Grant List
Represents Grant table in the DB
GET /v1/grants?page%5Bnumber%5D=1384&sort=program_reference_codes
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The high costs of available rapid testing strategies limit their use in population-based screening, particularly in the most vulnerable low-resource communities. In contrast, the test developed here will be relatively inexpensive because it requires neither external power nor additional sample preparation and analysis instrumentation. The test will be tuned to detect the entire range of viral loads observed in individuals with active infections. The project team aims to both design the prototype device and establish proof-of-principle within the first three months of the project. To do so, a diverse and multidisciplinary team including six graduate students, a post-doctoral fellow, a medical doctor, two undergraduate students, and two U.S.-based manufacturers will be mobilized. The project will provide a profound humanitarian and educational experience for the trainees as they work quickly but deliberately to demonstrate the feasibility of a COVID-19 test device urgently needed throughout the world. The overall goal of the proposal is to create a manufacturable microfluidic device capable of instantaneous diagnosis of active COVID-19 infections from nasal mucus or phlegm. The microfluidic device will employ ultrathin silicon 'nanomembranes' to identify the presence of SARS-CoV-2 spike proteins in the biofluid sample. Samples without the SARS-CoV-2 virus or with viral particles smaller than SARS-CoV-2 will not register a positive result. Modifications to the investigators' prior flow cell device designs will be made to ensure the two flow paths can be driven by the surface tension of the applied sample alone. The investigators have contracted with two U.S.-based companies for high-throughput manufacturing of the device components. Proof-of-concept demonstration will be conducted with nanoparticle SARS-CoV-2 surrogates and non-SARS-CoV2 spike protein controls. Studies will be conducted to define the sensitivity and specificity of the device, minimizing false negatives. Device manufacturing will be done using high-throughput strategies that can be readily translated to high-volume commercial manufacturing. A successful platform will be tested with samples from COVID-19 patients at a New York State flu center run by the University of Rochester.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": "1947", "attributes": { "award_id": "2032576", "title": "RAPID: Understanding and Mitigating the Effects of University Closures due to COVID-19 on Black Students in Physics at the Bachelor's Level", "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", "9134", "9178" ], "program_officials": [ { "id": 5173, "first_name": "Guebre", "last_name": "Tessema", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2020-06-15", "end_date": "2022-12-31", "award_amount": 136091, "principal_investigator": { "id": 5175, "first_name": "Tabbetha A", "last_name": "Dobbins", "orcid": null, "emails": "[email protected]", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 321, "ror": "https://ror.org/049v69k10", "name": "Rowan University", "address": "", "city": "", "state": "NJ", "zip": "", "country": "United States", "approved": true } ] }, "other_investigators": [ { "id": 5174, "first_name": "Thomas A Searles", "last_name": "Jr", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "awardee_organization": { "id": 321, "ror": "https://ror.org/049v69k10", "name": "Rowan University", "address": "", "city": "", "state": "NJ", "zip": "", "country": "United States", "approved": true }, "abstract": "NON-TECHNICAL DESCRIPTION: COVID-19 resulted in campus closures and online learning at many colleges and universities. These campus closures disrupted connections to educational support resources, mentorship (informal and formal), and co-curricular activities that enrich the students’ experiences. This project studies the impacts of the COVID-19 campus closures (and move to online learning) on African American (Black) students pursuing bachelor’s degrees in physics and astronomy. While the data is available, this study will survey students to examine and document the impact of campus closures to educational persistence and resilience for this population of students. Findings may provide insights into how support structures (e.g. mentoring, tutoring, intrusive advising) can be effectively implemented when students undergo crisis, such as death of a close family member or friend, disruption to home support structures due to mass incarceration or deportation of one or both parents, serious illness involving the student or close family member. This body of work will provide a basis for examining the methods used to support students from other underrepresented and marginalized groups during crisis. TECHNICAL DETAILS: This study is being undertaken within the framework of outcomes gained by the American Institute of Physics (AIP) TEAM UP Task Force’s two-year study. Recommendations from this study enable physics and astronomy departments to better support African American (Black) students, many