Grant List
Represents Grant table in the DB
GET /v1/grants?page%5Bnumber%5D=1385&sort=program_reference_codes
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Workforce, education, and social experiences are suddenly transformed, as millions of families are forced to take ownership of their children's education while working full-time or enduring hardships of new unemployment. Technology’s role is crucial, as this new social distancing context requires everything outside the family unit to now be performed remotely, over computer networks. This research project will develop understanding of the effects of these extraordinary circumstances on family life, educational outcomes, and work. The research team will investigate how digital technologies offer solutions, negatively impact, and perpetuate or reduce the digital divide and other disparities. To help families mitigate the negative effects of COVID-19 on society and the economy, the team will develop guidance—involving technologies in the home—about how to simultaneously support remote education, remote work, and family life. The research team will regularly communicate this guidance through blogging and social media targeted to families, educators, and technology designers.This research will contribute empirical understanding of the experiences of families during COVID-19 social distancing and other times of disruption, in conjunction with computing and information by (1) collecting and analyzing data about how families’ social and educational experiences are transformed by social distancing in the COVID-19 pandemic; (2) investigating aspects of technology designs that are helpful and unhelpful to families and their relationships to social and economic institutions during COVID-19; (3) designing and discovering new technologies to support work-life balance, education, and family connectedness; and (4) deriving theories and models of how families in a pandemic crisis adapt technologies for distance learning and workforce participation. Thirty diverse families--each with at least one child between the ages of 3 and 13--will be remotely engaged in interviews, surveys, and design activities. The Asynchronous Remote Communities (ARC) method will be used to collect experiences and co-design new technologies. This method can benefit participants through increased reflection, social support, and resource-sharing. The team will develop new design artifacts based on remote co-design sessions with families. The work will contribute new methodologies, as the researchers adapt the ARC method for use with families with younger children in rapidly evolving situations. The research will be informed by, and potentially extend and further validate theoretical frameworks, including strength of weak ties, joint media engagement, family resilience theory, and funds of knowledge.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": "1855", "attributes": { "award_id": "2029457", "title": "RAPID: Collecting Reliable COVID-19 Datasets in Crisis Conditions", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Office of the Director" ], "program_reference_codes": [ "096Z", "7914" ], "program_officials": [ { "id": 4904, "first_name": "Chaitanya", "last_name": "Baru", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2020-05-01", "end_date": "2020-10-31", "award_amount": 69998, "principal_investigator": { "id": 4905, "first_name": "Rastislav", "last_name": "Bodik", "orcid": null, "emails": "[email protected]", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 159, "ror": "https://ror.org/00cvxb145", "name": "University of Washington", "address": "", "city": "", "state": "WA", "zip": "", "country": "United States", "approved": true } ] }, "other_investigators": [], "awardee_organization": { "id": 159, "ror": "https://ror.org/00cvxb145", "name": "University of Washington", "address": "", "city": "", "state": "WA", "zip": "", "country": "United States", "approved": true }, "abstract": "This RAPID project enables approaches to mitigate the negative impacts of COVID-19 on public health, society, and the economy by deploying technologies to enable collecting reliable COVID-19-related data sets under crisis conditions. In the midst of a crisis, such as the COVID-19 pandemic, generators of critical new data, such as hospitals and critical health organizations, lack the time and resources to make this important data readily available for use by others. One cannot expect the already overburdened primary data providers to do the extra work needed to make the data more accessible for others to use. Even those who already publish data on their websites often do not have the time to edit/modify the data, for example to apply newly introduced tags, such as Schema.org’s new tags related to coronavirus. Yet, these data are critical in a crisis in order to inform the public; improve emergency response; and aid the scientific community in its efforts to find solutions. Currently, the teams that are engaged in dataset collection are employing slow, tedious, and painstaking manual techniques. The interactive dataset collection tools to be developed by this project will