Award Abstract # 1918749
Expeditions: Collaborative Research: Global Pervasive Computational Epidemiology

NSF Org: CCF
Division of Computing and Communication Foundations
Recipient: UNIVERSITY OF MARYLAND, COLLEGE PARK
Initial Amendment Date: March 23, 2020
Latest Amendment Date: May 21, 2024
Award Number: 1918749
Award Instrument: Continuing Grant
Program Manager: Mitra Basu
mbasu@nsf.gov
 (703)292-8649
CCF
 Division of Computing and Communication Foundations
CSE
 Directorate for Computer and Information Science and Engineering
Start Date: April 1, 2020
End Date: March 31, 2026 (Estimated)
Total Intended Award Amount: $406,395.00
Total Awarded Amount to Date: $462,395.00
Funds Obligated to Date: FY 2020 = $219,362.00
FY 2021 = $32,000.00

FY 2022 = $115,038.00

FY 2023 = $59,417.00

FY 2024 = $36,578.00
History of Investigator:
  • Aravind Srinivasan (Principal Investigator)
    srin@cs.umd.edu
  • Rita Colwell (Co-Principal Investigator)
Recipient Sponsored Research Office: University of Maryland, College Park
3112 LEE BUILDING
COLLEGE PARK
MD  US  20742-5100
(301)405-6269
Sponsor Congressional District: 04
Primary Place of Performance: University of Maryland College Park
MD  US  20742-5103
Primary Place of Performance
Congressional District:
04
Unique Entity Identifier (UEI): NPU8ULVAAS23
Parent UEI: NPU8ULVAAS23
NSF Program(s): Special Projects - CNS,
CYBERINFRASTRUCTURE,
Expeditions in Computing
Primary Program Source: 01002021DB NSF RESEARCH & RELATED ACTIVIT
01002223DB NSF RESEARCH & RELATED ACTIVIT

01002324DB NSF RESEARCH & RELATED ACTIVIT

01002425DB NSF RESEARCH & RELATED ACTIVIT

01002122DB NSF RESEARCH & RELATED ACTIVIT
Program Reference Code(s): 7723, 9178, 9251
Program Element Code(s): 171400, 723100, 772300
Award Agency Code: 4900
Fund Agency Code: 4900
Assistance Listing Number(s): 47.070

ABSTRACT

Infectious diseases cause more than 13 million deaths per year worldwide. Rapid growth in human population and its ability to adapt to a variety of environmental conditions has resulted in unprecedented levels of interaction between humans and other species. This rise in interaction combined with emerging trends in globalization, anti-microbial resistance, urbanization, climate change, and ecological pressures has increased the risk of a global pandemic. Computation and data sciences can capture the complexities underlying these disease determinants and revolutionize real-time epidemiology --- leading to fundamentally new ways to reduce the global burden of infectious diseases that has plagued humanity for thousands of years. This Expeditions project will enable novel implementations of global infectious disease computational epidemiology by advancing computational foundations, engineering principles, theoretical understanding, and novel technologies. The innovative tools developed will provide new analytical capabilities to decision makers and result in improved science-based decision making for epidemic planning and response. They will facilitate enhanced inter-agency and inter-government coordination and outbreak response. The team will work closely with many local, regional, national, and international public health agencies and universities to apply and deploy powerful technologies during epidemic outbreaks that can be expected to occur during the course of the project. International scientific networks linked to a comprehensive postdoctoral, graduate and undergraduate student training program will be established. Educational programs to foster interest in and increase understanding of computational science in addressing the complex societal challenges due to pandemics will also be developed. The team, with partners in Asia, Africa, Europe, and Latin America, will produce multidisciplinary scientists with diverse skills related to public health.

The novel implementations of this project will be enabled by the development of a rigorous computational theory of spreading and control processes on dynamic multi-scale, multi-layer (MSML) networks, along with tools from AI, machine learning, and social sciences. New techniques resulting from this research will make it possible to develop and apply large-scale simulations of epidemics and social interactions over MSML networks. These simulations, in turn, will provide fundamentally new insights into how to control epidemics. Pervasive computing technologies will be developed to support disease surveillance and real-time response. The computational advances will also be generalizable; that is, they will be applicable to other areas such as cybersecurity, ecology, economics and social sciences. The project will take into account emerging concerns and constraints that include: preserving privacy of individuals and vulnerable groups, enabling model predictions to be interpreted and explained, developing effective interventions under uncertain and unknown network data, understanding strategic and adversarial behaviors of individual agents, and ensuring fairness of the process across the entire population. The research team includes experts from multiple disciplines and will address these societal concerns and constraints in practical, impactful, and novel ways, including the development of computational tools and techniques to support sound, ethical science-based policy pertaining to public health infectious disease epidemiology. Center for Computational Research in Epidemiology (CoRE) at the University of Virginia will be established as a part of the project. CoRE will develop transformative ways to support real-time epidemiology and facilitate improved outbreak response to benefit the society.

