Grant List
Represents Grant table in the DB
GET /v1/grants?page%5Bnumber%5D=1384&sort=-funder_divisions
{ "links": { "first": "https://cic-apps.datascience.columbia.edu/v1/grants?page%5Bnumber%5D=1&sort=-funder_divisions", "last": "https://cic-apps.datascience.columbia.edu/v1/grants?page%5Bnumber%5D=1442&sort=-funder_divisions", "next": "https://cic-apps.datascience.columbia.edu/v1/grants?page%5Bnumber%5D=1385&sort=-funder_divisions", "prev": "https://cic-apps.datascience.columbia.edu/v1/grants?page%5Bnumber%5D=1383&sort=-funder_divisions" }, "data": [ { "type": "Grant", "id": "2147", "attributes": { "award_id": "2030790", "title": "IIBR: Informatics: RAPID: Genome-wide Structure and Function Modeling of the SARS-CoV-2 Virus", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Biological Sciences (BIO)" ], "program_reference_codes": [ "096Z", "1165" ], "program_officials": [ { "id": 5799, "first_name": "Jean", "last_name": "Gao", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2020-06-01", "end_date": "2022-02-28", "award_amount": 199785, "principal_investigator": { "id": 5800, "first_name": "Yang", "last_name": "Zhang", "orcid": null, "emails": "[email protected]", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 169, "ror": "", "name": "Regents of the University of Michigan - Ann Arbor", "address": "", "city": "", "state": "MI", "zip": "", "country": "United States", "approved": true } ] }, "other_investigators": [], "awardee_organization": { "id": 169, "ror": "", "name": "Regents of the University of Michigan - Ann Arbor", "address": "", "city": "", "state": "MI", "zip": "", "country": "United States", "approved": true }, "abstract": "The most recent outbreak of coronavirus disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has become a global pandemic. It has spread over more than 200 countries and caused numerous deaths worldwide. The central activities of SARS-CoV-2, including human cell invasion and viral duplication and infection, are conducted through the proteins coded by the viral genome as well as the protein-protein interactions between the virus and its human hosts. Determination of the structures, functions and interactions of protein molecules associated with coronaviruses can thus provide critically important knowledge to help elucidate and end the pandemic. This project will extend state-of-the-art structural bioinformatics methods to generate genome-wide protein structure and function models for SARS-CoV-2 and other human coronaviruses, which will help in understanding the general mechanisms and principles governing the virulence, diversity and evolution of these coronaviruses and facilitate the development of new treatments to cure infected individuals and terminate the COVID-19 pandemic. Multiple graduate and undergraduate students, including women and minorities, will be trained through participation in different Objectives of the project. The project results will be integrated with the bioinformatics core courses in the Bioinformatics and Biochemistry PhD Programs and the Museum of Natural History at the University of Michigan, with the purpose of enhancing the outreach and broad impacts of this research on both student and public education.Accurately modeling protein structure and function has been a long-term challenge in structural bioinformatics and computational biology. A classical approach to this problem is comparative modeling, i.e., deducing information of unknown target proteins from known homologous proteins that are evolutionarily related to the targets. This approach is built on the assumption that similar sequences have similar structures and functions. Although they work well in many applications, the comparative approaches cannot be applied to effectively model proteins associated with SARS-CoV-2 and other human coronaviruses, because viral genomes are highly mutable, and many of the genes and gene products belonging to these viruses do not have close homologous templates with other species. To address these issues, this project plans to extend multiple algorithms developed in the PI’s lab, which have been designed primarily for non-homology-based protein structure and function prediction. In particular, the methods will utilize cutting-edge deep convolutional neural-network (DCNN) models to generate amino acid-level contact and distance maps in order to improve protein structure and interaction network modeling accuracy. Since the DCNN models are trained only on sequence databases, the performance of the approaches does not rely on the availability of structural and functional templates and can therefore be effectively used to model the coronavirus proteins that lack homologous templates; successfully developing these methods will also benefit the field of structural bioinformatics in general due to the importance of non-homologous protein structure and function prediction. In summary, the success of this project will result in the development of an urgently needed knowledge base to improve the understanding of fundamental principles associated with human coronaviruses and facilitate the development of new treatments for the COVID-19 pandemic. The data and methods produced by the project will be accessible to the community at https://zhanglab.ccmb.med.umich.edu/COVID-19/. This RAPID award is made by the Division of Biological Infrastructure, using funds from the Coronavirus Aid, Relief, and Economic Security (CARES) Act.