Grant List
Represents Grant table in the DB
GET /v1/grants?page%5Bnumber%5D=1391&sort=program_reference_codes
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The symptoms of COVID-19 can vary dramatically in both presentation and severity. Individuals infected with COVID-19 may be asymptomatic while still able to transmit the virus. Population-based studies suggest the rate of asymptomatic cases of COVID-19 may be high, but data is lacking. In order to evaluate the true prevalence of the virus, characterize individuals that are affected and express severe symptoms and assess the effects of interventions to attenuate the proliferation of the spread of the virus, data regarding both the symptomatic and asymptomatic cases are critically important. Measurement of rates of COVID-19 are important for surveillance and predictive modeling in the general population as well as to inform scientific inquiry regarding the potential biological mechanisms underlying the expression of symptoms among select individuals. The current project will provide novel insights about COVID-19 by assessing test results of decedents referred for tissue or organ donation across the United States in an epidemiological framework. Broad dissemination of these results will inform ongoing national policy and will continue to expand the scope and accuracy of predictive modeling.This research project will provide unique insights into the epidemiology of COVID-19 in the United States. This will be accomplished by aggregating systematic test results of COVID-19 among decedents referred for tissue and organ donation. Organ procurement organizations (OPOs) are responsible for recovering deceased donors for transplantation in the United States. OPOs are now utilizing tests of COVID-19 for decedents referred as potential donors to assess risk prior to allocation to a potential recipient. Importantly, this includes donor tissue and organs from decedents with and without symptoms or known exposure to COVID-19. This process provides a unique opportunity to evaluate the incidence, progression and characteristic of COVID-19 including decedents from unrelated causes. These data will further be analyzed via statistics and predictive models to model the trajectory of COVID-19 in the general population based on demographic and clinical characteristics of the general population in different regions of the country. This RAPID award is made by the Ecology and Evolution of Infectious Disease Program in the Division of Environmental Biology, using funds from the Coronavirus Aid, Relief, and Economic Security (CARES) Act.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.", "keywords": [], "approved": true } }, { "type": "Grant", "id": "2208", "attributes": { "award_id": "2029918", "title": "RAPID: Interdependent social vulnerability of COVID-19 and weather-related hazards in New York City", "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": 5977, "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": "2020-05-01", "end_date": "2021-04-30", "award_amount": 197475, "principal_investigator": { "id": 5981, "first_name": "P. Timon", "last_name": "McPhearson", "orcid": null, "emails": "[email protected]", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 723, "ror": "", "name": "The New School", "address": "", "city": "", "state": "NY", "zip": "", "country": "United States", "approved": true } ] }, "other_investigators": [ { "id": 5978, "first_name": "Luis E", "last_name": "Ortiz", "orcid": null, "emails": "[email protected]", "private_emails": null, "keywords": "[]", "approved": true, "websites": "[]", "desired_collaboration": "", "comments": "", "affiliations": [ { "id": 723, "ror": "", "name": "The New School", "address": "", "city": "", "state": "NY", "zip": "", "country": "United States", "approved": true } ] }, { "id": 5979, "first_name": "Christopher", "last_name": "Kennedy", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, { "id": 5980, "first_name": "Ahmed M", "last_name": "Mustafa", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "awardee_organization": { "id": 723, "ror": "", "name": "The New School", "address": "", "city": "", "state": "NY", "zip": "", "country": "United States", "approved": true }, "abstract": "The study will integrate survey, social media, building infrastructure, energy demand and use, and social- demographic data with simulations of potential emerging weather-related extremes to examine interdependent social vulnerability to COVID-19 and weather in New York City (NYC). The research will leverage cutting-edge simulations, modeling, and visualizations of urban social and infrastructure systems to understand how human behavior changes in response to shelter-in-place policies may expose potential interdependent and cascading social vulnerability to COVID and weather extremes. The primary research question is: How will existing vulnerabilities to health, weather, and economic hazards be affected by new guidelines designed to reduce COVID-19 transmission rates in NYC? The primary outcome will be to advance knowledge for understanding COVID-19 impacts in the national epicenter of the virus outbreak and where solutions will be needed for many months as the pandemic begins to interact with weather dynamics and drive interdependent vulnerability over time. As the COVID-19 pandemic evolves rapidly in NYC, there is an urgent need to collect data on social and economic impacts as they emerge, and join these with existing local, regional, and national datasets to anticipate potential interdependent