Grant List
Represents Grant table in the DB
GET /v1/grants?page%5Bnumber%5D=1385&sort=program_reference_codes
{ "links": { "first": "https://cic-apps.datascience.columbia.edu/v1/grants?page%5Bnumber%5D=1&sort=program_reference_codes", "last": "https://cic-apps.datascience.columbia.edu/v1/grants?page%5Bnumber%5D=1419&sort=program_reference_codes", "next": "https://cic-apps.datascience.columbia.edu/v1/grants?page%5Bnumber%5D=1386&sort=program_reference_codes", "prev": "https://cic-apps.datascience.columbia.edu/v1/grants?page%5Bnumber%5D=1384&sort=program_reference_codes" }, "data": [ { "type": "Grant", "id": "2008", "attributes": { "award_id": "2031614", "title": "RAPID: Impact of CoVID-19 Stay-at-Home Orders on urban stream quality in Denver Metro Area with application for future urban living scenarios", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Geosciences (GEO)" ], "program_reference_codes": [ "096Z", "7914" ], "program_officials": [ { "id": 5370, "first_name": "Laura", "last_name": "Lautz", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2020-05-15", "end_date": "2021-04-30", "award_amount": 50000, "principal_investigator": { "id": 5371, "first_name": "John E", "last_name": "McCray", "orcid": null, "emails": "[email protected]", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 621, "ror": "https://ror.org/04raf6v53", "name": "Colorado School of Mines", "address": "", "city": "", "state": "CO", "zip": "", "country": "United States", "approved": true } ] }, "other_investigators": [], "awardee_organization": { "id": 621, "ror": "https://ror.org/04raf6v53", "name": "Colorado School of Mines", "address": "", "city": "", "state": "CO", "zip": "", "country": "United States", "approved": true }, "abstract": "The COVID-19 restrictions in the Denver Metro area present an unprecedented opportunity to understand how urban river water quality might improve during times of greatly reduced traffic. Cleaner, fishable and swimmable urban rivers would be another justification for sustainable living practices that entail much less driving, including working from home (telecommuting), remote education, increased on-line shopping and delivery, and enhanced public transportation. These urban-living practices are inevitable, but the COVID-19 pandemic could potentially accelerate these practices into near-term living scenarios. The information will be useful to urban planners regarding green infrastructure needed for cleaning urban water, and to public health officials and legislators for managing urban water. Additionally, recent trends for manufacturing, urban development, traffic design, and public transportation mean the historical data on urban water quality may no longer describe current conditions. Thus, continued study is critically necessary to ensure a future clean, resilient water supply, as well as a healthy urban river ecosystem. The project team will collect perishable water quality and flow data from urban streams and waterways during COVID-19 and evaluate the impact of reduced traffic by analyzing this data in combination with long-term, infrequent, historical data, as well as more frequent post-pandemic data collected for this study. The team will collect data on traffic-based pollutants, including heavy metals, selected organics (PAH, BTEX), total-dissolved solids, basic water-quality parameters, and macro-organisms that respond rapidly to pollution. Chosen sites are representative of the urban spectrum, and also are likely to be impacted by future stay-at-home living practices. Results will be scaled up to the city-scale using mass-balance hydrological and water quality models in combination with GIS-based traffic data. The data collected can be used in combination with complex hydrological and traffic models to evaluate numerous scenarios associated with future urban living practices.This award was co-funded by the Hydrologic Sciences and Environmental Sustainability programs.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.", "keywords": [], "approved": true } }, { "type": "Grant", "id": "2009", "attributes": { "award_id": "2029919", "title": "RAPID: Enhancing US manufacturing of small molecule active pharmaceutical ingredients (APIs) using Authoritative Systems Knowledge (ASK) - (ASK4APIs)", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Office of the Director" ], "program_reference_codes": [ "096Z", "7914" ], "program_officials": [ { "id": 5372, "first_name": "Lara", "last_name": "Campbell", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2020-05-01", "end_date": "2021-04-30", "award_amount": 155000, "principal_investigator": { "id": 5373, "first_name": "James K", "last_name": "Ferri", "orcid": null, "emails": "[email protected]", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 672, "ror": "https://ror.org/02nkdxk79", "name": "Virginia Commonwealth University", "address": "", "city": "", "state": "VA", "zip": "", "country": "United