Grant List
Represents Grant table in the DB
GET /v1/grants?page%5Bnumber%5D=1391&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=1424&sort=program_reference_codes", "next": "https://cic-apps.datascience.columbia.edu/v1/grants?page%5Bnumber%5D=1392&sort=program_reference_codes", "prev": "https://cic-apps.datascience.columbia.edu/v1/grants?page%5Bnumber%5D=1390&sort=program_reference_codes" }, "data": [ { "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 } }, { "type": "Grant", "id": "2036", "attributes": { "award_id": "2032598", "title": "RAPID: Assessing Ethical Challenges in Conducting Do-it-yourself (DIY) Science During the COVID-19 Pandemic", "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": 5461, "first_name": "Wenda K.", "last_name": "Bauchspies", "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": 149495, "principal_investigator": { "id": 5463, "first_name": "Anna", "last_name": "Wexler", "orcid": null, "emails": "[email protected]", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 232, "ror": "https://ror.org/00b30xv10", "name": "University of Pennsylvania", "address": "", "city": "", "state": "PA", "zip": "", "country": "United States", "approved": true } ] }, "other_investigators": [ { "id": 5462, "first_name": "Lisa M", "last_name": "Rasmussen", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "awardee_organization": { "id": 232, "ror": "https://ror.org/00b30xv10", "name": "University of Pennsylvania", "address": "", "city": "", "state": "PA", "zip": "", "country": "United States", "approved": true }, "abstract": "The United States works to ensure that the research it sponsors is conducted ethically by requiring oversight of certain kinds of research, such as that involving humans or animals. However, do-it-yourself (DIY) scientific research conducted by individuals in their homes, garages, or community labs often is not subject to these research ethics regulations. The ethical challenges that participants in DIY communities may encounter are particularly salient in the COVID-19 pandemic, which has spurred an international group of DIY researchers to come together in pursuit of diagnostics and treatments. Because DIYers participating in these efforts are primarily communicating via publicly accessible online outlets, the pandemic offers an excellent opportunity to learn more about ethical issues in DIY research in a concentrated and accelerated setting. The project aims to assess how unregulated DIY research communities negotiate ethical challenges during an ongoing pandemic. It will lead to a better understanding of how DIY scientists identify, approach, and resolve ethical issues in their work in crises, and to practical understanding of what barriers or facilitators of ethical research exist in unregulated domains. Through online observations and interviews, this project examines how participants in open-source COVID-19 projects negotiate three central ethical issues: biosafety and harm, validation and replication, and authorship and credit. Phase 1 consists of analyzing digital communications amongst project members on an international DIY science platform called Just One Giant Lab (JOGL), where thousands of individuals are collaborating on open-source COVID-19 projects. Phase 2 involves conducting follow-up interviews with approximately 40 JOGL participants to probe participants’ ethics-related comments, how ethical challenges were resolved or were unable to be resolved, and whether there was agreement with and satisfaction about eventual outcomes. The interviews will explore participants’ views regarding barriers to—and facilitators of—unregulated ethical research, and what tools they would have found helpful in that context. This research will significantly advance our understanding of how and where ethical issues arise in unregulated research during times of crisis, and how participants in open science communities negotiate these challenges. This project will result in recommendations for developing better tools and approaches to help ensure the ethical conduct of DIY research, and will assist regulators and policymakers with the development of more informed approaches to ethics in unregulated research.This proposal was funded through the ER2 program by the BIO and OISE directorates.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": "2037", "attributes": { "award_id": "2034625", "title": "RAPID: Understanding and Enhancing Internet Connectivity of Underserved Communities During the COVID-19 Crisis", "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": 5464, "first_name": "Deepankar", "last_name": "Medhi", "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": 199303, "principal_investigator": { "id": 5468, "first_name": "Konstantinos", "last_name": "Pelechrinis", "orcid": null, "emails": "[email protected]", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 272, "ror": "https://ror.org/01an3r305", "name": "University of Pittsburgh", "address": "", "city": "", "state": "PA", "zip": "", "country": "United States", "approved": true } ] }, "other_investigators": [ { "id": 5465, "first_name": "Prashant", "last_name": "Krishnamurthy", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, { "id": 5466, "first_name": "Martin B", "last_name": "Weiss", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, { "id": 5467, "first_name": "Eleanor", "last_name": "Mattern", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "awardee_organization": { "id": 272, "ror": "https://ror.org/01an3r305", "name": "University of