A note on Covid-19 by Professor Vincent Poor

Abstract

The Covid-19 pandemic has affected many aspects of our lives, and has also revealed shortcoming of many scientific and technological aspects of public health response. These include supply chain issues for food and medical supplies, the time scales of the development of tests for the virus, treatments and vaccines, technologies for contact tracing, networking technologies for working and learning from home, and accurate modeling of viral spread and the corresponding effectiveness of mitigation strategies. The readers and authors of IET Smart Cities can address many of these issues, and one approach to the latter one that is being pursued at Princeton University is described below.

One critical issue for many of the readers of IET Smart Cities, is the struggle that universities and schools have had in trying to maintain their curricular momentum. This has been particularly difficult for subjects such as electrical and electronic engineering where hands-on instruction is a key aspect of the training of students. Many institutions, including Princeton, have addressed this in the short by sending lab kits to students’ homes, using videos of lab experiments, and similar approaches, but these are not substitutes for the in-lab experiences that are so important to engineering and scientific education. For the coming academic year, many universities in the USA plan to bring students back into the lab, while expanding lab hours to allow for safe occupation via social distancing, mask-wearing and assuring that ventilation and air exchange equipment are able to keep the air safe during and between laboratory sessions. Other instruction (lectures, problem-solving sessions, etc.) will still be largely online at many institutions. A silver lining of this aspect of the pandemic is that online capabilities have been greatly improved through the experience of widespread online teaching, and this will undoubtedly become part of instruction even after the pandemic is conquered, to the extent that it can enhance the overall educational experience. Primary and secondary education has similarly been affected, and it raises the additional issue of the burden of parents working from home who have to oversee the education of their younger children.

I would like to now describe ongoing work at Princeton addressing the issue of modeling of the spread of Covid-19 and developing an understanding of the efficacy of optimization of mitigation strategies. This work started because of other work, reported in [1], in which together with colleagues at Carnegie-Mellon University, we proposed a refined model for viral spread that incorporates mutations. Although this work was done and the paper submitted before the outbreak of Covid-19, it is relevant to the spread of that disease, as mutations are a factor in its spread. This work had its genesis in a joint research project, funded by the U.S. Army Research Office, between Osman Yağan of Carnegie Mellon and myself, in which we are studying the spread of information in multi-layer social networks. Since information spread in social networks and viral spread in human contact networks can be studied with very similar mathematical models, the work in [1] grew naturally from that study.

The coincidence of this work with the rapid spread of Covid-19 motivated Prof. Yağan and me to join forces with computational biologists Simon Levin at Princeton and Joshua Plotkin at the University of Pennsylvania to develop more accurate models for this spread, incorporating not only mutations, but also factors such as delays in diagnoses, variation in how individuals react to the novel coronavirus, and the multi-layer and interacting nature of the networks over which the disease may spread (schools, workplaces, neighborhoods, family units, etc.). Such models can then be used to develop optimal and robust methods to control the spread by carefully timed interventions such as testing, contact tracing and quarantine. This work is being funded by several organizations that quickly developed rapid funding sources for research on COVID-19, including the U.S. Army Research Office, the C3.ai Digital Transformation Institute, and the U. S. National Science Foundation.

This is but one example of how our community can have a positive effect on the current crisis, and on how smart cities may be structured to respond to future crises of a similar nature. The tools we use – signal processing, artificial intelligence, dynamical modeling, network science, etc. – are exactly the kind of tools that can help provide better understanding of pandemics and how societies should respond to them.

H. Vincent Poor is the Michael Henry Strater University Professor of Electrical Engineering at Princeton University. He also holds an ongoing appointment as a Visiting Professor at Imperial College, and he has held visiting appointments at several other universities as well, including most recently at Berkeley and Cambridge. During 2006 to 2016, he served as Dean of Princeton's School of Engineering and Applied Science. His research interests are in the areas of information theory, machine learning and network science, and their applications in wireless networks, energy systems and related fields. Among his publications in these areas is the forthcoming book Advanced Data Analytics for Power Systems. (Cambridge University Press, 2021).

An IET Fellow, Dr. Poor is a Member of the U.S. National Academy of Engineering and the U.S. National Academy of Sciences, a Corresponding Fellow of the Royal Society of Edinburgh, an International Fellow of the Royal Academy of Engineering, and a Foreign Member of the Royal Society of London. In 2010, he received the IET Ambrose Fleming Medal, and in 2017 he received the IEEE Alexander Graham Bell Medal. He currently serves as the Founding Co-Editor-in-Chief of IET Smart Grid, and on the Advisory Board of IET Smart Cities. He has received honorary degrees and professorships from a number of universities in Asia, Europe and North America, including a D.Sc. honoris causa from the University of Edinburgh in 2011.