impacts of COVID-19 as weather dynamics shift over coming months. Social survey and social media data are especially critical to collect now as perspectives on location specific experiences and perspective of green space as critical infrastructure can change over time. Additionally, social media data can only be accessed cost-effectively via Twitter in weekly intervals and analyses are needed now to understand policy impacts in time to plan responses and strategies for resilience to interdependent COVID-weather extremes impacts. A convergent scientific approach is critical for examining how vulnerable populations may be further impacted as spring turns to summer with potential heat waves and extreme rainfall events. The analysis will examine how overlapping vulnerabilities interact with availability and usage of urban green spaces for physical and mental health during COVID-19 shelter-in-place policies. For example, data will include weekly geo-located tweets overlaid with buildings and green space spatial data to explore dominant locations of social media activity in NYC to understand which parks and open space are most used, and which will require additional resources to meet public need for physical and mental health. This data will provide input to real-time decision-making in NYC to impact current emergency responses, planning and policies that consider direct and indirect impacts of COVID-19, weather extremes, and interdependent vulnerabilities. There remains limited systemic understanding of what forms resilience to COVID-19 should take, especially when considering interactions with additional drivers of social vulnerability. Thus, the broader impacts of this research lie primarily in direct engagement with local practitioners—governmental officials, non-governmental organizations, community organizations—to improve their ability to conduct integrative planning and improve real-time decision-making to reduce social vulnerability and plan emergency response in the novel context of ongoing COVID-19 transmission that may be combined with weather-related extremes. Further, research will be provided to current NSF Growing Convergence Research (GCR) collaborators in Atlanta, Phoenix, San Juan (PR), and across the cities in the UREx Sustainability Research Network. (SRN), seeding opportunities to replicate methods and findings. The project PIs will train interdisciplinary graduate students and postdoctoral scholars in this convergent science approach and provide an important mechanism to bring scholars with advanced data science skills to gather important emerging data and advance novel research to understand the potential of interdependent COVID-19 and weather-related impacts on vulnerable populations in NYC.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": "2210", "attributes": { "award_id": "2029474", "title": "RAPID: Development of an ultrasensitive thermal contrast amplification lateral flow immunoassay for rapid, point-of-care COVID-19 diagnosis", "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": 5984, "first_name": "Ying", "last_name": "Sun", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2020-05-01", "end_date": "2022-04-30", "award_amount": 200000, "principal_investigator": { "id": 5985, "first_name": "John C", "last_name": "Bischof", "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": [], "awardee_organization": { "id": 227, "ror": "", "name": "University of Minnesota-Twin Cities", "address": "", "city": "", "state": "MN", "zip": "", "country": "United States", "approved": true }, "abstract": "Innovative and unique thermal contrast technology will be used to detect heat from laser irradiated gold nanoparticles to vastly improve the sensitivity of rapid diagnostics tests (i.e. lateral flow assays) designed to detect and diagnose COVID-19. This technology, which has been used to significantly improve lateral flow assays for HIV, malaria, Strep and influenza, will enable widespread, inexpensive (< $1/test), rapid (≤ 10 minutes), point-of-care detection of COVID-19. Such detection does not exist and cannot exist using the current methodologies for detecting both early (i.e. viral protein) and late (i.e. antibody) stages of COVID-19 infection. These new, thermal contrast lateral flow assays, along with new thermal contrast “reading” technology, will accelerate effective and targeted testing and surveillance of disease spread and management and bring enormous benefits to both national and global society. While first deployment will be in clinical settings, it is envisioned that this technology can eventually be used at schools, airports, sporting and entertainment events and even at home to assess the infection and spread of COVID-19 and other infectious diseases.The proposed research will develop new lateral flow assays to be used with innovative thermal contrast technology for highly sensitive, inexpensive, point-of-care detection of COVID-19. This new paradigm will, in the long term, be applied to a wide variety of diseases. Lateral flow assays are arguably the cheapest, fastest and easiest to use rapid diagnostic assays in the world. While they are used qualitatively for numerous diseases, they have weaknesses that include lack of sensitivity and quantification of disease burden. Both of these issues are addressed through our novel Thermal Contrast Assay technology. To address this specifically for COVID-19, two approaches are proposed: 1) optimizing gold nanoparticles and lateral flow assays for binding either COVID-19 protein or COVID-19 antibody analytes, and 2) the use of a new quantitative figure of merit called the “Binding Ratio” to rapidly identify reagents and components for optimized thermal contrast lateral flow assays. This project is part of a larger effort with industry partners to scale up the manufacturing of a proprietary thermal contrast reader so that it will be affordable for point-of-care clinical settings such as doctors’ offices, urgent care settings, emergency rooms, and field hospitals.