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": "2154", "attributes": { "award_id": "2028651", "title": "RAPID: Point-of-Need Detection of COVID-19 using CRISPR-Enabled Cell-Free Synthetic Biology", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Biological Sciences (BIO)" ], "program_reference_codes": [ "096Z", "7465", "7914" ], "program_officials": [ { "id": 5818, "first_name": "David", "last_name": "Rockcliffe", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2020-05-01", "end_date": "2022-04-30", "award_amount": 200000, "principal_investigator": { "id": 5820, "first_name": "Julius", "last_name": "Lucks", "orcid": "https://orcid.org/0000-0002-0619-6505", "emails": "[email protected]", "private_emails": "", "keywords": null, "approved": true, "websites": "['https://www.biorxiv.org']", "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 317, "ror": "https://ror.org/000e0be47", "name": "Northwestern University", "address": "", "city": "", "state": "IL", "zip": "", "country": "United States", "approved": true } ] }, "other_investigators": [ { "id": 5819, "first_name": "Michael", "last_name": "Jewett", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "awardee_organization": { "id": 317, "ror": "https://ror.org/000e0be47", "name": "Northwestern University", "address": "", "city": "", "state": "IL", "zip": "", "country": "United States", "approved": true }, "abstract": "The COVID-19 pandemic highlights a limitation of laboratory-based testing for the SARS-CoV-2 coronavirus in that it does not scale with a sudden and dramatic increase in volume. Laboratory tests, often based on quantitative polymerase chain reaction (qPCR), require equipment, time, expertise and infrastructure, resulting in severe logistical challenges and ultimately resulting in inadequate testing. There is a critical need for diagnostics that can be rapidly deployed at the point-of-need (PON) to enable global surveillance of infectious diseases. Towards addressing this need, this project incorporates a new CRISPR-based approach called CRISPR Isothermal Amplification (CIA) that enables one-pot diagnostic amplification and detection of the SARC-CoV-2 virus at ambient temperatures. This scheme builds off innovations in cell-free synthetic biology and biosensing that enable rapid, low-cost, and PON detection of chemical contaminants and viral pathogens. The platform technology under development could serve several purposes from a quick-screen technology to inform triage and isolation strategies, to a clinical test used on the front lines. There is also the potential for the technology to eventually serve as an at-home test to help inform which subpopulations and sectors of the economy could safely return to productivity towards the end of the pandemic. In addition, the test has the flexibility to be rapidly reprogrammed to detect new emerging pathogens. This work is pursued in close collaboration with a commercial partner, Stemloop, Inc., in order to quickly transition the technology for manufacturing, deployment, and distribution.This project employs a new CRISPR Isothermal Amplification (CIA) approach to enable a one-pot amplification and detection of the SARS-CoV-2 genome in an assay for the virus. CIA uses programmed Cas 13 systems to recognize SARS-CoV-2 RNA, trigger nucleic acid amplification and create a detectable output simultaneously in the same reaction where amplification occurs. Computational models of each process are developed alongside the experimental systems and used to guide optimizations to achieve attomolar sensitivity. The method is unique in that no DNA primers are required. The amplification and detection processes are designed to take less than one hour and cost less than one dollar per test to manufacture.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": "2160", "attributes": { "award_id": "2031420", "title": "RAPID: COVID-19 induced cessation of ecotourism and supplemental feeding: Implications for wildlife physiology, reproduction, and the microbiome", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Biological Sciences (BIO)" ], "program_reference_codes": [ "096Z", "7914", "9178" ], "program_officials": [ { "id": 5839, "first_name": "Theodore", "last_name": "Morgan", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2020-08-15", "end_date": "2022-07-31", "award_amount": 184519, "principal_investigator": { "id": 5842, "first_name": "Susannah", "last_name": "French", "orcid": null, "emails": "[email protected]", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 