Grant List
Represents Grant table in the DB
GET /v1/grants?page%5Bnumber%5D=1384&sort=-principal_investigator
{ "links": { "first": "https://cic-apps.datascience.columbia.edu/v1/grants?page%5Bnumber%5D=1&sort=-principal_investigator", "last": "https://cic-apps.datascience.columbia.edu/v1/grants?page%5Bnumber%5D=1405&sort=-principal_investigator", "next": "https://cic-apps.datascience.columbia.edu/v1/grants?page%5Bnumber%5D=1385&sort=-principal_investigator", "prev": "https://cic-apps.datascience.columbia.edu/v1/grants?page%5Bnumber%5D=1383&sort=-principal_investigator" }, "data": [ { "type": "Grant", "id": "509", "attributes": { "award_id": "2008456", "title": "III: Small: Data-Driven Control of Epidemic Processes over Complex Dynamic Networks", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Computer and Information Science and Engineering (CISE)" ], "program_reference_codes": [], "program_officials": [ { "id": 1042, "first_name": "Amarda", "last_name": "Shehu", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2020-06-01", "end_date": "2023-05-31", "award_amount": 439893, "principal_investigator": { "id": 1043, "first_name": "VICTOR M", "last_name": "PRECIADO", "orcid": null, "emails": "[email protected]", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 232, "ror": "https://ror.org/00b30xv10", "name": "University of Pennsylvania", "address": "", "city": "", "state": "PA", "zip": "", "country": "United States", "approved": true } ] }, "other_investigators": [], "awardee_organization": { "id": 232, "ror": "https://ror.org/00b30xv10", "name": "University of Pennsylvania", "address": "", "city": "", "state": "PA", "zip": "", "country": "United States", "approved": true }, "abstract": "Despite notable advances in medicine over the last century, recent pandemics such as COVID-19 remind us that the threat of infectious diseases to human populations is very real. While continuing advances in medicine are essential, information technologies can greatly improve our ability to detect and contain the devastating effects of infectious diseases. In this direction, public health agencies collect, periodically update, and publicly report field data containing geolocated information about the tested, infected, recovered, hospitalized, and deceased individuals in those areas affected by the disease. However, this data is unreliable, incomplete, and coarse-grained; therefore, health agencies can greatly benefit from information technologies to filter and analyze field data in order to make reliable predictions about the future spread of the disease. Moreover, the final objective of a health agency is to use this information to design efficient strategies to contain the spread of infectious diseases. To achieve this objective, health agencies have at their disposal epidemic-control resources, such as social distancing, traffic restrictions, and the distribution of pharmaceutical resources (whenever available). Due to the heterogeneity and high cost of these resources, finding the cost-optimal allocation of each type of resource throughout the population is a very challenging problem of utmost societal impact. In this project, we propose to develop an integrated framework for modeling, prediction, and cost-optimal control of epidemic outbreaks using finite resources and unreliable data.In order to implement practical epidemic-control tools, it is necessary to first develop mathematical models able to replicate salient geo-temporal features of disease transmission. These patterns are strongly influenced by the geography of the area over which the disease is spreading, as well as the mobility patterns of the population. In this direction, we will use complex contact graphs to model both realistic geographical constraints and mobility patterns. In particular, the vertices of this graph correspond to towns/districts and its links represent interactions between them. On top of this contact graph, we will build a dynamical model aiming to replicate the complex geo-temporal spread of the disease. In this direction, we will consider a system of stochastic processes, coupled through the edges of the contact graph, to model the evolution of the disease. Once the model of the spread is tuned, we will then proceed to the design of a coordinated strategy to contain the spread of the infection by distributing resources throughout the population. In this direction, we will design and implement an optimization program to find the cost-optimal allocation of heterogeneous resources given a finite budget. In this research task, we must deal with the inherent uncertainty of field data, as well as the presence of sampling biases that can have a dramatic impact on the fairness of the cost-optimal allocation of resources. The success of the proposed research program would greatly improve our ability to efficiently detect and appropriately react to epidemic outbreaks, whereupon a rapid control response can be deployed.