provide an alternative approach, empowering a community of volunteers to help with data collection efforts. The data collection tools developed can be used with only an internet connection, a web browser, and brief training, thereby putting the effort well within reach of a large population of potential volunteers. Existing automatic data extractors assume that (i) webpages in a single website are structured uniformly, because they were produced from the same template and (ii) relevant webpages originate from a single website. As a result, much of the prior work in the area of web data extraction and ingestion focuses on ‘syntactic’ extraction. Currently, dedicated data collection teams are collecting data with a combination of expertise and time-consuming and painstaking manual effort. Other teams are hiring call centers to call hospitals in each state to collect their capacities. Such high-cost, high-effort approaches do not scale well to all the datasets that one would like to be able to access and analyze. Many COVID-19-related datasets are scattered over thousands of websites with similar information but no structural similarities--e.g., each hospital’s website may look different but may contain very similar and related data. The technical challenge that this project will tackle will be to build a ‘semantic’ data extractor that locates the information of interest despite divergent website structures. The software tools that will be created for data ingestion can be used by the many individuals who are keen to contribute their time and effort to help combat COVID-19, without compromising their physical distancing efforts.This RAPID award is made by the Convergence Accelerator program in the Office of Integrative Activities and is associated with the Convergence Accelerator Track A: Open Knowledge Network.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.", "keywords": [], "approved": true } }, { "type": "Grant", "id": "1856", "attributes": { "award_id": "2029421", "title": "RAPID: Transmission and Immunology of COVID-19 in the Pandemic and Post-Pandemic Phase: Real-time Assessment of Social Distancing & Protective Immunity", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Biological Sciences (BIO)" ], "program_reference_codes": [ "096Z", "7914" ], "program_officials": [ { "id": 4906, "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": "2020-05-01", "end_date": "2021-07-31", "award_amount": 199992, "principal_investigator": { "id": 4908, "first_name": "Micaela E", "last_name": "Martinez", "orcid": null, "emails": "[email protected]", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 196, "ror": "https://ror.org/00hj8s172", "name": "Columbia University", "address": "", "city": "", "state": "NY", "zip": "", "country": "United States", "approved": true } ] }, "other_investigators": [ { "id": 4907, "first_name": "Markus", "last_name": "Hilpert", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "awardee_organization": { "id": 196, "ror": "https://ror.org/00hj8s172", "name": "Columbia University", "address": "", "city": "", "state": "NY", "zip": "", "country": "United States", "approved": true }, "abstract": "In March 2020 the COVID-19 pandemic took hold in the United States and threatened the lives a livelihood of all Americans. At this time, the pandemic is being driven by person-to-person transmission of the coronavirus known as SARS-CoV-2. The U.S. government, both state and federal, responded to the pandemic by launching social distancing as an intervention, ordering schools and non-essential businesses to close throughout most of the country. Mathematical models are required in order to measure the transmission of COVID-19 in the U.S. This research focuses on building mathematical models that include data on social distancing to measure how effective the intervention is at slowing the disease. The researchers will also evaluate strategies for reopening schools and workplaces. Importantly, the researchers will measure the risk for a second pandemic wave by accounting for how the immune system reacts to the infection. This research is important because it will provide the U.S. government with models that it can use to choose among options for reopening cities. These models will also reveal the number of deaths averted by social distancing policies. In addition to the great societal benefit of this research, it also brings scientific advancement and broader impacts by demonstrating how novel datasets can be collected in real-time and models can be deployed during a public health emergency. This project provides professional development opportunities for an early career scientist. Transmission models will be used to explore creative ways for phased reopening of cities in order to minimize disease-induced mortality and overburdening of hospitals. The researchers will focus on the 62 counties in New York state, the current epicenter of the pandemic. The researchers will fit a city-level COVID-19 transmission model which accounts for social distancing quantified by Google traffic data, and ground-truthed by public transportation data, live-webcam streams, and Google trends