375, "ror": "https://ror.org/00h6set76", "name": "Utah State University", "address": "", "city": "", "state": "UT", "zip": "", "country": "United States", "approved": true } ] }, "other_investigators": [ { "id": 5840, "first_name": "Karen", "last_name": "Kapheim", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, { "id": 5841, "first_name": "Charles", "last_name": "Knapp", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "awardee_organization": { "id": 375, "ror": "https://ror.org/00h6set76", "name": "Utah State University", "address": "", "city": "", "state": "UT", "zip": "", "country": "United States", "approved": true }, "abstract": "Wildlife feeding by tourists is a widespread phenomenon across various species and environments. While it can provide important economic benefits to countries and local communities, it also poses significant challenges for affected wildlife. These widespread practices may induce habituation, cause stress, impact health, and alter growth and survival, all of which can lead to population changes. However, the ability to control for extraneous factors in tourism-related wildlife feeding conditions is often difficult if not impossible, rendering scientific study challenging. Furthermore, the effects of termination of wildlife feeding have rarely been considered. The unprecedented cessation of tourism and food provisioning due to the COVID-19 global pandemic has created a natural experiment that meets the aforementioned challenges to directly test the effects of this phenomenon on critically endangered rock iguanas in The Bahamas. The proposed work will leverage this unique opportunity to test the effects of widescale resource restriction and dietary change on the health of free-living animals. The implications of this work reach far beyond that of the tourist sector and can inform the effects of resource restriction for natural populations across species on a broader scale. Thus, understanding the consequences of supplemental feeding cessation will help inform countries on a broader scale that are working at making tourism practices more sustainable, which is particularly important for endangered species. The COVID-19 pandemic has created a unique situation via the restriction of human movement and contact resulting in severely reduced global travel. As a result, in countries like The Bahamas, there has been the complete removal of tourist stress and feeding pressures on wildlife across the country. Recent findings by the PIs examining critically endangered iguanas across an insular landscape in The Bahamas have illustrated significant tourist-driven effects on physiology and gut microbial composition. This work has also revealed significant associations among tourism, bacterial abundance, and physiology. The recent and sudden release from supplemental feeding by tourists has created a rare natural experiment by which the PIs can directly test the effects of diet shifts and restriction in natural populations of long-lived iguanas where pre-pandemic data are already available. The PIs will specifically focus on the gut microbiome, and test for direct associations and directionality with physiological systems important to health and survival. Moreover, the ongoing 40-year demographic study in this system will allow the PIs to relate this to survival, growth, and recruitment at the population level. The research group will work to facilitate transfer of results from this study to The Bahamas via long standing research partnerships and will provide opportunistic clarity on potential management strategies moving forward. To reach the broader community, outcomes will also be reported via Shedd Aquarium media and exhibits.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": "2178", "attributes": { "award_id": "2028301", "title": "RAPID: Harnessing the power of multiple models for outbreak management", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Biological Sciences (BIO)" ], "program_reference_codes": [ "096Z", "7914" ], "program_officials": [ { "id": 5891, "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": "2020-04-15", "end_date": "2022-03-31", "award_amount": 200000, "principal_investigator": { "id": 5892, "first_name": "Katriona", "last_name": "Shea", "orcid": null, "emails": "[email protected]", "private_emails": "", "keywords": null, "approved": true, "websites": "['https://kshealab.wordpress.com/', 'https://midasnetwork.us/mmods/']", "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [], "awardee_organization": { "id": 219, "ror": "", "name": "Pennsylvania State Univ University Park", "address": "", "city": "", "state": "PA", "zip": "", "country": "United States", "approved": true }, "abstract": "For many of the most damaging or worrying pathogens, such as the SARS-CoV-2 virus that causes COVID-19, multiple scientific groups develop quantitative models to forecast disease dynamics and assess possible interventions. These models often differ significantly