If it were up to engineering, everything would be optimized. Efficiency would rule, and the best available outcomes would frictionlessly elevate scientific research, live at our fingertips and shape our surroundings.
But engineering is a field of inquiry rooted in practice. Its ideals are brought to life with tenacity and care by engineers, who ground aspirations for progress in the messiness of what’s possible.
Shirin Saeedi Bidokhti and Saswati Sarkar are electrical engineers. They analyze and design networks that knit together a world of Internet-enabled devices, creating intelligent infrastructures that send and receive information with reliability, security and speed. Bidokhti is an assistant professor in the Department of Electrical and Systems Engineering, with a secondary appointment in the Department of Computer and Information Systems. Sarkar is a professor in the Department of Electrical and Systems Engineering.
But their interest in connectivity is not limited to the Internet of Things. Together, they have produced a suite of studies that apply techniques from network and information theory to pandemic control and prevention.
Designed in partnership with medical experts, these tools, developed originally to boost the performance and safety of wireless objects, prove to be flexible and rigorous enough to care for networks of people. Bidokhti and Sarkar’s findings hold the potential to deliver life-saving benefits while accommodating the most human of constraints, including unequal access to medical resources, reluctance, budgetary constraints, testing errors and local practices.
Specialists in transmission, Bidokhti and Sarkar initiated these projects in response to the COVID-19 pandemic, leveraging similarities between how information travels through networks and how illness moves through populations. Their computational techniques and data-driven strategies offer insights for minimizing disease spread that improve on state-of-the-art practices in epidemiology.
“As network experts, we attend to variety and efficacy within a network in a much more precise way,” they explain. “Public health models tend to take the homogeneity of the infection network for granted—that is, they weight each person and each path between these people in relatively similar terms. But we can provide tools with more dimension to them. When the speed of a vaccination rollout is crucial or when the number of vaccines is limited—as it often is around the world—policymakers and practitioners need to know the optimal order in which to vaccinate to minimize spread.”
The research outlines tactics to make testing, vaccination and quarantine more precise, cost-efficient and equitable, with evidence pointing to significant benefits through straightforward adjustments to public health approaches.
Having disseminated their work at a conference hosted by the Centers for Disease Control and Prevention and authored peer-reviewed studies published and forthcoming in journals in the biological sciences to machine learning, the value of their research is clear. Next steps aim at collaboration with policymakers, practitioners and the public to adapt the research into reality.
This story is by Devorah Fischler. Read more at Penn Engineering Today.