indicating individuals are staying home. The researchers will explore transmission dynamics and hospitalization trajectories under different scenarios of adaptive immunity (e.g., long-lived sterilizing immunity, waning immunity, and immunity that reduces symptoms in subsequent infections). Models will be parameterized using data on testing, COVID-19 clinical cases, hospitalizations, and mortality via Maximum Likelihood by Iterated Particle Filtering (MIF). Modeling will be done in real time, requiring the statistical inference pipeline to be sufficiently nimble to account for the rapidly changing epidemiological situation. The researchers are interfacing with policy makers as part of CDC working groups on modeling COVID-19 and will actively share data through the NIH MIDAS coordination network.This RAPID award is made by the Ecology and Evolution of Infectious Diseases Program in the Division of Environmental Biology, using funds from the Coronavirus Aid, Relief, and Economic Security (CARES) ActThis 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": "1857", "attributes": { "award_id": "2031195", "title": "RAPID: Data-Driven Models to Optimize Ventilator Therapy in ICU COVID Patients", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Engineering (ENG)" ], "program_reference_codes": [ "096Z", "7914" ], "program_officials": [ { "id": 4909, "first_name": "Lawrence", "last_name": "Goldberg", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2020-06-15", "end_date": "2021-05-31", "award_amount": 200000, "principal_investigator": { "id": 4911, "first_name": "Sridevi V", "last_name": "Sarma", "orcid": null, "emails": "[email protected]", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 344, "ror": "https://ror.org/00za53h95", "name": "Johns Hopkins University", "address": "", "city": "", "state": "MD", "zip": "", "country": "United States", "approved": true } ] }, "other_investigators": [ { "id": 4910, "first_name": "Raimond L", "last_name": "Winslow", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "awardee_organization": { "id": 344, "ror": "https://ror.org/00za53h95", "name": "Johns Hopkins University", "address": "", "city": "", "state": "MD", "zip": "", "country": "United States", "approved": true }, "abstract": "The novel Coronavirus (COVID-19) is one of four infectious diseases caused by the SARS-CoV-2 virus. Although the clinical signs and patient symptoms of this complicated disease vary in presentation and severity, clinicians and investigators have reported constitutional symptoms (cough and fever), upper and lower respiratory tract symptoms, as well as gastrointestinal symptoms. Among the most concerning is the life threatening acute respiratory distress syndrome (ARDS) in patients. The pathophysiology of severe ARDS results from a rapid decline in pulmonary function and requires intubation of patients in critical condition for invasive mechanical ventilation to combat lung recruitability, reduced peripheral capillary oxygen saturation (SpO2) and risks of organ failure and death. Ventilator settings to increase SpO2 and oxygen delivery is achieved with positive end-expiratory pressure (PEEP). However, controlling ventilation at a high PEEP for extended periods of time significantly increases risk for ventilator-associated lung injury (VALI). This RAPID project will develop novel engineering strategies for optimal ventilator control to maximize SpO2 in minimal time, while minimizing PEEP and the duration of ventilator use are needed to minimize VALI and subsequent complications, and to improve favorable patient outcomes. In the management of patients with COVID-19, these strategies are significant to optimize oxygen delivery, minimal invasive ventilator use and mechanical lung injury. Further, the understanding of ventilator requirements and operative settings highlights the need for available ventilators. The management of severe ARDS is complicated and strategies and protocols are desperately needed.To achieve this goal, we will develop data-driven linear parameter-varying (LPV) dynamical systems models that relate patient clinical state and ventilator inputs to the output variable patient SpO2. Patient state will be characterized using data from the electronic health record (EHR) and minute-by-minute physiological time-series (PTS) data (e.g., heart rate, respiratory rate, SpO2) acquired from patient monitoring. We will first develop the LPV model using retrospective data from non-COVID-19 patients who are on ventilators to help treat conditions such as pneumonia and ARDS. Then, we will test the predictive capabilities of the LPV model in COVID-19 patients who are placed on ventilators. Finally, we will develop an optimal ventilator control strategy for COVID-19 patients to regulate SpO2 levels in ICU patients based on the LPV model. Attempting to control a complex biological system using control strategies based on mechanistic models is generally intractable. However, the LPV framework allows for sophisticated optimal strategies to be implemented that not only allow for better performance than other classical methods, but also provides stability and performance guarantees.