Grant List
Represents Grant table in the DB
GET /v1/grants?page%5Bnumber%5D=1391&sort=-keywords
{ "links": { "first": "https://cic-apps.datascience.columbia.edu/v1/grants?page%5Bnumber%5D=1&sort=-keywords", "last": "https://cic-apps.datascience.columbia.edu/v1/grants?page%5Bnumber%5D=1419&sort=-keywords", "next": "https://cic-apps.datascience.columbia.edu/v1/grants?page%5Bnumber%5D=1392&sort=-keywords", "prev": "https://cic-apps.datascience.columbia.edu/v1/grants?page%5Bnumber%5D=1390&sort=-keywords" }, "data": [ { "type": "Grant", "id": "9902", "attributes": { "award_id": "5T03OH008607-18", "title": "Yale Occupational and Environmental Medicine Residency Training Program", "funder": { "id": 4, "ror": "https://ror.org/01cwqze88", "name": "National Institutes of Health", "approved": true }, "funder_divisions": [], "program_reference_codes": [], "program_officials": [ { "id": 24278, "first_name": "ELIZABETH", "last_name": "MAPLES", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2021-07-01", "end_date": "2026-06-30", "award_amount": 237365, "principal_investigator": { "id": 24279, "first_name": "CARRIE A", "last_name": "REDLICH", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 452, "ror": "https://ror.org/03v76x132", "name": "Yale University", "address": "", "city": "", "state": "CT", "zip": "", "country": "United States", "approved": true } ] }, "other_investigators": [], "awardee_organization": { "id": 452, "ror": "https://ror.org/03v76x132", "name": "Yale University", "address": "", "city": "", "state": "CT", "zip": "", "country": "United States", "approved": true }, "abstract": "The Yale Occupational and Environmental Medicine (OEM) residency training program, based in the Department of Medicine at the Yale School of Medicine, is one of the oldest, most productive, stable and highly regarded occupational medicine residency training programs in the United States, training 66 OEM physicians over the past almost thirty-five years. There is an urgent need for OEM and public health / preventive medicine (PM) trained physicians in multiple settings across the U.S., further highlighted by the COVID-19 pandemic. The Yale OEM residency is an integrated two-year NIOSH-funded PM / OEM residency training program that is fully accredited by the Accreditation Council for Graduate Medical Education (ACGME), and leads to board eligibility for certification in PM and OEM by the American Board of Preventive Medicine as well as a Master of Public Health (MPH) Degree from the Yale School of Public Health. The program is currently approved for a total of four positions, two per year. Trainees admitted to the program are expected to have completed an initial ACGME accredited residency in an appropriate clinical specialty, typically Internal Medicine or Family Medicine. This NIOSH TPG 5-year renewal proposal describes the Yale OEM residency program. Highlighted are: 1) our success to date in training physicians for OEM careers, 2) our current OEM training program, which is continually evolving to meet changing needs, and 3) our plans for training future OEM physician leaders able to address the substantial challenges U.S. workers and employers will face in the future. The goal of Yale OEM Residency training program is to train physicians to be proficient in all aspects of the practice of occupational and environmental medicine, based on a sound fundamental knowledge of epidemiology, industrial hygiene, biostatistics, toxicology, human and organization behavior, clinical medicine, and evidence-based critical analysis. We train future physician leaders to serve in a range of OEM academic, clinical and public health roles, with a particular focus on developing skills as educators and scientific investigators, in preparation for successful academic careers.", "keywords": [], "approved": true } }, { "type": "Grant", "id": "9903", "attributes": { "award_id": "5R21OH012247-02", "title": "Implementing a Lifestyle Medicine Program via Telehealth to Optimize GERD Management in WTC First Responders", "funder": { "id": 4, "ror": "https://ror.org/01cwqze88", "name": "National Institutes of Health", "approved": true }, "funder_divisions": [], "program_reference_codes": [], "program_officials": [ { "id": 24208, "first_name": "JAMES", "last_name": "YIIN", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2021-07-01", "end_date": "2023-06-30", "award_amount": 249589, "principal_investigator": { "id": 24290, "first_name": "JOHN D", "last_name": "MEYER", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 625, "ror": "https://ror.org/04a9tmd77", "name": "Icahn School of Medicine at Mount Sinai", "address": "", "city": "", "state": "NY", "zip": "", "country": "United States", "approved": true } ] }, "other_investigators": [ { "id": 24291, "first_name": "Emily", "last_name": "Senay", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "awardee_organization": { "id": 625, "ror": "https://ror.org/04a9tmd77", "name": "Icahn School of Medicine at Mount