Grant List
Represents Grant table in the DB
GET /v1/grants?page%5Bnumber%5D=1384&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=1421&sort=program_reference_codes", "next": "https://cic-apps.datascience.columbia.edu/v1/grants?page%5Bnumber%5D=1385&sort=program_reference_codes", "prev": "https://cic-apps.datascience.columbia.edu/v1/grants?page%5Bnumber%5D=1383&sort=program_reference_codes" }, "data": [ { "type": "Grant", "id": "1905", "attributes": { "award_id": "2031536", "title": "RAPID: Modeling the Severity and Transmissibility of COVID-19 in the USA with Intrinsic Behavior Change", "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": 5053, "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": "2022-05-31", "award_amount": 199580, "principal_investigator": { "id": 5054, "first_name": "Peter", "last_name": "Riley", "orcid": null, "emails": "[email protected]", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 697, "ror": "", "name": "Predictive Science Incorporated", "address": "", "city": "", "state": "CA", "zip": "", "country": "United States", "approved": true } ] }, "other_investigators": [], "awardee_organization": { "id": 697, "ror": "", "name": "Predictive Science Incorporated", "address": "", "city": "", "state": "CA", "zip": "", "country": "United States", "approved": true }, "abstract": "As COVID-19 spreads through communities across the world, and particularly within the USA, a number of questions remain unanswered. Of particular importance is to what extent different mitigation and containment strategies affect the resulting number of ICU cases and/or deaths? This question will become ever more nuanced as communities begin to relax current “lockdown” orders to varying degrees. Additionally, to what extent do spatial and temporal changes in weather (temperature, precipitation, and humidity) as well as UV radiation modulate the disease’s evolution? Through a combination of unique data collection, model refinement, and scientific investigation, this study can shed valuable insight on these questions. The codes and derived data will be made available to the scientific community through GitHub repositories, CRAN packages, and web portals, and informal training will be provided for potentially interested stakeholders, such as county public health departments, the CDC, and DoD agencies.This investigation will use an existing state-of-the-art modeling and forecasting framework, Dynamics of Interacting Community Epidemics (DICE), to examine the human ecology of COVID-19 dynamics. DICE is a unique tool that can help reveal the impact of different containment and non-pharmaceutical mitigation strategies, as well as climate forcing, on the transmission of COVID-19. Uniquely, it is an arbitrarily scaled hybrid spatial metapopulation model in which individual communities experience deterministic disease dynamics, but between which the process of one community seeding an outbreak in another community is stochastic. DICE can be run at the county, state, region, or national level, or, various combinations of these sub-units can be coupled, depending on what data are available. DICE solves the system of SE1…EnI1…ImRX equations producing a modeled incidence profile and estimates of the reproduction number as a function of time, R(t), the severity of the outbreak, and parameters quantifying the efficacy of interventions. DICE already has the capability of incorporating school vacation data, and uses climate data from NASA and NOAA, and specific humidity, in particular, which has been shown to be important in forecasting the evolution of influenza. A range of methodologies for incorporating interventions, such as school closures, social distancing, and shelter-in-place orders have been recently tested and explored using a complementary single-population prototype tool (DRAFT), specifically developed to rapidly explore refinements that can be incorporated into DICE. DICE can both simulate possible future scenarios as well as fit to available data to estimate the efficacy of different intervention profiles, and also captures joint estimates of severity (Sev) and transmissibility (R(t)). As COVID-19 spreads across the U.S., community transmission can be evaluated in R-Sev space, which will provide crucial and strategic information to assist policymakers in making more informed decisions. Through the use of a Monte Carlo Markov Chain (MCMC) approach, DICE produces robust estimates of the uncertainties in the projections. Additionally, DICE is a multi-model algorithm, allowing the generation of forecasts for more than 32 model variants, which provides not only an estimate of the impact that various factors may play (e.g., climate), but also produces hyper-ensembles of model realizations, which, in turn provide additional estimates of uncertainty. 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": "1906", "attributes": { "award_id": "2029518", "title": "RAPID: Improving Transportation Equity to Enhance Food Security for Families Vulnerable to COVID-19", "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": 5055, "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": "2020-06-01", "end_date": "2021-05-31", "award_amount": 159955, "principal_investigator": { "id": 5059, "first_name": "Robert C", "last_name": "Hampshire", "orcid": "https://orcid.org/0000-0002-5269-3377", "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": [ { "id": 5056, "first_name": "H. V", "last_name": "Jagadish", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, { "id": 5057, "first_name": "Olutayo G", "last_name": "Fabusuyi", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, { "id": 5058, "first_name": "Aditi", "last_name": "Misra", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "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": "This