Pittsburgh", "address": "", "city": "", "state": "PA", "zip": "", "country": "United States", "approved": true }, "abstract": "In the absence of pharmaceutical interventions for COVID-19, governments and authorities had to rely on other measures for curbing the impact of the pandemic. Almost everywhere in the world, measures of social distancing and shelter-at-home orders were put in place, leading to the need for people to work from home (WFH), including schooling. This situation once again highlighted the issue of digital divide. Even today, not everyone in the US has access to computing and broadband Internet connection. The objective of this project is twofold; (a) understand how underserved communities in Pittsburgh gained Internet access and what problems they faced, and (b) identify ways to improve the connectivity of underserved communities, by utilizing and significantly enhancing existing wireless mesh technologies, that will allow to share highly underutilized bandwidth resources with communities in need. The project seeks to bridge local information divide – which is leading to well-being divide – in the pandemic situation, where reliance on networked technologies is impacting people’s livelihoods. The main expected technical contributions of this project is the design of networks that can “ship” network capacity from underutilized locations to underserved locations, using low-cost mesh networks. Unlike previous approaches to mesh networking, the constraints of this problem are the immutable locations and the tradeoffs between capacity that can be allocated and the capacity that is needed. The limitations on these degrees of freedom will influence network design, as well as, mechanisms (routing, wireless channelization, adaptive rates) to dynamically deliver capacity to location which have the demand. The project will also provide a data-driven way to identify and target underutilized network resources, as well as, underserved locations and populations in need for network access. The dimensions of this problem are numerous, not well understood, and span social, infrastructural and technological factor. This project will support the work of MetaMesh in operating and expanding PittMesh, which is a crucial resource for underserved communities especially in times of crisis. Furthermore, the findings can support the development of policies to promote connectivity of unserved communities. While this would be applicable to widespread emergencies such as the COVID-19 pandemic shutdown, the project will explore policy proposals that would support narrower emergency response, as well as, promoting resilient service to underserved communities. In a world that is changing to online interactions (either mid-term or long-term), broadband connectivity is practically an essential household utility. Additional information about the project can be found at: http://www.pitt.edu/~kpele/PROJECTS/covid-netaccess.htmlThis 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": "2040", "attributes": { "award_id": "2032179", "title": "RAPID: Learning to Teach During COVID-19: Leveraging Simulated Classrooms as Practice-Based Spaces for Preservice Elementary Teachers within Online Teacher Education Courses", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Education and Human Resources (EHR)" ], "program_reference_codes": [ "096Z", "7914" ], "program_officials": [ { "id": 5475, "first_name": "Michael", "last_name": "Steele", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2020-06-15", "end_date": "2021-05-31", "award_amount": 199440, "principal_investigator": { "id": 5477, "first_name": "Jamie N", "last_name": "Mikeska", "orcid": "https://orcid.org/0000-0002-8831-2572", "emails": "[email protected]", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [ { "id": 5476, "first_name": "Heather", "last_name": "Howell", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "awardee_organization": { "id": 296, "ror": "https://ror.org/03b5q4637", "name": "Educational Testing Service", "address": "", "city": "", "state": "NJ", "zip": "", "country": "United States", "approved": true }, "abstract": "School-based field experiences are a critical part of preservice teacher education. The COVID-19 pandemic has significantly disrupted the ability of teacher education programs to place their teacher candidates in typical K-12 teaching settings as a part of learning to teach. This project examines how simulated classroom field experiences for preservice teachers can be implemented in online and emergency remote teacher education courses. Elementary mathematics and science teacher educators are provided with opportunities to engage their preservice teachers in practice-based spaces using mixed-reality simulated classroom environments. These simulations are real-time lessons with animated student avatars that are voiced by an interactor who is responding to the teacher's lesson in real time in ways that represent authentic student thinking. This project aims to develop support materials for integrating simulated field experiences into elementary mathematics and science teacher education courses. The research will seek to understand what preservice teachers learn about teaching from these experiences, how teacher educators integrate the simulated field experiences into coursework, and how such simulated experiences can be integrated in remote, online courses in ways that support preservice teacher learning.This project advances knowledge through the development and deployment of simulation-based tools that develop preservice elementary