Additional Areas of Expertise

BIG DATA: Efficient Distributed Computation of Large-Scale Graph Problems in Epidemiology and Contagion Dynamics

Project Contact: Anil Vullikanti / Madhav Marathe
Funding Agency: National Science Foundation National Science Foundation logo

We aim to develop a rigorous algorithmic theory of distributed large-scale graph computation, specifically focusing on important graph problems that arise in computational epidemiology and contagion dynamics. At the onset of any epidemic and during the annual flu season, epidemiologists and public health officials are concerned with understanding how the disease spreads, who is likely to get infected, where it originated, and how to control its rapid spread. Mathematical models are commonly used for studying these problems, especially stochastic contagion models on real-world networks. Fundamental graph-theoretic problems arise naturally and crucially in analyzing these models and solving these questions. The graphs encountered are typically large (with tens of millions of nodes). Further, typical experimental analyses involve large designs with many parameters, leading to hundreds of thousands of graph computations. In this domain, fast response times are critical to effective decision making, and distributed and parallel computation meets this need.

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Broad One Health Endectocide-based Malaria Intervention in Africa (BOHEMIA)

Project Contact: Achla Marathe / Bryan Lewis
Funding agency: Unitaid Unitaid

The University of Virginia is one of 10 partner organizations working with the Barcelona Institute for Global Health to assess the effect of ivermectin mass drug administration on malaria transmission in sub-Saharan African malaria-endemic countries. The project aims to provide solid and systematic evidence to contribute toward a World Health Organization policy recommendation by 2022 on the use of antiparasitic drugs to reduce malaria transmission. Evidence on efficacy and safety will be supported by additional data on cost-effectiveness, acceptability, and environmental impact to facilitate analysis leading to a policy recommendation.

 

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CINES

Project Contact: Chris Kuhlman / Madhav Marathe
Funding Agency: National Science Foundation National Science Foundation

Networks are ubiquitous and part of our common vocabulary. Network science and engineering as a formal field has seen explosive growth over the last 20 years, playing a central role in the formation of companies such as Akamai, Twitter, Google, Facebook, and LinkedIn. The concepts have also been used to address fundamental problems in diverse fields (e.g., epidemiology, marketing, utility infrastructures, coupled human social-technical systems), and are now part of most university curricula. Network science is multi-disciplinary, yet resources for conducting network science are largely dispersed and standalone, of small scale, home-grown for personal use, and/or do not cover the broad range of operations that need to be performed on networks, much less compose these operations. Furthermore, many researchers who study networks are not computer scientists; as a result, they do not have easy access to computing and data resources. What is needed is a cyber infrastructure to bring together resources to provide a unifying ecosystem for network science that is greater than the sum of its parts. The goal of this project is to develop this community resource, where users and application developers can contribute codes and data to the benefit of the entire community.

 

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Complex Pathway Simulator (COPASI)

Project Contact: Stefan Hoops / Brian Klahn
Funding agency: National Institutes of Health: National Institute of General Medical Sciences (NIH(NIGMS))   National Institutes of Health National Institute of General Medical Sciences

COPASI is a software application for simulation and analysis of biochemical networks and their dynamics. COPASI is a stand-alone program that supports models in the SBML standard and can simulate their behavior using ODEs or Gillespie's stochastic simulation algorithm; arbitrary discrete events can be included in such simulations. A list of its many features can be found here.

 

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Coupled social and infrastructure approaches for enhancing solar energy adoption

Project Contact: Achla Marathe / Samarth Swarup
Funding Agency: Department of Energy (DOE)  Department of Energy logo

This research project is focused on enhancing the diffusion of rooftop solar panel adoption in rural areas of Virginia. This collaborative work, led by the University of Virginia, combines data-driven methods, highly detailed agent-based models of rural populations, and novel datasets from rural cooperatives to build a solar-adoption propensity score for households in rural areas.

 

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MIDAS

Project Contact: Stephen Eubank / Bryan Lewis
Funding agency: National Institutes of Health (NIH)   National Institutes of Health

The Models of Infectious Disease Agent Study (MIDAS) is a large research consortium–as of late 2018, 10 research projects, three research centers, and an informatics resource–funded by the National Institute of General Medical Sciences (NIGMS) of the National Institutes of Health (NIH). Our researchers have led research projects in MIDAS since its inception in 2005 with the goals to promote policymakers' use of large-scale simulation models capable of faithfully representing the interactions among people that lead to the spread and control of infectious diseases. Researchers have demonstrated the feasibility of conducting epidemiology experiments in silico to assess the comparative effectiveness of hypothetical spatio-temporally and demographically targeted mitigations according to many different measures of effectiveness.

 

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Next Generation Social Science (NGS2)

Project Contact: Chris Kuhlman / Madhav Marathe
Funding Agency: Defense Advanced Research Projects Agency (DARPA) DARPA

The overarching goal of this project is to develop methods to make social science research more rigorous, reproducible, and transparent. To make the work more concrete and to provide an exemplar, the project studies collective identity (CI) – roughly, an individual's mental and emotional connection with a broader community, category, or institution. We designed and constructed an online game platform where people play a novel group anagram game to foster the generation of CI within the participant group. The level of CI produced is measured by a proxy parameter called the DIFI (dynamic identity fusion index). We also model and simulate this game using various methods, including agent-based approaches. Finally, in support of rigorous methods for producing models of human behavior, various inference methods are studied to infer properties of human behavior models used in the social sciences. Inference methods are also used in the modeling of group anagram games: game data are used to devise models of human behavior and inference approaches are used to infer these models’ properties.

 

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Social Diffusion in Networked Cognitive Systems

Project Contact: Mark Orr / Samarth Swarup
Funding Agency: National Science Foundation National Science Foundation logo

This interdisciplinary research project examines social diffusion dynamics, including both the transmission of information within social systems and the impact of those information flows on the ways that the social system functions. The project will bridge the cognitive and social sciences by providing new knowledge about the ways that ideas, beliefs, traditions, behaviors, and information in general move through social channels from person to person and create patterns of knowledge within communities, nations, and the world as a whole.

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