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

PUBLICATIONS PRODUCED AS A RESULT OF THIS RESEARCH

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(Showing: 1 - 10 of 30)
Babay, A and Dinitz, M and Srinivasan, A and Tsepenekas, L and Vullikanti, A. "Controlling Epidemic Spread using Probabilistic Diffusion Models on Networks" Proc. International Conference on Artifical Intelligence and Statistics (AISTATS) , 2022 Citation Details
Baveja, Alok and Chavan, Amit and Nikiforov, Andrei and Srinivasan, Aravind and Xu, Pan "Improved Sample-Complexity Bounds in Stochastic Optimization" Operations research , 2023 https://doi.org/10.1287/opre.2018.0340 Citation Details
Brubach, B and Chakrabarti, D and Dickerson, J and Khuller, S and Srinivasan, A and Tsepenekas, L "A Pairwise Fair and Community-preserving Approach to k-Center Clustering" International Conference on Machine Learning (ICML) , 2020 https://doi.org/ Citation Details
Brubach, B and Chakrabarti, D and Dickerson, J and Srinivasan, A and Tsepenekas, L. "Fairness, Semi-Supervised Learning, and More:A General Framework for Clustering with Stochastic Pairwise Constraints" Proc. Thirty-Fifth AAAI Conference on Artificial Intelligence (AAAI) , 2021 https://doi.org/ Citation Details
Brubach, Brian and Grammel, Nathaniel and Harris, David G and Srinivasan, Aravind and Tsepenekas, Leonidas and Vullikanti, Anil "Stochastic Optimization and Learning for Two-Stage Supplier Problems" ACM Transactions on Probabilistic Machine Learning , v.1 , 2025 https://doi.org/10.1145/3604619 Citation Details
Brubach, Brian and Grammel, Nathaniel and Ma, Will and Srinivasan, Aravind "Online Matching Frameworks Under Stochastic Rewards, Product Ranking, and Unknown Patience" Operations Research , 2023 https://doi.org/10.1287/opre.2021.0371 Citation Details
Brumfield, Kyle D. and Chen, Arlene J. and Gangwar, Mayank and Usmani, Moiz and Hasan, Nur A. and Jutla, Antarpreet S. and Huq, Anwar and Colwell, Rita R. "Environmental Factors Influencing Occurrence of Vibrio parahaemolyticus and Vibrio vulnificus" Applied and Environmental Microbiology , v.89 , 2023 https://doi.org/10.1128/aem.00307-23 Citation Details
Brumfield, Kyle D. and Usmani, Moiz and Chen, Kristine M. and Gangwar, Mayank and Jutla, Antarpreet S. and Huq, Anwar and Colwell, Rita R. "Environmental parameters associated with incidence and transmission of pathogenic Vibrio spp ." Environmental Microbiology , v.23 , 2021 https://doi.org/10.1111/1462-2920.15716 Citation Details
Brumfield, Kyle D. and Usmani, Moiz and Santiago, Sanneri and Singh, Komalpreet and Gangwar, Mayank and Hasan, Nur A. and Netherland, Michael and Deliz, Katherine and Angelini, Christine and Beatty, Norman L. and Huq, Anwar and Jutla, Antarpreet S. and Co "Genomic diversity of Vibrio spp. and metagenomic analysis of pathogens in Florida Gulf coastal waters following Hurricane Ian" mBio , v.14 , 2023 https://doi.org/10.1128/mbio.01476-23 Citation Details
Chakrabarti, D and Dickerson, J. P. and Esmaeili, S. A. and Srinivasan, A. and Tsepenekas, L. "A New Notion of Individually Fair Clustering: alpha-Equitable k-Center" Proc. International Conference on Artifical Intelligence and Statistics (AISTATS) , 2022 Citation Details
Dickerson, John P. and Sankararaman, Karthik A. and Srinivasan, Aravind and Xu, Pan and Xu, Yifan "Matching Tasks and Workers under Known Arrival Distributions: Online Task Assignment with Two-sided Arrivals" ACM Transactions on Economics and Computation , 2024 https://doi.org/10.1145/3652021 Citation Details
(Showing: 1 - 10 of 30)

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