is a narrative review without formal meta-analysis; some thresholds and frameworks are author proposals requiring empirical validation.

Keywords

Infectious Disease Early Warning; Multi-source Data Fusion; Federated Learning; Health Code System; Standardized Response

References

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