COVID-19 RAPID research program will investigate, and begin to develop mechanisms to address, the lack of access to food (i.e. food insecurity) associated with COVID-19 and the role of transportation challenges leading to food insecurity. The pandemic has exposed some of the underlying inequities in health outcomes in our society. The lack of smartphone and internet access has cut off some families from food delivery services. Consequently, these households are at higher risk of exposure to the virus due to more frequent trips to the grocery store, often by public transit. The research objectives address food insecurity at both the local level and nationwide. The research team will provide research and technical assistance to the City of Detroit’s pilot program to deliver school lunches to vulnerable families. At a national level, the team aims to identify the geographical areas and people most affected by both food and transportation insecurity. The research results will be made available to the public via a nationwide website and database that captures COVID-19 related food insecurity mitigation strategies and best practices.This project aims to enable data driven discovery and practical insights by linking the available data on food security with information about when, where, how, and why people travel. The team will accomplish this by ingesting the Food Security Index, and related information scraped from tagged content on the Internet, into the teams’ Transportation Equity Open Knowledge Network (OKN) developed under Convergence Accelerator Phase I Pilot project 1936884. The OKN allows stored data, its relationship to other data and to real-world objects and concepts to be understood at a semantic level. This integration will support the development and evaluation of the previously mentioned school lunch delivery program, as well as the identification of people and places most at risk of food insecurity due to a lack of access to transportation. The results are expected to contribute to the response to the current COVID-19 pandemic and any future outbreaks.This RAPID award is made by the Convergence Accelerator program in the Office of Integrative Activities 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": "1910", "attributes": { "award_id": "2032639", "title": "RAPID: Remote Work in the Time of COVID-19", "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": 5067, "first_name": "Melanie", "last_name": "Hughes", "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": 199999, "principal_investigator": { "id": 5069, "first_name": "Wen", "last_name": "Fan", "orcid": null, "emails": "[email protected]", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [ { "id": 5068, "first_name": "Phyllis E", "last_name": "Moen", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "awardee_organization": { "id": 425, "ror": "https://ror.org/02n2fzt79", "name": "Boston College", "address": "", "city": "", "state": "MA", "zip": "", "country": "United States", "approved": true }, "abstract": "The COVID-19 pandemic has transformed where work is done. In response to stay-at-home orders mandated in many states, U.S. workers able to do so have spent some time working remotely. This sudden spike in working from home, combined with the closing of childcare facilities and the move to on-line schooling, represents a sea change in working conditions and family lives, effectively a large-scale social experiment. To begin to understand this social experiment unfolding in real time, this RAPID project will collect quantitative and qualitative data to advance science and inform policy development around three objectives. First, the project will investigate the experiences of remote work and technologies enabling it, including disparities by gender, age/life-course stage, socioeconomic status, and race/ethnicity. Second, the project will examine the relationships between work demands, work resources, and quality of life, including strategic adaptations to remote work and the importance of work resources for certain groups. Third, the project will examine the sustainability of working from home post COVID-19 in terms of workers’ preferences and strategic adaptations. Evidence on the work-, family-, and individual-level as well as technological mechanisms and their relative importance for women and men at different life stages will help organizations to develop effective interventions to help workers better adapt to the risks and possibilities of remote work jumpstarted by the COVID-19 crisis. These findings will lay the groundwork for future organizational interventions and regulatory response in developing and implementing new work designs for the 21st century, thus promoting U.S. economic competitiveness. This RAPID project will launch a baseline survey with a nationally representative sample of 2,000 remote workers, while also collecting qualitative data from 500 remote workers through Amazon Mechanical Turk (MTurk). Respondents for the online baseline survey will be drawn from the KnowledgePanel, the largest probability-based online panel in the U.S. Concurrently, 500 remote workers will be recruited through MTurk to complete an open-ended interview schedule regarding their remote work experiences. The multi-method data will promote understanding of the changing nature of work and technology use, novel tactics employers adopt to monitor workers, struggles and benefits associated with remote work, and employees’ strategic responses to the new working conditions. The proposed designing and fielding of a nationally representative survey, together with the textual data collected from MTurk, will provide original, real-time, and dynamic empirical evidence of workers’ work experiences and lives within the context of the COVID-19 outbreak. Together, findings will inform theories of enduring inequalities in the social organization of where, when, and how work is accomplished, as well as theories of social and organizational change. This study will also illuminate how a social disruption in combination with evolving communication technologies may act as a catalyst for profound transformations in mindsets and social change, specifically regarding future work flexibility.