of whom already face well-documented obstacles to completing their degrees (due to other factors which are independent of COVID-19). Moreover, findings of this study extend to better understand and support students who undergo personal crises that require short-term or long-term absences from campus (even during normal campus operations). The project advances understanding of how to increase resilience toward degree completion for African American (Black) students during crises. These understandings could extrapolate to other marginalized populations of students. The importance of this work is that people's lives will be changed by increasing the relative percentage of these students who enter the technical workforce after having completed bachelor's degrees. The future job outlook in physics in the U.S. is promising, and access to these careers will provide the potential for a secure and stable financial future. This research lessens the likelihood that students will be left out of these job prospects due to the occurrence of crises during their pursuit of the bachelor’s degree. Crises that students face are more often personal crises, such as death of a close family member, serious illness to the student or close family member, effects of mass incarceration on families, or deportation of one or both parents. Likewise, economic crises resulting in the closures of some physics departmental occurring due to disasters such as storms, hurricanes, and tornadoes also present a disruption to physics departments and their students. This study bridges the learning from a sudden and collective crisis (i.e. COVID-19 campus closures) to well-designed actions that physics departments can take to address crises of a personal nature. This study provides useful contributions to better serve students and to prevent differential effects of crises on underrepresented minority students.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": "1896", "attributes": { "award_id": "2031196", "title": "RAPID: Epidemic control strategies for COVID-19 in age-structured populations: A multi-model approach", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Biological Sciences (BIO)" ], "program_reference_codes": [ "096Z", "7914", "9150" ], "program_officials": [ { "id": 5025, "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": "2020-05-15", "end_date": "2023-04-30", "award_amount": 199019, "principal_investigator": { "id": 5027, "first_name": "Bret D", "last_name": "Elderd", "orcid": null, "emails": "[email protected]", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [ { "id": 5026, "first_name": "Bret D", "last_name": "Elderd", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 360, "ror": "https://ror.org/05ect4e57", "name": "Louisiana State University", "address": "", "city": "", "state": "LA", "zip": "", "country": "United States", "approved": true } ] } ], "awardee_organization": { "id": 360, "ror": "https://ror.org/05ect4e57", "name": "Louisiana State University", "address": "", "city": "", "state": "LA", "zip": "", "country": "United States", "approved": true }, "abstract": "The recent COVID-19 pandemic has led to the reliance on epidemiological models to forecast the potential benefits associated with various mitigation strategies to control the spread of this novel disease. The mathematical models focused on COVID-19 vary in their complexity from the relatively simple to more elaborate models that follow individuals and their movement over the course of the epidemic. Model performance depends both on model structure and the underlying data available. During the beginning of an epidemic when limited data are available, simple models should work best. As the epidemic progresses, more complex models may then perform better. Yet, most research focuses on a single model and does not consider comparing across multiple models. This research will address immediate societal concerns over the course of the epidemic by using a multi-model approach to ask questions regarding epidemic intensity (for example, the number of expected COVID-19 cases) and the impacts of mitigation strategies on these dynamics. While the research will develop models focused on COVID-19, the models along with the statistical approaches to fit models to data will be applicable for future outbreaks of novel diseases. Additionally, this project provides training opportunities for a graduate student and a post-doctoral researcher. In general, this research will use a multi-model approach that considers a range of models from relatively simple compartmental models to more complex models that incorporate age- and/or network-structure based on social contacts. The models considered will provide estimates of important epidemiological parameters (for example, R0) along with their associated uncertainty. Understanding this uncertainty and establishing if and when epidemic model complexity is a hindrance or utility for estimating epidemic parameters and epidemic trajectories is a key issue.This research will combine data with multiple epidemic models on the COVID-19 pandemic to:1.