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": "1859", "attributes": { "award_id": "2027540", "title": "Collaborative Research: RAPID: Building a Spatiotemporal Platform for Rapid Response to COVID-19", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Computer and Information Science and Engineering (CISE)" ], "program_reference_codes": [ "096Z", "7914" ], "program_officials": [ { "id": 4915, "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": "2021-05-31", "award_amount": 100000, "principal_investigator": { "id": 4916, "first_name": "Weihe", "last_name": "Guan", "orcid": null, "emails": "[email protected]", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 455, "ror": "https://ror.org/03vek6s52", "name": "Harvard University", "address": "", "city": "", "state": "MA", "zip": "", "country": "United States", "approved": true } ] }, "other_investigators": [], "awardee_organization": { "id": 455, "ror": "https://ror.org/03vek6s52", "name": "Harvard University", "address": "", "city": "", "state": "MA", "zip": "", "country": "United States", "approved": true }, "abstract": "The Spatiotemporal Innovation IUCRC develops novel spatiotemporal analytical tools to enable applications of national and global significance. In response to the COVID-19 crisis, Harvard University and George Mason University, university sites within this IUCRC, propose this collaborative project to collect and share COVID related data in near real time, conduct spatiotemporal analytics, and mine socioeconomic and environmental knowledge to facilitate decision support systems in response to the pandemic. This project will build a unique cloud-based platform composed of a data collection subsystem for collecting global, high quality COVID-19-related data; spatiotemporal analytics tools for analyzing the disease evolution and socioeconomic patterns; and, modeling tools for assessing medical supplies and logistics. Through web access services, the platform will provide capabilities for easy access to the data collected as well as access to the developed spatiotemporal analytical and modeling tools. Such capabilities will facilitate quick production of data-driven decision support systems for community preparedness. This project has secured participation of 50+ international researchers in developing the proposed platform. These researchers will help collect and validate data, analyze how policies influence the outbreaks, how the Earth environment is impacted, and how to balance reopening of the economy and controlling the spreading of the disease in the U.S. based on experiences from Asia and Europe. Over 200 undergraduate volunteers, including many from underrepresented groups, are already involved in this project through Harvard’s Coronavirus Visualization Team efforts. Data, information, and knowledge accumulated in this project have been, and will continue to be, archived long term in a comprehensive gateway (covid-19.stcenter.net). Such data include spatiotemporal distribution of confirmed cases, relevant social, economic and natural information from different resources, such as authoritative reports, news releases, Earth observation, and social media. Software and tools developed are posted on GitHub for open access. Sustained online collaboration is being conducted to produce replicable research using spatiotemporal analyses to mine patterns and relations between COVID-19 and social and natural factors for community response and preparedness.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": "1861", "attributes": { "award_id": "2030685", "title": "RAPID: An Ensemble Approach to Combine Predictions from COVID-19 Simulations", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Biological Sciences (BIO)" ], "program_reference_codes": [ "096Z", "7914" ], "program_officials": [ { "id": 4921, "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": "2021-07-31", "award_amount": 199998, "principal_investigator": { "id": 4924, "first_name": "Taylor M", "last_name": "Anderson", "orcid": null, "emails": "[email protected]", "private_emails": "", "keywords": null, "approved": true, "websites": "['http://covid-ensemble.gmu.edu']", "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [ { "id": 4922, "first_name": "Andreas", "last_name": "Zuefle", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, { "id": 4923, "first_name": "Hamdi", "last_name": "Kavak", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "awardee_organization": { "id": 239, "ror": "https://ror.org/02jqj7156", "name": "George Mason University", "address": "", "city": "", "state": "VA", "zip": "", "country": "United States", "approved": true }, "abstract": "Decision-makers and the general public rely on models to simulate the SARS-CoV-2 virus spread and predict the number of infections and fatalities. Model predictions are critical to rapidly develop policy interventions that mitigate COVID-19, to anticipate impacts on health care resources, and to strategize how best to impose and lift public health guidelines. Existing COVID-19 models produce radically different predictions, thus creating confusion and mistrust over