States", "approved": true } ] }, "other_investigators": [], "awardee_organization": { "id": 672, "ror": "https://ror.org/02nkdxk79", "name": "Virginia Commonwealth University", "address": "", "city": "", "state": "VA", "zip": "", "country": "United States", "approved": true }, "abstract": "This COVID-19 RAPID research program will develop a digital map of production requirements and domestic manufacturing capacity for the pharmaceutical active ingredient, hydroxychloroquine (HCQ). HCQ may provide a treatment option for COVID-19; however, the pandemic already appears to be disrupting the global pharmaceutical supply, which may cause shortages of this and other important medicines in the United States. Shortages of medicines occur in part because the domestic drug supply chain has become longer, more complex, and fragmented. The United States has sophisticated chemicals and pharmaceutical manufacturing capability, but many key raw materials and/or intermediates are manufactured elsewhere in the world. Less complicated markets could respond to a shortage by increasing production, but logistical challenges – especially the complexity of the supply chain – limit the ability of domestic drug manufacturers to easily increase production. Although the United States ranks second in global chemicals exports, much of its manufacturing capacity is unable to be readily leveraged to address anticipated shortfalls because there is no clear map of production requirements or domestic manufacturing capacity that is capable of meeting these requirements. HCQ will be “mapped” as an example case, with the potential to refine and apply the same methodology even more rapidly to other compounds of value to the public in treating COVID-19 or for application to other diseases or emergent challenges. The project team will develop a systematic ontology - and more specifically, a knowledge graph – of HCQ production of the active pharmaceutical ingredient (API) in hydroxycloroquine (HCQ). The project plans to start with HCQ, but the methodology should be readily applicable to other molecules such as Remdesivir or Favipiravir (which have also been proposed as potential COVID-19 therapies). In partnership with Procter & Gamble, the project will leverage an Authoritative System Knowledge (ASK) chemical systems approach, as well as data and resources from the “M-Print” open knowledge network Phase I Convergence Accelerator project (1937017). The ASK methodology is currently used by industry and government organizations to manage electromechanical systems manufacturing and other processes, but tools such as these are not yet widely used in the chemicals industry. The project team hypothesizes that suppliers of necessary source materials and intermediates in the US and abroad can be identified, and more importantly that the chemical transformations required for the HCQ process could be retooled within American facilities if that were a desirable outcome to ensure robust supply. To test the specific hypothesis that US manufacturing of HCQ could be optimized to minimize global supply chain challenges, the team will develop a model approach that uses raw materials available in the U.S., including the production requirements to transform these raw materials into HCQ. The greater insights into supply and manufacturing patterns are envisioned to help decision-makers both inside and outside the supply chain optimize processes. The overall goal is to rapidly address a critical need in API manufacturing and reimagine its optimization in times of crisis.This RAPID award is made by the Convergence Accelerator program in the Office of Integrative Activities using funds from the Coronavirus Aid, Relief, and Economic Security (CARES) Act, and is associated with the Convergence Accelerator Track A: Open Knowledge Network.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": "2011", "attributes": { "award_id": "2026225", "title": "RAPID: Multiphase flow physics driving respiratory infectious disease transmission", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Engineering (ENG)" ], "program_reference_codes": [ "096Z", "7914" ], "program_officials": [ { "id": 5377, "first_name": "William", "last_name": "Olbricht", "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": 5378, "first_name": "Lydia", "last_name": "Bourouiba", "orcid": null, "emails": "[email protected]", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 210, "ror": "https://ror.org/042nb2s44", "name": "Massachusetts Institute of Technology", "address": "", "city": "", "state": "MA", "zip": "", "country": "United States", "approved": true } ] }, "other_investigators": [], "awardee_organization": { "id": 210, "ror": "https://ror.org/042nb2s44", "name": "Massachusetts Institute of Technology", "address": "", "city": "", "state": "MA", "zip": "", "country": "United States", "approved": true }, "abstract": "The current COVID-19 coronavirus outbreak vividly demonstrates the acute global danger emanating from a local event in a hyperconnected world. A key public health challenge is the immediate reduction in host-to-host transmission events, particularly in front-line healthcare workers taking direct care of COVID-19 patients. Such patients often have to undergo medical procedures that induce respiratory emissions and thereby put healthcare workers at risk. This RAPID project aims to reduce the host-to-host transmission of COVID-19 during such “aerosol” emission-producing medical procedures by developing, testing, and deploying fluid-dynamics-based solutions at the patient’s bedside that can essentially syphon off the emissions coming from a patient and protect healthcare workers and other personnel.This RAPID project seeks to develop such non-pharmacologic containment approaches by first visualizing and quantifying the multiphase nature of respiratory emissions during exhalations, coughs, and aerosol-generating medical procedures pertinent to COVID-19 patients. Based on that quantification, the project will model and quantify the impact of locally applied multiphase flow control for containing, diverting, and collecting the pathogen-laden emissions from the patient and thus reduce exposure to healthcare providers. Finally, the project will develop rapid prototype systems for deployment at the patient’s bedside in clinically relevant settings. The benefits of the project to healthcare professionals and the public rest with the rapid development of concrete and actionable non-pharmacological interventions to curb host-to-host transmission of respiratory infectious diseases and in particular protection of healthcare workers at the frontline of this historic 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": "2016", "attributes": { "award_id": "2028713", "title": "RAPID: Coronavirus: Understanding aerosol transmission and potential control measures in indoor environments", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Engineering (ENG)" ], "program_reference_codes": [ "096Z", "7914" ], "program_officials": [ { "id": 5393, "first_name": "Mamadou", "last_name": "Diallo", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2020-05-15", "end_date": "2022-04-30", "award_amount": 108775, "principal_investigator": { "id": 5394, "first_name": "Donghyun", "last_name": "Rim", "orcid": null, "emails": "[email protected]", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 219, "ror": "", "name": "Pennsylvania State Univ University Park", "address": "", "city": "", "state": "PA", "zip": "", "country": "United States", "approved": true } ] }, "other_investigators": [], "awardee_organization": { "id": 219, "ror": "", "name": "Pennsylvania State Univ University Park", "address": "", "city": "", "state": "PA", "zip": "", "country": "United States", "approved": true }, "abstract": "COVID-19 is a worldwide pandemic caused by the Coronovirus SARS-CoV2. COVID-19 is reported to be transmitted through direct surface exposure and through close personal contact within a short distance. Recent studies demonstrate that the virus can survive in small airborne particles (less than 5 micrometers) for hours and accumulate indoors. This result suggests a strong possibility for airborne transmission of COVID-19 in occupied spaces. However, there is a present lack of science-based information on how the virus-laden particles disperse in indoor environments. This RAPID proposal responds to the urgent need to better understand the airborne transmission and potential SARS-CoV2 control measures in indoor environments. The goal of this project is to investigate the transport mechanisms of the virus particle transport around the human body and reveal how the concentrations of virus particles are affected by human coughing and breathing, as well as ventilation rates and indoor airflow patterns. This information will be used to evaluate the effectiveness of control measures such as ventilation, filtration, and zone partitioning on aerosol transmission in densely occupied environments. Results will be used to help protect vulnerable population groups in clinical settings and senior living facilities. Successful completion of this research will more broadly inform medical health professionals, scientists, engineers, and policymakers to make decisions regarding the types of ventilation strategies and personal protective equipment that can be used to prevent aerosol transmission indoors.The COVID-19 pandemic is a health emergency of global scale. Emerging science suggests a high potential for airborne exposure to SARS-CoV2 (the virus responsible for COVID-19) as a significant exposure pathway. However, there are major gaps in our understanding that prevent efficient use of control strategies for indoor environments. The overall objectives of this research project are