\tunderstand the relative performance of traditional compartmental approaches to network-based age-structured models during the course of an on-going epidemic; 2.\tquantify the most crucial sources of variation for estimating threshold vaccination criteria, epidemic trajectories, and potential mitigation strategies; and, 3.\tidentify if and when more complex network-based models outperform more traditional approaches as the epidemic progress over time and data availability changes.To meet the above goals, the analyses conducted will use a Bayesian approach of fitting models to data. A Bayesian approach will allow for the quantification of model and parameter uncertainty as well as assess the uncertainty of forecasted epidemic dynamics and mitigation strategies.This project is jointly funded by the Ecology and Evolution of Infectious Diseases Program (EEID), Division of Environmental Biology, Directorate of Biological Sciences and the Established Program to Stimulate Competitive Research (EPSCoR).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": "1909", "attributes": { "award_id": "2036801", "title": "RAPID: Enzyme-free detection of SARS-CoV2 using a PAINT-based single-molecule microscopy assay", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Biological Sciences (BIO)" ], "program_reference_codes": [ "096Z", "7914", "9150" ], "program_officials": [ { "id": 5064, "first_name": "Mamta", "last_name": "Rawat", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2020-07-15", "end_date": "2022-06-30", "award_amount": 199993, "principal_investigator": { "id": 5066, "first_name": "Jeffrey", "last_name": "Caplan", "orcid": null, "emails": "[email protected]", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 442, "ror": "https://ror.org/01sbq1a82", "name": "University of Delaware", "address": "", "city": "", "state": "DE", "zip": "", "country": "United States", "approved": true } ] }, "other_investigators": [ { "id": 5065, "first_name": "Kun", "last_name": "Huang", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "awardee_organization": { "id": 442, "ror": "https://ror.org/01sbq1a82", "name": "University of Delaware", "address": "", "city": "", "state": "DE", "zip": "", "country": "United States", "approved": true }, "abstract": "The novel coronavirus that causes COVID-19 is composed of a spherical envelope that surrounds a genome made of ribonucleic acids (RNAs). These RNAs control viral replication and have distinct regions for each coronavirus strain. Most rapid detection tests for COVID-19 amplify three unique RNA regions with a protein called an enzyme. The activity of enzymes greatly increases the sensitivity of COVID-19 detection, but have disadvantages of requiring cold storage, short shelf life, and potential for false negative results due to the loss of enzymatic activity. This research develops an enzyme-free COVID-19 test that uses the binding of stabilized deoxyribonucleic acid (DNA) molecules that are locked into place along the novel coronavirus genome. The novel coronavirus can be visualized using a micrscope when another marker DNA molecule constantly docks and undocks from the DNA attached to the coronavirus genetic material. The project will deliver a sensitive and stable test for rapid detection of COVID-19. A major goal of this project is to develop a flexible enzyme-free, shelf-stable viral test that can be readily adapted for the current as well as future viral outbreaks.The goal of this project is to develop an enzyme-free detection method for SARS-CoV-2 with single-molecule sensitivity that can detect single nucleotide polymorphisms of mutated strains in the United States. To achieve this goal, viral capture coverglasses will be engineered by covalently linking oligonucleotide (oligo) sequences that are reverse complementary to the 70-nt leader sequence (LS) and sequences 5’ to key regions of SARS-CoV-2 genome. Extracted viral RNA will be applied to the coverglass to pull down all viral transcripts that bind to the LS capture anchors. The specific regions for SARS-CoV-2 detection suggested by the Center of Disease Control & Prevention (CDC) will be used as target sequences to design probes for small RNA detection via DNA-based points accumulation in nanoscale topography (sRNA-PAINT). Single-molecule viral detection and amplification of target regions will be achieved with sRNA-PAINT by deploying highly specific locked-nucleic acid (LNA) oligonucleotide probes linked to oligo “docking strands”. Amplification is enzyme-free, and instead, the signal for any individual probe is amplified by the predictable binding and unbinding of oligo “imager strands” to the docking strands of the probe. Imager strands have a conjugated fluorophore and their binding is detected in thousands of images taken over 15 minutes on a microscope using a sensitive camera. The detection assay or kit will consist of an oligonucleotide (oligo)-linked capture coverglass, COVID-19 -specific oligo probes, fluorescent oligo imaging probes, buffers, and instructions on how to either deploy the assay on any microscope with single molecule sensitivity or build one.