Sinai", "address": "", "city": "", "state": "NY", "zip": "", "country": "United States", "approved": true }, "abstract": "Project Summary/Abstract: Gastroesophageal reflux disease (GERD) is the 2nd most common World Trade Center (WTC) certified chronic condition, with pathogenesis connected to WTC dust and fumes exposure and irritation of the esophageal lining leading to prolonged inflammation. Due to the clinical prevalence of GERD, WTC conditions associated with GERD, risk of medication side effects and costs, it is critical to consider Lifestyle Medicine (LM) to optimize management of GERD in certified patients. In addition, WTC first responders have increasingly voiced concerns about possible side-effects related to chronic use of some GERD medications. GERD, similar to the vast majority of chronic conditions, can be managed by adopting lifestyle measures that maximize nutrition, physical activity, sleep, and other healthy habits. Evidence demonstrates that clinicians can be powerful motivators to help patients make behavioral changes that can prevent, treat and even reverse chronic conditions. LM formalizes these efforts by giving clinicians tools that emphasize their role as “health coaches” to identify and support patient readiness and confidence in making lifestyle changes so that they can successfully adopt long-lasting healthy habits. LM techniques, delivered by trained clinicians, have been shown to help patients make significant and long-lasting behavioral change that improves clinical outcomes. As a result of the COVID-19 pandemic, telehealth was successfully integrated into the Mount Sinai WTC Health Program Clinical Center of Excellence (WTCHP CCE) and has become an essential and popular way for patients to receive care. Integrating a LM Program delivered via existing telehealth services would increase opportunity for patient encounters—a crucial factor in facilitating behavior changes that can improve GERD. Educating patients to successfully adhere to LM first-line therapies requires skills and time-opportunity not available to most WTC providers. The objective of this R21 is to evaluate the effectiveness and feasibility of a LM program in responders with certified GERD, to reduce symptoms and/or need for medications. This project will be novel in that: 1) it is based on the evidence-based LM intervention as advised by the American College of Lifestyle Medicine; including health habit assessments, goal identification, readiness/confidence to change, and agreeing to action plans with monthly follow-up for 6 months 2) will be delivered by LM-trained clinicians 3) and will occur via a virtual care platform that incorporates health tracking technology, and the ability to engage and support patients over an extended period of time. Program evaluation will consist of assessment of participant enrollment and retention, feasibility and participant satisfaction with intervention, and effectiveness in lowering GERD symptoms, medication use and helping participants reach their health goals.", "keywords": [], "approved": true } }, { "type": "Grant", "id": "9907", "attributes": { "award_id": "5R21OH012253-02", "title": "An Innovative Approach to Improving Asthma Control for World Trade Center Rescue and Recovery Workers through Telehealth Enriched Asthma Management (WTC-TEAM)", "funder": { "id": 4, "ror": "https://ror.org/01cwqze88", "name": "National Institutes of Health", "approved": true }, "funder_divisions": [], "program_reference_codes": [], "program_officials": [ { "id": 24208, "first_name": "JAMES", "last_name": "YIIN", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2021-07-01", "end_date": "2023-06-30", "award_amount": 257074, "principal_investigator": { "id": 24299, "first_name": "Erin", "last_name": "Thanik", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 625, "ror": "https://ror.org/04a9tmd77", "name": "Icahn School of Medicine at Mount Sinai", "address": "", "city": "", "state": "NY", "zip": "", "country": "United States", "approved": true } ] }, "other_investigators": [], "awardee_organization": { "id": 625, "ror": "https://ror.org/04a9tmd77", "name": "Icahn School of Medicine at Mount Sinai", "address": "", "city": "", "state": "NY", "zip": "", "country": "United States", "approved": true }, "abstract": "Asthma is prevalent among World Trade Center (WTC) rescue and recovery workers (responders). Moreover, this population exhibits poorly controlled disease with high health care utilization costs, particularly among responders affected by mental health conditions. Evidence from the general population supports that asthma self-management programs can improve asthma outcomes, including asthma control and health care utilization. However, asthma self- management is not currently part of the WTC Health Program (WTCHP). That asthma morbidity and mortality are higher in older adults compared to younger adults is particularly salient for the aging WTC cohort, underscoring the potential for asthma self- management programs to become vital