to address this knowledge gap by: (1) developing a mechanistic understanding of SARS-CoV2 aerosol transport in indoor environments due to coughing, talking, normal breathing, and breathing with a mask under various ventilation rates and air mixing conditions; (2) assessing airborne infection risk using inhalation intake of SARS-CoV2 aerosols released from an infector assuming steady-state, well-mixed air conditions; and (3) evaluating the effectiveness of ventilation, filtration, and zone partitioning for controlling aerosol transmission in densely occupied environments. This will be achieved using a mathematical infection risk model coupled with computational fluid dynamics simulations of aerosol transport to provide new information critical to our understanding of virus aerosol transport and associated airborne infection risk in indoor environments. The analysis will fill a critical information gap in our understanding of the transport mechanisms of infectious aerosols in the human breathing zone. Key parameters to be assessed include the emission mode of the infector (i.e. coughing, talking, breathing); the infectious aerosol mass and diameter; and the ventilation strategy and indoor air mixing rate. Parametric analysis of the effectiveness of infection control measures will inform guidelines for building system design and operations for the protection of human health.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.", "keywords": [], "approved": true } }, { "type": "Grant", "id": "2018", "attributes": { "award_id": "2030479", "title": "RAPID: Environmental Reservoirs of SARS-CoV-2", "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": 5400, "first_name": "Douglas", "last_name": "Levey", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2020-06-01", "end_date": "2022-05-31", "award_amount": 199999, "principal_investigator": { "id": 5402, "first_name": "Forest", "last_name": "Rohwer", "orcid": null, "emails": "[email protected]", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 448, "ror": "", "name": "San Diego State University Foundation", "address": "", "city": "", "state": "CA", "zip": "", "country": "United States", "approved": true } ] }, "other_investigators": [ { "id": 5401, "first_name": "Naveen K", "last_name": "Vaidya", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "awardee_organization": { "id": 448, "ror": "", "name": "San Diego State University Foundation", "address": "", "city": "", "state": "CA", "zip": "", "country": "United States", "approved": true }, "abstract": "In the midst of the CoVID-19 pandemic, important questions remain unanswered: Is the virus that causes the disease, lurking on that keypad at your local bank? What about that package from Amazon? While there is much speculation, we really do not know the answers to these very basic questions about how and where the virus lives outside of humans. The goal of this research is to determine if surfaces commonly touched by humans, but not routinely disinfected, are important for the spread of CoVID-19. Such surfaces will be tested for the presence of the virus. Results will be used to develop new mathematical models to determine if commonly touched surfaces are important for spreading CoVID-19. This research is important not only for society's response to CoVID-19 but also to plan for future pandemics caused by other viruses. The project will also help train the next generation of scientists who study the ecology and spread of disease. This project will collect thousands of surface swab samples from hundreds of sites around San Diego, USA. The samples will be screened for SARS-CoV-19 using the Reverse Transcription Polymerase Chain Reactions (RT-PCR). These data will be used to calibrate, fit and validate dynamical models that describe SARS-CoV-2 transmission from the environmental reservoirs. From these models, important characters of the SARS-CoV-2 epidemic, including risk of human infections from environmental reservoirs and contribution of environmental reservoirs to the basic reproduction number (R0) of the virus, will be estimated. The swab samples will also be characterized by metatranscriptomics for other viruses and microbes living on the same surfaces. This will be valuable for understanding the community ecology of these micro-environments and serve as a baseline for future studies. Together these intellectual products will have practical impact on controlling CoVID-19 by helping determine whether more effort should be put on disinfecting these surfaces or controlling the virus in other reservoirs.This RAPID award is made by the Population and Community Ecology 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": "2020", "attributes": { "award_id": "2030567", "title": "RAPID: Viral Particle Disrupting and Sequestering Polymer Materials applied to Coronaviruses", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Mathematical and