Grant List
Represents Grant table in the DB
GET /v1/grants?page%5Bnumber%5D=1404&sort=program_reference_codes
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Seasonal return of the coronavirus (SARS-CoV-2) will be even more devastating. One of the primary ways that SARS-CoV-2 appears to spread is through people touching surfaces with virus-laden droplets. Virus survivability on surfaces varies greatly by droplet size and composition, surface material and texture, and the ambient temperature and relative humidity. These factors impact the concentration of salt and other solutes in the process of droplets drying on surfaces, which strongly influences the survival of viruses in the droplets. This project will address survivability of viruses inside droplets and its relationship to droplet size, type of surface and ambient conditions representing seasonal variations. The team will especially seek to understand conditions under which virus survival in surface-adherent droplets is diminished. This information is crucial for public health officials, virologists, and other experts working on disinfection efforts to control and mitigate current and future COVID-19 outbreaks.This project will bring together an interdisciplinary team of engineers, virologists, and infectious disease experts to understand the mechanisms that determine virus survival/annihilation on different surfaces under varying seasonal conditions. The research plan combines computer simulations and experiments to reveal what happens inside droplets under conditions specific to COVID-19 scenarios. More specifically, this project will study the thermal and mass transport processes that occur in droplets drying on various surfaces. Project results will include quantitative information on the solute (salt, protein) concentration-time pathways in the interior of virus-carrying droplets drying on surfaces. These pathways will be quantified as functions of droplet size, composition, ambient temperature and relative humidity, and surface characteristics. Novel laser and chemical treatments of surfaces will be employed to modify hydrophobicity and hydrophilicity over a wide range to examine the effect of such modifications on droplet drying processes. This project will therefore reveal what types of seasonal conditions and surface modifications will contribute to diminished survival of viruses within droplets. The project team will transition project results to experts who study methods to deactivate viruses. Arming them with precise knowledge on the thermochemical conditions faced by the pathogens within drying droplets will enable them to focus attention on effective disinfection techniques.This project is jointly funded by the Thermal Transport Processes 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": "1634", "attributes": { "award_id": "2031794", "title": "RAPID: Understanding SARS-CoV2 transmission through a novel continuous monitoring system", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Engineering (ENG)" ], "program_reference_codes": [ "096Z", "7914", "9150" ], "program_officials": [ { "id": 4291, "first_name": "Stephanie", "last_name": "George", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2020-08-01", "end_date": "2021-10-31", "award_amount": 199994, "principal_investigator": { "id": 4292, "first_name": "Bapi", "last_name": "Pahar", "orcid": null, "emails": "[email protected]", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 305, "ror": "https://ror.org/04vmvtb21", "name": "Tulane University", "address": "", "city": "", "state": "LA", "zip": "", "country": "United States", "approved": true } ] }, "other_investigators": [], "awardee_organization": { "id": 305, "ror": "https://ror.org/04vmvtb21", "name": "Tulane University", "address": "", "city": "", "state": "LA", "zip": "", "country": "United States", "approved": true }, "abstract": "The coronavirus SARS-CoV-2 (severe acute respiratory syndrome–coronavirus-2), which is the cause of COVID-19, has a higher transmission rate compared to previous limited epidemics related to similar coronaviruses that are known to be transmitted by respiratory secretions, droplets, direct or indirect contacts. A recent study documented that SARS-CoV-2 can remain viable for several hours on various surfaces such as stainless steel, plastic, glass, and cardboards, suggesting that transmission can also happen through contaminated surfaces. Controversies remain whether SARS-CoV-2 can be transmitted through aerosols. Therefore, it is important to understand how soon an infected person starts shedding the virus, and how soon he or she transmits the infection to another person who is in direct or indirect contact with them. To address these major questions, this project will utilize a novel non-invasive monitoring system that can provide important information about the disease progression and new insights on disease staging, outcomes, and transmissions using a mouse model that has been engineered to have the human receptor that is key to allowing the virus to enter cells lining human airways. The monitoring system can continuously capture disease related data such as body temperature, head-body distance, activity patterns and breathing sounds. The data obtained will be used to identify meaningful patterns associated with disease onset and progression. To determine the difference between direct contact (DC) and indirect contact (IC), infected and uninfected animal groups will be housed in the same cage (DC) or in adjoining cages separated