in their projections and recommendations, reflecting different policy assumptions, as well as scientific, logistical, and other uncertainty about biological and management processes. Such uncertainty can be challenging for policymakers, hindering intervention planning and response. Policymakers may thus choose to rely on single trusted sources of advice, or on consensus where it appears, without confidence that decisions will be the best possible. However, less-than-optimal decisions mean more lives may be lost or more resources used than needed. In the face of biological, epidemiological, and operational uncertainties, systematic strategies to formalize the process of using multiple models to develop policy can improve the effectiveness and efficiency of policy responses to outbreaks. The COVID-19 pandemic outbreak is a major health issue for most countries in the world. This work is intended to directly address this current problem in real time. The work will also provide a framework for future outbreak response. Many models to address the COVID-19 pandemic are in development, or recently published. This project will develop multiple-model elicitation protocols, embedded in a strong framework for decision making, that formally acknowledges our uncertainty about this novel pathogen, and avoids known sources of bias. A full acknowledgment and accounting of uncertainty is critical both for decision making and for public communication. Nationally relevant objectives (e.g., minimizing deaths) and interventions (e.g., social distancing) will be assessed during this process. The project will merge formal expert elicitation methods (usually used to elicit opinions from individual experts) with modeling analyses from multiple research groups to enhance decision making for outbreak management. Groups will project disease dynamics under different interventions, and the ensemble of outputs will be analyzed using decision analysis to provide an evaluation of interventions against the policy makers’ objectives. The project will conduct this exercise to address key policy decision-making needs in the face of uncertainty during the COVID-19 pandemic.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": "2194", "attributes": { "award_id": "2035361", "title": "RAPID: Collaborative Research: Mitigation and Suppression of Coronavirus Pandemic with Data-driven RAPID Decisions Using COVID-19 Simulator", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Biological Sciences (BIO)" ], "program_reference_codes": [ "096Z", "7914" ], "program_officials": [ { "id": 5938, "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": "2020-07-01", "end_date": "2022-06-30", "award_amount": 103858, "principal_investigator": { "id": 5939, "first_name": "Jagpreet", "last_name": "Chhatwal", "orcid": null, "emails": "[email protected]", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 736, "ror": "https://ror.org/002pd6e78", "name": "Massachusetts General Hospital", "address": "", "city": "", "state": "MA", "zip": "", "country": "United States", "approved": true } ] }, "other_investigators": [], "awardee_organization": { "id": 736, "ror": "https://ror.org/002pd6e78", "name": "Massachusetts General Hospital", "address": "", "city": "", "state": "MA", "zip": "", "country": "United States", "approved": true }, "abstract": "Coronavirus disease 2019 (COVID-19), a global pandemic, has affected every sector of human life. While our understanding of the spread of COVID-19 is still evolving, policymakers need to make strategic decisions amidst this uncertainty to mitigate the pandemic. Appropriate policy decisions can reduce morbidity, mortality, and damage to the healthcare system. This study will estimate the underlying prevalence of COVID-19 at the county level and project future trajectories of COVID-19 under various sequences of non-pharmaceutical and pharmaceutical interventions. In addition, these new estimations and projections will be incorporated into our existing online COVID-19 Simulator (www.covid19sim.org) to inform county-level decisions. In addition, the COVID-19 Simulator will detect early signs of community-level COVID-19 outbreaks and predict hotspots in different jurisdictions. From a societal perspective, this research will improve our understanding of COVID-19 transmission and inform policies to mitigate the spread of the virus, ultimately leading to a reduction in COVID-19 disease burden.This research will use epidemiological and operations research modeling approaches to simulate the spread of COVID-19 at the county level and the effects of different interventions on mitigation of COVID-19. In addition, causal decision tree and mixed-integer programing-based machine learning algorithms will be used to identify county level hotspots of transmission. The proposed research will not only inform key decisions with important public health implications, but also contribute to intellectual merits, including 1) linking and using various