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.", "keywords": [], "approved": true } }, { "type": "Grant", "id": "508", "attributes": { "award_id": "2032481", "title": "Virtual Workshops for GEO REU Students and PIs During COVID-19", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Geosciences (GEO)" ], "program_reference_codes": [], "program_officials": [ { "id": 1040, "first_name": "Elizabeth", "last_name": "Rom", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2020-06-01", "end_date": "2021-05-31", "award_amount": 48449, "principal_investigator": { "id": 1041, "first_name": "Valerie F", "last_name": "Sloan", "orcid": null, "emails": "[email protected]", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 275, "ror": "", "name": "University Corporation For Atmospheric Res", "address": "", "city": "", "state": "CO", "zip": "", "country": "United States", "approved": true } ] }, "other_investigators": [], "awardee_organization": { "id": 275, "ror": "", "name": "University Corporation For Atmospheric Res", "address": "", "city": "", "state": "CO", "zip": "", "country": "United States", "approved": true }, "abstract": "In response to the COVID-19 national emergency, many Research Experiences for Undergraduates (REU) Site programs have cancelled their programs for the summer 2020 due to a lack of housing and travel restrictions. Some REU sites that are funded by the Division of Ocean Sciences have created virtual internships for students who will be able to access and analyze data remotely. The University Corporation for Atmospheric Research (UCAR) will organize an on-line professional development workshop that is designed to support about sixty undergraduates and faculty mentors who are participating in virtual REU internships during the summer 2020. This professional development workshop series will ensure that students who are participating in the virtual internships have the professional development opportunities that are normally part of an in-person REU program.This project will support a workshop series that will focus on two topics. 1. Student Professional Development: providing a series of weekly professional development seminars for students participating in the virtual REUs. These seminars will serve the dual purpose of developing a sense of cohort amongst students, and 2. Faculty Support and Materials: supporting faculty mentors in preparing to run a virtual REU, providing faculty with seminars on facilitating professional development topics, and providing some webinar recordings and materials for guided student activities. The project will also be a model for future virtual internship programs.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.", "keywords": [], "approved": true } }, { "type": "Grant", "id": "507", "attributes": { "award_id": "2031245", "title": "EAGER/Collaborative Research: Experimentally Validated Modeling of the Dynamics of Carbon Dioxide Removal from the Bloodstream via Peritoneal Perfluorocarbon Circulation", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Engineering (ENG)" ], "program_reference_codes": [], "program_officials": [ { "id": 1038, "first_name": "Harry", "last_name": "Dankowicz", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2020-06-01", "end_date": "2022-05-31", "award_amount": 150000, "principal_investigator": { "id": 1039, "first_name": "Joseph S", "last_name": "Friedberg", "orcid": null, "emails": "[email protected]", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 262, "ror": "", "name": "University of Maryland at Baltimore", "address": "", "city": "", "state": "MD", "zip": "", "country": "United States", "approved": true } ] }, "other_investigators": [], "awardee_organization": { "id": 262, "ror": "", "name": "University of Maryland at Baltimore", "address": "", "city": "", "state": "MD", "zip": "", "country": "United States", "approved": true }, "abstract": "This EArly-concept Grant for Exploratory Research (EAGER) project brings together a multidisciplinary team of control engineers, biomedical engineers, medical researchers, and clinicians to explore a novel pulmonary-independent method for supplementing gas exchange in an animal. Specifically, the research team will study whether the circulation of oxygenated perfluorocarbon (PFC) through the abdomen (the peritoneal cavity) of a large animal, can serve as a pathway for clearing carbon dioxide (CO2) from the animal’s bloodstream; and what are the governing dynamics of this CO2 clearing process. The peritoneal cavity essentially acts as a “third lung” in this scenario, providing critical life support for patients whose compromised lung function has exceeded the support achievable through mechanical ventilation. There is currently a critical need for this treatment, within the context of the COVID-19 pandemic, but this system also has potential to emerge as a standard modality in the critical care of hundreds of thousands of patients in pulmonary failure. Furthermore, the medical community will benefit from the deep fundamental understanding of the CO2 removal capabilities of peritoneal oxygenated PFC circulation, which will be an essential element in bringing this technology into future clinical trials.This project addresses the challenge of building an experimentally validated model of the dynamics of carbon dioxide transport