to meeting the unique needs of this population. To address this need, we developed an innovative approach to optimizing asthma care: the Telehealth Enriched Asthma Management (TEAM) program. This intervention will utilize the existing clinical infrastructure of the WTCHP, including partnering with the WTC Mental Health Program, to provide asthma self-management to enhance current asthma care. The TEAM program will also leverage the recently augmented telehealth capabilities in the WTCHP (implemented in response to the SARS-CoV-2 pandemic) by delivering a series of virtual visits to provide asthma self-management education. Within this robust telehealth and clinical infrastructure, TEAM will provide care coordinated with other components of the WTCHP— Treatment Program, Mental Health Program, smoking cessation services, and social work— via a multifactorial approach that will promote uptake and adherence. The specific aims of the TEAM program are to: 1) develop an intervention that will expand telehealth capabilities to improve asthma care; 2) collect and analyze a variety of subjective and objective asthma outcome measures to evaluate the effectiveness of the TEAM intervention; 3) evaluate current barriers to telehealth platform utililzation as well as satisfaction with virtual encounters to understand how to improve WTCHP telehealth delivery. Our proposal is evidence-based and responds to the specific needs of those most significantly impacted by the WTC disaster. We anticipate that this program will improve asthma care for this vulnerable population, while optimizing telehealth capabilities that can be implemented for other health conditions affecting WTC responders.", "keywords": [], "approved": true } }, { "type": "Grant", "id": "9917", "attributes": { "award_id": "3OT2HD108097-01S1", "title": "SCALE-UP Counts: A health information technology approach to increasing COVID-19 testing in elementary and middle schools serving disadvantaged communities", "funder": { "id": 4, "ror": "https://ror.org/01cwqze88", "name": "National Institutes of Health", "approved": true }, "funder_divisions": [ "NIH Office of the Director" ], "program_reference_codes": [], "program_officials": [ { "id": 10382, "first_name": "Sonia S", "last_name": "Lee", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2021-07-21", "end_date": "2023-07-20", "award_amount": 1374508, "principal_investigator": { "id": 21047, "first_name": "David W", "last_name": "Wetter", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 202, "ror": "https://ror.org/03r0ha626", "name": "University of Utah", "address": "", "city": "", "state": "UT", "zip": "", "country": "United States", "approved": true } ] }, "other_investigators": [ { "id": 21048, "first_name": "Yelena Ping", "last_name": "Wu", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "awardee_organization": { "id": 202, "ror": "https://ror.org/03r0ha626", "name": "University of Utah", "address": "", "city": "", "state": "UT", "zip": "", "country": "United States", "approved": true }, "abstract": null, "keywords": [], "approved": true } }, { "type": "Grant", "id": "9920", "attributes": { "award_id": "5U60OH009855-12", "title": "Minnesota Occupational Health and Safety Surveillance Program", "funder": { "id": 4, "ror": "https://ror.org/01cwqze88", "name": "National Institutes of Health", "approved": true }, "funder_divisions": [], "program_reference_codes": [], "program_officials": [ { "id": 22791, "first_name": "Linda", "last_name": "West", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2021-07-01", "end_date": "2023-06-30", "award_amount": 160000, "principal_investigator": { "id": 24321, "first_name": "Erik", "last_name": "Zabel", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 1722, "ror": "", "name": "MINNESOTA STATE DEPT OF HEALTH", "address": "", "city": "", "state": "MN", "zip": "", "country": "United States", "approved": true } ] }, "other_investigators": [], "awardee_organization": { "id": 1722, "ror": "", "name": "MINNESOTA STATE DEPT OF HEALTH", "address": "", "city": "", "state": "MN", "zip": "", "country": "United States", "approved": true }, "abstract": "A strategic surveillance goal of the National Institute for Occupational Safety and Health is to strengthen the capacity of state health departments to conduct occupational surveillance and related prevention activities. The purpose of this project is to enhance the capacity of the Minnesota Department of Health to promote occupational health and safety through surveillance of a core set of occupational health indicators and dissemination of the findings to appropriate stakeholders for use in setting priorities for education and prevention activities. The project will expand the use of industry and occupation reporting in established datasets and will explore new sources of data in order to include previously underserved worker populations in surveillance of occupational health and safety. The major aims of this program include: (1) Collect, analyze, disseminate