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": "1911", "attributes": { "award_id": "2032082", "title": "Collaborative Research: RAPID: Integrative Modeling of Intervention Serology and the Role of Shield Immunity in Reducing COVID-19 Epidemic Spread", "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": 5070, "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": 99430, "principal_investigator": { "id": 5071, "first_name": "Joshua S", "last_name": "Weitz", "orcid": "https://orcid.org/0000-0002-3433-8312", "emails": "[email protected]", "private_emails": "", "keywords": "['Theoretical ecology']", "approved": true, "websites": "['https://ecotheory.biosci.gatech.edu', 'https://github.com/WeitzGroup']", "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": "Developing intervention strategies that can reduce transmission and alleviate the impacts of social distancing is an essential goal of near- and long-term public health responses to the ongoing COVID-19 pandemic. This project combines epidemic models of COVID-19 with serological testing modules to develop actionable policies to robustly identify recovered individuals who have protective antibodies to SARS-CoV-2019 as a means to (i) reduce infection transmission; (ii) facilitate interaction substitution that can reduce transmission risk (the basis for ‘shield immunity’); (iii) enable safer return to normal economic activity. Findings from shield immunity modeling studies will be released via open-source software, disseminated via policy-making documents that guide the design, interpretation, and action-taking from serological testing initiatives, and communicated with local, state, and national decision makers (e.g. CDC) via rapid response reports. The project will help assess feasibility of scaling up targeted serological testing and shield immunity-based interventions for the public good and economic re-engagement. Additionally, this project will provide training opportunities for graduate students and a postdoctoral scholar.Mitigation and suppression have emerged as the primary means to control and contain local spread of COVID-19. Public health interventions including lockdowns and shelter-in-place orders both reduce infections and raise questions of sustainability and long-term tactics, given the drastic consequences for socio-economic health and well-being. This project expands intervention approaches by extending the conceptual and practical foundations for ‘shield immunity’, i.e., the identification and deployment of recovered individuals as focal points for sustaining safer interactions via interaction substitution of otherwise risky contacts between individuals of unknown disease status. This project evaluates the practical potential of shield immunity by combining realistic scale-out of test capacity and reliability into an integrated framework with explicit consideration of individual status both with respect to disease status and with respect to serological test status. Altogether, the research integrates epidemiological dynamics, nonlinear dynamics, and statistical test analytics. The analysis of combined effects of interaction substitution, test scale, and test reliability will help inform efforts to prudently leverage information on seroconversion and immunity to help control COVID-19 spread while facilitating the safer return of individuals back to economic and social activities. 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": "1912", "attributes": { "award_id": "2032084", "title": "Collaborative Research: RAPID: Integrative Modeling of Intervention Serology and the Role of Shield Immunity in Reducing COVID-19 Epidemic Spread", "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": 5072, "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": 99999, "principal_investigator": { "id": 5073, "first_name": "Benjamin", "last_name": "Lopman", "orcid": null, "emails": "[email protected]", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 265, "ror": "https://ror.org/03czfpz43", "name": "Emory University", "address": "", "city": "", "state": "GA", "zip": "", "country": "United States", "approved": true } ] }, "other_investigators": [], "awardee_organization": { "id": 265, "ror": "https://ror.org/03czfpz43", "name": "Emory University", "address": "", "city": "", "state": "GA", "zip": "", "country": "United States", "approved": true }, "abstract": "Developing intervention strategies that can reduce transmission and alleviate the impacts of social distancing is an essential goal of near- and long-term public health responses to the ongoing COVID-19 pandemic. This project combines epidemic models of COVID-19 with serological testing modules to develop actionable policies to robustly identify recovered individuals who have protective antibodies to SARS-CoV-2019 as a means to (i) reduce infection transmission; (ii) facilitate interaction substitution that can reduce transmission risk (the basis for ‘shield immunity’); (iii) enable safer return to normal economic activity. Findings from shield immunity modeling studies will be released via open-source software, disseminated via policy-making documents that guide the design, interpretation, and action-taking from serological testing