Henning S. Mortveit

About

Henning S. Mortveit is an associate professor in the Department of Systems and Information Engineering and the UVA Biocomplexity Institute at the University of Virginia. He received his doctorate in mathematics from the Norwegian University of Science and Technology in 2000. His research interests include the theory, modeling, and simulation of massively interacting systems, computational architectures for systems of systems, and the development of theories and formalisms for capturing, analyzing, and simulating networked systems, including applications in machine learning. Prior to joining UVA, he was a technical staff member at Los Alamos National Laboratory and an associate professor at Virginia Tech.

Research topics

• Computer Security
• Computer Science
• Virology
• Medicine
• Marketing
• International trade
• Transport engineering
• Business
• Geography
• Engineering

Selected publications

• Prioritizing allocation of COVID-19 vaccines based on social contacts increases vaccination effectiveness

  medRxiv (Cold Spring Harbor Laboratory) · 2021 · 70 citations

  We study allocation of COVID-19 vaccines to individuals based on the structural properties of their underlying social contact network. Even optimistic estimates suggest that most countries will likely take 6 to 24 months to vaccinate their citizens. These time estimates and the emergence of new viral strains urge us to find quick and effective ways to allocate the vaccines and contain the pandemic. While current approaches use combinations of age-based and occupation-based prioritizations, our s…

  DOI

• Evaluation of the US COVID-19 Scenario Modeling Hub for informing pandemic response under uncertainty

  Nature Communications · 2023-11-20 · 59 citations

  articleOpen access

  Our ability to forecast epidemics far into the future is constrained by the many complexities of disease systems. Realistic longer-term projections may, however, be possible under well-defined scenarios that specify the future state of critical epidemic drivers. Since December 2020, the U.S. COVID-19 Scenario Modeling Hub (SMH) has convened multiple modeling teams to make months ahead projections of SARS-CoV-2 burden, totaling nearly 1.8 million national and state-level projections. Here, we fin…

  PublisherOA PDFDOI

• Potential impact of annual vaccination with reformulated COVID-19 vaccines: Lessons from the US COVID-19 scenario modeling hub

  PLoS Medicine · 2024-04-17 · 14 citations

  articleOpen accessCorresponding

  BACKGROUND: Coronavirus Disease 2019 (COVID-19) continues to cause significant hospitalizations and deaths in the United States. Its continued burden and the impact of annually reformulated vaccines remain unclear. Here, we present projections of COVID-19 hospitalizations and deaths in the United States for the next 2 years under 2 plausible assumptions about immune escape (20% per year and 50% per year) and 3 possible CDC recommendations for the use of annually reformulated vaccines (no recomme…

  PublisherOA PDFDOI

• An agent-based framework to study forced migration: A case study of Ukraine

  PNAS Nexus · 2024-02-29 · 8 citations

  articleOpen access

  The ongoing Russian aggression against Ukraine has forced over eight million people to migrate out of Ukraine. Understanding the dynamics of forced migration is essential for policy-making and for delivering humanitarian assistance. Existing work is hindered by a reliance on observational data which is only available well after the fact. In this work, we study the efficacy of a data-driven agent-based framework motivated by social and behavioral theory in predicting outflow of migrants as a resu…

  PublisherOA PDFDOI

• <scp>Epihiper</scp>—A high performance computational modeling framework to support epidemic science

  PNAS Nexus · 2024-12-11 · 6 citations

  articleOpen access

  This paper describes Epihiper, a state-of-the-art, high performance computational modeling framework for epidemic science. The Epihiper modeling framework supports custom disease models, and can simulate epidemics over dynamic, large-scale networks while supporting modulation of the epidemic evolution through a set of user-programmable interventions. The nodes and edges of the social-contact network have customizable sets of static and dynamic attributes which allow the user to specify intervent…

  PublisherOA PDFDOI

Frequent coauthors

• Madhav Marathe

  218 shared

• Anil Vullikanti

  University of Virginia

  121 shared

• Samarth Swarup

  University of Virginia

  109 shared

• Abhijin Adiga

  Warwick Hospital

  98 shared

• Jiangzhuo Chen

  University of Virginia

  95 shared

• Stephen Eubank

  University of Virginia

  90 shared

• Bryan Lewis

  Biocom

  88 shared

• Srinivasan Venkatramanan

  Biocom

  78 shared

Education

• Ph.D.

  Norwegian University of Science and Technology

  2000

Awards & honors

• Best paper award at the 39th ACM International Conference on…
• Honorable mention award at AAMAS 2020

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• Laura Barnesh-32
• Roman Krzysztofowiczh-30