from the bloodstream of a large animal into oxygenated perfluorocarbon perfused through the animal’s abdominal (peritoneal) cavity. Using the experimental data obtained as part of this project, the team will develop and parameterize a control-oriented, multi-compartment model of the transport dynamics governing CO2 removal. While previous experiments on peritoneal oxygenated PFC circulation have predominantly examined quasi-steady conditions, the research team will ensure the richness of its data by deliberately designing the underlying experiments to maximize the identifiability of the CO2 removal dynamics. The result will be a dataset better suited for the modeling and estimation of underlying system dynamics than the quasi-steady datasets. The system dynamics and control community will benefit from the opportunity to apply its scientific tools and methods to the dynamic modeling of a novel ventilation technology. Particularly important is the degree to which such modeling can help broaden the interdisciplinary impact of the dynamic systems and controls discipline to a new health-related application technology. Addressing this research challenge urgently, but rigorously, has the potential to provide critical assistance to the medical research community, particularly considering the COVID-19 crisis.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.", "keywords": [], "approved": true } }, { "type": "Grant", "id": "506", "attributes": { "award_id": "2030282", "title": "EAGER-Development of Antiviral Functionalized Carbon Nanotubes (CNTs) for Generating Virus-free Medical Grade Water and Preventing the Spread of COVID-19", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Engineering (ENG)" ], "program_reference_codes": [], "program_officials": [ { "id": 1036, "first_name": "Nora", "last_name": "Savage", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2020-06-01", "end_date": "2023-05-31", "award_amount": 138573, "principal_investigator": { "id": 1037, "first_name": "Somenath", "last_name": "Mitra", "orcid": null, "emails": "[email protected]", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 228, "ror": "https://ror.org/05e74xb87", "name": "New Jersey Institute of Technology", "address": "", "city": "", "state": "NJ", "zip": "", "country": "United States", "approved": true } ] }, "other_investigators": [], "awardee_organization": { "id": 228, "ror": "https://ror.org/05e74xb87", "name": "New Jersey Institute of Technology", "address": "", "city": "", "state": "NJ", "zip": "", "country": "United States", "approved": true }, "abstract": "At this point there is an urgent need to address many issues related to the Corona virus (COVID-19) outbreak including stopping the spread of the virus, and helping the overwhelmed healthcare industry cope with different problems. The goal of this project is to generate antiviral functionalized carbon nanotubes which will have several applications related to the COVID-19 pandemic. First, the functionalized nanotubes can be used as self-cleaning sorbents in personal protection equipment (PPE) such as medical masks. Many commercial paints/coatings formulations contain carbon nanotubes, and the anti-viral nanotubes will be effective materials for preventing the spread of COVID-19 via surface contacts. An immediate application is carbon nanotube-enhanced membrane distillation for inexpensive bacteria/endotoxin/virus-free medical-grade water generation. This water is used for cleaning medical equipment and as injectable water in patient treatment. Most importantly the membrane distillation with antiviral nanotubes will be developed into a point-of-care technology where a domestic water heater or a microwave oven can be used to generate medical grade water in field hospitals. The Objective of this project is to develop functionalized carbon nanotubes with high anti-viral activity for stopping the spread of Covid-19 and helping the overwhelmed healthcare industry. The work will be an extension of work already underway with bacteria and endotoxins. The two major proposed tasks are the development of specific carbon nanotube functional forms with antiviral activity, and the development of carbon nanotube enhanced microwave induced membrane distillation for generating bacteria/endotoxins/virus free medical grade water. In the first task, functionalized carbon nanotubes will be synthesized by the incorporation of different antiviral agents. In the second task these nanotubes will be used to synthesize biocidal membranes for membrane distillation. In the proposed process, as the hot contaminated water will pass over the antiviral nanotube membrane, it will be partially transformed to water vapor that will pass through as purified water while the hydrophobic membrane will prevent the aqueous phase from permeating through. In this project we also propose to use microwaves to heat the water because this has additional biocidal effects. The novel membranes developed by immobilizing the antiviral carbon nanotubes will not only serve as molecular transporters for pure water generation, but also have biocidal properties that will generate highly