Coevolving spreading dynamics of negative information and epidemic on multiplex networks

Area of Science:

• Epidemiology
• Network Science
• Public Health Communication

Background:

• Negative information about vaccines increases public concern and vaccine hesitancy, potentially impeding epidemic control efforts.
• Existing research on information-epidemic dynamics primarily examines the suppressive impact of information on disease spread.

Purpose of the Study:

• To investigate the effects of negative vaccination information on epidemic spreading using a coupled propagation model.
• To analyze how negative information influences vaccination willingness and disease transmission dynamics on multiplex networks.

Main Methods:

• Developed a two-layer multiplex network model incorporating negative information propagation (virtual layer) and disease spread (physical layer).
• Modeled individuals spreading negative information after adverse events and reducing vaccination willingness upon exposure.
• Employed the microscopic Markov chain method for analytical prediction of epidemic threshold and final infection density.

Main Results:

• Negative information spread lowers the epidemic outbreak threshold and increases final infection density.
• Vaccination activities, rather than negative information spread alone, primarily impact epidemic dynamics.
• Significant impact of negative information on epidemic spread occurs only when vaccination willingness is substantially reduced.

Conclusions:

• Negative vaccine information can exacerbate epidemics by lowering the outbreak threshold, but its overall impact is contingent on reduced vaccination rates.
• Public health strategies should focus on maintaining high vaccination willingness to effectively counter both disease spread and the influence of misinformation.