by a permeable partition (IC). This novel information will open the door for further studies where a greater number of experimental factors can be investigated, enabling more humane endpoints. Furthermore, the novel SARS-CoV-2 - mouse transmission models can be applied to other contagious diseases. The goal of this proof-of-principle project is to generate novel information about the SARS-CoV-2 transmission rate, frequencies and, disease progression through direct or indirect contact (DC or IC) in murine models. Results obtained are expected to provide fundamental insight into how soon an infected person starts shedding the virus, and how soon he or she transmits the infection to another person who is in direct or indirect contact with them. The project’s goal will be accomplished using hACE2 (human angiotensin-converting enzyme 2) transgenic mice expressing the ACE2 receptor and a non-invasive and continuous monitoring system (CMS) designed for multi-sensor data collection that can capture several external phenotypes, including body temperature, head-body distance, activity patterns, breathing sounds, and others to characterize infectious disease progression. Algorithms have been developed for time-series decomposition and classification that can identify meaningful patterns associated with disease staging. The project hypothesizes that the novel CMS can be used to better characterize the SARS-CoV-2 transmission time, frequencies, and to study disease progression variability between DC and IC mice-to-mice transmission models. The hypothesis will be tested with two specific aims. The FIRST aim is to quantify the SARS transmission time, frequencies, and resulting disease progression dynamics in a DC mice-to-mice transmission model. This aim will be achieved by infecting one of the two housed hACE2 transgenic mice per cage with SARS-CoV-2, after which the animals will be continuously monitored for 21 days. Real time data analysis will be used to determine disease onset (viral transmission) for other uninfected hACE2 mice, which are housed in the same cage and have DC with the infected mice. The SECOND Aim will quantify the SARS transmission time, frequencies, and disease progression in an IC mice-to-mice transmission model. This aim will be accomplished by housing uninfected hACE2 mice in cages with a permeable partition separating them from infected hACE2 mice. In both aims, the morality rate will be quantified. Tissues will be collected from euthanized animals to measure tissue viral loads, tissue pathogenesis by histology, and to detect tissue viral antigens by immunohistochemistry (IHC) staining.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": "1747", "attributes": { "award_id": "2030313", "title": "RAPID: Graduate student experiences of support and stress during the COVID-19 pandemic", "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", "9150" ], "program_officials": [ { "id": 4582, "first_name": "Daniel", "last_name": "Denecke", "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": 163033, "principal_investigator": { "id": 4586, "first_name": "Craig A", "last_name": "Ogilvie", "orcid": null, "emails": "[email protected]", "private_emails": "", "keywords": null, "approved": true, "websites": "['https://www.montana.edu/covid19_rapid/index.html']", "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 259, "ror": "https://ror.org/02w0trx84", "name": "Montana State University", "address": "", "city": "", "state": "MT", "zip": "", "country": "United States", "approved": true } ] }, "other_investigators": [ { "id": 4583, "first_name": "Rachel A", "last_name": "Smith", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, { "id": 4584, "first_name": "Sarah", "last_name": "Rodriguez", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, { "id": 4585, "first_name": "Kelly E", "last_name": "Knight", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "awardee_organization": { "id": 259, "ror": "https://ror.org/02w0trx84", "name": "Montana State University", "address": "", "city": "", "state": "MT", "zip": "", "country": "United States", "approved": true }, "abstract": "RAPID: Understanding Graduate Student Experiences of Support and Stress During the COVID-19 Pandemic COVID-19 has rapidly upended graduate students’ learning, research, and teaching. Carefully crafted career paths that often require substantial investments by both students and their institutions have been placed at risk due to newly imposed challenges. These challenges include limited access to classrooms and labs, disruption to current and planned collaborations, increased stress and anxiety, and new responsibilities for children and other family members. This impact is particularly acute for graduate students with marginalized identities (e.g., students of color, low-income students) who are often already struggling with existing inequalities. To compound matters, these hardships confront a graduate student population that multiple studies suggest was already facing a mental health crisis. If graduate students do not feel supported during this critical period, the U.S. may suffer a dramatic decline in the effectiveness and inclusiveness of its graduate-level training, resulting in long-term negative impacts on the U.S. scientific knowledge base, society, and the broader U.S. economy. This National Science Foundation Rapid Response Research (RAPID) award to Montana State University, Iowa State University, and Texas A&M-Commerce will support research on: postsecondary