Examining the impact of emotion-driven resource allocation on information-behavior-disease coupling dynamics

Area of Science:

• Epidemiology
• Network Science
• Public Health

Background:

• Medical and informational resources are vital in public emergency management and disease spread.
• Emotion-driven resource allocation significantly influences disease dynamics, rumor spread, and panic buying.
• Existing models lack comprehensive analysis of multiplex networks and functional resource heterogeneity.

Purpose of the Study:

• To propose a mUAU-DKD-rSIS model for investigating emotion-driven resource allocation in multiplex networks.
• To analyze the impact of emotional heterogeneity on resource allocation strategies.
• To explore the coupled evolution of rumor dissemination, panic buying, and disease spread.

Main Methods:

• Development of the mUAU-DKD-rSIS model.
• Examination of emotional heterogeneity's impact on resource allocation (anxious vs. calm individuals).
• Classification of informational (learning, review) and medical (protective, curative) resources.
• Incorporation of common pool resources at the group level.
• Application of the microscopic Markov chain approach (MMCA) and numerical simulations.

Main Results:

• Rational resource allocation is crucial for effective disease control.
• Learning informational resources induce a meta-critical point, modulating network coupling strength.
• Curative medical resources demonstrate a second-order phase transition and an optimal utilization rate.
• Emotional states significantly influence resource provision and allocation effectiveness.

Conclusions:

• The study provides theoretical insights into optimizing resource allocation strategies during public health emergencies.
• Understanding emotion-driven resource allocation is essential for mitigating disease spread and panic buying.
• The model highlights the critical roles of specific resource types and network structures in emergency response.