Physical Sciences (MPS)" ], "program_reference_codes": [ "096Z", "7914" ], "program_officials": [ { "id": 5405, "first_name": "Andrew", "last_name": "Lovinger", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2020-06-01", "end_date": "2022-05-31", "award_amount": 181849, "principal_investigator": { "id": 5407, "first_name": "Dominik", "last_name": "Konkolewicz", "orcid": null, "emails": "[email protected]", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 183, "ror": "https://ror.org/05nbqxr67", "name": "Miami University", "address": "", "city": "", "state": "OH", "zip": "", "country": "United States", "approved": true } ] }, "other_investigators": [ { "id": 5406, "first_name": "Richard C", "last_name": "Page", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "awardee_organization": { "id": 183, "ror": "https://ror.org/05nbqxr67", "name": "Miami University", "address": "", "city": "", "state": "OH", "zip": "", "country": "United States", "approved": true }, "abstract": "This is an NSF RAPID award in response to the 2020 CARES Act and is managed by the Polymers Program in the Division of Materials Research of the Directorate for Mathematical and Physical Sciences.PART 1: NON-TECHNICAL SUMMARYSince the first cases of coronavirus disease 2019 (COVID-19) appeared in late 2019, the disease has infected millions globally. The virus responsible for COVID-19 can stay active, capable of causing infections, on various surfaces for days, during which time indirect contact transmission could occur. Coronaviruses contain both a surface envelope of lipids and surface presented proteins which resemble spikes. Both of these features of the virus can be used to trap and destroy the viruses within synthetic materials. Synthetic polymer materials capable of inactivating and sequestering the virus causing COVID-19 will be developed in this project. These materials will form tough structures, with the materials containing synthetic and natural groups to both disrupt the lipid molecules on the surface of the virus and to bind and trap the coronavirus spike proteins. The polymers will form a tough network, ensuring the material performs for an extended period of time. This research involves design and synthesis of polymers as well as characterization and study of their mechanical properties and focuses on developing materials that could be adapted or coated onto existing high-touch surfaces. Additionally, the project will create publicly accessible virtual presentations and content on how polymer materials are critical for the health care industry and innovations in materials for biomedical applications. With the development of materials with excellent durability and robust ability to disrupt and trap the coronavirus, a reduction in COVID-19 infection by mitigating the indirect contact transmission mechanism is possible.PART 2: TECHNICAL SUMMARYSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2) exhibits active lifetimes of over 24 hours. This enables transmission to occur hours or days after a virus containing droplet is deposited from an infected individual. Materials that destroy the virus and sequester the virus to the surface could reduce the transmission rate of coronavirus disease 2019 (COVID-19). This project will develop virus trapping and disrupting tough networks which could be used to coat commonly encountered surfaces. The polymer materials will disrupt the lipid envelope of SARS-CoV-2 viral particles and bind the spike on the surface of SARS-CoV-2 with high affinity. Both purely synthetic materials as well as hybrid peptide/synthetic materials approaches will be investigated. The polymers will include tough network forming functionalities as well as peptide or synthetic polymers for both lipid envelope disruption and spike protein binding. The scientific focus of the project is to determine how a polymer material's microstructure and functionality impacts its ability to: form tough and mechanically robust networks; disrupt viral lipid envelopes; and immobilize SARS-CoV-2 through the surface spike proteins. A library of polymer materials containing distinct crosslink densities and macromolecular architectures will be used to determine how polymer structure impacts a material's mechanical property, lipid particle rupturing capability, and ability to bind to SARS-CoV-2 spike proteins. This will guide the design of materials for optimal mechanical performance and coronavirus disrupting capabilities, and will facilitate the design of surface coatings that can hinder indirect contact transmission with long lifetimes of the structures. To remotely engage with the public on the importance of polymer materials, a series of monthly YouTube videos will be developed to convey how polymer materials are critical to health and safety, highlighting developments in materials for healthcare and biomedical applications.This grant is being awarded using funds made available by the Coronavirus Aid, Relief, and Economic Security (CARES) Act supplement allocated to MPS.