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)
(Showing: 1 - 30 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
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
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
Dinitz, M. and Srinivasan, A. and Tsepenekas, L. and Vullikanti, A.
"Fair Disaster Containment via Graph-Cut Problems"
Proc. International Conference on Artifical Intelligence and Statistics (AISTATS)
, 2022
Citation
Details
Duppala, Sharmila and Grammel, Nathaniel and Luque, Juan and MacRury, Calum and Srinivasan, Aravind
"Proportionally Fair Matching via Randomized Rounding"
, 2025
Citation
Details
Duppala, Sharmila and Li, George and Luque, Juan and Srinivasan, Aravind and Valieva, Renata
"Concentration of Submodular Functions and Read-k Families Under Negative Dependence"
, 2025
Citation
Details
Esmaeili, S. and Duppala, S. and Cheng, D. and Nanda, V. and Srinivasan, A. and Dickerson, J.
"Rawlsian Fairness in Online Bipartite Matching: Two-sided, Group, and Individual"
Proc. Thirty-Seventh AAAI Conference on Artificial Intelligence (AAAI)
, 2023
Citation
Details
Esmaeili, Seyed A. and Brubach, Brian and Srinivasan, Aravind and Dickerson, John P.
"Fair Clustering Under a Bounded Cost"
Proc. Conference on Neural Information Processing Systems (NeurIPS)
, 2021
Citation
Details
Feehan, A.K. and Rose, R. and Nolan, D.J. and Spitz, A.M. and Graubics, K. and Colwell, R.R. and Garcia-Diaz, J. and Lamers, S.L.
"Nasopharyngeal Microbiome Community Composition and Structure Is Associated with Severity of COVID-19 Disease and Breathing Treatment"
Applied microbiology
, v.1
, 2021
https://doi.org/10.3390/applmicrobiol1020014
Citation
Details
Gowda, K. and Pensyl, T. and Srinivasan, A. and Trinh, K.
"Improved Bi-Point Rounding Algorithms and a Golden Barrier for k-median"
Proc. ACM-SIAM Symposium on Discrete Algorithms (SODA)
, 2023
https://doi.org/10.1137/1.9781611977554.ch38
Citation
Details
Jayakumar, Jane M and Martinez-Urtaza, Jaime and Brumfield, Kyle D and Jutla, Antarpreet S and Colwell, Rita R and Cordero, Otto X and Almagro-Moreno, Salvador
"Climate change and Vibrio vulnificus dynamics: A blueprint for infectious diseases"
PLOS Pathogens
, v.20
, 2024
https://doi.org/10.1371/journal.ppat.1012767
Citation
Details
Jutla, Antarpreet and Usmani, Moiz and Brumfield, Kyle D. and Singh, Komalpreet and McBean, Fergus and Potter, Amy and Gutierrez, Angelica and Gama, Samuel and Huq, Anwar and Colwell, Rita R.
"Anticipatory decision-making for cholera in Malawi"
mBio
, v.14
, 2023
https://doi.org/10.1128/mbio.00529-23
Citation
Details
Leonard, M and Valitutti, F and Karathia, H and Pujolassos, M and Kenyon, V and Fanelli, B and Troisi, J and Subramanian, P and Camhi, S and Colucci, A and Serena, G and Cucchiara, S and Trovato, C. M and Malamisura, B and Francavilla, R and Elli, L and H
"Microbiome signatures of progression toward celiac disease onset in at-risk children in a longitudinal prospective cohort study"
Proceedings of the National Academy of Sciences of the United States of America
, v.118
, 2021
https://doi.org/10.1073/pnas.2020322118
Citation
Details
Li, G. and Haddadan, A. and Li, A. and Marathe, M. and Srinivasan, A. and Vullikanti, A. and Zhao, Z.
"Theoretical Models and Preliminary Results for Contact Tracing and Isolation"
Proc. International Conference on Autonomous Agents and Multiagent Systems (AAMAS)
, 2022
Citation
Details
Li, G. and Li, A. and Marathe, M. and Srinivasan, A. and Tsepenekas, L. and Vullikanti, A.
"Deploying Vaccine Distribution Sites for Improved Accessibility and Equity to Support Pandemic Response"
Proc. International Conference on Autonomous Agents and Multiagent Systems (AAMAS)
, 2022
Citation
Details
Li, George and Li, Ann and Marathe, Madhav and Srinivasan, Aravind and Tsepenekas, Leonidas and Vullikanti, Anil
"Deploying vaccine distribution sites for improved accessibility and equity to support pandemic response"
Autonomous Agents and Multi-Agent Systems
, 2023
https://doi.org/10.1007/s10458-023-09614-9
Citation
Details
Monir, Md Mamun and Islam, Mohammad Tarequl and Mazumder, Razib and Mondal, Dinesh and Nahar, Kazi Sumaita and Sultana, Marzia and Morita, Masatomo and Ohnishi, Makoto and Huq, Anwar and Watanabe, Haruo and Qadri, Firdausi and Rahman, Mustafizur and Thoms
"Genomic attributes of Vibrio cholerae O1 responsible for 2022 massive cholera outbreak in Bangladesh"
Nature Communications
, v.14
, 2023
https://doi.org/10.1038/s41467-023-36687-7
Citation
Details
Morgado, Michele E and Brumfield, Kyle D and Chattopadhyay, Suhana and Malayil, Leena and Alawode, Taiwo and Amokeodo, Ibiyinka and He, Xin and Huq, Anwar and Colwell, Rita R and Sapkota, Amy R
"Antibiotic resistance trends among Vibrio vulnificus and Vibrio parahaemolyticus isolated from the Chesapeake Bay, Maryland: a longitudinal study"
Applied and Environmental Microbiology
, v.90
, 2024
https://doi.org/10.1128/aem.00539-24
Citation
Details
Morgado, Michele E and Brumfield, Kyle D and Mitchell, Clifford and Boyle, Michelle M and Colwell, Rita R and Sapkota, Amy R
"Increased incidence of vibriosis in Maryland, U.S.A., 20062019"
Environmental Research
, v.244
, 2024
https://doi.org/10.1016/j.envres.2023.117940
Citation
Details
Naor, Joseph and Srinivasan, Aravind and Wajc, David
"Online Dependent Rounding Schemes for Bipartite Matchings, with Applications"
, 2025
Citation
Details
Usmani, M and Brumfield, K and Jamal, Y and Huq, A and Colwell, R and Jutla, A
"A review of the environmental trigger and transmission components for prediction of cholera"
Tropical medicine and infectious disease
, v.6
, 2021
Citation
Details
Usmani, Moiz and Brumfield, Kyle D and Magers, Bailey and Zhou, Aijia and Oh, Chamteut and Mao, Yuqing and Brown, William and Schmidt, Arthur and Wu, Chang-Yu and Shisler, Joanna L and Nguyen, Thanh H and Huq, Anwar and Colwell, Rita and Jutla, Antarpreet
"Building Environmental and Sociological Predictive Intelligence to Understand the Seasonal Threat of SARS-CoV-2 in Human Populations"
The American Journal of Tropical Medicine and Hygiene
, v.110
, 2024
https://doi.org/10.4269/ajtmh.23-0077
Citation
Details
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(Showing: 1 - 30 of 30)
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