pure medical grade water. The developed approach will open the door to specific carbon nanotube functionalization to deal with different bacteria and virus. These functionalized nanotubes can be used in adsorbents and membranes to provide effective virus protection in air purifiers, in personal protection equipment (PPE) such as gas masks and for water treatment. Since many commercial paints/coatings formulations contain carbon nanotubes, the anti-viral nanotubes in surface coating will serve to prevent the spread of COVID-19 (or other viruses) that transmit via surface contacts. The educational goal of the project is the expansion of nanotechnology into disease prevention and medical infrastructure, which has not been emphasized in the past but has significant potential benefits.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.", "keywords": [], "approved": true } }, { "type": "Grant", "id": "505", "attributes": { "award_id": "2030859", "title": "Computing Innovation Fellows 2020 Project", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Computer and Information Science and Engineering (CISE)" ], "program_reference_codes": [], "program_officials": [ { "id": 1030, "first_name": "Mitra", "last_name": "Basu", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2020-05-15", "end_date": "2024-04-30", "award_amount": 15936600, "principal_investigator": { "id": 1035, "first_name": "Ellen W", "last_name": "Zegura", "orcid": null, "emails": "[email protected]", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 274, "ror": "https://ror.org/00agrkd75", "name": "Computing Research Association", "address": "", "city": "", "state": "DC", "zip": "", "country": "United States", "approved": true } ] }, "other_investigators": [ { "id": 1031, "first_name": "Mark D", "last_name": "Hill", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, { "id": 1032, "first_name": "Andrew", "last_name": "Bernat", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, { "id": 1033, "first_name": "Elizabeth", "last_name": "Bradley", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, { "id": 1034, "first_name": "Ann W", "last_name": "Schwartz", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "awardee_organization": { "id": 274, "ror": "https://ror.org/00agrkd75", "name": "Computing Research Association", "address": "", "city": "", "state": "DC", "zip": "", "country": "United States", "approved": true }, "abstract": "The current coronavirus (COVID-19) pandemic is disrupting aspects of daily life and work, including having a serious impact on the current faculty-recruiting season in the computing-research community. A Computing Research Association (CRA) survey completed on April 1st 2020 counted about 100 academic-computing job positions being pulled from the market due to hiring freezes, and universities continue to forecast major financial losses for the upcoming academic year, with likely negative impacts on the academic-computing job market for the next year as well. Thus what is needed is a bridge that keeps highly trained researchers in the academic pipeline to preserve future computing innovation and to meet the training needs of future computing professionals, as these will be the backbone of the future economy. CRA and its Computing Community Consortium (CCC) provided such a bridge for the severe economic downturn a decade ago, using NSF funding to administer three cohorts of Computing Innovation Fellows (CIFellows). That postdoctoral project kept 127 young scholars in research with career-enhancing programs. The current project, CIFellows 2020, is intended to provide similar support for the academic-computing pipeline in light of the damage it is sustaining in the wake of the current pandemic. The CIFellows 2020 project takes inspiration from the original CIFellows project but adapts it to the current uncertain situation, by incorporating more flexibility, allowing the option of doing a postdoc at the applicant’s current institution, and providing a significant mentoring/cohort-building component that is based on best practices that emerged from the original effort. Fellows may come from any research area under the umbrella of NSF Computing and Information Science and Engineering (CISE). Fellows will engage in a 1-2 year postdoctoral experience that furthers their career development in new ways. An application process will be implemented, and selection of successful applicants will be made using a technical program-committee style with strict adherence to conflicts of interest and based on a holistic evaluation of merit and diversity along many dimensions, with major emphasis on intellectual merit and broader impacts in applicant materials.