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": "2022", "attributes": { "award_id": "2034518", "title": "RAPID: Facilitating Rapid and Actionable Responses to Social, Behavioral, and Economic-Related COVID Questions: The Societal Experts Action Network (SEAN)", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Social, Behavioral, and Economic Sciences (SBE)" ], "program_reference_codes": [ "096Z", "7914" ], "program_officials": [ { "id": 5410, "first_name": "Robert", "last_name": "O'Connor", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2020-07-01", "end_date": "2021-06-30", "award_amount": 197739, "principal_investigator": { "id": 5411, "first_name": "Monica N", "last_name": "Feit", "orcid": null, "emails": "[email protected]", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 339, "ror": "https://ror.org/038mfx688", "name": "National Academy of Sciences", "address": "", "city": "", "state": "DC", "zip": "", "country": "United States", "approved": true } ] }, "other_investigators": [], "awardee_organization": { "id": 339, "ror": "https://ror.org/038mfx688", "name": "National Academy of Sciences", "address": "", "city": "", "state": "DC", "zip": "", "country": "United States", "approved": true }, "abstract": "The COVID-19 pandemic has disrupted nearly every aspect of life across the globe. As decision-makers at the federal, state, and local level respond, they are grappling with numerous complex scientific questions. Many of these questions are grounded in the social, behavioral, and economic (SBE) sciences. Questions such as: • What strategies are most likely to restart economic growth in critically important sectors that were heavily affected by COVID-19? • How can governments more effectively encourage use of masks and other strategies to reduce disease transmission? • What recent knowledge will allow public and private sector educational entities to improve on-line learning outcomes? The Division of Behavioral and Social Sciences and Education (DBASSE) at the National Academies of Sciences, Engineering, and Medicine (NASEM), in collaboration with the National Science Foundation, establishes the Societal Experts Action Network (SEAN). SEAN produce products designed to provide actionable responses to urgent policy questions asked by federal, state, and local decision-makers. SEAN is unique in its focus on rapid, readable, and research-based insights in response to questions on issues such as the reopening of businesses and economic growth, the education of children, the mental health and resilience of our communities, and many more. The resulting products are made publicly available and widely disseminated, which benefits not only the requesting official, but a broad range of decision-makers and the public. The value of SBE sciences in addressing problems of national importance is shared with an expansive audience, thus contributing to our nation’s overarching understanding of how best to deploy SBE knowledge in pandemic and other crisis situations.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": "2026", "attributes": { "award_id": "2028040", "title": "RAPID: Real-time phylogenetic inference and transmission cluster analysis of COVID-19", "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": 5420, "first_name": "Shannon", "last_name": "Fehlberg", "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": 5422, "first_name": "Alexander N", "last_name": "Moshiri", "orcid": "https://orcid.org/0000-0003-2209-8128", "emails": "[email protected]", "private_emails": "", "keywords": null, "approved": true, "websites": "['https://github.com/niemasd/ViReport-COVID-19', 'https://github.com/niemasd/ViReport', 'https://github.com/niemasd/ViralMSA']", "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 258, "ror": "", "name": "University of California-San Diego", "address": "", "city": "", "state": "CA", "zip": "", "country": "United States", "approved": true } ] }, "other_investigators": [ { "id": 5421, "first_name": "Tajana S", "last_name": "Rosing", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "awardee_organization": { "id": 258, "ror": "", "name": "University of California-San Diego", "address": "", "city": "", "state": "CA", "zip": "", "country": "United States", "approved": true }, "abstract": "As the COVID-19 pandemic spreads rapidly around the world, public health officials need to be able to answer questions such as “How is COVID-19 spreading through the population?” and “How many individual outbreaks exist within a given community?”