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Home
Network-based Epidemic Control Through Optimal Travel And Quarantine Management.

Home
Network-based Epidemic Control Through Optimal Travel And Quarantine Management.

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Network-Based Epidemic Control Through Optimal Travel and Quarantine Management.

Mahtab Talaei¹, Apostolos I Rikos¹, Alex Olshevsky¹

¹Department of Electrical and Computer Engineering, and the Division of Systems Engineering, Boston University, Boston, MA, USA.

IEEE Transactions on Control of Network Systems

|January 26, 2026

View abstract on PubMed

Summary

This summary is machine-generated.

This study introduces network-based epidemic control strategies, optimizing travel rates and quarantine measures to minimize infectious disease spread. Simulations using Massachusetts data validate the effectiveness of these network-structure-dependent approaches.

Keywords:

Epidemics compartmental models networked control systems optimization stability of nonlinear systems

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Area of Science:

Epidemiology
Network Science
Mathematical Biology

Background:

Swift global transmission of infectious diseases necessitates advanced control strategies.
Existing models often lack comprehensive network-based optimization for epidemic containment.

Purpose of the Study:

To develop and analyze a network-based framework for epidemic control.
To investigate two distinct approaches: travel rate optimization and SIR model enhancement with quarantine.

Main Methods:

Network optimization for reducing travel rates and analyzing convergence.
Expanding the SIR model with optimized quarantined states, reducing it to matrix balancing.
Utilizing augmented primal-dual gradient dynamics for optimal quarantine convergence.
Validating models with public county-level data from Massachusetts.

Main Results:

Travel rate reduction strategy effectiveness is dependent on network structure.
The optimized quarantine problem links to matrix balancing and the epidemic's reproduction number.
Augmented primal-dual gradient dynamics guarantee exponential convergence for quarantine optimization.
Simulation studies confirm the practical applicability of the proposed framework.

Conclusions:

Network structure is a critical factor in epidemic dynamics and control.
Optimized travel reduction and quarantine strategies offer effective means to curb infectious disease spread.
The developed framework provides a robust, data-driven approach to epidemic control.