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.", "keywords": [], "approved": true } }, { "type": "Grant", "id": "10147", "attributes": { "award_id": "2127309", "title": "Computing Innovation Fellows Project 2021", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Computer and Information Science and Engineering (CISE)", "CISE Education and Workforce" ], "program_reference_codes": [], "program_officials": [ { "id": 1030, "first_name": "Mitra", "last_name": "Basu", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2021-05-01", "end_date": "2025-04-30", "award_amount": 19998659, "principal_investigator": { "id": 1033, "first_name": "Elizabeth", "last_name": "Bradley", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [ { "id": 1034, "first_name": "Ann W", "last_name": "Schwartz", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, { "id": 1035, "first_name": "Ellen W", "last_name": "Zegura", "orcid": null, "emails": "[email protected]", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 274, "ror": "https://ror.org/00agrkd75", "name": "Computing Research Association", "address": "", "city": "", "state": "DC", "zip": "", "country": "United States", "approved": true } ] }, { "id": 26052, "first_name": "Randal E", "last_name": "Bryant", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, { "id": 26053, "first_name": "Kenneth L", "last_name": "Calvert", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "awardee_organization": { "id": 274, "ror": "https://ror.org/00agrkd75", "name": "Computing Research Association", "address": "", "city": "", "state": "DC", "zip": "", "country": "United States", "approved": true }, "abstract": "Due to the pandemic and the resulting uncertain circumstances, academic institutions are facing major hiring disruptions. This leaves the computing research community at risk of losing yet another class of young researchers who cannot afford to wait out the current disruption and thus may leave the research career path permanently. It is not a stretch to anticipate that the loss of this workforce in the research pipeline will have long-lasting downstream effects on computing innovation and impact. The Computing Innovation Fellows (CIFellows) Program addresses these issues by providing funding for two-year postdoctoral positions for recent PhD graduates in computer information science and engineering to provide a career-enhancing bridge experience that will give them the opportunity to remain in the academic research community and retain a cohort of early career professionals in areas under the umbrella of NSF CISE. \n\nWith funding by the National Science Foundation, the CIFellows 2021 program will offer two-year postdoctoral opportunities in computing, with cohort activities to support career development and community building. This program is open to researchers whose work falls under the National Science Foundation Computing and Information Science and Engineering Directorate and who have completed or plan to complete their PhD between 1/1/20 - 12/31/21. Applicants will work with a mentor from a US academic institution for their postdoc.\n\nThis 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.", "keywords": [], "approved": true } }, { "type": "Grant", "id": "504", "attributes": { "award_id": "2025693", "title": "Ecology of MERS-CoV in camels, humans, and wildlife in Ethiopia", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Biological Sciences (BIO)" ], "program_reference_codes": [], "program_officials": [ { "id": 1028, "first_name": "Katharina", "last_name": "Dittmar", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2019-08-25", "end_date": "2023-08-31", "award_amount": 2308740, "principal_investigator": { "id": 1029, "first_name": "Amira", "last_name": "Roess", "orcid": null, "emails": "[email protected]", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [], "awardee_organization": { "id": 239, "ror": "https://ror.org/02jqj7156", "name": "George Mason University", "address": "", "city": "", "state": "VA", "zip": "", "country": "United States", "approved": true }, "abstract": "Zoonotic diseases are diseases that exist in animals and can be transmitted to humans. Zoonoses currently account for approximately 75 percent of all emerging infections worldwide. However, the factors that trigger zoonotic disease emergence and spread remain poorly understood, in part because they involve multiple species, complex inter-species and intra-species relationships, and many interacting environmental, behavioral, social, and economic factors. Middle Eastern Respiratory Syndrome-Coronavirus (MERS-CoV) presents a case in point. MERS-CoV was first identified in human beings in 2012 and has killed 36 percent of those infected, but little is known about what has led to its emergence. For example, several human cases have been traced to contact with camels imported from Africa; studies also have detected MERS-CoV in African wildlife; and while camels are a known reservoir host for MERS-CoV, it is not known if there are additional reservoirs or intermediate hosts, or how frequently and under what conditions MERS-CoV spillover occurs. This project will investigate the natural ecology of MERS-CoV in relation to broader social, economic, and environmental changes; its potential wildlife reservoirs in close contact with camels; and the potential for spillover to humans who consume, herd, and trade camels and camel products. This work is of direct importance to national security with respect to disease threats; MERS-CoV is currently on the World Health Organization's priority shortlist