. With increasing access to sequencing technologies, scientists can analyze the genome sequences of collected SARS-CoV-2 viral samples in order to gain information about to aid in the development of vaccines and drugs as well as to infer the most likely evolutionary history of the virus, which can help epidemiologists track the spread of the virus across populations. The epidemiological use of the evolutionary history of the virus is only useful if it can be updated in real-time, but as the sheer volume of available data rapidly grows, scientists will require scalable computational tools to conduct these analyses. The goal of this project is to develop novel algorithms, software tools, and hardware systems that will scale to the massive amounts of data that are rapidly being generated in this pandemic, which will in turn aid in phylogenomic analysis of the virus, the effective tracking of the spread of the virus as well as in the development of novel vaccines and drugs in this pandemic. As a broader impact, this project will help with replicability and reproducibility of genetic and epidemiological research results. Furthermore, the existence of such a system will aid in fighting future viral outbreaks. This project provides professional development opportunities for an early career scientist.The standard viral phylogenetic inference workflow consists of quality checking and filtering, multiple sequence alignment, phylogenetic inference, phylogenetic rooting, phylogenetic dating, and transmission clustering. The researchers have identified that the computational bottlenecks of the workflow are multiple sequence alignment and phylogenetic inference, which scale poorly as a function of the number of input sequences. The objective of this project is the development of a user-friendly, scalable, and modular workflow for conducting a real-time computational phylogenetic analysis of assembled viral genomes, with a primary focus of SARS-CoV-2. The project solution includes: (1) the development of a novel software tool for orchestrating the automated end-to-end workflow, (2) the development of novel algorithms (and software implementations of these algorithms) to speed up the computational bottlenecks of the workflow, (3) the development of novel hardware systems for accelerating the workflow, and (4) a real-time publicly-accessible repository in which researchers can access the most up-to-date analysis results (with intermediate files) of all SARS-CoV-2 genomes currently available to prevent repeat computation efforts. The analysis infrastructure that will be built in this project will be broadly applicable to any viral pathogen for which phylogenetic inference is biologically and epidemiologically meaningful.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": "2027", "attributes": { "award_id": "2030015", "title": "RAPID: Time-Sensitive Human Forest and Model Forecasts for COVID-19 Vaccine and Treatment Trials", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Social, Behavioral, and Economic Sciences (SBE)" ], "program_reference_codes": [ "096Z", "7914" ], "program_officials": [ { "id": 5427, "first_name": "Robert", "last_name": "O'Connor", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2020-08-15", "end_date": "2022-08-31", "award_amount": 200000, "principal_investigator": { "id": 5430, "first_name": "Sauleh", "last_name": "Siddiqui", "orcid": null, "emails": "[email protected]", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [ { "id": 5428, "first_name": "Pavel D", "last_name": "Atanasov", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, { "id": 5429, "first_name": "Regina", "last_name": "Joseph", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "awardee_organization": { "id": 509, "ror": "https://ror.org/052w4zt36", "name": "American University", "address": "", "city": "", "state": "DC", "zip": "", "country": "United States", "approved": true }, "abstract": "Accurate, time-specific predictions are important for planning and decision making during fast-moving pandemics. In particular, whether an effective COVID-19 vaccine will be available in 9, 12, or 18 months is an issue of vital national interest. The main objective of this project is to compare the accuracy of a new method for crowd-based forecasting of time-specific outcomes–such as clinical trial transitions of COVID-19 treatments and vaccines–to that of new machine learning models. The research will examine the relative strengths of crowd and modeling methods and explore combinations of the two in predicting clinical trial results. A forecasting tournament is the project’s main method for human data collection. It starts in 2020 and continues until 2021. People with interest in forecasting and clinical trials are encouraged to sign up for participation, independently of their background. Study participants complete surveys and forecasting training, and will then have the opportunity to make probabilistic forecasts on specific trial events over several months, with regular accuracy feedback. To broaden the impacts of this work, the research team disseminates the aggregate forecasts about clinical trial phase transition of