institutional policies and practices that are designed to help graduate students feel supported during the COVID-19 pandemic and the influence of these efforts on graduate student educational and career decision-making. The study will contribute to a research base for graduate school administrators and faculty about how to immediately help graduate students during a crisis, as well as what strategies might be most effective for supporting graduate students in STEM fields and more broadly as they make degree and career-related decisions as society emerges from the crisis. The project is a multi-institution, two-phase explanatory mixed method study of graduate students’ experiences during the COVID-19 pandemic, their perceptions of institutional support, and near-term impact of this support on educational and career decision-making. The research team will leverage existing institutional higher education networks to solicit the participation of 15 institutions and disseminate questionnaires to 30,000 graduate students in late spring/early summer 2020. The research is informed by scholarship on trauma and disaster response. The survey, which will include validated trauma scales that measure post-traumatic stress disorder-like symptoms, anxiety, and depression, will be designed by an interdisciplinary team that includes trauma and mental health experts. Team members will then conduct virtual focus groups using semi-structured qualitative interview protocols to explore students’ experiences and reactions to the COVID-19 pandemic in-depth, and how these experiences relate to their identities, persistence, and career aspirations. Researchers will integrate both quantitative and qualitative data to further interpret, explain, and provide new insights to understanding graduate student experiences during the COVID-19 pandemic. Analyses will pay particular attention to student experiences related to race, class, gender, and other socio-demographic factors. Multiple efforts will be made at each stage to recruit and support participation of underrepresented minority STEM graduate students in the study. Results of the research will be disseminated through peer reviewed scholarship, a white paper, and workshops providing graduate college leadership and STEM faculty with research-based guidelines on how to support the broadest range of their graduate students during a time of crisis. This Rapid Response Research (RAPID) award is made by the Innovations in Graduate Education (IGE) program in the Division of Graduate Education/Directorate of Education and Human Resources 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": "1806", "attributes": { "award_id": "2027059", "title": "RAPID: Determine Community Disease Burden of COVID-19 by Probing Wastewater Microbiome", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Engineering (ENG)" ], "program_reference_codes": [ "096Z", "7914", "9150" ], "program_officials": [ { "id": 4771, "first_name": "Mamadou", "last_name": "Diallo", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2020-05-01", "end_date": "2023-04-30", "award_amount": 151956, "principal_investigator": { "id": 4772, "first_name": "Tao", "last_name": "Yan", "orcid": null, "emails": "[email protected]", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 684, "ror": "", "name": "University of Hawaii", "address": "", "city": "", "state": "HI", "zip": "", "country": "United States", "approved": true } ] }, "other_investigators": [], "awardee_organization": { "id": 684, "ror": "", "name": "University of Hawaii", "address": "", "city": "", "state": "HI", "zip": "", "country": "United States", "approved": true }, "abstract": "The global COVID-19 pandemic is an urgent global health and economic crisis. The impacts of the current outbreak are exacerbated by the potential for infection outbreaks to seasonally reoccur. This potential seasonal cycling presents a great challenge to the current clinic-based disease surveillance approach which does not adequately test asymptomatic and mildly symptomatic patients. There is a critical need for alternative methods to assess spread, as undiagnosed infections are driving disease transmission in the COVID-19 pandemic. To directly address this need, this project will develop the science to assess community infection prevalence by analyzing the wastewater microbiome, promising a rapid, sensitive, and comprehensive approach for microbial disease surveillance. Successful development will allow assessment of infection rates in the population served by the wastewater treatment plant, including those with mild or no symptoms. Such capabilities would dramatically improve surveillance of pandemics by indicating in real time where the disease is emerging in new hotspots. Such information would inform intervention strategies for controlling the current COVID-19 pandemic, and help the Nation manage future outbreaks more effectively. This RAPID project has two specific research objectives in responding to the COVID-19 pandemic. The first objective is to develop and optimize a highly efficient concentration and detection method for enveloped viruses (like coronavirus) in the wastewater matrix. There is urgent need for this knowledge given that the most commonly used concentration methods for viruses in water samples were developed for the non-enveloped viruses. Successful completion of this phase of the research will thus fill a major technological gap and significantly advance the capability to detect and monitor the SARS-CoV2 in water and wastewater. The second objective is to collect time-sensitive wastewater samples from communities impacted by the disease. The wastewater will be used to determine the abundance, diversity, and temporal dynamics of SARS-CoV2 and other enveloped viruses in the wastewater. These data will be analyzed to reconstruct the extent of COVID-19 community transmission. The research will generate much-needed information on community transmission of COVID-19, as well as serve to validate the wastewater-based surveillance approach. If successful, this research will provide a new avenue to protect the public health of the Nation through wastewater surveillance.