initiatives, and communicated with local, state, and national decision makers (e.g. CDC) via rapid response reports. The project will help assess feasibility of scaling up targeted serological testing and shield immunity-based interventions for the public good and economic re-engagement. Additionally, this project will provide training opportunities for graduate students and a postdoctoral scholar.Mitigation and suppression have emerged as the primary means to control and contain local spread of COVID-19. Public health interventions including lockdowns and shelter-in-place orders both reduce infections and raise questions of sustainability and long-term tactics, given the drastic consequences for socio-economic health and well-being. This project expands intervention approaches by extending the conceptual and practical foundations for ‘shield immunity’, i.e., the identification and deployment of recovered individuals as focal points for sustaining safer interactions via interaction substitution of otherwise risky contacts between individuals of unknown disease status. This project evaluates the practical potential of shield immunity by combining realistic scale-out of test capacity and reliability into an integrated framework with explicit consideration of individual status both with respect to disease status and with respect to serological test status. Altogether, the research integrates epidemiological dynamics, nonlinear dynamics, and statistical test analytics. The analysis of combined effects of interaction substitution, test scale, and test reliability will help inform efforts to prudently leverage information on seroconversion and immunity to help control COVID-19 spread while facilitating the safer return of individuals back to economic and social activities. 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": "1913", "attributes": { "award_id": "2029850", "title": "RAPID: Extreme water use patterns and their impact on the microbial and chemical ecology of drinking water.", "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": 5074, "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": 199268, "principal_investigator": { "id": 5077, "first_name": "Kelsey", "last_name": "Pieper", "orcid": null, "emails": "[email protected]", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 184, "ror": "https://ror.org/04t5xt781", "name": "Northeastern University", "address": "", "city": "", "state": "MA", "zip": "", "country": "United States", "approved": true } ] }, "other_investigators": [ { "id": 5075, "first_name": "Aron", "last_name": "Stubbins", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, { "id": 5076, "first_name": "Ameet", "last_name": "Pinto", "orcid": null, "emails": "[email protected]", "private_emails": null, "keywords": "[]", "approved": true, "websites": "[]", "desired_collaboration": "", "comments": "", "affiliations": [] } ], "awardee_organization": { "id": 184, "ror": "https://ror.org/04t5xt781", "name": "Northeastern University", "address": "", "city": "", "state": "MA", "zip": "", "country": "United States", "approved": true }, "abstract": "The COVID-19 pandemic has infected over a million people in the United States to date. Stay-at-home orders and the closing of non-essential businesses have been implemented in many municipalities to limit the spread of COVID-19. An unintentional impact of these interventions is a drastic change in where and how much drinking water is used. For example, water use in commercial buildings has decreased while water use in homes has increased. There is limited understanding of how changes in water use across densely populated cities impact drinking water quality, and what actions must be taken to lower any potential risks. Changes in water use can change the type and number of chemicals or harmful organisms present in the system, affecting water quality and public health. The primary goal of this RAPID project is to understand the effect of drastic changes in water use patterns across residential and commercial locations on the quality of drinking water. Drinking water quality will be monitored at multiple residential and commercial locations in the City of Boston, during and after the pandemic. Results from this study will have a direct impact on post-COVID-19 recovery. Results inform efforts to protect public health and water infrastructure in future scenarios where water use patterns change drastically over short time periods.Stay-at-home advisories and related cessation of all non-essential businesses in response to the global COVID-19 pandemic have dramatically altered drinking water use patterns across the United States. An unintended consequence of these changes is the potential public health concerns of water non-use. Specifically, water use in commercial buildings has decreased dramatically, leading to extended stagnation and loss of disinfection capacity. Stagnation can lead to the growth of biofilms in plumbing containing opportunistic pathogens or corrosion due to the potential for anoxic/anaerobic conditions. At the same time, water use in homes has increased greatly which may also affect biofilms in building plumbing. The objectives of this RAPID project are to determine the impact of (1) changes in water use patterns on the microbial and chemical ecology of drinking water; and (2) growth of biofilms on the bulk water microbial community as a function of varying water use patterns. The research team will leverage ongoing efforts at chemical and integrated metagenomic monitoring of drinking water in the City of Boston to address the project objectives. In the short-term, this research will generate much-needed data and insights to inform recommissioning strategies. This is particularly important in the short-term as the individuals suffering or recovering from COVID-19 may be exposed to opportunistic respiratory pathogens from drinking water. Results will also inform guidance for water utilities on flushing and disinfecting water that was not used in large commercial buildings during the public heath interventions. In the long term, the ability to compare the impact of extreme changes in water use patterns on the chemical and microbial ecology of drinking water in a full-scale drinking water system has the potential to significantly enhance understanding of the biological stability of drinking water. Thus, results from this study will have a direct impact on post-COVID-19 recovery while also leveraging the current situation to provide lasting advances in drinking water quality management.