and utilize Minnesota data for 24 occupational health indicators (OHIs) based on criteria established by the Council of State and Territorial Epidemiologists (CSTE); (2) Collaborate with the MDH Lead and Healthy Homes Program to participate in the Adult Blood Lead Epidemiology and Surveillance (ABLES) program; (3) Advance the inclusion and use of industry and occupation information in health informatics; (4) Disseminate surveillance findings to appropriate audiences; (5) Build and maintain collaborations and partnerships to improve state surveillance activities; (6) Develop and use new data sources to address underserved worker populations, including agricultural workers, gig workers, and salon workers; (7) Evaluate the Minnesota Occupational Health and Safety Surveillance Program using CDC guidelines to continuously improve the program; (8) Assess COVID-19 outcomes and experience by industry and occupation sector by comparing risk of infection, comparing case outcomes, and studying the impact of safety measures and business closures; and (9) Measure the impact of statewide agricultural safety policies and rural mental health interventions by developing consistently trackable indicators of farm injuries and farm community mental health status.", "keywords": [], "approved": true } }, { "type": "Grant", "id": "9941", "attributes": { "award_id": "2200169", "title": "PIPP Phase I: Center for Pandemic Decision Science - Developing Robust Paradigms for Anticipating and Mitigating Uncertain Pathogen Threats", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Computer and Information Science and Engineering (CISE)", "PIPP-Pandemic Prevention" ], "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": "2022-09-01", "end_date": "2024-02-29", "award_amount": 1000000, "principal_investigator": { "id": 25697, "first_name": "Lauren", "last_name": "Meyers", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [ { "id": 23390, "first_name": "Holly A", "last_name": "Wichman", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 984, "ror": "https://ror.org/03hbp5t65", "name": "University of Idaho", "address": "", "city": "", "state": "ID", "zip": "", "country": "United States", "approved": true } ] }, { "id": 25694, "first_name": "David P", "last_name": "Morton", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, { "id": 25696, "first_name": "Mark E", "last_name": "Escott", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, { "id": 25698, "first_name": "Akihiro", "last_name": "Nishi", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "awardee_organization": { "id": 156, "ror": "", "name": "University of Texas at Austin", "address": "", "city": "", "state": "TX", "zip": "", "country": "United States", "approved": true }, "abstract": "Despite decades of pandemic preparedness efforts, COVID-19 took the world by surprise. The national and global health community did not foresee the extend of challenges associated with charting ecosystems of potential threats, elucidating interdependent behavioral and political dynamics, and equipping decision makers with nimble science, strategies, and training. This project imagines a better prepared future for responding to pathogen threats and aims to build the basis for a Center for Pandemic Decision Science that will break down the persistent silos separating the academic, government, and industry institutions that have collectively, but not always collaboratively, guided pandemic preparedness and response efforts. Over the next 18 months, a team of 35 natural scientists, social scientists, computer scientists, engineers, physicians, and public health officials from 10 institutions will host a series of interdisciplinary workshops and undertake pilot studies that will lay the intellectual and organizational groundwork for tackling three fundamental research questions - How can we anticipate the vast universe of potential pathogen threats and detect them at their source? How will people, communities, and leaders behave and respond to emerging threats? How can we integrate science into decision making across the preparedness, containment, and response spectrum? For each of these questions, the team will identify immediate and long-term goals for basic research, training of scientists and decision makers, and development of predictive intelligence capabilities. These activities will establish a new research paradigm that is grounded in complex systems modeling, integrate perspectives and methods across diverse disciplines, and engage extensively with decision makers to ensure that the science is both relevant and practical. The project will broadly engage the research and public health communities through workshops and colloquia, train a diverse group of students, develop an undergraduate teaching module in pandemic decision science, and disseminate resulting insights and products through online platforms, media, and peer-reviewed publications. \n\nThroughout the COVID-19 pandemic, this interdisciplinary