datasets in near real-time to improve our understanding of disease epidemiology, 2) estimating the underlying prevalence of COVID-19 and projecting future trajectories under various interventions, 3) developing innovative approaches for real time parameter estimations, model calibrations, and projections, and (4) detecting early signs of community-level COVID-19 outbreaks using epidemiological, statistical and machine learning based modeling techniques.This RAPID award is made by the Ecology and Evolution of Infectious Diseases Program in the Division of Environmental Biology, using funds from the Coronavirus Aid, Relief, and Economic Security (CARES) ActThis 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": "2195", "attributes": { "award_id": "2035360", "title": "RAPID: Collaborative Research: Mitigation and Suppression of Coronavirus Pandemic with Data-driven RAPID Decisions Using COVID-19 Simulator", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Biological Sciences (BIO)" ], "program_reference_codes": [ "096Z", "7914" ], "program_officials": [ { "id": 5940, "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": "2020-07-01", "end_date": "2022-06-30", "award_amount": 92030, "principal_investigator": { "id": 5941, "first_name": "Turgay", "last_name": "Ayer", "orcid": null, "emails": "[email protected]", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 294, "ror": "", "name": "Georgia Tech Research Corporation", "address": "", "city": "", "state": "GA", "zip": "", "country": "United States", "approved": true } ] }, "other_investigators": [], "awardee_organization": { "id": 294, "ror": "", "name": "Georgia Tech Research Corporation", "address": "", "city": "", "state": "GA", "zip": "", "country": "United States", "approved": true }, "abstract": "Coronavirus disease 2019 (COVID-19), a global pandemic, has affected every sector of human life. While our understanding of the spread of COVID-19 is still evolving, policymakers need to make strategic decisions amidst this uncertainty to mitigate the pandemic. Appropriate policy decisions can reduce morbidity, mortality, and damage to the healthcare system. This study will estimate the underlying prevalence of COVID-19 at the county level and project future trajectories of COVID-19 under various sequences of non-pharmaceutical and pharmaceutical interventions. In addition, these new estimations and projections will be incorporated into our existing online COVID-19 Simulator (www.covid19sim.org) to inform county-level decisions. In addition, the COVID-19 Simulator will detect early signs of community-level COVID-19 outbreaks and predict hotspots in different jurisdictions. From a societal perspective, this research will improve our understanding of COVID-19 transmission and inform policies to mitigate the spread of the virus, ultimately leading to a reduction in COVID-19 disease burden.This research will use epidemiological and operations research modeling approaches to simulate the spread of COVID-19 at the county level and the effects of different interventions on mitigation of COVID-19. In addition, causal decision tree and mixed-integer programing-based machine learning algorithms will be used to identify county level hotspots of transmission. The proposed research will not only inform key decisions with important public health implications, but also contribute to intellectual merits, including 1) linking and using various datasets in near real-time to improve our understanding of disease epidemiology, 2) estimating the underlying prevalence of COVID-19 and projecting future trajectories under various interventions, 3) developing innovative approaches for real time parameter estimations, model calibrations, and projections, and (4) detecting early signs of community-level COVID-19 outbreaks using epidemiological, statistical and machine learning based modeling techniques.This RAPID award is made by the Ecology and Evolution of Infectious Diseases Program in the Division of Environmental Biology, using funds from the Coronavirus Aid, Relief, and Economic Security (CARES) ActThis 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": "2206", "attributes": { "award_id": "2032726", "title": "RAPID: Epidemiology of COVID-19 based on Surveillance of Deceased Donors in the United States", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Biological Sciences (BIO)" ], "program_reference_codes": [ "096Z", "7914" ], "program_officials": [ { "id": 5973, "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": "2020-06-01", "end_date": "2021-05-31", "award_amount": 198244, "principal_investigator": { "id": 5974, "first_name": "Jesse D", "last_name": "Schold", "orcid": null, "emails": "[email protected]", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 737, "ror": "", "name": "Cleveland Clinic Foundation", "address": "", "city": "", "state": "OH", "zip": "", "country": "United States", "approved": true } ] }, "other_investigators": [], "awardee_organization": { "id": 