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": "1916", "attributes": { "award_id": "2027306", "title": "RAPID:Comparative quantitative microbial risk assessment of COVID-19 transmission through droplets, aerosols and contaminated surfaces", "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": 5084, "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": "2020-05-01", "end_date": "2023-04-30", "award_amount": 172088, "principal_investigator": { "id": 5085, "first_name": "Sunny", "last_name": "Jiang", "orcid": null, "emails": "[email protected]", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 177, "ror": "", "name": "University of California-Irvine", "address": "", "city": "", "state": "CA", "zip": "", "country": "United States", "approved": true } ] }, "other_investigators": [], "awardee_organization": { "id": 177, "ror": "", "name": "University of California-Irvine", "address": "", "city": "", "state": "CA", "zip": "", "country": "United States", "approved": true }, "abstract": "The global COVID-19 pandemic caused by the virus SARS-CoV2 has caused considerable human health and economic impacts because of its rapid spread and high fatality rate. Examining the trend of COVID-19 data suggests other routes of transmission beyond direct person-to-person transmission are likely. Recent reports show that aerosols containing SARS-CoV2 could still infect an individual for up to 3 hours later, and plastic surfaces contaminated with SARS-CoV2 can remain infective for up to 3 days. Trace evidence of the virus was recovered from Diamond Princess cruise ship cabins up to 17 days after the passengers left. The goal of this project is to understand COVID-19 transmission through virus-contaminated respiratory droplets, fine solid or liquid particles in air, and contact with virus-contaminated surfaces. This study will model human behaviors effecting disease transmission, the properties of the virus, and environmental factors that may affect disease transmission to predict infection risk. If successful, this project will help describe transmission patterns of SARS-CoV2 and provide new ways to slow or halt the spread of this disease in the US. The goal of this project is to identify the infection risk of the novel coronavirus SARS-CoV2 through different exposure routes in order to prioritize measures for public health protection. The specific objectives of this work are to: 1) model the spread of SARS-CoV2 through droplets and aerosols from coughing/sneezing and through aerosols generated from toilet flushing under different environmental conditions; 2) develop exposure models through inhalation of droplets and aerosols to determine the dose of single and repeated exposure; and 3) model repeated exposure through contact with contaminated surfaces and hands-to-face transmission. The models will incorporate mechanistic understanding of aerosol generation, transport, and fate under different conditions, as well as data fitting to predict aerosol size and concentration under different test scenarios. SARS-CoV2 viral shedding rate from patients and its persistence in different environmental media will be used to model the viral load through aerosol and contact exposure. Human physiology and habits will be coupled with viral attack rates to quantify the risk using a Monte Carlo probability simulator. The sensitivity analysis will also identify data gaps for rapid data collection in collaboration with teams of researchers from different disciplines. The results of this project will contribute to the understanding of global spread of infectious disease beyond short and long-term remediation strategies on COVID-19.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": "1917", "attributes": { "award_id": "2028909", "title": "RAPID: Rapid Assay for RNA Extraction and Concentration for COVID-19 Molecular Diagnostics", "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": 5086, "first_name": "Christina", "last_name": "Payne", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2020-05-01", "end_date": "2021-07-31", "award_amount": 200000, "principal_investigator": { "id": 5090, "first_name": "Abdennour", "last_name": "Abbas", "orcid": null, "emails": "[email protected]", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 227, "ror": "", "name": "University of Minnesota-Twin Cities", "address": "", "city": "", "state": "MN", "zip": "", "country": "United States", "approved": true } ] }, "other_investigators": [ { "id": 5087, "first_name": "Andrew C", "last_name": "Nelson", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, { "id": 5088, "first_name": "Ryan", "last_name": "Langlois", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, { "id": 5089, "first_name": "Sophia", "last_name": "Yohe", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "awardee_organization": { "id": 227, "ror": "", "name": "University of Minnesota-Twin Cities", "address": "", "city": "", "state": "MN", "zip": "", "country": "United