Grant List
Represents Grant table in the DB
GET /v1/grants?page%5Bnumber%5D=1383&sort=program_reference_codes
{ "links": { "first": "https://cic-apps.datascience.columbia.edu/v1/grants?page%5Bnumber%5D=1&sort=program_reference_codes", "last": "https://cic-apps.datascience.columbia.edu/v1/grants?page%5Bnumber%5D=1419&sort=program_reference_codes", "next": "https://cic-apps.datascience.columbia.edu/v1/grants?page%5Bnumber%5D=1384&sort=program_reference_codes", "prev": "https://cic-apps.datascience.columbia.edu/v1/grants?page%5Bnumber%5D=1382&sort=program_reference_codes" }, "data": [ { "type": "Grant", "id": "1917", "attributes": { "award_id": "2028909", "title": "RAPID: Rapid Assay for RNA Extraction and Concentration for COVID-19 Molecular Diagnostics", "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": 5086, "first_name": "Christina", "last_name": "Payne", "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": 200000, "principal_investigator": { "id": 5090, "first_name": "Abdennour", "last_name": "Abbas", "orcid": null, "emails": "[email protected]", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 227, "ror": "", "name": "University of Minnesota-Twin Cities", "address": "", "city": "", "state": "MN", "zip": "", "country": "United States", "approved": true } ] }, "other_investigators": [ { "id": 5087, "first_name": "Andrew C", "last_name": "Nelson", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, { "id": 5088, "first_name": "Ryan", "last_name": "Langlois", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, { "id": 5089, "first_name": "Sophia", "last_name": "Yohe", "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": "Access to coronavirus testing is integral to the effort to curb and control the spread of the 2019 coronavirus disease (COVID-19). COVID-19 diagnostic tests currently implement a technique called reverse transcription-polymerase chain reaction (RT-PCR). This technique requires two reagent kits. The first is used to extract the genetic material, ribonucleic acid (RNA), from the coronavirus, and the second is used to amplify the RNA to enable its detection with RT-PCR. The current diagnostic crisis is due, in part, to shortages of the RNA extraction kits and the time required to extract the RNA using the kits. Increasing both the availability and time-efficiency of these kits is vital to improve testing accessibility and enhance the reliability of clinical diagnostics. In this project, a novel process for producing porous sorbent materials will be used to develop an alternative extraction kit for efficient and rapid extraction of nucleic acids from patient samples. The performance will be further optimized by studying the molecular mechanisms governing nucleic acid capture and release by the sorbents and applying this fundamental knowledge. The development and clinical validation of a novel extraction kit will be followed by mass production, addressing the current shortage and improving access to COVID-19 testing. The close collaboration between engineers developing the diagnostic technologies and clinicians implementing the prototypes, as enabled by this project, will streamline the transition of scientific knowledge into solutions that benefit the health and well-being of society. The project will also provide workforce development opportunities through training researchers in novel diagnostic techniques for coronaviruses.The goal of this project is to develop a novel, scalable approach to nucleic acid separation and concentration and mass-produce prototype kits for immediate implementation in clinical settings. The concept relies on the use of sorbent materials instead of the current filter- and silica column-based approach. Unlike filters that use pore size to physically separate the target (here nucleic acids) from the media, sorbents are a porous material that captures the target by chemical affinity and interactions. As a result, the use of a sorbent enables larger water flow rates, enhanced nucleic acid capture efficiency, and faster sample processing, and overcomes the need for multiple buffers or extraction steps. Functionalization of conventional filters with a combination of metal oxide nanoparticles and organosiloxane polymers will be used to produce the nucleic acid sorbent. The functionalization will be achieved by a new method for supported synthesis of nanoparticles by thermolysis and polymer conjugation. The efficiency of the sorbent will be assessed by the extraction and detection of nucleic acid using quantitative RT-PCR. Validation of the alternative sorbent kit will assess the effectiveness of the rapid separation and concentration of the novel coronavirus (SARS-CoV-2) RNA. The project will also explore the fundamentals of nucleic acid dynamics in porous sorbents as it relates to nucleic acid size, sorbent chemistry, porosity and pore size, and the effect of transport phenomena in porous media. Enhanced understanding of the factors that affect the retention, release, and transport of nucleic acids in porous media is critical to producing reliable and efficient nucleic acid extraction and detection kits and the development of other bio-separation processes. Training of postdoctoral researchers and graduate and undergraduate students will focus on how the combination of transdisciplinary collaboration and a clear understanding of the fundamental aspects can lead to disruptive technologies.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": "1920", "attributes": { "award_id": "2037261", "title": "RAPID: Collaborative Research: The Transformation of Essential Work: Managing the Introduction of AI in 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": 5096, "first_name": "Andruid", "last_name": "Kerne", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2020-07-15", "end_date": "2022-09-30", "award_amount": 86373, "principal_investigator": { "id": 5097, "first_name": "Samantha", "last_name": "Shorey", "orcid": null, "emails": "[email protected]", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 156, "ror": "", "name": "University of Texas at Austin", "address": "", "city": "", "state": "TX", "zip": "", "country": "United States", "approved": true } ] }, "other_investigators": [], "awardee_organization": { "id": 156, "ror": "", "name": "University of Texas at Austin", "address": "", "city": "", "state": "TX", "zip": "", "country": "United States", "approved": true }, "abstract": "Millions of people deemed “essential workers” in the COVID-19 pandemic perform manual labor, such as sorting, cleaning, garbage collection, and recycling. To mitigate risks associated with this work, there is an accelerated push to introduce artificial intelligence (AI) to safeguard the public and workers from disease transmission. Yet, decades of human-computer interaction and organizational communication research shows that the introduction of new technologies into workplaces is not an easy transition; instead technologies transform and displace existing work practices. This research project investigates