A multiscale modeling framework to study the interdependence of brain, behavior, and pandemic

Area of Science:

• Neuroscience
• Epidemiology
• Behavioral Science

Background:

• Traditional epidemiological models assume static human behavior, which is inaccurate during pandemics.
• Human behavior is dynamic and influenced by emotions, particularly fear, impacting disease transmission.

Purpose of the Study:

• To develop a theoretical framework integrating neuronal plasticity into epidemiological models.
• To investigate how dynamic human behavior affects disease transmission during pandemics.
• To optimize public health awareness campaigns by considering behavioral responses.

Main Methods:

• Proposed a multi-time-scale theoretical framework assimilating neuronal plasticity.
• Coupled brain region connectivity and firing frequencies with a susceptible-infected-recovered (SIR) model.
• Simulated a fear conditioning model alongside awareness campaigns.

Main Results:

• Identified an optimal duration for awareness campaigns to effectively mitigate pandemics under fear conditioning.
• Sensitivity analysis revealed learning rate and firing frequency as key regulators of pandemic waves.
• Demonstrated the impact of neuronal dynamics on contact transmission rates.

Conclusions:

• Neuronal dynamics provide a basis for understanding and modeling behavioral immune responses.
• Incorporating behavioral plasticity is crucial for accurate epidemiological modeling.
• Findings offer implications for designing more effective public health interventions and awareness campaigns.