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": "2211", "attributes": { "award_id": "2039851", "title": "RAPID: Understanding the Disparate Impact 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": 5986, "first_name": "Nancy", "last_name": "Lutz", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2020-09-01", "end_date": "2021-08-31", "award_amount": 199910, "principal_investigator": { "id": 5988, "first_name": "William A", "last_name": "Darity", "orcid": null, "emails": "[email protected]", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 246, "ror": "https://ror.org/00py81415", "name": "Duke University", "address": "", "city": "", "state": "NC", "zip": "", "country": "United States", "approved": true } ] }, "other_investigators": [ { "id": 5987, "first_name": "Pablo", "last_name": "Beramendi", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "awardee_organization": { "id": 246, "ror": "https://ror.org/00py81415", "name": "Duke University", "address": "", "city": "", "state": "NC", "zip": "", "country": "United States", "approved": true }, "abstract": "There is anecdotal evidence that the COVID-19 pandemic has disproportionately affected communities of color and poor communities than relatively affluent communities. This research will use two projects to systematically study the causes and consequences of disparate effects of the COVID-19 pandemic on different socioeconomic groups. The first project uses new cross state survey data to investigate whether access to public health insurance for the poor reduces the chances that individuals contract the COVID-19 disease. The unique way the researchers collect the data and the methodology they use allows them to establish a causal relationship between public health insurance access and COVID-19 spread. The second project will use a different cross-state survey data to investigate the effects of COVID-19 on political and ideological preferences. The results of this research project will provide important inputs into policies to reduce the disparate effects of the pandemic on different groups and indirectly reduce the rate of spread and its negative impact in the aggregate.This research project investigates the disparate effects of COVID-19 on different communities. The project will collect data from adjoining counties across states that expanded Medicaid under the Affordable Care Act (ACA) and those that did not and use regression discontinuity (RD) design to estimate the causal effects of access to public health insurance on the probability of getting COVID-19. The second project will use panel survey data to investigate the effects of the COVID-19 pandemic on political ideology, beliefs, and participation. This project is based on panel data collected from 1000 individuals in 8 states. The sampling frame, and details of the data collected allows the PIs to establish a causal relationship while controlling for a wide variety of socioeconomic and policy variables. By studying the causes and consequences of the disparate effects of COVID-19, this research provides a broader and more nuanced effects of COVID-19 than had hitherto been provided. The results of this research project will provide important inputs into policies to reduce the disparate effects of the pandemic on different groups and indirectly reduce the rate of spread and its negative impact in the aggregate.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": "2216", "attributes": { "award_id": "2033926", "title": "RAPID: Psychosocial Determinants of Successful Remote Learning During the Covid-19 Pandemic: A Study of Promoters and Barriers faced by HBCU Students", "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": 6003, "first_name": "Michelle", "last_name": "Rogers", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2020-06-15", "end_date": "2021-08-31", "award_amount": 199997, "principal_investigator": { "id": 6008, "first_name": "Adrienne A", "last_name": "Morgan", "orcid": null, "emails": "[email protected]", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 708, "ror": "", "name": "North Carolina Agricultural & Technical State University", "address": "", "city": "", "state": "NC", "zip": "", "country": "United States", "approved": true } ] }, "other_investigators": [ { "id": 6004, "first_name": "Christopher", "last_name": "Doss", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, { "id": 6005, "first_name": "Anna", "last_name": "Lee", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, { "id": 6006, "first_name": "Stephanie M", "last_name": "Teixeira-Poit", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, { "id": 6007, "first_name": "Jeannette M", "last_name": "Wade", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "awardee_organization": { "id": 708, "ror": "", "name": "North Carolina Agricultural & Technical State University", "address": "", "city": "", "state": "NC", "zip": "", "country": "United States", "approved": true }, "abstract": "The unprecedented and fast evolving nature of the COVID-19 pandemic disrupted STEM education, negatively impacting