Real-time end-to-end pandemic planning, prediction, and response
The COVID-19 pandemic has brought forth the need for a sustainable capability for pandemic planning, response, and mitigation at various geographic, temporal, and social scales. The social, economic, and health impact of the pandemic has been immense and will continue to be felt for decades to come. Since February 2020, our group has been providing local, state, and federal authorities continuous modeling and analytics support as they work assiduously to contain the pandemic. Based on this experience, I will describe the scientific and engineering challenges and opportunities in developing an end-to-end program to better prepare and respond to future pandemics and epidemic outbreaks.
Madhav Marathe is an endowed Distinguished Professor in Biocomplexity, Director of the Network Systems Science and Advanced Computing (NSSAC) Division, Biocomplexity Institute and Initiative, and a tenured Professor of Computer Science at the University of Virginia. Dr. Marathe is a passionate advocate and practitioner of transdisciplinary team science. During his 25-year professional career, he has established and led a number of large transdisciplinary projects and groups. His areas of expertise are network science, artificial intelligence, high-performance computing, computational epidemiology, biological and socially coupled systems, and data analytics.
From January 2005 to September 2018, he was a Professor of Computer Science at Virginia Tech. Concurrently, at Virginia Tech, he was the Deputy Director (2005-2014) and then the Director (2014-2018) of the Network Dynamics and Simulation Science Laboratory at the Biocomplexity Institute of Virginia Tech. He obtained his Bachelor of Technology degree in 1989 in Computer Science and Engineering from the Indian Institute of Technology, Madras, and his Ph.D. in 1994 in Computer Science from the University at Albany -SUNY, under the supervision of Professors Harry B. Hunt III and Richard E. Stearns. Before coming to Virginia Tech in 2005, he worked in the Basic and Applied Simulation Science Group (CCS-5) in the Computer and Computational Sciences Division at Los Alamos National Laboratory where he was the team leader in a theory-based, advanced simulation program to represent, design, and analyze extremely large socio-technical and critical infrastructure systems. He holds adjunct appointments at Chalmers University and the Indian Institute of Public Health.
Dr. Marathe has published more than 350 articles in peer-reviewed journals, conferences, and workshops. Mentoring and training next-generation scientists have been his life-long passion. He has mentored more than a dozen staff scientists, and (co)-advised more than 20 doctoral students, 20+ MS students, and 10+ postdoctoral fellows.
Dr. Marathe and his division focus on developing the scientific foundations and the associated engineering principles to study large-scale biological, information, social, and technical (BIST) systems. His current interests span five broad themes: (i) methods to construct various BIST networks using partial and noisy data as well as procedural information; (ii) understanding the general form and structure of dynamical processes over BIST networks (e.g., key network/pathway properties and typical pathways that impact dynamics); (iii) algorithmic theory of optimization and control as it pertains to the dynamical processes, including methods to detect, enhance, arrest, and mitigate dynamics; (iv) general conceptual and algorithmic foundations to understand the co-evolution of networks and dynamics; and (v) high-performance services-based computing solutions that can be delivered seamlessly to end-users and policymakers.