their use. Therefore, there is an urgent need to compare between the wide-range of existing COVID-19 models and their predictions. The goal of this project is to find a consensus among various model predictions and to make the different model assumptions and uncertainty transparent. An interactive web-based dashboard will serve as an open and accessible tool to inform the public, fellow researchers, and decision-makers where existing models agree and disagree on predictions. The predictions that are agreed upon by existing models can then be used with greater confidence and trust as the basis for effective decision-making to save lives and resources. Apart from the practical importance for the implementation of effective pandemic control measures and public health strategies, other broader impacts are professional development opportunities for early career researchers and training opportunities for a post-doctoral scholar. To find a consensus among various model predictions, this project will develop a novel ensemble prediction approach that (1) aligns different COVID-19 simulation models and (2) uses a time series clustering technique to unify model predictions. In the model alignment stage, a range of open-source and publicly available COVID-19 simulation models will be selected, aligned based on their parameters, and run as needed. A broad set of predictions will be obtained from the model results, where each prediction represents a possible world with a corresponding number of new cases, fatalities, and other quantities of interest. The time series clustering stage will project possible worlds into a feature space and apply clustering algorithms to find similar possible worlds. For each cluster, a representative possible world will be selected, enriched with measures of uncertainty, and visualized using the dashboard. This project advances the theoretical knowledge base for model alignment approaches and representative uncertain clustering for simulation model predictions. The unification of model predictions into a scientific consensus can be used to inform decision-makers better so that they can develop life-saving interventions.This RAPID award is made by the Ecology and Evolution of Infectious Diseases Program in the Division of Environmental Biology, using funds from the Coronavirus Aid, Relief, and Economic Security (CARES) Act.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.", "keywords": [], "approved": true } }, { "type": "Grant", "id": "1863", "attributes": { "award_id": "2030545", "title": "RAPID: Personal SARS-CoV-2 Exposure Assessment using a PDMS Wristband", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Mathematical and Physical Sciences (MPS)" ], "program_reference_codes": [ "096Z", "7914" ], "program_officials": [ { "id": 4927, "first_name": "Tomasz", "last_name": "Durakiewicz", "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": 200000, "principal_investigator": { "id": 4928, "first_name": "Krystal", "last_name": "Pollitt", "orcid": null, "emails": "[email protected]", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 452, "ror": "https://ror.org/03v76x132", "name": "Yale University", "address": "", "city": "", "state": "CT", "zip": "", "country": "United States", "approved": true } ] }, "other_investigators": [], "awardee_organization": { "id": 452, "ror": "https://ror.org/03v76x132", "name": "Yale University", "address": "", "city": "", "state": "CT", "zip": "", "country": "United States", "approved": true }, "abstract": "NON-TECHNICAL ABSTRACTPeople with COVID-19 can spread the disease through droplets expelled when coughing. There is also a possibility that infection of the novel coronavirus SARS-CoV-2 can be spread through smaller droplets, called aerosols, released by simply speaking or breathing. While larger droplets quickly fall to the ground, these smaller sized aerosols can stay in the air for longer and travel distances far greater than six feet. This is especially concerning because infected people with no or mild symptoms could unknowingly be contributing to the spread of COVID-19. Airborne SARS-CoV-2 aerosols have important implications for healthcare workers and people that work in proximity to the public, such as grocery clerks or transit workers. One way to test a person’s risk for infection is to determine the amount of virus in the air around them. This project will develop a low-cost wristband made from a soft silicone rubber that can be used to rapidly detect a person’s exposure to airborne SARS-CoV-2 aerosols. This wristband’s collection efficiency will be determined in our lab, and the ability to detect airborne viruses in a real-world setting will be tested with healthcare workers in a hospital COVID-19 patient ward. The simple design of the wristband could shape how COVID-19 infection is assessed. This research is critical, as cases in the US peak and we prepare for a possible second wave of infections as shelter in place orders are eased. Results from this project will be shared with hospital networks and more broadly with public health organizations. Wristbands will also be given to elementary school children during cold