737, "ror": "", "name": "Cleveland Clinic Foundation", "address": "", "city": "", "state": "OH", "zip": "", "country": "United States", "approved": true }, "abstract": "A novel coronavirus, Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), emerged at the end of 2019, causing the clinical illness of Coronavirus Disease 2019 (COVID-19). The symptoms of COVID-19 can vary dramatically in both presentation and severity. Individuals infected with COVID-19 may be asymptomatic while still able to transmit the virus. Population-based studies suggest the rate of asymptomatic cases of COVID-19 may be high, but data is lacking. In order to evaluate the true prevalence of the virus, characterize individuals that are affected and express severe symptoms and assess the effects of interventions to attenuate the proliferation of the spread of the virus, data regarding both the symptomatic and asymptomatic cases are critically important. Measurement of rates of COVID-19 are important for surveillance and predictive modeling in the general population as well as to inform scientific inquiry regarding the potential biological mechanisms underlying the expression of symptoms among select individuals. The current project will provide novel insights about COVID-19 by assessing test results of decedents referred for tissue or organ donation across the United States in an epidemiological framework. Broad dissemination of these results will inform ongoing national policy and will continue to expand the scope and accuracy of predictive modeling.This research project will provide unique insights into the epidemiology of COVID-19 in the United States. This will be accomplished by aggregating systematic test results of COVID-19 among decedents referred for tissue and organ donation. Organ procurement organizations (OPOs) are responsible for recovering deceased donors for transplantation in the United States. OPOs are now utilizing tests of COVID-19 for decedents referred as potential donors to assess risk prior to allocation to a potential recipient. Importantly, this includes donor tissue and organs from decedents with and without symptoms or known exposure to COVID-19. This process provides a unique opportunity to evaluate the incidence, progression and characteristic of COVID-19 including decedents from unrelated causes. These data will further be analyzed via statistics and predictive models to model the trajectory of COVID-19 in the general population based on demographic and clinical characteristics of the general population in different regions of the country. This RAPID award is made by the Ecology and Evolution of Infectious Disease Program in the Division of Environmental Biology, using funds from the Coronavirus Aid, Relief, and Economic Security (CARES) Act.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": "12298", "attributes": { "award_id": "1U18HS029905-01", "title": "Improving Access to Multidisciplinary Care for patients with long COVID", "funder": { "id": 4, "ror": "https://ror.org/01cwqze88", "name": "National Institutes of Health", "approved": true }, "funder_divisions": [ "Agency for Healthcare Research and Quality (AHRQ)" ], "program_reference_codes": [], "program_officials": [ { "id": 28197, "first_name": "Leeann", "last_name": "Comfort", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2023-09-30", "end_date": "2028-09-29", "award_amount": 999951, "principal_investigator": { "id": 28198, "first_name": "Jessica Anne", "last_name": "Bender", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [ { "id": 28199, "first_name": "Janna L", "last_name": "Friedly", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, { "id": 28200, "first_name": "Nikki", "last_name": "Gentile", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "awardee_organization": { "id": 159, "ror": "https://ror.org/00cvxb145", "name": "University of Washington", "address": "", "city": "", "state": "WA", "zip": "", "country": "United States", "approved": true }, "abstract": "The proposed project seeks to expand the existing UW multidisciplinary long COVID clinic (the Post COVID Rehabilitation and Recovery clinic) to improve access and coordination of care for patients with long COVID. This project will allow the team to improve care delivery for all patients, but will emphasize improving access to multidisciplinary care for underserved patients including patients in rural communities, Alaska, and in Latinx, Native American, and SE Asian communities. With additional funding, we will make an enormous impact on a large region throughout WWAMI, focusing initially on Washington and Alaska, and Idaho, Montana, Wyoming in the later years. The aims of this proposed project include Aim 1: Improve clinical care delivery and expand access to the UW PCRRC. The goal of this aim is to improve access and care delivery for patients from Washington and Alaska that are referred to the Post-COVID Rehabilitation and Recovery clinic. The key areas that we will be expanding