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An Open-Source Data Driven Hybrid Modeling System for Infectious Disease Surveillance and Early Warning.

Jianyi Zhang1, Haoliang Cui1, Yiwen Xing1

  • 1Department of Global Health, School of Public Health, Peking University, Beijing, China.

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Summary
This summary is machine-generated.

This study introduces an open-source hybrid modeling system for early epidemic detection, integrating diverse data for faster, more reliable alerts. The system complements China's national framework, improving public health surveillance and response capabilities.

Keywords:
Early warningHybrid modelingInfectious diseaseOpen-source Data

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Area of Science:

  • Epidemiology
  • Public Health Surveillance
  • Computational Biology

Background:

  • Globalization increases imported epidemic risks.
  • Current surveillance systems are fragmented and rely on lab confirmation.
  • China's multipoint trigger early-warning framework needs enhancement.

Purpose of the Study:

  • To develop an open-source, data-driven hybrid modeling system for earlier and more reliable epidemic alerts.
  • To complement China's existing national early-warning system.
  • To integrate diverse data sources for improved surveillance.

Main Methods:

  • Integrated heterogeneous signals: official epidemiology, digital traces, mobility, meteorology, and pathogen genomics.
  • Utilized semantic harmonization and a hybrid analytic stack.
  • Employed seasonality-adjusted baselines, anomaly detection, SEIR models, and short-horizon learners for early-warning scores.

Main Results:

  • Achieved 83.3% sensitivity and 76.9% positive predictive value.
  • Provided a median lead time of 9.3 days before official confirmation.
  • Demonstrated high forecasting accuracy for COVID-19 and SFTSV.

Conclusions:

  • An open-source hybrid modeling system provides calibrated, timely alerts for diverse pathogens.
  • The system enhances China's national early-warning system and has potential for scaling.
  • Broadened inputs and cross-agency linkage improve public health response and resource allocation.