COVID-19 treatments and vaccines through public health information channels. These forecasts, combined with predictive training and accuracy feedback provided to study participants, may aid the coordination of public health and clinical development efforts to overcome the pandemic.The primary research goal of the project is to improve the predictive performance of crowd-based methods, machine models and ensembles of the two. Psychologists have shown that taking the outside view, by examining a prediction problem in context of historical reference classes, improves accuracy. The crowd-based approach, referred to as human forest, combines reference class forecasting and collective intelligence approaches to produce data-driven estimates from a group of forecasters. The time-specific human forest variant employs a survival analysis approach, enabling forecasters to construct reference classes and obtain unbiased historical estimates in the presence of missing data. On the decision science front, the research goals include testing the effects of interfaces featuring historical estimates; understanding the psychology of reference class selection; examining time-scope sensitivity in judgmental forecasting; and assessing the relative importance of subject matter expertise versus general predictive competence. On the machine-modeling front, the research goals include integrating survival-type models into machine learning and improving their performance using bi-level optimization to choose hyper-parameters. The results are released as soon as they become available.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": "2035", "attributes": { "award_id": "2034228", "title": "RAPID: Reconstructing the contemporary history and progenitor of SARS-CoV-2 strains causing COVID-19", "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": 5458, "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": "2020-07-15", "end_date": "2021-06-30", "award_amount": 200000, "principal_investigator": { "id": 5460, "first_name": "Sudhir", "last_name": "Kumar", "orcid": null, "emails": "[email protected]", "private_emails": "", "keywords": "['Phylogenetics']", "approved": true, "websites": "['http://sars2evo.datamonkey.org/', 'https://igem.temple.edu/COVID-19']", "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 277, "ror": "https://ror.org/00kx1jb78", "name": "Temple University", "address": "", "city": "", "state": "PA", "zip": "", "country": "United States", "approved": true } ] }, "other_investigators": [ { "id": 5459, "first_name": "Sayaka", "last_name": "Miura", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "awardee_organization": { "id": 277, "ror": "https://ror.org/00kx1jb78", "name": "Temple University", "address": "", "city": "", "state": "PA", "zip": "", "country": "United States", "approved": true }, "abstract": "Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) is the root cause of the COVID-19 disease that has caused many deaths in the US and millions of infections worldwide. It is expected to infect many more and is feared to inflict a higher death toll, requiring immediate research efforts to understand its genome biology and evolution. Experimental laboratories have quickly assembled tens of thousands of CoV-2 genomes to characterize its variation and to track the spread of COVID-19. Now a meaningful analysis of this enormous dataset is needed to understand patterns of coronavirus change over the last few months. These evolutionary patterns are the key to making predictions and developing products to fight COVID-19. The discovery of evolutionary patterns requires new methods explicitly designed to exploit salient features of coronavirus genomes and the history of the outbreaks. This project will provide professional development opportunities for two early career scientists, and a public webinar for broader education and training on how to use the new software to study viral evolution will be hosted.Novel analytical approaches for inferring the contemporary evolutionary history of SARS-CoV-2 strains will be developed. In preliminary investigations, the new procedures and protocols show higher power in resolving early evolutionary events in the SARS-CoV-2 history. New methods will be tested by using empirical and computer-simulated datasets. An extensive collection of coronavirus strains will be analyzed to reconstruct the earliest evolutionary events in its origin and divergence. The new software implementing the new methods will be integrated into the Molecular Evolutionary Genetics Analysis (MEGA) software that is used extensively in virology. This will place sophisticated techniques at the fingertips of scientists via a graphical user interface and command-line versions.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.", "keywords": [], "approved": true } } ], "meta": { "pagination": { "page": 1385, "pages": 1419, "count": 14184 } } }