students’ persistence and success. Social aspects of race and class predict health and education outcomes. Public health crises, like COVID-19, disproportionately impact African Americans (AAs), lower income Americans, and individuals with psychosocial risk factors. This disparate impact has been linked to poor baseline health and environmental factors that make it difficult to adhere to social distancing orders. National education-related disparities show similar patterns across sociodemographic groups. Low-income college students have unequal access to computers, electronic devices, and internet service. They also encounter, higher instances of barriers, such as family violence and hunger, and greater concerns around stress and coping. At North Carolina A&T State University (NC A&T), the nation’s largest Historically Black College and University (HBCU), over 1,700 students failed to show up for classes when COVID-19 forced transition of all classes to online instruction modes in March 2020, placing them at risk for dropping out of college or extending their time in school; an additional financial burden. Furthermore, many of the students at HBCUs work part-time while attending school, mostly in the essential services sector. This places them at greater risk for exposure to COVID-19, exacerbating their anxieties and stress levels. A multidisciplinary team of investigators from NC A&T seeks to understand the risk factors for these college students and use the information to develop effective interventions to prevent disruptions in remote learning success in preparation for fall 2020 and subsequent semesters. This is critical because resumption of classes using “normal” face-to-face formats is unlikely to happen until a drug or vaccine for COVID-19 is developed, a scenario that could take years. If the barriers to online and remote learning are not addressed, these students will literally be left behind. This study seeks to highlight the extent to which psychosocial risk factors, including poor access to technology resources, related to the current COVID-19 pandemic and extenuating circumstances are having an impact on student learning outcomes. To better understand the correlations between psychosocial risk factors and remote learning success in times of crisis, this study will utilize a mixed-methods approach for students at NC A&T. An online survey will be administered to collect data on various metrics: sociodemographic characteristics, experience with COVID-19, attitudes and behaviors in response to COVID-19, perceived stress and coping, depression, family violence perpetration and victimization, food insecurity and self-efficacy, and familiarity with computers and technology. This will be followed by interviews with randomly selected survey respondents to contextualize quantitative findings and uncover additional barriers and promoters. Survey data will be analyzed using regression modeling, and interview data will be analyzed using content analyses. The survey and interview data will be triangulated by comparing and contrasting findings to develop a comprehensive understanding of psychosocial risk factors that impact remote learning success. The outcomes will inform development of a mobile Application (App) to engage these students and connect them to resources needed to sustain their learning in these disruptive conditions. Findings from this study will produce new knowledge regarding psychosocial determinants of successful remote learning during a critical disruption in the academic journey. As the nation’s largest HBCU and top producer of African American STEM undergraduates, NC A&T is positioned to evaluate how the COVID-19 pandemic has impacted vulnerable students and develop suitable interventions for academic success. This work will inform future policy and development of prevention strategies so that institutions are better prepared for future pandemics that make remote and online learning necessary for all students. This work will produce insights that may be applied more generally to how HBCU students’ and students from similar backgrounds transition to online learning. This research will aid intervention development by identifying factors impacting student success under extreme disruptive conditions.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": "1428", "attributes": { "award_id": "2028344", "title": "RAPID: Exploring Impacts of the COVID-19 Pandemic on Undergraduate STEM Education by Student Gender, Race/Ethnicity, and Socioeconomic Status", "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", "7914", "7914" ], "program_officials": [ { "id": 3698, "first_name": "Bonnie", "last_name": "Green", "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": 127748, "principal_investigator": { "id": 3699, "first_name": "Nathanial P", "last_name": "Brown", "orcid": null, "emails": "[email protected]", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 219, "ror": "", "name": "Pennsylvania State Univ University Park", "address": "", "city": "", "state": "PA", "zip": "", "country": "United States", "approved": true } ] }, "other_investigators": [], "awardee_organization": { "id": 219, "ror": "", "name": "Pennsylvania State Univ University Park", "address": "", "city": "", "state": "PA", "zip": "", "country": "United States", "approved": true }, "abstract": "The COVID-19 pandemic presents unprecedented challenges to undergraduate education. Although the disruption affects the entire academic community, the impacts are not equal. For example, students with low