teachers' abilities to teach mathematics and science. Preservice teachers use performance tasks to deliver instruction in the simulated classroom. The project develops support materials for teacher educators to integrate this work into online and/or emergency remote teacher education courses (in response to COVID-19) in ways that support engagement in ambitious teaching practice. The project assesses impact on preservice teachers' ambitious teaching practice through artifacts of the simulated classroom practice, including observations and recordings of the simulated interactions and preservice teacher surveys and assessments of their use of ambitious teaching practices. The project evaluates the ways in which teacher educators integrate the simulated field experience into their emergency remote teacher education courses through surveys and interviews. The research addresses the immediate COVID-19 pandemic challenges in providing field experiences for students and provides long-term support for the ongoing challenge of finding field experience settings that are conducive to preparing highly-qualified elementary mathematics and science teachers.The Discovery Research preK-12 program (DRK-12) seeks to significantly enhance the learning and teaching of science, technology, engineering and mathematics (STEM) by preK-12 students and teachers, through research and development of innovative resources, models and tools. Projects in the DRK-12 program build on fundamental research in STEM education and prior research and development efforts that provide theoretical and empirical justification for proposed projects.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": "2057", "attributes": { "award_id": "2028892", "title": "RAPID: Collaborative Research: Quarantined Networks and the Spread of COVID-19", "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": 5523, "first_name": "Pawel", "last_name": "Hitczenko", "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": 59541, "principal_investigator": { "id": 5525, "first_name": "Matthew S", "last_name": "Junge", "orcid": null, "emails": "[email protected]", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 714, "ror": "https://ror.org/04yrgt058", "name": "Bard College", "address": "", "city": "", "state": "NY", "zip": "", "country": "United States", "approved": true } ] }, "other_investigators": [ { "id": 5524, "first_name": "Felicia", "last_name": "Keesing", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "awardee_organization": { "id": 714, "ror": "https://ror.org/04yrgt058", "name": "Bard College", "address": "", "city": "", "state": "NY", "zip": "", "country": "United States", "approved": true }, "abstract": "As the global community weighs the necessary extent of quarantine and social distancing to fight the spread of COVID-19, the critical question is how disease transmission is mitigated by these measures. Recent predictions suggest that without serious interventions, a large portion of the world population will become infected, resulting in millions of deaths. To mitigate this worst-case scenario, key policy decisions are being guided by mathematical models. However, several prominent models make unrealistic assumptions about human contacts i.e., that an individual is equally likely to infect a close family member as a complete stranger on the other side of the country. Such assumptions are useful for calculations, but fail to take into account the full geographic complexity of the outbreak. Furthermore, many models do not consider the consequences of the quarantine of healthy individuals. This project will use rigorous analysis and simulation to address these shortcomings by describing a more realistic structure of quarantined networks and how disease spreads in them. The proposed research will use real-world data about contact networks to make predictions and recommendations for controlling the COVID-19 outbreak, improving our understanding of how best to contain the current as well as future pandemics. The project will involve the training of undergraduate students.This research will describe the effect of quarantine on connectivity and disease transmission on more realistic networks than have previously been considered. Of particular importance will be locating critical thresholds which, when exceeded, allow large epidemics to occur. There is recent study of these thresholds, but for networks that model digital infrastructure and social networks. The first objective of the research will be to determine the effect of biased site percolation on graph structure, especially how different percolation rules influence the size of the largest component of a given graph. The second part will then focus on how the critical threshold and size of the epidemic for an SIR model change after percolation. This will be explored rigorously on graphs generated from the configuration model as well as random spatial networks such as Gilbert graphs. Additionally, these questions will be investigated on real world face-to-face networks using data specific to the current COVID-19 pandemic. Answering them will help test robustness of previous models, while also exploring the effectiveness of stronger preemptive distancing.This grant is being awarded using funds made available by the Coronavirus Aid, Relief, and Economic Security (CARES) Act supplemental funds 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": "2058", "attributes": { "award_id": "2028880", "title": "RAPID: Collaborative Research: Quarantined Networks and the Spread of