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": "1976", "attributes": { "award_id": "2031548", "title": "RAPID: Machine Learning Methods to Understand, Predict and Reduce the Spread of COVID-19 in Small Communities", "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", "9150" ], "program_officials": [ { "id": 5270, "first_name": "Debasis", "last_name": "Majumdar", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2020-05-15", "end_date": "2022-04-30", "award_amount": 185747, "principal_investigator": { "id": 5273, "first_name": "Gil R", "last_name": "Gallegos", "orcid": null, "emails": "[email protected]", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 706, "ror": "https://ror.org/016tyxe19", "name": "New Mexico Highlands University", "address": "", "city": "", "state": "NM", "zip": "", "country": "United States", "approved": true } ] }, "other_investigators": [ { "id": 5271, "first_name": "Tatiana", "last_name": "Timofeeva", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, { "id": 5272, "first_name": "Orit", "last_name": "Tamir", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "awardee_organization": { "id": 706, "ror": "https://ror.org/016tyxe19", "name": "New Mexico Highlands University", "address": "", "city": "", "state": "NM", "zip": "", "country": "United States", "approved": true }, "abstract": "The ongoing COVID-19 outbreak has recently reached pandemic status spreading all around the world. The severity of the pandemic, along with an enormous impact on world’s economy and society, has forced governments to introduce emergency measures. It is essential to utilize the available statistical data from trusted sources in order to model and evaluate the dynamics of the pandemic spread, to not only better understand such complex systems, but to learn and develop possible solutions to prevent further spread of current and/or similar future outbreaks. Thus, this research, devoted to the development of mathematical models of COVID-19 pandemic spread, addresses an urgent national need. Faculty and students in computer science, anthropology, and computational chemistry at New Mexico Highlands University have formed a diverse group for finding a solution to the complicated problems of the description and prediction of COVID-19 spread. This multidisciplinary project is expected to yield a better understanding of the interconnections among many factors that contribute to the spread of COVID-19. Statistical data will be collected in regions of Northern New Mexico, including San Juan and McKinley Counties in the Navajo Nation and Los Alamos county outside of the Navajo Nation. Analysis of the collected statistical data along with socio-cultural assessment from this project will be presented to New Mexico (NM) tribal and health authorities. The project will aim to provide a scientific basis for the prediction of disease spread and will consider scenarios associated with the possibility of another wave of the pandemic. Students from this minority-serving institution involved in the project will obtain valuable experience in the application of advanced machine learning models and methods in providing fast robust reaction to a national health, economic, and societal crisis.In this study, machine learning methods will be used to analyze pandemic spread scenarios in different regions and to glean the most important features of the data characterizing the spread. The research team will use both traditional machine learning techniques and advanced methods, such as artificial neural networks, allowing development of virus incidence model capturing dependencies in both linear and nonlinear domains. The work will concentrate on understanding disease spread with regard to multiple socioeconomic factors. The problem can be treated as a sequence modeling one; so, recurrent neural networks and more complex models based on their recurrent cells might be one promising direction. The next step will be to assemble datasets for small isolated communities with different socioeconomic backgrounds and ethnicities – comparing Navajo Indians living on the Navajo reservation to Los Alamos County (NM) – and to test the applicability of the developed model to these regions. The spatiotemporal data available on the spread is heterogeneous in character. An important goal of this research is to classify the collected data with respect to the similarity in the epidemic curve behavior and then build separate models for different regions according to this classification. The proposed model will be used for prediction of future incidents and to produce the most effective non-medical recommendations for suppression and prevention of future viral outbreaks.This research is supported by the Partnerships for Research and Education in Materials (PREM) program and the Condensed Matter and Materials Theory (CMMT) program in the Division of Materials Research in the Directorate for Mathematical and Physical Science using supplemental funds made available by 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": "1979", "attributes": { "award_id": "2028297", "title": "RAPID: COVID-19 Behavior, Perception, and Control Across Geographic and Economic Gradients", "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", "9150" ], "program_officials": [ { "id": 5284, "first_name": "Zhilan", "last_name": "Feng", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2020-05-01", "end_date": "2023-04-30", "award_amount": 199999, "principal_investigator": { "id": 5287, "first_name": "Folashade", "last_name": "Agusto", "orcid": null, "emails": "[email protected]", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 415, "ror": "", "name": "University of Kansas Center for Research Inc", "address": "", "city": "", "state": "KS", "zip": "", "country": "United States", "approved": true } ] }, "other_investigators": [ { "id": 5285, "first_name": "A. Townsend", "last_name": "Peterson", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, { "id": 5286, "first_name": "Jarron M Saint", "last_name": "Onge", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "awardee_organization": { "id": 415, "ror": "", "name": "University of Kansas Center for Research Inc", "address": "", "city": "", "state": "KS", "zip": "", "country": "United States", "approved": true }, "abstract": "COVID-19 is a respiratory disease caused by a recently discovered, novel coronavirus. Since its discovery in Wuhan, China, in 2019, COVID-19 has already led to over 2 million cases globally. It has spread globally, including to many vulnerable countries without adequate healthcare infrastructures. Many different responses have been tried, including social distancing, school and event closings, and travel bans. This project will develop mathematical models to address three fundamental questions: 1) how much participation and coordinated control is needed for effective protection? 2) what independent control efforts can compensate for lack of coordination to achieve effective protection? and 3) how do community population demographics, socioeconomic conditions, and health care infrastructure impact outcomes? This project aims to inform coordination of disease control policies at all scales (local, regional, national, international) to aid in curtailing the ongoing and future outbreaks. This project will advance fundamental understanding of the impacts of control efforts via a new risk perception-driven infectious disease model, and predict which drivers of public demand for community-level control efforts might lead to potentially harmful long-term decisions. The project will involve training two doctoral students in techniques related to mathematical modeling of disease dynamics and spread. Many mitigation options are being weighed and implemented for COVID-19, with different decisions made at different administrative levels, including alternative quarantine strategies and different degrees of “lockdown”. All of these decisions come with different perceptions of risk. The PIs will develop and analyze disease transmission models that incorporate various factors including public perception of risk, age-structure with a hospitalized population, and spatial structure with different scales spanning local communities to an entire country. These models will be used to explore impacts of community population demographics, socioeconomic conditions, and health care infrastructures, and how these factors impact control efforts under different social and economic settings. With the results of these models, policy- and decision-makers can consider the impacts of specific features of the communities under their administration as contributors to a broader network of public health efforts and choose the optimal mitigation steps. This work will inform coordination of disease control policies to curtail the ongoing outbreak directly. The results of this project, while tailored specifically to inform COVID-19 virus control strategies, will be applicable to any novel infectious disease outbreak in the future.This award is being funded by the 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": "1981", "attributes": { "award_id": "2028728", "title": "RAPID: Epidemiological and Phylogenetic Models for Contact-Based Control 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", "9150" ], "program_officials": [ { "id": 5290, "first_name": "Zhilan", "last_name": "Feng", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2020-04-15", "end_date": "2022-09-30", "award_amount": 199009, "principal_investigator": { "id": 5292, "first_name": "Cameron J", "last_name": "Browne", "orcid": null, "emails": "[email protected]", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 707, "ror": "https://ror.org/01x8rc503", "name": "University of Louisiana at Lafayette", "address": "", "city": "", "state": "LA", "zip": "", "country": "United States", "approved": true } ] }, "other_investigators": [ { "id": 5291, "first_name": "Hayriye", "last_name": "Gulbudak", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "awardee_organization": { "id": 707, "ror": "https://ror.org/01x8rc503", "name": "University of Louisiana at Lafayette", "address": "", "city": "", "state": "LA", "zip": "", "country": "United States", "approved": true }, "abstract": "The current COVID-19 pandemic shows that effective and responsive public health systems are crucial for curbing emergent outbreaks. Increased case detection, contact tracing, and broad