of diseases in urgent need of accelerated research. To understand when and under what conditions MERS-CoV emerges and spreads from animals to humans, the researchers ask, (1) How do social, cultural and behavioral characteristics of camel economics shape virus ecologies? (2) Which intra- and inter-species interactions increase MERS-CoV emergence and transmission? and (3) What climatic and environmental variables are associated with transmission? Researchers will conduct field studies of wild and domestic (camel) reservoirs, collecting roughly 800 samples from targeted wildlife (ungulates and eulipotyphlads) over two years at four locations. They will carry out socio-behavioral studies across the camel value chain and follow individual camels longitudinally to determine when the same individuals seroconvert to MERS-CoV positive status. They will conduct laboratory analyses and do viral sequencing. The researchers will integrate the data into mathematical models using the 'Method of Plausible Parameter Sets' (MPPS), which will determine which mechanistic scenarios are consistent with observed patterns of MERS-CoV in camels and humans. This broadly inclusive approach expands upon traditional studies of zoonotic disease emergence. The transmission models will be applicable to other zoonotic diseases linked to livestock production and will help to identify interventions to reduce disease emergence and transmission.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.", "keywords": [], "approved": true } }, { "type": "Grant", "id": "503", "attributes": { "award_id": "2011069", "title": "US-UK Collaboration: Integrating ecology, epidemiology, and human interests to guide strategic management of zoonoses in complex wildlife reservoirs", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Biological Sciences (BIO)" ], "program_reference_codes": [], "program_officials": [ { "id": 1023, "first_name": "Samuel", "last_name": "Scheiner", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2020-09-01", "end_date": "2024-08-31", "award_amount": 1412940, "principal_investigator": { "id": 1027, "first_name": "Jorge E", "last_name": "Osorio", "orcid": null, "emails": "[email protected]", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 263, "ror": "", "name": "University of Wisconsin-Madison", "address": "", "city": "", "state": "WI", "zip": "", "country": "United States", "approved": true } ] }, "other_investigators": [ { "id": 1024, "first_name": "Tonie E", "last_name": "Rocke", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, { "id": 1025, "first_name": "Daniel P", "last_name": "Walsh", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, { "id": 1026, "first_name": "Robin E", "last_name": "Russell", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "awardee_organization": { "id": 263, "ror": "", "name": "University of Wisconsin-Madison", "address": "", "city": "", "state": "WI", "zip": "", "country": "United States", "approved": true }, "abstract": "Wild animals host a wide variety of pathogens that can spread to other animals and humans. Suchdiseases, including Ebola and COVID-19, significantly affect human health, agriculture and wildlifeconservation. Historically, disease control methods (e.g. vaccination, therapeutics) have focused onhumans or livestock rather than wild animal reservoirs. Focusing on disease control in wildlife couldbe more effective in preventing disease emergence in humans, but that approach is currently limitedby three factors. First, many diseases are maintained in cycles that spread across landscapes, butwildlife diseases are notoriously difficult to assess at these large spatial scales, making responses tointerventions unpredictable. Second, tools like vaccines have been difficult to administer to sufficientnumbers of animals to actually reduce disease transmission in the wild. Third, interventions areusually bounded by societal constraints, both financial (e.g., limited funds to invest) and sociological(e.g., conflicting stakeholder interests). New technologies, including vaccines that can spread amongwildlife and miniaturized animal-borne tracking systems, have unrealized potential to overcome theselimitations. This project will focus on reducing vampire bat transmitted rabies, which has significanthuman health and agricultural impacts across Latin America, but the methods developed for this studycould be applied to other important wildlife diseases. The project will strengthen researchcapacity through training of students and early career scientists in field, laboratory and quantitative methodologies.This project will conduct field and laboratory research to test specific hypotheses about theepidemiology and management of vampire bat-transmitted rabies. The researchers will: (1) Use fieldexperiments with animal-borne GPS tags and large-scale data on bat presence from questionnairesand historical rabies outbreaks to generate models that can be used to determine how humandisturbance influences bat abundance and dispersal; (2) Conduct studies using captive and wildvampire bats to determine host and ecological factors that will influence the use of self-spreadingrabies vaccines that target bats; and (3) Use parameters estimated from fieldwork and captive studiesto optimize strategies for localized control and regional elimination of vampire bat rabies that preservediverse stakeholder requirements, e.g. wildlife conservation goals as well as improved human andlivestock health.