Collaborative Research: Modeling and Control of Non-Passive Networks with Distributed Time-Delays: Application in Epidemic Control


 S. Farokh Atashzar, assistant professor of electrical and computer engineering at NYU Tandon, has received a major NSF award (~$400K) to conduct fundamental research on the control of networked dynamic systems in the presence of distributed delays.

The COVID pandemic, an example of large-scale disease propagation, can be seen as a "mega-network" where complex interactions and distributed delays in the interconnections lead to hard-to-predict, echoing “waves” of disease spread. S. Farokh Atashzar, with the support of a collaborative National Science Foundation Civil, Mechanical and Manufacturing Innovation (NSF CMMI) grant, and in collaboration with Northeastern University, will dive into these "waves" by developing novel approaches to computational network modeling and designing optimal mitigation control to minimize the spread.

This research seeks to develop a comprehensive framework for data-driven control of large-scale networks where time delays and complex behavior play an important role. In the COVID pandemic, such effects lead to ``reflective" spreading waves, resulting in hard to predict and control phases of infection spread. But accurate network models of society and disease spread are necessary to enhancing pandemic preparedness and making healthcare systems and governments ready to respond well to potential future airborne epidemic diseases.

Effective mitigation of pandemics spread over networks requires: (a) unveiling the topology, dynamics, and delays of the underlying network from experimental data; (b) using this information to design networks that can robustly minimize the systemic effects of localized infection foci; and (c) synthesizing real-time optimal control laws that adjust local parameters to prevent the onset of delay-induced echoing waves of pandemic spread. This research seeks to achieve these objectives by embedding the problem into a more general one: data-driven control synthesis, based on nonlinear passivity control theory, for networked systems in the presence of delay-induced non-minimum phase/non-passive behavior.