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": "1811", "attributes": { "award_id": "2028077", "title": "RAPID: A Virtual Reality simulator to train first responders involved in health care efforts related to the COVID-19 virus outbreak", "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": 4787, "first_name": "Balakrishnan", "last_name": "Prabhakaran", "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": 103339, "principal_investigator": { "id": 4788, "first_name": "J.", "last_name": "Cecil", "orcid": null, "emails": "[email protected]", "private_emails": "", "keywords": null, "approved": true, "websites": "['https://vrice.okstate.edu/content/rapid-creation-virtual-reality-simulator-trai…']", "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [], "awardee_organization": { "id": 387, "ror": "https://ror.org/01g9vbr38", "name": "Oklahoma State University", "address": "", "city": "", "state": "OK", "zip": "", "country": "United States", "approved": true }, "abstract": "All health-care workers may not have the necessary background and training to handle contagious diseases, and different contagious diseases need different types of precautions to be taken. In this context, training these responders to handle the novel virus COVID-19 infected patients become paramount. This project involves the creation of a Virtual Reality based simulation environment to support training of first responders including nurses in hospitals, communities, and cities in the US in order to respond more effectively to the recent COVID-19 outbreak. The COVID-19 virus pandemic has placed an overwhelming strain on the Nation's ability to treat patients; the number of patients who need to be tested continues to rise (and is expected to increase substantially in the coming weeks and months). Currently, there is an urgent need to train such responders (nurses, physician assistants and others) to perform the screening/testing activities in a methodical, safe and efficient manner. By creating a Virtual Reality simulator for such training, this project will accomplish two objectives: (i) increase the pool of first responders involved in COVID-19 testing; and (ii) develop a more effective process to train and prepare such first responders. The simulator's training modules will also be used to introduce university students to the process of designing and building such Virtual Reality simulators for medical and healthcare contexts.With the number of COVID-19 patients continuing to increase rapidly, it is critical that the Nation have a larger pool of trained first responders. The creation of such a Virtual Reality training simulator will address this urgent need. This innovative simulator will provide a user-friendly and effective training experience for the nurses and other health care assistants to perform various triage-related screening and testing activities. An interdisciplinary team of researchers including first responders, nurses and triage coordinators (from a hospital partner) will collaborate in a unique participatory design-based approach to designing and building this training simulator. With the involvement of COVID-19 first responders and medical specialists, an information centric process model (ICPM) will be created that captures the functional and temporal relationships of various activities involved in the triage-based patient interaction process including screening, testing, and treatment. The creation of such an ICPM will provide a robust and structured foundation to create the simulation environments, which will be distributed to hospitals nationwide. On-line workshops will be held (after creation of this simulator) to help with the use of this simulator by hospitals and clinics. Conference and workshop papers targeting medical and healthcare professionals will be presented to highlight the design and use of this training simulator.This project is co-funded by 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": "1835", "attributes": { "award_id": "2034247", "title": "RAPID: Democratizing Genome Sequence Analysis for COVID-19 Using CloudLab", "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": 4846, "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": "2020-06-15", "end_date": "2023-05-31", "award_amount": 179999, "principal_investigator": { "id": 4851, "first_name": "Praveen R", "last_name": "Rao", "orcid": null, "emails": "[email protected]", "private_emails": "", "keywords": "['Big data']", "approved": true, "websites": "['https://sites.google.com/view/raopraveen', 'https://github.com/MU-Data-Science/EVA']", "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 292, "ror": "", "name": "University of Missouri-Columbia", "address": "", "city": "", "state": "MO", "zip": "", "country": "United States", "approved": true } ] }, "other_investigators": [ { "id": 4847, "first_name": "Wesley