in this project include: 1) coordination of care; 2) behavioral health support; 3) vocational services; and 4) electronic consults. Aim 2: Engage underserved communities to enhance long COVID education and connection to local resources. The overall goal of our community engagement activities will be to foster authentic longitudinal relationships, promote ongoing collaborations and address the needs of community members. We will accomplish this by: 1) forming a community advisory board to guide our engagement activities; 2) producing publicly accessible educational webinars (community conversations) addressing COVID and long COVID; 3) producing culturally responsive patient education materials on long COVID in English, Spanish, and SE Asian languages; and 4) participating in community events including town hall meetings, community health fairs, pop-up vaccination events to share information about the UW long COVID clinic and long COVID. Aim 3: Improve patient access in the WWAMI region by training a network of clinicians specializing in long COVID care. In this aim, we will provide ongoing education and support to community PCPs, psychologists, and physical therapists with a focus on providers in under-resourced areas across the WWAMI region, including rural areas. We will accomplish this by: 1) In partnership with the Washington State DOH, we will create an advanced training certificate program for long COVID. 2) We will conduct monthly interactive case-based webinars. 3) We will also identify local physician and physical therapy leads who can be trained to be local experts in the care of patients with long COVID and are a part of our extended UW PCRRC network. 4) We will hold weekly virtual office hours that are pre-specified with a scheduling system to allow community providers to discuss cases and ask questions that will help them with their own management of patients with long COVID.", "keywords": [], "approved": true } }, { "type": "Grant", "id": "12317", "attributes": { "award_id": "1R13HS029599-01A1", "title": "Telehealth Post-Pandemic: A Roadmap for the Coming Decade", "funder": { "id": 4, "ror": "https://ror.org/01cwqze88", "name": "National Institutes of Health", "approved": true }, "funder_divisions": [ "Agency for Healthcare Research and Quality (AHRQ)" ], "program_reference_codes": [], "program_officials": [ { "id": 28226, "first_name": "Poonam", "last_name": "Pardasaney", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2023-09-30", "end_date": "2024-09-29", "award_amount": 49668, "principal_investigator": { "id": 28227, "first_name": "Bree Elizabeth-Allen", "last_name": "Holtz", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [], "awardee_organization": { "id": 521, "ror": "https://ror.org/05hs6h993", "name": "Michigan State University", "address": "", "city": "", "state": "MI", "zip": "", "country": "United States", "approved": true }, "abstract": "The COVID-19 pandemic necessitated the rapid implementation of telehealth, leading to limited reflection on how to sustain quality care and what permanent policies are needed for continuing these services. Therefore, the objective of this proposed conference is to convene a Think Tank to develop and disseminate best practices for providing high-quality telehealth care and promoting policy changes that support equitable access to quality, safe, and efficient healthcare for all Americans. This Think Tank will assemble a multi-disciplinary group of decision leaders from health systems, physician groups, researchers, patient advocates, technology groups, insurers, and policymakers from Michigan. Michigan is an ideal setting for this conference because of its racial, political, cultural, economic, and geographic diversity. Thus, the guidance developed through this Think Tank will be able to be translated nationally. The rationale is that this conference provides a timely opportunity for these groups to reflect on the quick shift to providing telehealth during the pandemic and chart a course for best practices and next steps to ensure its sustainability. There are two specific aims for this Think Tank: 1) Develop practice, policy, and research priorities to guide evidence-based guidelines for providing telehealth services, and 2) Actively distribute these priorities to a national audience. For the first aim, Think Tank will cultivate connections among healthcare providers, health systems, government, industry, and researchers engaged in telehealth. Through a pre-conference literature review, key topics will be developed, and workgroups will be formed to delve into the issues and document gaps and evidence-based practices. For the second aim, the team will leverage partnerships with Connect America, Blue Cross Blue Shield, and the Journal of Telemedicine and Telecare to disseminate Think Tank findings and recommendations to a national audience. The team will also disseminate findings at national conferences, through social media, and on its website to improve