COVID-19", "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": 5526, "first_name": "Pawel", "last_name": "Hitczenko", "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": 22554, "principal_investigator": { "id": 5527, "first_name": "Nicole", "last_name": "Eikmeier", "orcid": null, "emails": "[email protected]", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 715, "ror": "https://ror.org/04tmmky42", "name": "Grinnell College", "address": "", "city": "", "state": "IA", "zip": "", "country": "United States", "approved": true } ] }, "other_investigators": [], "awardee_organization": { "id": 715, "ror": "https://ror.org/04tmmky42", "name": "Grinnell College", "address": "", "city": "", "state": "IA", "zip": "", "country": "United States", "approved": true }, "abstract": "As the global community weighs the necessary extent of quarantine and social distancing to fight the spread of COVID-19, the critical question is how disease transmission is mitigated by these measures. Recent predictions suggest that without serious interventions, a large portion of the world population will become infected, resulting in millions of deaths. To mitigate this worst-case scenario, key policy decisions are being guided by mathematical models. However, several prominent models make unrealistic assumptions about human contacts i.e., that an individual is equally likely to infect a close family member as a complete stranger on the other side of the country. Such assumptions are useful for calculations, but fail to take into account the full geographic complexity of the outbreak. Furthermore, many models do not consider the consequences of the quarantine of healthy individuals. This project will use rigorous analysis and simulation to address these shortcomings by describing a more realistic structure of quarantined networks and how disease spreads in them. The proposed research will use real-world data about contact networks to make predictions and recommendations for controlling the COVID-19 outbreak, improving our understanding of how best to contain the current as well as future pandemics. The project will involve the training of undergraduate students.This research will describe the effect of quarantine on connectivity and disease transmission on more realistic networks than have previously been considered. Of particular importance will be locating critical thresholds which, when exceeded, allow large epidemics to occur. There is recent study of these thresholds, but for networks that model digital infrastructure and social networks. The first objective of the research will be to determine the effect of biased site percolation on graph structure, especially how different percolation rules influence the size of the largest component of a given graph. The second part will then focus on how the critical threshold and size of the epidemic for an SIR model change after percolation. This will be explored rigorously on graphs generated from the configuration model as well as random spatial networks such as Gilbert graphs. Additionally, these questions will be investigated on real world face-to-face networks using data specific to the current COVID-19 pandemic. Answering them will help test robustness of previous models, while also exploring the effectiveness of stronger preemptive distancing.This grant is being awarded using funds made available by the Coronavirus Aid, Relief, and Economic Security (CARES) Act supplemental funds 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": "2074", "attributes": { "award_id": "2032107", "title": "RAPID: Energy-Efficient Disinfection of Viral Bioaerosols in Public Spaces: Vital for Lifting of the “Stay-at-Home” Orders during the Covid-19 Outbreak", "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": 5567, "first_name": "Bruce", "last_name": "Hamilton", "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": 200000, "principal_investigator": { "id": 5568, "first_name": "Jelena", "last_name": "Srebric", "orcid": "https://orcid.org/0000-0001-6825-5091", "emails": "[email protected]", "private_emails": "", "keywords": null, "approved": true, "websites": "['https://city.umd.edu/covid-19']", "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 297, "ror": "https://ror.org/047s2c258", "name": "University of Maryland, College Park", "address": "", "city": "", "state": "MD", "zip": "", "country": "United States", "approved": true } ] }, "other_investigators": [], "awardee_organization": { "id": 297, "ror": "https://ror.org/047s2c258", "name": "University of Maryland, College Park", "address": "", "city": "", "state": "MD", "zip": "", "country": "United States", "approved": true }, "abstract": "This project will provide an analytical framework to assess potential reduction of infection risks from COVID-19 viral bioaerosols in public spaces, including school buses, classrooms, and retail stores. Viral bioaerosols may cause infection for occupants staying both near and far away from infected people, whether staying indoors at the same time or not. Upper-room germicidal ultraviolet (UR-GUV) light can provide a real-time air disinfection solution with a relatively small energy footprint if its light effectively interacts with the bioaerosol both in the air and on surfaces. This project will develop and disseminate an open-source numerical analytical framework including assessment of UR-GUV disinfection and make it publicly available online to provide a free resource useful for helping to control the spread of airborne COVID-19 infections in public spaces. An effective, real-time, and sustainable