socioeconomic status, first generation students, and women may be affected more strongly by the disruptions than other students. Thus, based on students’ demographics, the pandemic may be more likely or less likely to exacerbate existing or create new differential impacts on students. This study seeks to investigate this likely unequal impact among a large sample of students enrolled in calculus courses in spring 2020. A survey will gather student voices by probing how this disaster has affected students in STEM career pathways. The survey needs to be conducted immediately to gather the information from the students as they experience the disruption in their collegiate education. The goal of this study is to conduct a timely mixed-methods study to collect data from a representative sample of undergraduate STEM students from varied backgrounds. The data will include the student voice about their experiences including: 1) the transition away from campus; 2) the challenges experienced; 3) the degree to which challenges affected attendance, academic performance, withdrawal rates, and college dropout rates; and 4) factors that facilitated success or failure among among all students, including underresourced and underrepresented students. After gathering data via the survey, the project team will facilitate 15 focus groups of about six students, balanced by socioeconomic status, race or ethnicity, and gender. Using a socioecological framework, the project team will analyze factors across multiple levels, ranging from the individual to public policy, and use the survey and focus group data to develop a quantitative survey. This work represents novel STEM-education research in an urgent and unique context. The findings may immediately inform interventions to address the needs of current undergraduate STEM students in the US. Formal reports and recommendations that arise will be published and disseminated in Fall 2020 and Spring 2021. In the longer term, results may inform evidence-based recommendations regarding distance versus on-campus learning for students, including students from underresourced and underrepresented backgrounds. This RAPID award is made by the Improving Undergraduate STEM Education program in the Division of Undergraduate Education (Education and Human Resources Directorate), 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": "1963", "attributes": { "award_id": "2027541", "title": "RAPID: COVID-19 Response Support: Building Synthetic Multi-scale Networks", "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", "7923" ], "program_officials": [ { "id": 5227, "first_name": "Seung-Jong", "last_name": "Park", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2020-07-01", "end_date": "2021-12-31", "award_amount": 173640, "principal_investigator": { "id": 5230, "first_name": "Madhav V", "last_name": "Marathe", "orcid": null, "emails": "[email protected]", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [ { "id": 5228, "first_name": "Henning S", "last_name": "Mortveit", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, { "id": 5229, "first_name": "Srinivasan", "last_name": "Venkatramanan", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "awardee_organization": { "id": 517, "ror": "", "name": "University of Virginia Main Campus", "address": "", "city": "", "state": "VA", "zip": "", "country": "United States", "approved": true }, "abstract": "The Novel 2019 Coronavirus (COVID-19) has already caused unprecedented global social, economic, and health impact. This project will develop synthetic global multi-scale social contact networks. The synthetic but realistic social contact networks can capture human interactions either at an individual or community level. The networks can be used in conjunction with agent-based models to simulate the ongoing COVID-19 pandemic. The simulations can in-turn be used to design and assess various interventions that balance health benefits with social and economic costs. Data will be made available to the scientific community. The PIs will also work with other research groups and continue their partnership with other federal and state agencies to support their response efforts. Developing synthetic social contact networks is a statistically and algorithmically challenging problem. This project will synthesize ensembles of two classes of synthetic social contact networks -- patch-based meta-population networks and individualized synthetic social contact populations and networks using a combination of machine learning and data driven modeling techniques. The need for such data driven mechanistic modeling methods has become abundantly clear in regimes when the available data is sparse and noisy. The project will undertake a detailed statistical analysis of the algorithms and the synthetic networks they produce. This includes methods to conduct global sensitivity analysis and methods to quantify the uncertainty in the outcomes as a function of the network structure. One of the many uses of this resource, is to support individual-based as well as meta-population-based simulation models for epidemic spread in general, and COVID-19 in particular. Beyond supporting ongoing COVID-19 outbreaks, these synthetic social contact networks will be useful in responding to other epidemics. The PIs plan to make this data available to the global research community so that researchers around the world can immediately use it to assess the pandemic and the response efforts in their respective regions.