team of scientists, engineers, social scientists, and clinicians has been developing mathematical models to provide situational awareness, actionable forecasts, and time-sensitive policy analyses for decision makers on all scales, from local to global. The team has partnered closely with government agencies, healthcare systems, and schools to provide predictive intelligence as the virus, human behavioral responses, and the arsenal of effective countermeasures continually shifted. This work has elucidated three interlinked grand challenges. The first is the global failure of imagination in anticipating novel pathogen threats, despite decades of concerted preparedness efforts. The second is the fundamental inability to anticipate individual, collective, and governmental behavioral responses during the threats. The third is the persistent gap between science and the decisions made by individuals, agencies, and policymakers. This project will launch a Center for Pandemic Decision Science that tackles these grand challenges by advancing the integration of complex systems science into pandemic decision making. As a first step, the Center will conduct a series of inclusive, multidisciplinary workshops and pilot studies that will spur innovative interdisciplinary research into the emergence and detection of novel threats, the dynamics of people’s behavior, and the design and adoption of adaptive decision paradigms for preventing, tracking and mitigating pathogen threats under uncertainty. These activities will hone the Center’s vision, identify key research priorities, and embark on a diverse portfolio of educational and community building activities to advance the science and practice of pathogen preparedness. \n\nThis award is supported by the cross-directorate Predictive Intelligence for Pandemic Prevention Phase I (PIPP) program, which is jointly funded by the Directorates for Biological Sciences (BIO), Computer Information Science and Engineering (CISE), Social, Behavioral and Economic Sciences (SBE) and Engineering (ENG). This project was also funded in collaboration with the CDC to support research projects to further advance federal infectious disease modeling, prevention and response capabilities.\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": "9942", "attributes": { "award_id": "2200274", "title": "PIPP Phase I: An End-to-End Pandemic Early Warning System by Harnessing Open-source Intelligence", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Computer and Information Science and Engineering (CISE)", "PIPP-Pandemic Prevention" ], "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": "2022-09-01", "end_date": "2024-02-29", "award_amount": 995988, "principal_investigator": { "id": 2631, "first_name": "Wei", "last_name": "Wang", "orcid": null, "emails": "[email protected]", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 151, "ror": "", "name": "University of California-Los Angeles", "address": "", "city": "", "state": "CA", "zip": "", "country": "United States", "approved": true } ] }, "other_investigators": [ { "id": 25699, "first_name": "Chen", "last_name": "Li", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, { "id": 25700, "first_name": "Andrew", "last_name": "Noymer", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, { "id": 25701, "first_name": "Quanquan", "last_name": "Gu", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, { "id": 25702, "first_name": "Nanyun", "last_name": "Peng", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "awardee_organization": { "id": 151, "ror": "", "name": "University of California-Los Angeles", "address": "", "city": "", "state": "CA", "zip": "", "country": "United States", "approved": true }, "abstract": "The ongoing global COVID-19 outbreak highlights the need to prepare for pandemics. Early detection, and, to the extent possible, prediction, are the key. While it is crucial to get as much early warning as possible in advance of the planet’s next pandemic, the odds are overwhelmingly against the next global outbreak being an exact repeat of the current COVID-19 crisis. Open source intelligence (OSINT) is vital to the provision of pandemic early warning. Diseases, especially infectious diseases, are socio-biological phenomena and leave both social and microbiological footprints. This Predictive Intelligence for Pandemic Prevention (PIPP) Phase I: Development Grants project will explore the feasibility of developing an open-source intelligence system, informed by biological, environmental, socio-economical, behavioral, and media data from diverse sources, to monitor human society for signs of unusual activities that reflect the emergence of novel pathogens with pandemic potential. This multidisciplinary research will bridge biology, social sciences, epidemiology, and computer science to address this grand challenge and start construction of a semi-autonomous system to give an early warning of the next pandemic.