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Understanding the mechanisms of infodemics: Equation-based vs. agent-based models.

Cristian Berceanu1, Francesco Bertolotti2, Nadia Arshad3,4

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Agent-based models (ABMs) integrating psycho-social factors and networks better capture infodemic dynamics than equation-based models (EBMs). Enhanced ABMs show superior real-world data fit for understanding misinformation spread during health crises.

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Area of Science:

  • Computational epidemiology
  • Network science
  • Public health modeling

Background:

  • Digital communication accelerates the spread of misinformation and disinformation, impacting public health and policy.
  • Understanding these propagation mechanisms is crucial, especially during global health crises like the COVID-19 pandemic.
  • Traditional equation-based models (EBMs) often overlook crucial psycho-social factors and communication network structures.

Purpose of the Study:

  • To introduce a novel enhanced agent-based model (ABM) integrating psycho-social factors and communication networks to study misinformation and disinformation diffusion.
  • To compare the performance of enhanced ABMs, simple ABMs, and EBMs in modeling infodemic dynamics.
  • To evaluate model fit using real-world data on vaccine acceptance.

Main Methods:

  • Developed an enhanced agent-based model (ABM) incorporating psycho-social elements and network structures.
  • Developed a simple ABM to emulate EBM structures for comparative analysis.
  • Conducted 11,110 experiments comparing ABMs and EBMs across parameter ranges.
  • Utilized a multi-objective optimization procedure to fit models to 36 weeks of real-world vaccine acceptance data.

Main Results:

  • A weak overall equivalence was found between ABMs and EBMs, with similar outcomes under specific conditions.
  • The enhanced ABM demonstrated a significantly better fit to real-world infodemic data (r=0.99, NRMSE=0.055) compared to EBMs (r=0.78, NRMSE=0.418) and simple ABMs (r=0.95, NRMSE=0.103).
  • Model structure critically influences the accuracy of infodemic dynamics simulation.

Conclusions:

  • Agent-based models (ABMs) are more effective than equation-based models (EBMs) for capturing complex infodemic phenomena.
  • Psycho-social factors and network interactions are vital components for accurate modeling of misinformation and disinformation spread.
  • The enhanced ABM provides a robust framework for public health policy and communication strategies during health crises.