engineering solution for air indoor space disinfection is an important precaution to help prevent the spread of COVID-19, particularly in the context of efforts to restart the nation's economy. The project will develop numerical methods based on Computational Fluid Dynamics (CFD) to reproduce the processes for viral bioaerosols spread by indoor airflow, removed by exhaust, inactivated by UR-GUV, inhaled by the occupants, and deposited onto surfaces in public spaces of varied spatial scales, ventilation systems, as well as population size and density. This project will also optimize the application of ceiling fans to improve UR-GUV disinfection efficacy. The investigation will provide new insight on infection risk due to viral aerosols and infection control by UR-GUV for surfaces contaminated by viral bioaerosols. In addition, the project will consider two UV-C sources, one by traditional mercury vapor UV-C lamps (UV-C-MV) and another by UV-C-LED for their energy efficiency. The comparison of the two UV-C sources in terms of disinfection, energy efficiencies, and operation cost holds promise for a sustainable UR-GUV solution for minimizing infection risk in public spaces.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": "2078", "attributes": { "award_id": "2027420", "title": "RAPID: COVID Response: Identifying practices that minimize exposure to disinfection byproducts", "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": 5579, "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": "2021-04-30", "award_amount": 190337, "principal_investigator": { "id": 5582, "first_name": "Lea Hildebrandt", "last_name": "Ruiz", "orcid": "https://orcid.org/0000-0001-8378-1882", "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": [ { "id": 5580, "first_name": "Atila", "last_name": "Novoselac", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, { "id": 5581, "first_name": "Pawel K", "last_name": "Misztal", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "awardee_organization": { "id": 156, "ror": "", "name": "University of Texas at Austin", "address": "", "city": "", "state": "TX", "zip": "", "country": "United States", "approved": true }, "abstract": "To curb the spread of COVID-19, communities across the world are disinfecting buildings and other public places. Cleaning workers wear different levels of personal protection ranging from simple surgical masks to professional high efficiency filtration masks. Depending on the mask type, cleaning process, surfaces, and disinfectants used, cleaning workers can be exposed to disinfection byproducts that form as a result of reactions between disinfectants, the surfaces being cleaned, and the mask materials. This project will quantify inhalation of disinfection byproducts during disinfection while wearing different classes of masks and use this information to make recommendations on which mask to wear and how often to change or clean the mask or mask components. These data are directly relevant to the current worldwide COVID-19 crisis and similar future pandemic challenges. The core project team includes at least three graduate research assistants, one postdoctoral scholar, and five professors with different backgrounds and at different stages of their careers. Thus, a further impact of this project will be advancing education and training the next generation of scientists and engineers in techniques to respond to pandemic challenges. This project consists of a two-phase approach to address potential health threats to cleaning workers resulting from the large-scale use of disinfection in response to the global COVID-19 pandemic. The goal of the first phase is to define possible concentration ranges of chemicals that flow through personal protection masks based on: 1) the type of disinfectant used; 2) the application process (e.g. wiping, spraying, or fogging); 3) chemical processes forming byproducts (such as reaction of cleaning products on surfaces); 4) environmental conditions (such as ventilation/dilution rate); and 5) proximity of the cleaning worker to the chemical source. The goal of phase two is to characterize the inhalation of disinfection byproducts. A full-scale thermal manikin equipped with a nose and mouth breathing simulation system will be exposed to uniform concentrations of byproducts determined in phase one in an environmental chamber. Surface chemical reaction processes and kinetics will be studied with different masks and byproduct inhalation will be assessed via mass spectrometry. The manikin will be equipped with four different classes of masks: surgical mask, dust mask without exhalation relief valve, dust mask with exhalation relief valve, and professional mask. It is likely that some masks decrease inhalation exposure to disinfection byproducts while others increase exposure via adsorption of cleaning product vapors to the mask and by addition of moisture due to exhalation. These processes lead to a mask chemistry that produces additional chemical products whose concentration is much greater in the mask than in the ambient air. These secondary products are potentially more harmful than the primary vapors of cleaning products. Successful completion of this study will provide timely and critical data relevant to the current crisis by identifying risks for different types of masks and cleaning products. Such information will inform recommendations on mask use to protect the health of cleaning workers.