\n\nThe PIPP Phase I project will develop a prototype of an end-to-end pandemic-early-warning system powered by artificial intelligence (AI), machine learning, data science, and open-source technologies, which can simultaneously look for signs of an emerging infectious disease or a known disease, predict its spread, and detect and monitor risk factors over space and time. The system will embrace human intelligence as an integrated component and will be transferable and extensible to support additional data sources and machine learning models, making it suitable for detecting and predicting outbreaks of a new disease regardless of its nature and scale. The Phase I activities include four synergistic pilot projects highlighting innovative approaches to addressing the grand challenges, as well as a detailed plan to develop communities and capacity of a full center.\n\nThis award is supported by the cross-directorate Predictive Intelligence for Pandemic Prevention Phase I (PIPP) program, which is jointly funded by the Directorates for Biological Sciences (BIO), Computer Information Science and Engineering (CISE), Social, Behavioral and Economic Sciences (SBE) and Engineering (ENG).\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": "9943", "attributes": { "award_id": "2200338", "title": "PIPP Phase I: Coupling Predictive Intelligence with Adaptive Response to Create Pandemic-Resilient Cities", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Computer and Information Science and Engineering (CISE)", "PIPP-Pandemic Prevention" ], "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": "2022-09-01", "end_date": "2024-02-29", "award_amount": 1000000, "principal_investigator": { "id": 25706, "first_name": "Benjamin", "last_name": "Dalziel", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [ { "id": 4533, "first_name": "Tyler S", "last_name": "Radniecki", "orcid": "https://orcid.org/0000-0002-5295-3562", "emails": "[email protected]", "private_emails": "", "keywords": "['Wastewater microbiology']", "approved": true, "websites": "['https://cbee.oregonstate.edu/people/tyler-radniecki', 'https://arxiv.org', 'https://www.medrxiv.org', 'https://trace.oregonstate.edu/testing-results', 'https://public.tableau.com/profile/oregon.health.authority.covid.19#!/vizhome/O…']", "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 154, "ror": "https://ror.org/00ysfqy60", "name": "Oregon State University", "address": "", "city": "", "state": "OR", "zip": "", "country": "United States", "approved": true } ] }, { "id": 25703, "first_name": "Jeffrey W", "last_name": "Bethel", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, { "id": 25704, "first_name": "Justin", "last_name": "Sanders", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, { "id": 25705, "first_name": "Katherine R", "last_name": "McLaughlin", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "awardee_organization": { "id": 154, "ror": "https://ror.org/00ysfqy60", "name": "Oregon State University", "address": "", "city": "", "state": "OR", "zip": "", "country": "United States", "approved": true }, "abstract": "When the first cases of COVID-19 appeared in cities across the United States, it triggered a process of exponential spread that could not be reversed using available technologies, interventions, and solutions. As climate change accelerates the emergence of pathogens with pandemic potential, cities and urban areas will provide the media and conduits that collect and transmit novel pathogens. Recent advances in pathogen sensing/surveillance and epidemiological forecasting along with the early success in the use of non-pharmaceutical interventions to reduce the spread of COVID-19 suggest that feedback loops between predictive intelligence and adaptive response could enable cities to efficiently attenuate pandemic threats if the feedback is sufficiently localized and rapid. The overarching goal of this project is to lay the foundation for the establishment of a Center that will combine and integrate mathematical/computational modelling with engineering, public health, and public engagement to explore the design and prototyping of city-scale feedback loops that could proactively attenuate the rates of transmission of pathogens with pandemic potential. The proposed PIPP Phase I Center development activities will include targeted research projects, workshops, and workforce development including the mentoring of four graduate students and the establishment of a graduate student rotation program at Oregon State University that will provide cross-training in transdisciplinary pandemic science and enable the development and facilitation of bi-directional trainings and exchanges on pandemic dynamics between scientists, engineers, and public health professionals and stakeholders.