quarantine and social distancing measures, have been vital for containing COVID-19 outbreaks. Mathematical modeling and analysis can provide important insights into the efficacy of these “contact-based” non-pharmaceutical interventions. However, model parameterization requires detailed case data, which is often challenged by inconsistent, unreported and asymptomatic cases. To overcome these difficulties, the researchers will: develop outbreak models of COVID-19 with quarantine, tracing and social distancing and derive new results on final epidemic size and reproduction number under quarantine measures to flatten and shrink the epidemic curve. The researchers will also integrate case and phylogenetic data for an innovative framework that reveals region-specific control and predicts how to best implement contact-based measures for efficiently containing the outbreaks. Results will be communicated with policymakers, along with scientific and public audiences, in order to have a real-time impact on the coronavirus pandemic.This project will consider compartmental outbreak models of COVID-19 with responsive quarantine, tracing and social distancing measures. The novel formulation allows for derivation of outbreak size, dependent on contact tracing and broad quarantine intervention parameters. Reproduction number estimates and model fitting of case, tracing and quarantine data will quantify region-specific control characteristics. Sensitivity of epidemic size to combinations of contact tracing and social distancing measures will be assessed to understand viable strategies for flattening and shrinking epidemic curves. Long term scenarios of loosening and tightening of quarantine interventions for sustainable control will be investigated through analysis and simulation of the models. In addition, through interdisciplinary collaboration, the researchers will incorporate multi-region stochastic versions of the epidemiological models with phylodynamic computations of genomic data to improve model projections in the presence of possible unknown chains of transmission and area-specific responsive interventions. A “forward simulation-backward coalescent” approach will track migration events and produce phylogenetic tree signatures for matching model simulations to phylogenetic data. The overall combined phylodynamic and epidemic model analysis will quantify heterogeneous disease spread and contact-based control efficacy to inform public health authorities.This award is being funded by the 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": "2004", "attributes": { "award_id": "2027688", "title": "RAPID: Visual Analytics Approach to Real-Time Tracking of COVID-19", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Computer and Information Science and Engineering (CISE)" ], "program_reference_codes": [ "096Z", "7914", "9150" ], "program_officials": [ { "id": 5353, "first_name": "Behrooz", "last_name": "Shirazi", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2020-06-01", "end_date": "2022-05-31", "award_amount": 187477, "principal_investigator": { "id": 5357, "first_name": "Raju", "last_name": "Gottumukkala", "orcid": "https://orcid.org/0000-0003-0794-4015", "emails": "[email protected]", "private_emails": "", "keywords": null, "approved": true, "websites": "['http://crest.vastream.net']", "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 707, "ror": "https://ror.org/01x8rc503", "name": "University of Louisiana at Lafayette", "address": "", "city": "", "state": "LA", "zip": "", "country": "United States", "approved": true } ] }, "other_investigators": [ { "id": 5354, "first_name": "Vijay V", "last_name": "Raghavan", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, { "id": 5355, "first_name": "Satya S", "last_name": "Katragadda", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, { "id": 5356, "first_name": "Ravi Teja", "last_name": "Bhupatiraju", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "awardee_organization": { "id": 707, "ror": "https://ror.org/01x8rc503", "name": "University of Louisiana at Lafayette", "address": "", "city": "", "state": "LA", "zip": "", "country": "United States", "approved": true }, "abstract": "COVID-19 data, related to infection rates, at-risk populations, mobility, and commute dynamics are rapidly becoming available from several sources. However, there is a lack of interactive visual decision-making environments integrated with data-driven tools to help public health and community leaders understand how various factors such as physical distancing and other mitigation strategies, impact the spread of disease, help flatten the curve, enabling economic recovery while minimizing public health risk due to reopening. This project will develop visual analytic tools for tracking COVID-19 and propose balanced intervention strategies for effective containment of the outbreak. The proposed visual analytics system integrates heterogeneous datasets and enables the application of relevant analytical models and data-engineering for decision support in a complex and evolving crisis. The objectives include the development of (1) forecasting models for recovery based on