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": "2055", "attributes": { "award_id": "2028145", "title": "RAPID: Measuring the Effects of the COVID-19 Pandemic on Broadband Access Networks to Inform Robust Network Design", "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", "7923" ], "program_officials": [ { "id": 5519, "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": "2020-05-01", "end_date": "2022-04-30", "award_amount": 187395, "principal_investigator": { "id": 5520, "first_name": "Nicholas G", "last_name": "Feamster", "orcid": null, "emails": "[email protected]", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 289, "ror": "https://ror.org/024mw5h28", "name": "University of Chicago", "address": "", "city": "", "state": "IL", "zip": "", "country": "United States", "approved": true } ] }, "other_investigators": [], "awardee_organization": { "id": 289, "ror": "https://ror.org/024mw5h28", "name": "University of Chicago", "address": "", "city": "", "state": "IL", "zip": "", "country": "United States", "approved": true }, "abstract": "This project is to study the effects of the shifts in Internet traffic resulting from the COVID-19 pandemic on the broadband Internet access infrastructure. Specifically, the project investigates how traffic volumes are changing as a result of changes in daily patterns of life, as well as how those shifts in traffic volume affect network utilization, network performance, and application performance. These questions have become increasingly critical during the COVID-19 pandemic, as large fractions of the population have come to depend on reliable Internet access that performs well for a variety of applications, from video conferencing to remote learning and healthcare. It is important to understand how various policies involving isolation, distancing, and closing of schools, libraries and services are affecting the usage patterns on the Internet, right at the time when the entire society is coming to rely on the network more than ever.This project will involve an unprecedented coordination of data about network traffic load under crisis, through granular measurements, propriety data sharing agreements, as well as extensive baseline data spanning over ten years. This project aims to study two fundamental research questions: (1) What is the effect of the COVID-19 pandemic on network traffic patterns on broadband access networks? and (2) How well do (and will) the infrastructure and applications sustain these changes in traffic patterns? To study these questions, the project plans to take a multi-faceted approach, exploring issues including access performance and interconnect capacity when using critical applications when the network is under strain. This measurement study will shed light on the extent to which the existing Internet infrastructure can sustain exogenous shocks that dramatically shift the location, nature, and scale of network traffic and help identify vulnerabilities. Insights into the state of broadband Internet performance will yield important insights about how broader government policies to close schools and rely on remote e-learning may affect the broader population. Understanding the increased strain on the Internet on other application areas in medicine and public health, including patient compliance and remote caregiving, may lead to improved design of the Internet for such application areas. The project's activities will be posted to https://cdac.uchicago.edu/broadband.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": "1319", "attributes": { "award_id": "2037374", "title": "RAPID: Bioinformatic Search for Epitope-based Molecular Mimicry in the SARS-CoV-2 Virus using Chameleon", "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", "7923", "9102" ], "program_officials": [ { "id": 3392, "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": 238798, "principal_investigator": { "id": 3397, "first_name": "Giri", "last_name": "Narasimhan", "orcid": null, "emails": "[email protected]", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 207, "ror": "https://ror.org/02gz6gg07", "name": "Florida International University", "address": "", "city": "", "state": "FL", "zip": "", "country": "United States", "approved": true } ] }, "other_investigators": [ { "id": 3393, "first_name": "Kalai", "last_name": "Mathee", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, { "id": 3394, "first_name": "Prem P", "last_name": "Chapagain", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, { "id": 3395, "first_name": "Ananda M", "last_name": "Mondal", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, { "id": 3396, "first_name": "Jessica", "last_name": "Liberles", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "awardee_organization": { "id": 207, "ror": "https://ror.org/02gz6gg07", "name": "Florida International University", "address": "", "city": "", "state": "FL", "zip": "", "country": "United States", "approved": true }, "abstract": "Cross-reactive immunity is a process by which an individual who was vaccinated by an unrelated vaccine or who has recovered from an infection from an unconnected pathogen in the past is seemingly protected against an infection by SARS-CoV-2. A key step for an individual to mount a successful immune response to a pathogenic infection is for a human antibody to recognize and bind to a specific epitope (fragment) from an antigenic protein from the infecting pathogen. Cross-reactivity by molecular mimicry may occur when an antibody fortuitously binds to an epitope from SARS-CoV-2 because of a structural similarity at the binding