States", "approved": true }, "abstract": "Access to coronavirus testing is integral to the effort to curb and control the spread of the 2019 coronavirus disease (COVID-19). COVID-19 diagnostic tests currently implement a technique called reverse transcription-polymerase chain reaction (RT-PCR). This technique requires two reagent kits. The first is used to extract the genetic material, ribonucleic acid (RNA), from the coronavirus, and the second is used to amplify the RNA to enable its detection with RT-PCR. The current diagnostic crisis is due, in part, to shortages of the RNA extraction kits and the time required to extract the RNA using the kits. Increasing both the availability and time-efficiency of these kits is vital to improve testing accessibility and enhance the reliability of clinical diagnostics. In this project, a novel process for producing porous sorbent materials will be used to develop an alternative extraction kit for efficient and rapid extraction of nucleic acids from patient samples. The performance will be further optimized by studying the molecular mechanisms governing nucleic acid capture and release by the sorbents and applying this fundamental knowledge. The development and clinical validation of a novel extraction kit will be followed by mass production, addressing the current shortage and improving access to COVID-19 testing. The close collaboration between engineers developing the diagnostic technologies and clinicians implementing the prototypes, as enabled by this project, will streamline the transition of scientific knowledge into solutions that benefit the health and well-being of society. The project will also provide workforce development opportunities through training researchers in novel diagnostic techniques for coronaviruses.The goal of this project is to develop a novel, scalable approach to nucleic acid separation and concentration and mass-produce prototype kits for immediate implementation in clinical settings. The concept relies on the use of sorbent materials instead of the current filter- and silica column-based approach. Unlike filters that use pore size to physically separate the target (here nucleic acids) from the media, sorbents are a porous material that captures the target by chemical affinity and interactions. As a result, the use of a sorbent enables larger water flow rates, enhanced nucleic acid capture efficiency, and faster sample processing, and overcomes the need for multiple buffers or extraction steps. Functionalization of conventional filters with a combination of metal oxide nanoparticles and organosiloxane polymers will be used to produce the nucleic acid sorbent. The functionalization will be achieved by a new method for supported synthesis of nanoparticles by thermolysis and polymer conjugation. The efficiency of the sorbent will be assessed by the extraction and detection of nucleic acid using quantitative RT-PCR. Validation of the alternative sorbent kit will assess the effectiveness of the rapid separation and concentration of the novel coronavirus (SARS-CoV-2) RNA. The project will also explore the fundamentals of nucleic acid dynamics in porous sorbents as it relates to nucleic acid size, sorbent chemistry, porosity and pore size, and the effect of transport phenomena in porous media. Enhanced understanding of the factors that affect the retention, release, and transport of nucleic acids in porous media is critical to producing reliable and efficient nucleic acid extraction and detection kits and the development of other bio-separation processes. Training of postdoctoral researchers and graduate and undergraduate students will focus on how the combination of transdisciplinary collaboration and a clear understanding of the fundamental aspects can lead to disruptive technologies.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": "1920", "attributes": { "award_id": "2037261", "title": "RAPID: Collaborative Research: The Transformation of Essential Work: Managing the Introduction of AI in Response to COVID-19", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Computer and Information Science and Engineering (CISE)" ], "program_reference_codes": [ "096Z", "7914" ], "program_officials": [ { "id": 5096, "first_name": "Andruid", "last_name": "Kerne", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2020-07-15", "end_date": "2022-09-30", "award_amount": 86373, "principal_investigator": { "id": 5097, "first_name": "Samantha", "last_name": "Shorey", "orcid": null, "emails": "[email protected]", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 156, "ror": "", "name": "University of Texas at Austin", "address": "", "city": "", "state": "TX", "zip": "", "country": "United States", "approved": true } ] }, "other_investigators": [], "awardee_organization": { "id": 156, "ror": "", "name": "University of Texas at Austin", "address": "", "city": "", "state": "TX", "zip": "", "country": "United States", "approved": true }, "abstract": "Millions of people deemed “essential workers” in the COVID-19 pandemic perform manual labor, such as sorting, cleaning, garbage collection, and recycling. To mitigate risks associated with this work, there is an accelerated push to introduce artificial intelligence (AI) to safeguard the public and workers from disease transmission. Yet, decades of human-computer interaction and organizational communication research shows that the introduction of new technologies into workplaces is not an easy transition; instead technologies transform and displace existing work practices. This research project investigates both beneficial innovations and liabilities arising in waste management industries, as they deploy AI technologies in response to the COVID-19 crisis. It develops a set of best practices for