incidence, population vulnerabilities, mobility patterns, and mitigation activities, (2) social-media tools to understand public sentiment and risk perceptions, (3) visual interface for model-refinement & diagnosis through data engineering and visual analytics principles. The decision-making framework will offer new insights, close the gap between data and decisions, and is driven-by inputs from extensive partnerships & collaborations to improve reliability and usability. The data-driven tools will help improve decision makersí understanding of disease dynamics from multiple variables. Epidemiologists could potentially leverage these insights to create higher-fidelity models based on interventional factors and their effect on population behaviors. Local authorities could also utilize the models to make life-saving decisions while minimizing impact to the economy. The project will enable new public and private partnerships including the City of New Orleans, and Industry Advisory Board of NSF Center for Visual and Decision Informatics. The project will benefit graduate and undergraduate students through hands-on research experience with the development of analytical products. The project outcomes will include analytics dashboards, source code, models, and data collected from multiple sources. The dashboards, project descriptions, and a list of data sources along with their metadata will be made publicly available on www.vastream.net for a period of two years. The public facing portion of the portal for COVID-19 component will be moved to Amazon cloud in event of disruptions from outages, for the duration of the project. A new public repository will be created on GitHub, and the source code and publicly available datasets will be made available on this project repository.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": "2021", "attributes": { "award_id": "2027598", "title": "RAPID: A Comparative Study of How Context Shapes Responses to 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", "9150" ], "program_officials": [ { "id": 5408, "first_name": "Frederick", "last_name": "Kronz", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2020-05-01", "end_date": "2023-04-30", "award_amount": 100218, "principal_investigator": { "id": 5409, "first_name": "Wesley M", "last_name": "Shrum", "orcid": null, "emails": "[email protected]", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [], "awardee_organization": { "id": 360, "ror": "https://ror.org/05ect4e57", "name": "Louisiana State University", "address": "", "city": "", "state": "LA", "zip": "", "country": "United States", "approved": true }, "abstract": "The primary objective of this collaborative RAPID research project is to further our understanding of how cultural and political contexts shape the ways people perceive and respond to the COVID-19 pandemic. The research team will conduct 400 interviews across multiple urban and rural areas for an analysis oriented towards understanding how individuals construct and modify their perceptions given information from a wide variety of sources and their network of interactions with friends and family. The results of this project will provide a deeper understanding of the social mechanisms that shape perceptions and behaviors to public policy responses in culturally and politically diverse contexts. Perceptions of disease (i.e., perceived seriousness, susceptibility, and threat) shape perceived barriers and benefits to action, which in turn affect behavior. While it is given that these perceptions are important to understand health-related behaviors as well as support for various treatments and actions, it is crucial to understand how these perceptions are generated. The project interviews and the analysis are to be widely distributed to provide grounded guidance for policy makers and others seeking to understand the diversity of fears, risk perceptions, preparedness, and acceptable actions in pandemics.The research team will collect qualitative and quantitative data on groups selected for their importance to pandemic processes (transmission and treatment of information and infectious agents). The groups are stratified into five categories: scientists, medical professionals, teachers, the informal sector, and the unemployed. The purposes of the Interviews are to Identify the principal sources of information about COVID-19 transmission, treatment, and risk, including both new and old media as well as the factors associated with their relative importance across regions, occupations, and rural/urban areas; to examine factors that impact the credibility of sources in both absolute and relative terms; and to assess the level of knowledge and preparation for COVID-19 transmission, treatment, and risk. They will be used to Identify primary fears and concerns relevant to the current spectrum of treatment, isolation, and containment technologies. The results of this project will contribute to an understanding of the different ways cultural and political contexts shape response to outbreak information and directives, and they will provide guidance for policy makers seeking to understand these differences.This project is jointly funded by the Science and Technology Studied Program, and the Established Program to Stimulate Competitive Research (EPSCoR).