C", "last_name": "Warren", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, { "id": 4848, "first_name": "Peter J", "last_name": "Tonellato", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, { "id": 4849, "first_name": "Eduardo J.", "last_name": "Simoes", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, { "id": 4850, "first_name": "Deepthi", "last_name": "Rao", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "awardee_organization": { "id": 292, "ror": "", "name": "University of Missouri-Columbia", "address": "", "city": "", "state": "MO", "zip": "", "country": "United States", "approved": true }, "abstract": "The COVID-19 pandemic has caused numerous deaths in the United States and around the world. Human genetic information may hold the answers for COVID-19 drug discovery. With the ability to sequence the human genome at low cost, this project aims to democratize genome sequence (GS) analysis on CloudLab (https://cloudlab.us/, an NSF-funded cloud computing research infrastructure) for accelerating the process of finding a cure for COVID-19. As a result, any researcher will be able to study differences in the genetic information of individuals affected by COVID-19 using CloudLab at no charge. This project will investigate efficient computing solutions to perform the data-intensive task of analyzing differences in individuals' genomes. It will also provide training opportunities for students.The project will democratize genome sequence (GS) analysis using CloudLab. Deep insights from genomic information of individuals can be extracted by researchers at scale. This work will lead to improved understanding of how commodity clusters, cloud infrastructure, and open-source software could be designed for large-scale GS storage, processing, and analysis. Specifically, it will result in new algorithms for exhaustive variant analysis (EVA), scheduling strategies for efficient execution of variant analysis tasks, and optimization techniques to speedup EVA and maximize resource utilization in a commodity cluster. It will provide low-level measurement data for network optimization when processing large-scale GS workloads.By empowering researchers with publicly available software tools and computing infrastructure for variant analysis at scale, this project could advance the understanding of how individuals respond to COVID-19 infection by uncovering deep relationships in genomic variants of individuals. It can enable new drug discovery and treatment strategies for COVID-19. A prototype of the tool will be made publicly available for research and education. The findings will be disseminated in the form of publications and software packages. New course modules will be developed; a workshop for high school students will be conducted.The project website is at https://github.com/MU-Data-Science/EVA. This repository will be maintained for 5 years after the completion of the project.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": "1842", "attributes": { "award_id": "2030362", "title": "Collaborative Research: RAPID: Compounding Human and Natural Disasters: Implication on agriculture sectors", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Engineering (ENG)" ], "program_reference_codes": [ "096Z", "7914", "9150" ], "program_officials": [ { "id": 4867, "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": "2020-06-15", "end_date": "2023-05-31", "award_amount": 100000, "principal_investigator": { "id": 4868, "first_name": "Ashok", "last_name": "Mishra", "orcid": null, "emails": "[email protected]", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 290, "ror": "https://ror.org/037s24f05", "name": "Clemson University", "address": "", "city": "", "state": "SC", "zip": "", "country": "United States", "approved": true } ] }, "other_investigators": [], "awardee_organization": { "id": 290, "ror": "https://ror.org/037s24f05", "name": "Clemson University", "address": "", "city": "", "state": "SC", "zip": "", "country": "United States", "approved": true }, "abstract": "One of the key sectors being significantly affected by the COVID-19 disaster is the agriculture sector. The COVID-19 human disaster is further posing significant challenges to mitigating ongoing natural disasters (e.g., drought) in different parts of the world, including the USA. COVID-19 will have a significant impact on multiple agricultural sub-sectors as farm prices will be impacted, supply chains will likely slow down, farm workforce disruptions will occur, and the odds of farm bankruptcies will increase. The reduction of demand for crops and the reduction of supply of labor is likely to reduce revenue and increase costs. Drought conditions will further exacerbate the hardship of the broader agricultural sector as water availability declines due to drought and costs continue to rise.The resilience of agricultural systems in the face of natural hazards (e.g., drought, hot days) has improved over the decades but the ongoing COVID-19 pandemic presents a new and unexpected challenge to the farming community. The combination of drought and COVID-19 can lead to a compounding impact on farming sectors. Droughts reduce crop yield and create financial losses, and COVID-19 further compounds this challenge by impacting farm price, supply chains, health risk, and loss of farm work force. This project will study how the combination of ongoing drought and COVID-19 will affect the agricultural sectors that play an important role in the nation's food security. The research team will investigate