and flu season to test for SARS-CoV-2 and other airborne viruses as part of a learning module on the spread of respiratory viruses.TECHNICAL ABSTRACTThis RAPID proposal concerns the urgent need for monitoring levels of airborne SARS-CoV-2-laden aerosol as a critical strategy for assessing an individual’s risk of infection and identifying hotspots of exposure. The PI's vision is to develop a wearable, low-cost, highly deployable sampling device called the Fresh Air wristband. The envisioned device would passively concentrate airborne constituents onto a polymeric membrane sorbent bar while being worn. The critical component is the composition of this membrane, polydimethylsiloxane (PDMS)-based material, which was chosen given its efficient sorption of non-polar compounds that potentially include lipid enveloped viruses, such as SARS-CoV-2. An important focus of this project is identification of the physical properties and specific configuration of PDMS-based materials appropriate for a passive sampler, useful for simple and rapid exposure assessment of airborne SARS-CoV-2-laden aerosols. The goal of this NSF RAPID project is to develop a wearable low-cost environmental sampling device for detecting personal SARS-CoV-2 exposure to inform individuals of their potential risk of COVID-19 infection by determining the sorption characteristics of PDMS. The urgent nature of this work stems from the need to limit exposure to SARS-CoV-2 in order to decrease the incidence of new cases, especially in health care and other high-risk workers. Specific research objectives include the following: (1) Determine the size-resolved efficiency of viral collection on the PDMS-based sampler, and (2) Demonstrate utility of the Fresh Air sampler to assess health care providers’ exposure to airborne SARS-CoV-2. The major outcomes of this research project include an inexpensive exposure assessment tool and a rapid portable test protocol that individuals or groups can use to protect their health. Project results will be disseminated by sharing exposure data and sampler design with stakeholders at the Yale New Haven Hospital and more broadly to other hospital networks. To increase participation, the PI plans to introduce wristbands into a 5th grade New Haven Public School class during the traditional cold and flu season, analyze for Rhinovirus exposure, and develop and nationally distribute a learning module on the transmission of respiratory viruses, including SARS-CoV-2. This Rapid Response Research (RAPID) grant supports research that will develop a wearable low-cost environmental sampling device for detecting personal SARS-CoV-2 exposure with funding from the CARES Act managed by the Condensed Matter Physics Program in the Division of Materials Research of the Mathematical and Physical Sciences Directorate.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": "1873", "attributes": { "award_id": "2032264", "title": "RAPID: COVID-19 comparative modeling and analyses of outbreaks using mechanistic and ensemble machine learning and the development of a platform for projection and management", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Biological Sciences (BIO)" ], "program_reference_codes": [ "096Z", "7914" ], "program_officials": [ { "id": 4954, "first_name": "Katharina", "last_name": "Dittmar", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2020-06-01", "end_date": "2022-05-31", "award_amount": 200000, "principal_investigator": { "id": 4956, "first_name": "Wayne M", "last_name": "Getz", "orcid": "https://orcid.org/0000-0001-8784-9354", "emails": "[email protected]", "private_emails": "", "keywords": "['Dynamical systems']", "approved": true, "websites": "['https://vcresearch.berkeley.edu/faculty/wayne-m-getz']", "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 176, "ror": "", "name": "University of California-Berkeley", "address": "", "city": "", "state": "CA", "zip": "", "country": "United States", "approved": true } ] }, "other_investigators": [ { "id": 4955, "first_name": "Alan", "last_name": "Hubbard", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "awardee_organization": { "id": 176, "ror": "", "name": "University of California-Berkeley", "address": "", "city": "", "state": "CA", "zip": "", "country": "United States", "approved": true }, "abstract": "Numerical projections of reliable infection and disease mortality rates within cities, counties, states, and countries, as well as identification of the factors most responsible for these rates are critical to rational management of the ongoing Covid-19 pandemic. In the absence of effective therapeutics and vaccines, a deeper understanding of the impact of societal measures (distancing, contact tracing, quarantining, etc.) on local Covid-19 outbreaks are needed by administrators and healthcare professionals in making decisions that affect the tradeoff between the physical health of individuals and the economic health of communities. The aims of the proposal are twofold. First, to use cutting-edge statistical models to uncover the factors that most affect SARS-CoV-2 transmission and mortality rates. Second, to provide decisions makers