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Spreading predictability in complex networks.

Na Zhao1,2, Jian Wang3, Yong Yu2

  • 1Electric Power Research Institute of Yunnan Power Grid Co., Ltd, Kunming, 650200, People's Republic of China.

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Summary
This summary is machine-generated.

This study introduces a novel snapshot-based prediction model to identify individuals likely to be infected in the future. The model accurately forecasts future infections, aiding in effective disease and rumor control strategies.

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Area of Science:

  • Epidemiology
  • Network Science
  • Computational Social Science

Background:

  • Current infectious disease and rumor models often assume initial infection parameters are known.
  • Real-world scenarios frequently involve detecting outbreaks after they have already begun spreading.

Purpose of the Study:

  • To develop a prediction model that identifies potentially infected individuals from a single network snapshot.
  • To move beyond macro-scale infection prediction to individual-level forecasting.

Main Methods:

  • A novel prediction model utilizing a network snapshot as input.
  • Experimental validation on both synthetic and real-world network data.

Main Results:

  • The model demonstrates high consistency between predicted and actual infected individuals in simulations.
  • The approach effectively forecasts individual infections without prior knowledge of initial conditions.

Conclusions:

  • Snapshot-based prediction is a viable and effective strategy for identifying future infections.
  • This model offers a valuable tool for proactive infectious disease and rumor control efforts.