the use of telehealth, telehealth access, and the equitable coordination of care. The conference deliverables will include a literature review, a website including blogs and podcasts, at least six publications to be published in Telemedicine and e-Health, policy briefs, an updated telehealth curriculum, and presentations at national meetings. This contribution is significant because the work done at this Think Tank conference will directly inform policymakers’ and practice leaders’ decisions for improving telehealth solutions to provide better access and healthcare to all patients. This will have tangible impacts on and guidance for the sustained implementation of telehealth nationally.", "keywords": [], "approved": true } }, { "type": "Grant", "id": "12318", "attributes": { "award_id": "1R01HS029111-01A1", "title": "The Safety Integration Stakeholders (SAINTS) program to integrate worker and patient safety in Oregon rural hospitals", "funder": { "id": 4, "ror": "https://ror.org/01cwqze88", "name": "National Institutes of Health", "approved": true }, "funder_divisions": [ "Agency for Healthcare Research and Quality (AHRQ)" ], "program_reference_codes": [], "program_officials": [ { "id": 28228, "first_name": "Erofile", "last_name": "Gripiotis", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2023-09-30", "end_date": "2028-08-31", "award_amount": 379224, "principal_investigator": { "id": 28229, "first_name": "David Alejandro", "last_name": "Hurtado", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [], "awardee_organization": { "id": 765, "ror": "https://ror.org/009avj582", "name": "Oregon Health & Science University", "address": "", "city": "", "state": "OR", "zip": "", "country": "United States", "approved": true }, "abstract": "The COVID-19 pandemic accentuated the inextricable link between worker and patient safety. Yet most hospitals have separate initiatives for either patient or worker safety. A case in point is safe patient handling and mobility (SPHM), where most programs are implemented by Employee Health and Safety without coordination with other quality or patient safety departments. This disconnect is significant because adverse and costly outcomes such as patient falls or workers’ patient-assist injuries have overlapping risk factors that must be addressed together. One promising response is through a program that integrates stakeholders, including influential front-line workers, to address overlapping risk factors for patient falls and patient-assist injuries. Our preliminary research pre-tested an integrated safety program at a rural hospital in Oregon that demonstrated 12-month effectiveness in improving leading indicators and reduction of patient-assist injury rates. The program had four main components: 1) the involvement of patient and worker safety stakeholders; 2) the innovative application of Social Network Analysis (SNA) to identify peer-recognized safety leaders instead of self- or supervisor-nominated workers who may not have the influence required to effect change; 3) training stakeholders on safety leadership, and 4) implementation of quality improvement (QI) cycles to reduce safety barriers. Based on these promising results, we plan to build a new scalable intervention to be tested in a rigorous multi-hospital trial. The objective of this proposal is to conduct a cluster randomized controlled trial in six rural hospitals to evaluate the effectiveness and process of a safety program titled Safety Integration Stakeholders (SAINTS). We will test the central hypothesis that relative to usual practice controls, the three hospitals randomly assigned to the SAINTS program will have reduced risk factors for patient falls and patient- assist injuries 12 months after the end of the program. The rationale is that the SAINTS program will positively impact outcomes by identifying and training key players on strategies to boost environmental, administrative, and educational precautions relevant to SPHM. We propose two aims: Aim 1: Determine the SAINTS' effectiveness in reducing overlapping risk factors for patient falls and patient-assist injuries. Effectiveness will be determined by surveying in-patient unit workers on risk factors for patient-assist injuries and patient falls at baseline, six months after program initiation, and 12 months after the end of implementation (primary endpoint). The trial is powered to detect changes in leading indicators, but we will also examine lagging effects on the incidence rates of patient falls, patient-assist injuries, entries in reporting systems, and staff turnover. Aim 2: Identify safety precaution measures produced by the SAINTS program. Given the semi-structured design, we will conduct a multi-method process evaluation to identify the extent to which the program was delivered as planned and if it led to the adoption of preventative measures.", "keywords": [], "approved": true } } ], "meta": { "pagination": { "page": 1384, "pages": 1442, "count": 14420 } } }