the consequences of compounding drought and COVID 19 on farmers' socio-economic indicators at the county scale, and what strategies can be implemented to minimize the impacts. This project will advance knowledge of the combined influence of human hazard and natural hazard on agricultural sub-sectors and provides an excellent opportunity to study the compounding effect of two different types of hazards in different parts of the USA. Research results will be used to develop strategies for improving awareness about this unique extreme compounding, allowing stakeholders to take precautionary measures for such events in the future. The research findings will be shared with key stakeholders (e.g., Department of Agriculture, agriculture extension specialists) and is targeted to benefit those most affected by this ongoing disaster in the USA and worldwide, assisting in the development of precautionary measures that can be taken in the near future.This project is jointly funded by the Chemical, Bioengineering, Environmental and Transport Systems (CBET) Division 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": "1340", "attributes": { "award_id": "2040503", "title": "RAPID: development of a local epidemiological population balance model informed by UAV and WVD data", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Engineering (ENG)" ], "program_reference_codes": [ "096Z", "7914", "9150" ], "program_officials": [ { "id": 3448, "first_name": "William", "last_name": "Olbricht", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2020-08-01", "end_date": "2021-07-31", "award_amount": 100000, "principal_investigator": { "id": 3451, "first_name": "Norman J", "last_name": "Wagner", "orcid": null, "emails": "[email protected]", "private_emails": "", "keywords": "['Agent based model']", "approved": true, "websites": "['https://sites.udel.edu/udcovidmodel/']", "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": 3449, "first_name": "Antony N", "last_name": "Beris", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, { "id": 3450, "first_name": "Richard R Suminski", "last_name": "Jr", "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": "Decision making and policy setting by universities and surrounding localities requires knowledge of how people move and interact in the environment. This RAPID project adapts new scientific approaches in population balance modeling to model human movement and interaction on a university campus and a surrounding town with the goal of providing new tools to help develop rational strategies for mitigation and eventual elimination of the novel corona virus, as well as future biological threats. Data for the model input will be obtained from high-definition video footage of public, outdoor areas including green spaces/parks, sidewalks/streets, and campus walkways/congregating spaces analyzed by artificial intelligence algorithms. Highly efficient tools that were originally developed to study complex fluids will enable determination key parameters needed for epidemiological models including effective transmission rates. Epidemiological modeling will be translated into a dashboard for use by policy makers as well as for public education about mitigation strategies. This RAPID project will provide a computational tool and example for use more broadly by communities and in additional and future, challenging public health issues.\nA multivariate population balance model applied to a college and local municipality will generate key parameters for agent-based epidemiological models. Multivariate balance modeling will be challenged with new data sets of local population density and motion for model parameter estimation using parallel tempering developed under prior and current NSF support. In addition to the usual distinctions of immune, susceptible, exposed, infected, and recovered classes, additional variables to consider include: age, especially relevant for University students, face-covering, inside and outside, and spatial-temporal population distributions afforded by real time updates of aerial (unmanned aerial vehicle) and ground (stationary camera augmented by wearable video devices) surveillance data. While it is common to include coarse-grained information afforded by transportation networks in large-scale epidemiology models, this project will explore opportunities afforded by social force models combined with epidemic population balance modeling. Advanced parallel tempering algorithms will be run on a GPU cluster to challenge the model with daily data streams to update parameters for epidemiological models and scenario projections. A project dashboard will be made available for policy decision making and public education. Broader impacts include computational tools that can be applied to a broad range of public health issues.