with a simple-to-use, extensive instruction supported, data and scenario analysis (DASA) platform for evaluating the implications of different policy measures, including the implementation and relaxation of social distancing behavior, surveillance, contact tracing, patient isolation, and vaccination (once suitable vaccines are available). Additionally, this DASA platform will be suitable for training students at the undergraduate and graduate levels in public health and allied programs, as well as providing an analytical tool for students carrying out epidemiological research.The epidemiological model that underpins the Numerus Model Builder DASA Covid-19 platform includes modifications of the standard SEIR (Susceptible, Exposed/Latent, Infectious, Recovered) formulation to incorporate an explicit contact (C) class, as well as dividing infectious individuals into pre/asymptomatic (A) and symptomatic infectious (I) disease states to yield a SCLAIV model (where V refers to naturally vaccinated/recovered class). Individuals in the C class can either thwart (return to the S class) or succumb to (move onto the L=E class) pathogen invasion after making contact with the SARS-CoV-2 pathogen. The formulation also includes a parallel series of Sr, Cr, Lr, Ar, Ir and Vr classes that correspond to individuals moving into these reduced-exposure SCLAIV-response classes at rates determined by the driving actions of Covid-19 policy measures that have been put in place. The values of the SCLAIV+reponse model parameters are influenced by various factors that will be identified using statistical machine learning methods. In particular, “superlearners” that are a mix of parametric and nonparametric ensemble machine learning methods will be used to identify the factors responsible for observed spatio-temporal patterns across different, regional Covid-19 outbreaks, using appropriate data scraped from the worldwide web. This RAPID award is made by the Ecology and Evolution of Infectious Disease Program in the Division of Environmental Bioloy, 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": "1874", "attributes": { "award_id": "2031204", "title": "RAPID: Analyses of polymorphism and divergence to illuminate molecular evolution permissive of zoonoses in SARS and COVID-19", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Biological Sciences (BIO)" ], "program_reference_codes": [ "096Z", "7914" ], "program_officials": [ { "id": 4957, "first_name": "Katharina", "last_name": "Dittmar", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2020-06-01", "end_date": "2021-05-31", "award_amount": 122716, "principal_investigator": { "id": 4958, "first_name": "Jeffrey P", "last_name": "Townsend", "orcid": "https://orcid.org/0000-0002-9890-3907", "emails": "[email protected]", "private_emails": "", "keywords": "['Evolutionary biology']", "approved": true, "websites": "['https://publichealth.yale.edu/profile/jeffrey_townsend/']", "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 452, "ror": "https://ror.org/03v76x132", "name": "Yale University", "address": "", "city": "", "state": "CT", "zip": "", "country": "United States", "approved": true } ] }, "other_investigators": [], "awardee_organization": { "id": 452, "ror": "https://ror.org/03v76x132", "name": "Yale University", "address": "", "city": "", "state": "CT", "zip": "", "country": "United States", "approved": true }, "abstract": "COVID-19 disease has caused the third major outbreak in the past two decades resulting from a spillover of an animal coronavirus to humans. Among them, it is by far the most severe. Novel diseases typically appear because of such spillovers. However, it is unknown what genetic changes in the virus causing COVID-19 enabled it to infect humans. To identify those genetic changes, this project will use newly developed computational methods to compare the genes in coronaviruses infecting humans to the genes in coronaviruses infecting other animal hosts. This comparison of viral genomes will provide insight into which sites within coronavirus genomes enable the switching of host species as well as which sites change following host switches. Awareness of these genetic mechanisms is critical to determining if general rules underlie the ability of coronaviruses to switch hosts, as well as to provide necessary historical context for the changes coronavirus genomes have experienced following the onset of human infections. The ensuing knowledge provides precise guidance on new targets for ongoing decisions regarding vaccine design and drug development. Results of this project will also be incorporated into multiple engaging and educational exhibits at the North Carolina Museum of Natural Sciences. This project will apply molecular evolutionary approaches to reveal the rates of evolution of SARS-CoV-1, SARS-CoV-2, and of viruses sequenced from reservoir hosts, revealing the strength of selection across sites occurring proximate to zoonosis within the viral genomes. First, phylogenetic comparisons of extant SARS-causing, SARS-like, and COVID-19-causing viral sequences collected from infected humans and from animal reservoirs will be used to