MATHEMATICAL MODELING OF EPIDEMIC DISEASES ON COMPLEX NETWORKS

We wrote a survey paper on analysis and control of epidemics on complex networks: 

• Zino L. and Cao M., Analysis, Prediction, and Control of Epidemics: A Survey from Scalar to Dynamic Network Models. IEEE Circuits and Systems Magazine, 21(4), 4–23, 2021
   

We developed a framework for modeling human behavior in epidemic models using a co-evolutionary model, and we studied it:

• Ye M., Zino L., Rizzo A., and Cao M., Game-theoretic modeling of collective decision making during epidemics. Physical Review E, 104, 024314, 2021

• Frieswijk K., Zino L., Ye M., Rizzo A., Cao M., A mean-field analysis of a network behavioural–epidemic model. IEEE Control Systems Letters, 6, 2533–2538, 2022

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We developed and studied a realistic model of human decision making concerning vaccination: 

• Frieswijk K., Zino L., Cao M., Modelling the Effect of Vaccination and Human Behaviour on the Spread of Epidemic Diseases on Temporal Networks. Proceedings of the 21st European Control Conference, Jul 2022, London, UK.

• Frieswijk K., Zino L., Cao M., A Polarized Temporal Network Model to Study the Spread of Recurrent
  Epidemic Diseases in a Partially Vaccinated Population. IEEE Transactions on Network Science and Engineering, 10(6), 3732-3743, 2023.

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We developed a model for sexually transmitted disease, incorporating human behavior, and we studied it:

• Frieswijk K., Zino L., and Cao M., Modelling Behavioural Preferences in Epidemic Models for Sexually Transmitted Infections on Temporal Networks. Proceedings of the 20th European Control Conference, Jun-Jul 2021, Rotterdam, The Netherlands

• Frieswijk K., Zino L., and Cao M., A time-varying network model for sexually transmitted infections accounting for behavior and control actions. International Journal of Robust and Nonlinear Control, 2021
  Wiley Top Downloaded Article 2021

 

We developed thoeretical tools to study different methods to control epidemic processes, including NPIs and vaccination:

• Zino L., Rizzo A., and Porfiri M., On assessing control actions for epidemic models on temporal networks. IEEE Control Systems Letters, 4(4), 797–802, 2020

• Cenedese C., Zino L., Cucuzzella M., and Cao M., Optimal policy design to mitigate epidemics on networks using an SIS model. Proceedings of the 60th IEEE Conference on Decision and Control, Dec 2021, Austin TX, US

• Calafiore G.C., Parino F., Zino L., and Rizzo A., Dynamic Planning of a Two-Dose Vaccination Campaign with Uncertain Supplies. European Journal of Operational Research, 304(3), 1269–1278, 2023

• Parino F., Zino L., Rizzo A., Optimal control of endemic epidemics diseases with behavioral response. IEEE Open Journal of Control Systems, 3, 483–496, 2024.

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We studied stocastic epidemic models on networks:

• Fagnani F., and Zino L., Time to extinction for the SIS epidemic model: new bounds on the tail probabilities. IEEE Transactions on Network Science and Engineering, 6(1), 74–81, 2019

​We expanded the theory of activity-driven networks by proposing a continuous-time formulation. Then, we added new analytically-trectable features to improve the modeling of human behavior:

• Zino L., Rizzo A., and Porfiri M., A continuous-time discrete-distribution theory for activity-driven networks. Physical Review Letters, 117(22), 228302, 2016

• Zino L., Rizzo A., and Porfiri M., An analytical framework for the study of epidemic models on activity driven networks. Journal of Complex Networks, 5(6), 924–952, 2017

• Zino L., Rizzo A., and Porfiri M., Modeling memory effects in activity driven networks. SIAM Journal on Applied Dynamical Systems, 17(4), 2830–2854, 2018

• Zino L., Rizzo A., and Porfiri M., Effect of self-excitement and behavioral factors on epidemics on activity driven networks. Proceedings of the 18th European Control Conference, Jun 2019, Naples, Italy

• Zino L., Rizzo A., and Porfiri M., Analysis and control of epidemics in temporal networks with self-excitement and behavioral changes. European Journal of Control 54, 1–11, 2020

• Nadini M., Zino L., Rizzo A., and Porfiri M., A multi-agent model to study epidemic spreading and vaccination strategies in an urban-like environment. Applied Network Science, 5, 68, 2020

• Zino L., Rizzo A., Porfiri M., The impact of deniers on epidemics: A temporal network model. IEEE Control Systems Letters, 7, 685–690, 2022

• Zino L. and Rizzo A., On a Susceptible-Infected-Susceptible Epidemic Model with Reactive Behavioral Response on Higher-Order Temporal Networks. To appear in the Proceedings of the 63rd IEEE Conference on Decision and Control, 2024.

 

We developed and studied multivirus models, in which multiple competing variants or strains spread in the same population:​

• Zino L., Ye M., and Anderson B.D.O., On a bi-virus epidemic model with partial and waning immunity. Proceedings of the 22nd IFAC World Congress, Jul 2023, Yokohama, Japan

• Zino L., Ye M., and Anderson B.D.O., Modeling and analyzing competitive epidemic diseases with partial and waning virus-specific and cross-immunity. IFAC Journal of Systems and Control, 28, 100262, 2024. 

• Burbano Lombana D.A., Zino L., Butail S., Caroppo E., Jiang Z.P., Rizzo A., and Porfiri M., Activity-driven network modeling and control of the spread of two concurrent epidemic strains. Applied Network Science, 7, 66, 2022

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We developed and studied a mathematical model for the spread of vector-borne diseases:

• Zino L., Casu A., and Rizzo A., A Human-Vector Susceptible--Infected--Susceptible Model for Analyzing and Controlling the Spread of Vector-Borne Diseases. Under Review, 2024