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Award ID contains: 2028738

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  1. ; ; (, IEEE Transactions on Network Science and Engineering)
    Free, publicly-accessible full text available November 1, 2025
  2. ; ; ; ; ; (, IEEE Transactions on Control of Network Systems)
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  3. ; ; ; (, IEEE Transactions on Control of Network Systems)
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  4. ; ; ; (, IFAC-PapersOnLine)
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  5. ; ; ; (, 2022 European Control Conference (ECC))
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  6. ; ; (, 2022 American Control Conference (ACC))
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  7. ; ; (, 2022 American Control Conference (ACC))
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  8. ; ; ; (, SIAM Journal on Control and Optimization)
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  9. ; ; ; ; ; (, 2021 60th IEEE Conference on Decision and Control (CDC))
    In this paper, we present a discrete-time networked SEIR model using population flow, its derivation, and assumptions under which this model is well defined. We identify properties of the system’s equilibria, namely the healthy states. We show that the set of healthy states is asymptotically stable, and that the value of the equilibria becomes equal across all sub-populations as a result of the network flow model. Furthermore, we explore closed-loop feedback control of the system by limiting flow between sub-populations as a function of the current infected states. These results are illustrated via simulation based on flight traffic between major airports in the United States. We find that a flow restriction strategy combined with a vaccine roll-out significantly reduces the total number of infections over the course of an epidemic, given that the initial flow restriction response is not delayed. 
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  10. ; ; ; (, 2021 60th IEEE Conference on Decision and Control (CDC))
    We propose an SIR epidemic model coupled with opinion dynamics to study an epidemic and opinions spreading in a network of communities. Our model couples networked SIR epidemic dynamics and opinions towards the severity of the epidemic. We develop an epidemic-opinion based threshold condition to capture the moment when a weighted average of the epidemic states starts to decrease exponentially fast over the network, namely the peak infection time. We define an effective reproduction number to characterize the behavior of the model through the peak infection time. We use both analytical and simulation-based results to illustrate that the opinions reflect the recovered levels within the communities after the epidemic dies out. 
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