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": "2028", "attributes": { "award_id": "2032133", "title": "RAPID: A participatory study of how policy-makers and marginalized communities in the American South consume and act on scientific information in the context 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", "9150" ], "program_officials": [ { "id": 5431, "first_name": "Mary", "last_name": "Feeney", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2020-09-01", "end_date": "2022-08-31", "award_amount": 149995, "principal_investigator": { "id": 5434, "first_name": "Kelly H", "last_name": "Dunning", "orcid": null, "emails": "[email protected]", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 273, "ror": "https://ror.org/02v80fc35", "name": "Auburn University", "address": "", "city": "", "state": "AL", "zip": "", "country": "United States", "approved": true } ] }, "other_investigators": [ { "id": 5432, "first_name": "Janna", "last_name": "Willoughby", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, { "id": 5433, "first_name": "Sarah P", "last_name": "Bergquist", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, { "id": 31235, "first_name": "Katie", "last_name": "Corvey", "orcid": null, "emails": "[email protected]", "private_emails": null, "keywords": "[]", "approved": true, "websites": "[]", "desired_collaboration": "", "comments": "", "affiliations": [ { "id": 612, "ror": "https://ror.org/008s83205", "name": "University of Alabama at Birmingham", "address": "", "city": "", "state": "AL", "zip": "", "country": "United States", "approved": true } ] } ], "awardee_organization": { "id": 273, "ror": "https://ror.org/02v80fc35", "name": "Auburn University", "address": "", "city": "", "state": "AL", "zip": "", "country": "United States", "approved": true }, "abstract": "Decision makers play a crucial role in interpreting and communicating information to the public to signal (or compel) collective action in a crisis. However, they face a critical knowledge gap on the most effective strategies for communicating scientific recommendations within the most vulnerable communities. This project examines 1) how decision makers responded to COVID-19 in the three poorest states in the United States (U.S.) Southeast (i.e. Alabama, Louisiana, and Mississippi) and 2) how marginalized, low income communities consumed, interpreted, and acted on science-based messaging from decision makers. Specifically, the effectiveness of science-based messages will be analyzed, focusing on how influential various sources of messages were and the perceived trustworthiness of the institutions delivering COVID-19 information in low income communities compared to their wealthier counterparts. The project is participatory in its design, ensuring end users are involved in shaping, implementing, and learning from research through the duration of the project. The project will advance knowledge on how leaders can become more effective at engaging the public and will provide insights on how to compel individuals in some of the nation’s most vulnerable communities to undertake collective action to improve community well-being in the face of crises. This project includes five objectives. The first objective is to scope questions for surveys and interviews through a remote focus group workshop with at least 12 decision makers from federal, state, and local agencies. This workshop will allow stakeholders to include their informational needs directly into our project. Second, the researchers will implement a mail out survey to a large, representative sample of individuals from the Southeast (n=9,000). Experimental methods will be used to predict differences in the effectiveness of science-based messages, trust in institutions, and message sources as a function of demographics, different levels of socio-economic vulnerability, and other variables. Third, they will interview decision makers in these states to determine the challenges they faced responding to COVID-19, and the scientific messages that convinced them to take particular policy making actions (such as giving a “stay at home order”). Fourth, a remote webinar with decision makers from federal, state, local, and tribal jurisdictions will be hosted to share the findings. Fifth, the project will create a web application for decision makers across the U.S. to apply our data to their community. It will allow them to select demographic and socio-economic factors that represent their communities and provide suggested messaging and media characteristics that will increase the effectiveness of their policy decisions. Findings can help decision-makers better engage marginalized communities as the pandemic continues to unfold. This knowledge may be applied to other crises where voluntary collective action leads to increases in aggregate human well-being despite private costs.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.", "keywords": [], "approved": true } } ], "meta": { "pagination": { "page": 1384, "pages": 1405, "count": 14046 } } }