Interplay between the local information based behavioral responses and the epidemic spreading in complex networks

Area of Science:

• Epidemiology
• Mathematical Modeling
• Network Science

Background:

• Infectious disease transmission is influenced by human behavioral responses.
• Classical epidemic models often do not account for these dynamic behavioral changes.

Purpose of the Study:

• To investigate the impact of human behavioral responses on epidemic dynamics.
• To introduce and analyze a new state, S(F), representing susceptible individuals taking protective measures.

Main Methods:

• Modification of the classical susceptible-infected-recovered (SIR) model to include the S(F) state.
• Derivation of theoretical formulas for epidemic threshold and prevalence using the percolation method.
• Validation of analytical results through numerical simulations.

Main Results:

• An increased response rate (transition to S(F) state) enhances the epidemic threshold.
• Higher response rates lead to a reduced prevalence of the epidemic.
• Mean-field methods yield incorrect results by neglecting dynamic correlations, failing to show the impact of the S(F) state on the threshold.

Conclusions:

• Human behavioral responses, modeled by the S(F) state, are critical in controlling epidemic spread.
• The percolation method provides accurate predictions, unlike mean-field approximations.
• Encouraging protective behaviors can effectively mitigate epidemics.