\n\nThis PIPP Phase I project will lay the foundation for a Center that addresses the “Grand Challenge” of transforming cities from pandemic amplifiers to attenuators. To advance this goal, the project team proposes to design, build, and evaluate feedback loops between predictive intelligence and adaptive response that could attenuate pandemic threats in cities and urban areas by leveraging the networks of interacting components in urban systems. The specific objectives of the research are to: 1) Build and scale up community-academic partnerships with public health professionals and community leaders to advance pandemic predictive intelligence and adaptive response in cities and urban areas; 2) Develop mathematical and computational models that could simulate the process of stepping back across epidemic tipping points in urban systems; and 3) Design and prototype feedback loops that could predict and attenuate the transmission of infectious diseases in cities and urban areas. The successful completion of the proposed research has the potential for transformative impact through the establishment of community-academic partnerships to develop and validate disease contagion prediction-response systems and evaluate their effectiveness and adoptability. \n\nThis award is supported by the cross-directorate Predictive Intelligence for Pandemic Prevention Phase I (PIPP) program, which is jointly funded by the Directorates for Biological Sciences (BIO), Computer Information Science and Engineering (CISE), Engineering (ENG), and Social, Behavioral and Economic Sciences (SBE).\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": "9944", "attributes": { "award_id": "2200197", "title": "PIPP Phase I: Collective Intelligence for Pandemic Prediction Prevention and Response", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Computer and Information Science and Engineering (CISE)", "PIPP-Pandemic Prevention" ], "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": "2022-08-01", "end_date": "2024-01-31", "award_amount": 1000000, "principal_investigator": { "id": 25707, "first_name": "Eliah", "last_name": "Aronoff-Spencer", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [], "awardee_organization": { "id": 258, "ror": "", "name": "University of California-San Diego", "address": "", "city": "", "state": "CA", "zip": "", "country": "United States", "approved": true }, "abstract": "This Predictive Intelligence for Pandemic Prevention (PIPP) Phase I: Development Grant aims to test the feasibility and performance of a new platform that can merge currently siloed tools, in order to transform infectious-disease surveillance and public-health intervention. The digital platform will be an evolving resource that provides the toolkit for four key pandemic-prevention intelligence areas. The first area targets surveillance, such as contact tracing (human interaction) and wastewater detection of pathogens. The second area is diagnosis, such as rapid testing for disease. The third area is developing analytic intelligence, such as knowledge graphs or prediction methods. The final area is action, such as policy and public health recommendations. As a key feature of the platform, all stakeholders from researchers and medical doctors to politicians and the general public will be able to both contribute data to the platform and draw information from it. If successful, such a system would provide critical scientific evidence and become a testbed to foster further public-health investigations via a future Predictive Intelligence for Pandemic Prevention (PIPP) Center that combines social sciences with lab-oriented natural science and brings together expertise from across disciplines. In addition to research, training to prepare a future pandemic-ready workforce and outreach activities that educate and integrate the community into pandemic prevention efforts will be important parts of future PIPP Center activities.\n\nThis project will advance convergence science for pandemic intelligence by uniting public-health implementers, the community, and experts in infectious disease, bioengineering, statistical modeling, public health, computer science, human-centered design, policy, and gender, equity to explore an operational framework for a future PIPP Center. Project activities will be implemented according to the following objectives: 1) Create a collective intelligence cyberinfrastructure prototype and test it through a tabletop simulation in a real-world setting; 2) Use knowledge translation and prototype-community interaction to generate new knowledge, plans, and infrastructure for a future PIPP center; 3) Develop a team-science approach and ethics-focused framework for Center activities and policies, and build community and stakeholder engagement to guide Center development. The collaborative, interdisciplinary discussions held as part of the development activities will result in the identification of knowledge gaps, research challenges, and new opportunities for education and public engagement regarding pandemic prevention and response. Development activities will result in a prototype collective intelligence platform that will provide important insights into effective human-technology integration and closed-loop methods of pandemic prediction and response. Finally, a critical roadmap imbued with ethics and people-centered vision will be produced and converted to practice as a framework for stitching together participatory design, team science, and cyber-physical system development. Through this practice-to-learn approach, this project will advance the science of pandemic intelligence and support the education and training of a pandemic-ready workforce.