(l-r)Jiangzhuo Chen, Achla Marathe, Bryan Lewis, Dustin Machi, Andrew Warren, Stefan Hoops, Abhijin Adiga, Mandy Wilson, Stephen Eubank, Madhav Marathe, S.S. Ravi,Samarth Swarup, Srinivasan Venkatramanan, Parantapa Bhattacharya, Anil Vullikanti, Hannah Baek, Mark Orr, Chunhong Mao, Henning Mortveit, Dawen Xie, Erin Raymond, Christopher Kuhlman (not pictured: Allan Dickerman, Ron Kenyon, Brian Klahn, Jacob Porter, Rebecca Wattam)
In an effort to support the planning and response efforts for the recent Coronavirus outbreak, the Network Systems Science and Advanced Computing (NSSAC) division of the Biocomplexity Institute and Initiative at the University of Virginia has prepared a visualization tool that provides an alternate way of examining data curated by JHU and NSSAC. Check out our COVID-19 Surveillance Dashboard here.
Key features of our tool include:
- A visualization of all reported Coronavirus incidence data, filtered by date;
- A heatmap of selected attributes on an interactive map;
- A Query tool that allows users to focus on regions of interest;
- The ability to select regions by clicking on the map; to select multiple regions at once, hold the “command” key on the Mac or the “ctrl” key on Windows while clicking;
- Users can export subsets of the data for analysis on external tools.
We hope our tool will encourage researchers worldwide to explore the surveillance datasets made available by the World Health Organization (WHO), the Centers for Disease Control and Prevention (CDC), the European Centre for Disease Prevention and Control (ECDC), NHC, and DXY, and curated by Johns Hopkins CSSE and us.
The Network Systems Science and Advanced Computing division uses Twitter to share news about ongoing research, recent publications, events, awards and accomplishments, conferences, grant awards, and anything of interest to the research and scientific community. For additional information, check out the Biocomplexity Institute’s homepage and follow our social media accounts found below.
NSSAC is pursuing an advanced research and development program for interaction-based modeling, simulation and associated analysis, experimental design, and decision support tools for understanding large biological, information, social, and technological (BIST) systems. Extremely detailed, multi-scale computer simulations allow formal and experimental investigation of these systems. The need for such simulations is derived from questions posed by scientists, policymakers, and planners involved with very large complex systems. Our team is comprised of a diverse group of researchers from various domains including computer science, cognitive science, mathematics, biology, health science, electrical engineering, and economics.
When will a social media protest transition to a civil crisis? What is the tipping point for rhetoric to become action? How do we design effective, massive health messaging campaigns to make the public’s health more robust? Effective analysis and understanding of such questions must do more than compile data; it should be driven by theoretical knowledge and first principles of what motivates human behavior. This implies multiple levels of scale that coincide with key disciplines of human behavior—the cognitive, behavioral and social sciences—each of which offer unique insights into understanding and prediction. Our sophisticated computational simulations are designed to account for such theoretical first principles, allowing us to provide decision-makers with robust predictions of the influence of policies and the specifics of their implementation on human conduct.
Networks are ubiquitous in our modern vocabulary. From social media to transportation and resource distribution networks, they are the fabric of the interdependent infrastructures we rely on to navigate the complexities of our daily lives. Our work streamlines network analysis, offering effective tools to assist policymakers as they grapple with the most pressing issues: How does a city recover from a natural disaster? How does climate change impact society? How can we best fight infectious disease outbreaks? The versatile tools we have developed interpret, forecast, and explain the dynamics of massively interacting systems.
Revolutions in medical technology that promise patient-centered, personalized medicine are producing an unfathomable amount of data. New techniques that identify previously unknown organisms in our environment, and even in our bodies, are expanding our knowledge of the complexities of ecosystems and living organisms. As the data grows exponentially, our user-friendly tools help research scientists, healthcare professionals, and policymakers distill this information down to manageable and actionable relationships.
In any public health crisis, timely knowledge is power. Our transdisciplinary team leverages predictive modeling tools to inform and support public health policymakers as they identify and deploy essential response measures. Our distinguished record of success is visible in the support we provided key government agencies as they tackled H1N1 in the United States and cholera in Haiti, and in the data and analysis we are provided to help contain the Ebola outbreak in western Africa.
Additional Areas of Expertise
Researchers at the Biocomplexity Institute have a long history of project expertise. See here for a list of additional contributions to science.
Computing for Global Challenges Program
Computing for Global Challenges is a summer undergraduate research program that trains undergraduates in the use of computational methods to address real-world problems. Students work with faculty mentors on a range of ongoing research projects, and learn about cutting-edge methods in machine learning, data science, computational biology, and more.
Join Our Team
Please see our UVA job listings for opportunities with the Biocomplexity Institute and Initiative. Use query term "biocomplexity".
Proceedings of the 36th International Conference on Machine Learning, PMLR 97. (2019) A. Adiga, C. Kuhlman, M. Marathe, S.S. Ravi, A. Vullikanti
Proceedings of the 18th International Conference On Autonomous Agents And Multiagent Systems, 1635-43. (2019) P. Bhattacharya, S. Ekanayake, C. Kuhlman, C. Lebiere, D. Morrison, S. Swarup, M. Wilson, M. Orr
BMC Medical Informatics And Decision Making, 19(1):28. (2019) J. Cadena, D. Falcone, A. Marathe, A. Vullikanti
Indiana University Technical Report. (2019) G. Fox, J. Glazier, J. Kadupitiya, V. Jadhao, M. Kim, J. Qiu, J. Sluka, E. Somogyi, M. Marathe, A. Adiga, J. Chen
IEEE Transactions On Computer-Aided Design Of Integrated Circuits And Systems. (2019) P. Joshi, S.S. Ravi, Q. Liu, U. Bordoloi, S. Samii, S. Shukla, H. Zeng
Bulletin Of Mathematical Biology, 81(5):1442-1460. (2019) H. Mortveit, R. Pederson
Modeling Biomolecular Site Dynamics, 1945:119-139. (2019) A. Palmisano, S. Hoops, L. Watson, T. Jones, J. Tyson, C. Shaffer
eLIFE, 8:E43570. (2019) Y. Qi, Y. Wu, R. Saunders, Xg. Chen, C. Mao, J. Biedler, Z. Tu
BMC Infectious Diseases, 19(1):221. (2019) M. Singh, P. Sarkhel, G. Kang, A. Marathe, K. Boyle, P. Murray-Tuite, K. Abbas, S. Swarup
Crop Protection. (2019) S. Venkatramanan, S. Wu, B. Shi, A. Marathe, M. Marathe, S. Eubank, Lp. Sah, A. Giri, L. Colavito, K. Nitin, V. Sridhar
Annals of the American Association of Geographers, 109(3):875-886. (2019) C. Wu, B. Zaitchik, S. Swarup, J. Gohlke