reconstruct the history of coronavirus evolution. Second, the rate of change of each nucleotide within each gene—and each amino acid site within each protein—in viruses that were transmitting within the animal reservoir, in viruses transmitted among humans during the SARS outbreak, and in viruses transmitted among humans during the COVID-19 pandemic will quantified. Third, virus gene sequences that bracketed (pre- and post-) the host transition events for both SARS and COVID-19 will be estimated through phylogenetic ancestral state reconstruction methods. Finally, application of a novel computational approach comparing the divergence between reconstructed ancestors to the polymorphism present during the outbreak will identify genomic sites under selection that are associated with host transitions. This divergence will be mapped to known protein domains and structures, illuminating sites important to human infection and transmission, and thereby aiding molecularly targeted vaccine and therapy development. This RAPID award is made by the Systematics and Biodiversity Science Cluster in the Division of Environmental Biology, using funds from the Coronavirus Aid, Relief, and Economic Security (CARES) Act.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.", "keywords": [], "approved": true } }, { "type": "Grant", "id": "1875", "attributes": { "award_id": "2030509", "title": "RAPID: The effect of contact network structure on the spread of COVID-19: balancing disease mitigation and socioeconomic well-being", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Biological Sciences (BIO)" ], "program_reference_codes": [ "096Z", "7914" ], "program_officials": [ { "id": 4959, "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": "2022-11-30", "award_amount": 199136, "principal_investigator": { "id": 4962, "first_name": "Meggan E", "last_name": "Craft", "orcid": "https://orcid.org/0000-0001-5333-8513", "emails": "[email protected]", "private_emails": "", "keywords": null, "approved": true, "websites": "['https://github.com/', 'https://arxiv.org/']", "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 227, "ror": "", "name": "University of Minnesota-Twin Cities", "address": "", "city": "", "state": "MN", "zip": "", "country": "United States", "approved": true } ] }, "other_investigators": [ { "id": 4960, "first_name": "Eva", "last_name": "Enns", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, { "id": 4961, "first_name": "Matthew J", "last_name": "Michalska-Smith", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "awardee_organization": { "id": 227, "ror": "", "name": "University of Minnesota-Twin Cities", "address": "", "city": "", "state": "MN", "zip": "", "country": "United States", "approved": true }, "abstract": "What makes COVID-19 spread rapidly in some places, yet slowly in others? How should society lessen social distancing while limiting an increase in infections? To answer these questions, this Rapid Response Research (RAPID) project seeks to understand how patterns of interpersonal interaction (“structure”) in social contact networks affect disease spread in a population. The researchers will simulate a disease spreading through a variety of social contact networks, and use machine learning to relate each network’s structure to the number and timing of new infections. By limiting structures related to increased disease, societies may be able to reopen other parts of their economies while still curbing overall disease spread. The researchers will produce an interactive web application for the public and decision-makers to visualize trade-offs between reducing disease and maintaining social cohesion. This research will support the professional development of an early career scientist.This research aims to determine the inherent risk of SARS-CoV-2 spread based on contact network structure. The researchers will use machine learning to 1) identify network structures that influence disease spread and 2) predict disease spread on empirical contact networks. Important network structures will serve as targets for simulated disease mitigation interventions (e.g. reducing structures that increase levels of disease or increasing structures that reduce disease levels). Finally, the researchers will investigate whether future outbreaks of COVID-19 or other diseases could be alleviated through optimizing social contact networks ahead of time. The outcomes of this research will inform and facilitate quick, efficient interventions to reduce the social and economic costs of COVID-19. This research will develop a general framework for relating disease to network structure. Thus, results can be generalized beyond the current pandemic, serving to further our understanding of potential future waves of COVID-19, as well as other directly-transmitted diseases in humans, livestock, and wildlife.This RAPID award is made by the Ecology and Evolution of Infectious Diseases Program in the Division of Environmental Biology, using funds from the Coronavirus Aid, Relief, and Economic Security (CARES) ActThis 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": 1385, "pages": 1424, "count": 14236 } } }