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.", "keywords": [], "approved": true } }, { "type": "Grant", "id": "502", "attributes": { "award_id": "2029739", "title": "RAPID/Collaborative Research: Quantifying Social Media Data for Improved Modeling of Mitigation Strategies for the COVID-19 Pandemic", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Engineering (ENG)" ], "program_reference_codes": [], "program_officials": [ { "id": 1020, "first_name": "Eva", "last_name": "Kanso", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2020-05-15", "end_date": "2022-04-30", "award_amount": 141527, "principal_investigator": { "id": 1022, "first_name": "Konstantinos", "last_name": "Mykoniatis", "orcid": "https://orcid.org/0000-0001-8875-3178", "emails": "[email protected]", "private_emails": "", "keywords": null, "approved": true, "websites": "['http://covidatanalyze.auburn.edu']", "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 273, "ror": "https://ror.org/02v80fc35", "name": "Auburn University", "address": "", "city": "", "state": "AL", "zip": "", "country": "United States", "approved": true } ] }, "other_investigators": [ { "id": 1021, "first_name": "Alice E", "last_name": "Smith", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "awardee_organization": { "id": 273, "ror": "https://ror.org/02v80fc35", "name": "Auburn University", "address": "", "city": "", "state": "AL", "zip": "", "country": "United States", "approved": true }, "abstract": "This Rapid Response Research (RAPID) grant will support research that will contribute new knowledge related to modeling social behavior and community activity during the COVID-19 pandemic, as well as future pandemics with COVID-19 characteristics. The model focuses on compliance with mitigation strategies and public health guidelines, thus enabling the selection of policies that are most effective in promoting both the progress of science and advancing national health and prosperity. Various pandemic models are currently being used to predict the spread of a virus and establish which mitigation strategies are the most effective. These models are heavily based on assumptions and may include an oversimplified reality of how populations react and behave. This research will provide needed knowledge and methods for the development of a model of how individuals in the U.S. react to certain mitigation strategies, such as social-distancing, stay-at-home orders, quarantines, and travel advisories, by mining and analyzing social media data during the COVID-19 crisis. This enhanced modeling approach and its resultant model will be of great value to disaster response managers and policy/decision makers to understand human social behavior. This work allows assessment of the effectiveness of mitigation strategies and public health guidelines during pandemics (and other crises). This project will also form the basis of a publicly available case study suitable for university level students that can be widely incorporated in courses. Although individual-based and homogeneous mixing pandemic models provide useful insights and predictive capabilities within a range of possibilities, they are highly sensitive to people’s actions. This research aims to provide an enhanced approach to model social behavior and community activity during a pandemic in terms of compliance with mitigation strategies and public health guidelines. Social media data present a brief window of opportunity for research on how, and to what extent, the public does or does not comply with the recommended mitigation strategies and public health guidelines. The research team will collect real-time data from social media related to COVID19-exposed regional populations in the U.S. The data will be analyzed using machine learning techniques to identify non-mutually exclusive clusters of people based on similarity of their demographic, geographic, and time information, and establish relationships among clusters. The analyzed data will form the basis of a data-driven multi-paradigm simulation model that captures changes in public sentiment over time, quantifies the resistance/compliance with mitigation strategies and health guidelines, and gauges overall effectiveness of various mitigation strategies and advice over time.