Multi-Agent Consensus Algorithms for a Dual-Layer Epidemic Model

This paper introduces a dual-layer approach for a multi-agent-based SIS model, comprising two distinct layers: the person-to-person contact layer and the information layer. The person-to-person contact layer represents interpersonal interactions within cities, while the information layer captures the exchange of data, such as the number of confirmed cases, among healthcare officials in each city. Furthermore, a state-observer-based consensus mechanism is employed to estimate the unknown states—specifically, the number of infected individuals—within the person-to-person contact layer, which is then utilized in the information layer. A numerical example is provided to validate the theoretical findings.