\nThis 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": "1389", "attributes": { "award_id": "2027664", "title": "RAPID: Collaborative Research: COVID-19, Crises, and Support for the Rule of Law", "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": 3586, "first_name": "Reginald", "last_name": "Sheehan", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2020-04-15", "end_date": "2021-03-31", "award_amount": 29876, "principal_investigator": { "id": 3587, "first_name": "Amanda", "last_name": "Driscoll", "orcid": null, "emails": "[email protected]", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 343, "ror": "https://ror.org/05g3dte14", "name": "Florida State University", "address": "", "city": "", "state": "FL", "zip": "", "country": "United States", "approved": true } ] }, "other_investigators": [], "awardee_organization": { "id": 343, "ror": "https://ror.org/05g3dte14", "name": "Florida State University", "address": "", "city": "", "state": "FL", "zip": "", "country": "United States", "approved": true }, "abstract": "The rule of law is at the foundation of modern liberal democracy. Crises like the Coronavirus (COVID-19) pandemic, however, pose a challenge to this long-standing norm that buttresses modern democracies the world over. In the midst of a crisis, a person’s support for the rule of law is tested as their fundamental concern for the health, safety, and welfare of themselves, their families, and their friends is pitted against an abstract belief that the government “check all the boxes” before carrying out potentially lifesaving policies. In these situations, one’s desire for decisive government action may overwhelm, and subsequently lead to a decline in, one’s commitment to abstract democratic principles like the rule of law. Consequently, evaluating how crises affect support for fundamental democratic norms is critical for understanding their impact on the health and stability of the liberal democratic order.This project leverages the COVID-19 outbreak to examine this relationship and determine (a) whether governmental responses to crises affect citizens’ support for the rule of law; (b) whether citizens’ faith in government efforts is buttressed or undermined in response to elite and expert cues; and (c) whether citizens’ attitudes change after a crisis has dissipated. Each of these theoretical aims is tied to one of three unique features of the research design, which relies upon surveys of European democracies. First, to examine the effects of governmental responses, the project will make collect survey data on support for the rule of law across four Western democracies in April 2020: Germany, Spain, the United Kingdom, and the United States. Second, an original panel survey in Germany will enable the evaluation of changes to individual-level rule of law judgments in the short, medium, and long term. Lastly, embedded survey experiments will provide causal evidence on how elite and expert cues affect both the acceptance of policies and support for key aspects of the rule of law like compliance with laws and support for judicial constraints on executive and legislative power. Findings from each part of the project will provide insights into the individual-level dynamics crises activate in citizens’ relationship with democratic principles.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": "1390", "attributes": { "award_id": "2027671", "title": "RAPID: Collaborative Research: COVID-19, Crises, and Support for the Rule of Law", "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": 3588, "first_name": "Reginald", "last_name": "Sheehan", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2020-04-15", "end_date": "2021-03-31", "award_amount": 24087, "principal_investigator": { "id": 3589, "first_name": "Michael", "last_name": "Nelson", "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 rule of law is at the foundation of modern liberal democracy. Crises like the Coronavirus (COVID-19) pandemic, however, pose a challenge to this long-standing norm that buttresses modern democracies the world over. In the midst of a crisis, a person’s support for the rule of law is tested as their fundamental concern for the health, safety, and welfare of themselves, their families, and their friends is pitted against an abstract belief that the government “check all the boxes” before carrying out potentially lifesaving policies. In these situations, one’s desire for decisive government action may overwhelm, and subsequently lead to a decline in, one’s commitment to abstract democratic principles like the rule of law. Consequently, evaluating how crises affect support for fundamental democratic norms is critical for understanding their impact on the health and stability of the liberal democratic order.This project leverages the COVID-19 outbreak to examine this relationship and determine (a) whether governmental responses to crises affect citizens’ support for the rule of law; (b) whether citizens’ faith in government efforts is buttressed or undermined in response to elite and expert cues; and (c) whether citizens’ attitudes change after a crisis has dissipated. Each of these theoretical aims is tied to one of three unique features of the research design, which relies upon surveys of European democracies. First, to examine the effects of governmental responses, the project will make collect survey data on support for the rule of law across four Western democracies in April 2020: Germany, Spain, the United Kingdom, and the United States. Second, an original panel survey in Germany will enable the evaluation of changes to individual-level rule of law judgments in the short, medium, and long term. Lastly, embedded survey experiments will provide causal evidence on how elite and expert cues affect both the acceptance of policies and support for key aspects of the rule of law like compliance with laws and support for judicial constraints on executive and legislative power. Findings from each part of the project will provide insights into the individual-level dynamics crises activate in citizens’ relationship with democratic principles.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": 1404, "pages": 1424, "count": 14236 } } }