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.", "keywords": [], "approved": true } }, { "type": "Grant", "id": "501", "attributes": { "award_id": "2027399", "title": "RAPID/Collaborative Research: Agency COVID-19 Risk Communication on Social Media: Characterizing Drivers of Message Retransmission and Engagement", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Unknown" ], "program_reference_codes": [], "program_officials": [], "start_date": "2020-05-01", "end_date": "2020-08-31", "award_amount": 0, "principal_investigator": { "id": 1019, "first_name": "Jeannette", "last_name": "Sutton", "orcid": null, "emails": "[email protected]", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 178, "ror": "", "name": "University of Kentucky Research Foundation", "address": "", "city": "", "state": "KY", "zip": "", "country": "United States", "approved": true } ] }, "other_investigators": [], "awardee_organization": { "id": 178, "ror": "", "name": "University of Kentucky Research Foundation", "address": "", "city": "", "state": "KY", "zip": "", "country": "United States", "approved": true }, "abstract": "Public health and emergency management agencies are on the front lines of informing and educating the public about the science of virus transmission and prevention. In response to a threat such as COVID-19, their mission requires the communication of accurate and credible information to local populations using a variety of media channels. Increasingly, social media is a critical component of their communication toolbox - but using it to rapidly and effectively inform the public in a crowded media environment remains a significant challenge. In prior work on online communication associated with the Zika and Ebola outbreaks, the PIs established that effective messaging depended upon employing a combination of content, style, and structure features - but that the right mix seemed to depend upon properties of the disease event (including the uncertainty and ambiguity of the threat, the nature of the consequences involved, and the need for public information). COVID-19 poses a distinct risk profile, with a disruption potential to the American public and the built environment not seen by any threat within decades. This Rapid Response Research (RAPID) project will identify the key drivers of effective messaging in an emerging pandemic, and strategies for improving effectiveness in social media communication involving COVID-19 by public agencies. The specific focus will be on the outcomes of message retransmission (essential for both high levels of message penetration and ensuring multiple exposures critical for behavioral influence) and engagement (a critical indicator of attention and a driver of trust), both of which are measurable and established as core outcomes in prior studies of effective social media communication. By establishing evidence-based guidance for agencies to effectively warn, inform, and engage the general public during an emerging pandemic, this project will provide critical guidance needed to mount effective interventions that save lives, reduce economic losses, and protect the security of the nation against health threats, in alignment with the broader mission of the NSF.This objective will be pursued through the following core activities: (1) collection of perishable social media data on COVID-19 messaging by public agencies, and public engagement with/retransmission of those messages; (2) content coding of COVID-19 messages, to typologize information that is specific to the present event; (3) characterization of messaging strategies used by public agencies in the evolving COVID-19 response; (4) predictive analysis of message outcomes based on message context, content, style, and structure; and (5) development of evidence-based guidance for effective social media messaging by public agencies in response to this and similar events. This research strategy builds on successful prior work in response to emergent infectious disease threats and in the context of anthropogenic and natural hazard events. The intellectual merit of the research includes: Risk communication messages on social media are real time traces of online in/formal communication shared under conditions of imminent and ongoing threat; Research on communication and messaging dynamics online provides insights into the social amplification of risk, via diffusion of information; and strategies to design effective messages. This project will test the risk communication on social media model in response to a global pandemic by analyzing official communication from state, local, and national public health and emergency management Twitter accounts. The findings from this work will lead to the further development and refinement of the social amplification of risk framework and the risk communication on social media model. The broader impacts of this work most prominently include the accumulation of an evidence base for social media messaging, as noted above. This research will have immediate benefits to organizations and agencies tasked with communicating to at risk populations about emergent infectious disease in the context of the built environment. Our findings will inform the design and dissemination of risk communication messages and will be immediately applicable to public health and safety organizations in the context of COVID-19. Results will be shared via fact sheets, webinars, published papers, and presentations with academic and practitioner audiences.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.", "keywords": [], "approved": true } } ], "meta": { "pagination": { "page": 1384, "pages": 1405, "count": 14046 } } }