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": "2080", "attributes": { "award_id": "2029301", "title": "RAPID: Examining How Access to Green Space Impacts Subjective Well-Being During the COVID-19 Pandemic", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Engineering (ENG)" ], "program_reference_codes": [ "096Z", "7914" ], "program_officials": [ { "id": 5585, "first_name": "Bruce", "last_name": "Hamilton", "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": 99790, "principal_investigator": { "id": 5589, "first_name": "Brian J", "last_name": "Mailloux", "orcid": null, "emails": "[email protected]", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 717, "ror": "https://ror.org/04rt94r53", "name": "Barnard College", "address": "", "city": "", "state": "NY", "zip": "", "country": "United States", "approved": true } ] }, "other_investigators": [ { "id": 5586, "first_name": "Patricia J", "last_name": "Culligan", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, { "id": 5587, "first_name": "Benjamin S", "last_name": "Orlove", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, { "id": 5588, "first_name": "Elizabeth M", "last_name": "Cook", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "awardee_organization": { "id": 717, "ror": "https://ror.org/04rt94r53", "name": "Barnard College", "address": "", "city": "", "state": "NY", "zip": "", "country": "United States", "approved": true }, "abstract": "The aim of this project is to explore the connections among green space, perception of risk, and well-being in times of a public health emergency that require people to stay indoors and isolated. In a crisis like the current COVID-19 pandemic, the role of urban green spaces in promoting public health and well-being may be an especially important co-benefit of green infrastructure (GI) programs. However, there is simultaneous acknowledgement that being outdoors, even in green space, is a continuing source of risk for exposure and/or spread of COVID-19. Therefore, this research asks, how do variations in access to green space, whether due to lack of safe, nearby green space and/or the perceived risk associated with being outdoors in particular kinds of spaces during a public pandemic, impact well-being? To answer these questions, this study uses online surveys and video interviews with college students. These students have traveled home from their campuses, returning to a wide variety of residential and landscape forms, presenting an opportunity to conduct comparative study of how access to green space influences responses to current conditions and well-being. The survey, distributed via email, includes questions about well-being, outdoor activity, risk perception, and personal responses to social distancing/self-isolation measures. Interviewees, solicited from survey participants, will be asked questions about available outdoor green space and activity, lifestyle changes in response to COVID-19 and their effect on well-being, the role of outdoor activity in subjects' well-being, and barriers to outdoor activity under present circumstances. Both statistical analyses and qualitative coding analyses will be used to determine (a) subjects' access to different types of green space; (b) subjects' willingness to utilize green space with respect to type, accessibility, and risk perception; and (c) the association of (a) and (b) with subjects' well-being during the pandemic. This study will expand knowledge on the co-benefits of green infrastructure (GI) programs, with an emphasis on the co-benefit of health and well-being during times of great stress. This study will allow determination of the impacts on well-being for a wide-range of green space types and accessibility, across and within landscape forms (urban, suburban, rural). As such, this study will contribute to understandings of how novel, large-scale stress-inducing events intersect with differences in green space type and access to contribute to inequalities in human well-being. By collecting data from a diverse study population located across a broad area this study will be able to make comparisons across a range of green space types and access. This will further understanding of the mechanisms by which green space affects well-being, applicable to a range of fields like public health, civil engineering, urban ecology, and urban design and planning. This research will address two primary broader implications for well-being in the face of current and future global pandemic-related challenges. First, this study examines how green space access affects well- being during social distancing/stay-at-home conditions, such as those experienced by more than three-fifths of the US population. Second, this study will provide detailed information on subjects' choices and behaviors regarding outdoor recreation during social distancing/stay-at-home conditions, including risk perception and beliefs about the importance of green space access. Together these data will be used to inform current and future preparations and responses to pandemics and other extreme emergency circumstances through recommendations on outdoor recreation, well-being, and current and future planned GI design. Attention to characteristics of neighborhoods by type of building and by population characteristics will support GI planning and implementation by providing fine spatial resolution for the integration of GI contributions to well-being with GI contributions to stormwater management and other environmental benefits.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.", "keywords": [], "approved": true } } ], "meta": { "pagination": { "page": 1391, "pages": 1424, "count": 14236 } } }