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Machine learning based regional epidemic transmission risks precaution in digital society.

Zhengyu Shi1, Haoqi Qian2,3,4, Yao Li5

  • 1School of Data Science, Fudan University, Shanghai, 200433, China.

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|November 28, 2022
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Human contact networks, modeled using mobile phone data, can predict COVID-19 spread. This approach assesses regional transmission risk by analyzing user movement and close contacts, offering efficient epidemic precautions.

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Area of Science:

  • Epidemiology
  • Network Science
  • Data Science

Background:

  • Human interaction is a key factor in epidemic transmission.
  • Modeling individual activity heterogeneity is crucial for epidemiological risk assessment.
  • Big data enables large-scale analysis of human contact patterns.

Purpose of the Study:

  • To dynamically model human contact networks using mobile phone data.
  • To predict regional transmission risk during disease outbreaks.
  • To develop efficient and cost-effective epidemic precautions.

Main Methods:

  • Utilized mobile phone signaling data to track user trajectories.
  • Constructed dynamic contact networks to represent daily individual interactions.
  • Extracted individual feature matrices for extreme event learning and risk prediction.

Main Results:

  • Revealed spatiotemporal contact features of 7.5 million mobile phone users during the COVID-19 outbreak in Shanghai.
  • Successfully predicted regional transmission risk, decomposing it into inflow and local contact risks.
  • Demonstrated the flexibility and adaptability of the proposed method.

Conclusions:

  • Mobile phone data and network analysis provide a powerful tool for understanding and predicting epidemic spread.
  • The developed method offers a flexible, adaptive, and cost-effective approach for epidemic surveillance and control.
  • This approach can serve as an efficient precaution before large-scale outbreaks.