the coordination of human labor and AI to address the pandemic, transforming the future of work. The best practices will be presented as guidance on how to incorporate AI into critical economic institutions to mitigate the negative effects of COVID-19 on public health, society, and the economy. This guidance will regularly be communicated to workers, industry leaders, and the public through an open access toolkit, a workshop series, press releases, and social media. This will potentially benefit essential industries that employ or serve tens of millions of workers, including waste labor, shipping, manufacturing, retail, and food service.This project will be conducted through a multi-site ethnographic study, examining how two American waste management organizations negotiate the introduction of automated technologies, in an effort to mitigate risks associated with the COVID-19 pandemic. The first involves automated “floor care” robots at Pittsburgh International Airport. The second involves AI sorting systems in a single stream recycling plant, in Austin, Texas. By studying two sites, the research team is expected to gain comparative insight into how automation is introduced and attuned, according to professional, regional, and institutional norms. Data collection will include ethnographic fieldnotes, interview transcripts, and media materials. Extending theories of technological diffusion and invisible labor, the research team will qualitatively analyze the technology dissemination process, drawing insights from the actions and perspectives of workers as they negotiate the changing shape of their daily work. Through reflexive memos and “constant comparative” coding, the research will identify patterns of action and build a set of transferable observations. This is expected to yield (1) empirical findings on factors that promote or hinder rapid technological introduction in response to crisis, with specific insights on the human labor required to make automated technologies work (e.g., calibration, troubleshooting, and maintenance), (2) theoretical findings that contribute core understandings of the diffusion of innovation and how workplace technologies are reinvented through use, and (3) design recommendations for a variety of essential work sectors.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": "1921", "attributes": { "award_id": "2029336", "title": "RAPID: Science and Social Networks: COVID-19 in an Urban Epicenter", "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": 5098, "first_name": "Frederick", "last_name": "Kronz", "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": 99995, "principal_investigator": { "id": 5100, "first_name": "Kavita", "last_name": "Sivaramakrishnan", "orcid": null, "emails": "[email protected]", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 196, "ror": "https://ror.org/00hj8s172", "name": "Columbia University", "address": "", "city": "", "state": "NY", "zip": "", "country": "United States", "approved": true } ] }, "other_investigators": [ { "id": 5099, "first_name": "Merlin", "last_name": "Chowkwanyun", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "awardee_organization": { "id": 196, "ror": "https://ror.org/00hj8s172", "name": "Columbia University", "address": "", "city": "", "state": "NY", "zip": "", "country": "United States", "approved": true }, "abstract": "The primary objective of this RAPID research project is to further our understanding of the influences that shape policy making concerning public health during the COVID 19 pandemic with a focus on New York City as a global epicenter. The researchers plan to provide a time sensitive, living record and analysis that documents the unfolding of social and political aspects of the present crisis. They will study relational encounters in social and political realms during the pandemic that shape multiple interpretations of events, images, and broadcasts in a networked world; and how they in turn affect public responses, programmatic decisions, and policies that relate to public health in an urban context. The results of the project will be disseminated to policy makers, community members, media spokespersons, public health and medical experts in New York City, and global health stakeholders through publications in policy journals, newspapers, access to radio and news programs, and online platforms. This project aims to integrate insights from STS studies, history of global public health, and cultural and media studies to analyze political responses, public health communications, and policy decisions concerning COVID-19. It will contribute to STS frameworks by arguing that COVID-19 needs to be understood not only in terms of epidemiological and biological evidence-building regarding its contagiousness and virulence; it also needs to be understood in terms of social and political virality and transmissibility of social modes of thought that shape its interpretations. To achieve these ends, the researchers will draw from media studies of social susceptibility, diffusion, and differential impact. They will engage in content analysis of traditional media and social media, and they will conduct interviews with 50 key actors. They will produce an initial account of policymaking in a time of health and social crisis while generating critical material for future scholars of COVID-19. Their interview materials are to be donated to the Columbia University Center for Oral History Research; their print/digital materials are to be donated to the Special Collections division at the University’s Health Sciences Library.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.", "keywords": [], "approved": true } } ], "meta": { "pagination": { "page": 1384, "pages": 1421, "count": 14201 } } }