Project Description

The COVID-19 pandemic has changed the way the world operates. Using ideas from network science, systems and control theory, and data science, mathematical models can be developed to model the spread of a disease. Given effective models, optimization theory can be used to develop control algorithms that mitigate viral spread.

The objective of this project is to develop mathematical models that are detailed enough to capture the underlying interaction of people via contact networks inferred from various big data sources, as well as how the interactions of social, human, and urban networks affect the spread of the epidemic, while still being computationally tractable to inform policy making. The proposed models will be validated by employing social media data and high-resolution human movement data collected from cellular devices. This data will also give insight into how the interaction of people and ideas evolved from pre-outbreak times through the different stages of the COVID-19 pandemic, as well as the effects of the different implemented control measures on health outcomes. Finally, given the validated framework, optimization techniques will be used to design distributed algorithms to mitigate/eradicate the virus and identify the best control measures that are effective while considering the heterogeneity of cities.

Start Date

Summer 2021

Postdoc Qualifications

Networked systems and control theoretic expertise
Distributed optimization algorithms experience
Experience with real data

Co-Advisors

Philip E. Paré, philpare@purdue.edu, School of Electrical and Computer Engineering, https://sites.google.com/view/philpare/

Satish V. Ukkusuri, sukkusur@purdue.edu, Lyles School of Civil Engineering, http://www.satishukkusuri.com/

References

Nowzari, Cameron, Victor M. Preciado, and George J. Pappas. "Analysis and control of epidemics: A survey of spreading processes on complex networks." IEEE Control Systems Magazine 36.1 (2016): 26-46.

Paré, Philip E., Carolyn L. Beck, and Angelia Nedić. "Epidemic processes over time-varying networks." IEEE Transactions on Control of Network Systems 5.3 (2017): 1322-1334.

Paré, Philip E., Ji Liu, Carolyn L. Beck, Angelia Nedić, and Tamer Başar. "Multi-competitive viruses over static and time-varying networks." 2017 American Control Conference (ACC). IEEE, 2017.

Qian, Xinwu, Lijun Sun, and Satish V. Ukkusuri. "Scaling of contact networks for epidemic spreading in urban transit systems." arXiv preprint arXiv:2002.03564 (2020).

Qian, Xinwu and Satish Ukkusuri. "Modeling the spread of infectious disease in urban areas with travel contagion." arXiv preprint arXiv:2005.04583 (2020).