\n\nThis award is supported by the cross-directorate Predictive Intelligence for Pandemic Prevention Phase I (PIPP) program, which is jointly funded by the Directorates for Biological Sciences (BIO), Computer Information Science and Engineering (CISE), Engineering (ENG) and Social, Behavioral and Economic Sciences (SBE).\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": "9945", "attributes": { "award_id": "2200038", "title": "PIPP Phase I: Real-time Analytics to Monitor and Predict Emerging Plant Disease", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Computer and Information Science and Engineering (CISE)", "PIPP-Pandemic Prevention" ], "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": "2022-08-01", "end_date": "2024-01-31", "award_amount": 1000000, "principal_investigator": { "id": 25712, "first_name": "Jean", "last_name": "Ristaino", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [ { "id": 25708, "first_name": "Ignazio", "last_name": "Carbone", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, { "id": 25709, "first_name": "Jason A", "last_name": "Delborne", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, { "id": 25710, "first_name": "Qingshan", "last_name": "Wei", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, { "id": 25711, "first_name": "Chris M", "last_name": "Jones", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "awardee_organization": { "id": 245, "ror": "https://ror.org/04tj63d06", "name": "North Carolina State University", "address": "", "city": "", "state": "NC", "zip": "", "country": "United States", "approved": true }, "abstract": "Plant disease outbreaks are increasing and threatening food security for the vulnerable in many areas of the world and in the US. A stable, nutritious food supply is needed to both lift people out of poverty and improve health outcomes. Plant diseases cause crop losses from 20% to 30% in staple food crops. Plant diseases, both common and recently emerging, are spreading and exacerbated by climate change, transmission with global food trade networks, and emergence of new strains that may be difficult to control. This team of researchers will develop better ways to detect and predict when and where plant diseases will emerge. This research will characterize how human attitudes and social behavior of stakeholders impacts plant disease transmission and adoption of sensor, surveillance and disease prediction technologies. The team will engage a diverse group of postdoctoral associates, graduate students and research staff through research and workshop participation and foster partnerships for a future Plant Disease Pandemic Preparedness Center.\n\nPrediction of plant disease pandemics is unreliable due to the lack of real-time detection, surveillance, and data analytics to inform decision-making and prevent spread. This is the grand challenge that the convergence research team will tackle in this Predictive Intelligence for Pandemic Prevention (PIPP) planning grant. In order to improve pandemic prediction and tackle this grand challenge, a new set of predictive tools is needed. In the PIPP Phase I project, the multidisciplinary team will develop a pandemic prediction system called the “Plant Aid Database (PAdb)” that links pathogen detection by in-situ plant disease sensors and remote sensing of crop health, genomic surveillance, real-time spatial and temporal data analytics and climate data to develop predictive simulations of plant disease pandemics. The team plans to validate the PAdb using several model plant pathogens including novel lineages of Phytophthora infestans and the cucurbit downy mildew pathogen Pseudoperonospora cubensis. They plan to engage a broad group of stakeholders including scientists, growers, extension specialists, the USDA APHIS Plant Protection and Quarantine personnel, the Department of Homeland Security inspectors, and diagnosticians in the National Plant Diagnostic Network in a Pandemic Preparedness workshop. Differences in response and spread of pathogens and stakeholder experiences will be examined using current methods and the aid of the new PAdb.\n\nThis award is supported by the cross-directorate Predictive Intelligence for Pandemic Prevention Phase I (PIPP) program, which is jointly funded by the Directorates for Biological Sciences (BIO), Computer Information Science and Engineering (CISE), Engineering (ENG) and Social, Behavioral and Economic Sciences (SBE).\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 } } ], "meta": { "pagination": { "page": 1391, "pages": 1419, "count": 14184 } } }