interface with the epitope for which it was intended. If verified, a rapid repurposing of drugs and vaccines designed for the other pathogens can be quickly validated and applied to the current pandemic. This project plans to use bioinformatic techniques to investigate how molecular mimicry may play a role in cross-reactive immunity.The software pipeline will use the high-performance computing resources in the Chameleon cloud computing platform to run computationally-intensive molecular dynamics simulations within a machine learning framework and help identify occurrences of molecular mimicry in SARS-CoV-2. The pipeline can be divided into two main parts. The first part involves extracting useful features from structures of known complexes available from public databases such as Protein Data Bank (PDB). The second part involves building machine learning models from these features so that molecular mimicry, if present, can be detected in SARS-CoV-2.The machine learning framework will result in reusable models of molecular mimicry and is expected to assist in vaccine development. If successful, the project can potentially (a) explain global disparities in hospitalizations and death rates; (b) lead to quick repurposing of drugs to fight the current pandemic; (c) be replicated for other pathogens; (d) lead to faster vaccine development; (e) impact development of novel bioinformatic strategies for the current and future pandemics.An interdisciplinary team with expertise in computational biophysics, bioinformatics, machine learning, evolutionary biology, infectious diseases, computational epigenetics, glycobioogy, high-performance computing and software engineering will drive this project. All results will be made available through the project website at: http://biorg.cs.fiu.edu/lemom, including examples of molecular mimicry, software for replicating the experiments, and performance benchmarking results on Chameleon.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": "1626", "attributes": { "award_id": "2033946", "title": "RAPID: Neighborhood-level U.S. Internet Accessibility Assessment through Dataset Aggregation and Statistical and Predictive Modeling", "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", "7923", "9102" ], "program_officials": [ { "id": 4271, "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": 149439, "principal_investigator": { "id": 4272, "first_name": "Elizabeth M", "last_name": "Belding", "orcid": null, "emails": "[email protected]", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 320, "ror": "", "name": "University of California-Santa Barbara", "address": "", "city": "", "state": "CA", "zip": "", "country": "United States", "approved": true } ] }, "other_investigators": [], "awardee_organization": { "id": 320, "ror": "", "name": "University of California-Santa Barbara", "address": "", "city": "", "state": "CA", "zip": "", "country": "United States", "approved": true }, "abstract": "The U.S. has long suffered from digital inequities in multiple dimensions: rural and tribal regions are far less likely than urban cities to have high speed Internet access. Internet availability and quality within communities can often be predicted based on demographic and socioeconomic factors. The COVID-19 pandemic has brought to the forefront these inequalities; due to shelter-in-place orders, the lack of high quality Internet access has had dramatic impacts, including on the ability to participate in remote learning, remote work, and telehealth. While new government programs have been created to try to broaden access, a fundamental problem persists: no one accurately knows who does and does not have high quality access. There are many datasets of Internet measurements, but each on its own represents too incomplete a picture to provide the fine-grained information needed to discern which communities, or, ideally, neighborhoods lack quality Internet access. However, these datasets, when combined, is expected to provide a rich and geographically broad data source through which it may be possible to accurately assess Internet connectivity and performance. Furthermore, this study can let one learn trends from these datasets to predict Internet accessibility in regions for which no measurement data is currently available. The goal of this project is threefold: (i) to aggregate data from public and private sources to produce the most fine-grained analysis and detailed maps, to date, within states, at the community and, ideally, neighborhood level, of where fixed and mobile Internet access exists, where it does not, and where it is of too poor quality to be usable; (ii) to build statistical models that use demographic and other social variables to understand variation in Internet availability and quality; and (iii) to use what is learned to build predictive models of Internet service in areas for which there exist insufficient measurement data from available sources. This work will have broad impacts, including the informing of local, state and federal governments about where investments must be made to ensure all Americans have access to high quality mobile and/or fixed Internet. The project website, digitalaccess.cs.ucsb.edu, will contain information about research methodology and outcomes, including a report on what is learned about the state of California, the first state of focus for this award. Prediction models will also be made 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 } } ], "meta": { "pagination": { "page": 1391, "pages": 1419, "count": 14184 } } }