both beneficial innovations and liabilities arising in waste management industries, as they deploy AI technologies in response to the COVID-19 crisis. It develops a set of best practices for the coordination of human labor and AI to address the pandemic, transforming the future of work. The best practices will be presented as guidance on how to incorporate AI into critical economic institutions to mitigate the negative effects of COVID-19 on public health, society, and the economy. This guidance will regularly be communicated to workers, industry leaders, and the public through an open access toolkit, a workshop series, press releases, and social media. This will potentially benefit essential industries that employ or serve tens of millions of workers, including waste labor, shipping, manufacturing, retail, and food service.This project will be conducted through a multi-site ethnographic study, examining how two American waste management organizations negotiate the introduction of automated technologies, in an effort to mitigate risks associated with the COVID-19 pandemic. The first involves automated “floor care” robots at Pittsburgh International Airport. The second involves AI sorting systems in a single stream recycling plant, in Austin, Texas. By studying two sites, the research team is expected to gain comparative insight into how automation is introduced and attuned, according to professional, regional, and institutional norms. Data collection will include ethnographic fieldnotes, interview transcripts, and media materials. Extending theories of technological diffusion and invisible labor, the research team will qualitatively analyze the technology dissemination process, drawing insights from the actions and perspectives of workers as they negotiate the changing shape of their daily work. Through reflexive memos and “constant comparative” coding, the research will identify patterns of action and build a set of transferable observations. This is expected to yield (1) empirical findings on factors that promote or hinder rapid technological introduction in response to crisis, with specific insights on the human labor required to make automated technologies work (e.g., calibration, troubleshooting, and maintenance), (2) theoretical findings that contribute core understandings of the diffusion of innovation and how workplace technologies are reinvented through use, and (3) design recommendations for a variety of essential work sectors.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": "1921", "attributes": { "award_id": "2029336", "title": "RAPID: Science and Social Networks: COVID-19 in an Urban Epicenter", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Social, Behavioral, and Economic Sciences (SBE)" ], "program_reference_codes": [ "096Z", "7914" ], "program_officials": [ { "id": 5098, "first_name": "Frederick", "last_name": "Kronz", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2020-07-01", "end_date": "2022-06-30", "award_amount": 99995, "principal_investigator": { "id": 5100, "first_name": "Kavita", "last_name": "Sivaramakrishnan", "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": 5099, "first_name": "Merlin", "last_name": "Chowkwanyun", "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": "The primary objective of this RAPID research project is to further our understanding of the influences that shape policy making concerning public health during the COVID 19 pandemic with a focus on New York City as a global epicenter. The researchers plan to provide a time sensitive, living record and analysis that documents the unfolding of social and political aspects of the present crisis. They will study relational encounters in social and political realms during the pandemic that shape multiple interpretations of events, images, and broadcasts in a networked world; and how they in turn affect public responses, programmatic decisions, and policies that relate to public health in an urban context. The results of the project will be disseminated to policy makers, community members, media spokespersons, public health and medical experts in New York City, and global health stakeholders through publications in policy journals, newspapers, access to radio and news programs, and online platforms. This project aims to integrate insights from STS studies, history of global public health, and cultural and media studies to analyze political responses, public health communications, and policy decisions concerning COVID-19. It will contribute to STS frameworks by arguing that COVID-19 needs to be understood not only in terms of epidemiological and biological evidence-building regarding its contagiousness and virulence; it also needs to be understood in terms of social and political virality and transmissibility of social modes of thought that shape its interpretations. To achieve these ends, the researchers will draw from media studies of social susceptibility, diffusion, and differential impact. They will engage in content analysis of traditional media and social media, and they will conduct interviews with 50 key actors. They will produce an initial account of policymaking in a time of health and social crisis while generating critical material for future scholars of COVID-19. Their interview materials are to be donated to the Columbia University Center for Oral History Research; their print/digital materials are to be donated to the Special Collections division at the University’s Health Sciences Library.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": "1929", "attributes": { "award_id": "2037348", "title": "RAPID: Collaborative Research: The Transformation of Essential Work: Managing the Introduction of AI in 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": 5122, "first_name": "Andruid", "last_name": "Kerne", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2020-07-15", "end_date": "2022-07-31", "award_amount": 129616, "principal_investigator": { "id": 5123, "first_name": "Sarah E", "last_name": "Fox", "orcid": null, "emails": "[email protected]", "private_emails": "", "keywords": "['Human-computer interaction']", "approved": true, "websites": "['https://www.sarahfox.info/']", "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [], "awardee_organization": { "id": 243, "ror": "", "name": "Carnegie-Mellon University", "address": "", "city": "", "state": "PA", "zip": "", "country": "United States", "approved": true }, "abstract": "Millions of people deemed “essential workers” in the COVID-19 pandemic perform manual labor, such as sorting, cleaning, garbage collection, and recycling. To mitigate risks associated with this work, there is an accelerated push to introduce artificial intelligence (AI) to safeguard the public and workers from disease transmission. Yet, decades of human-computer interaction and organizational communication research shows that the introduction of new technologies into workplaces is not an easy transition; instead technologies transform and displace existing work practices. This research project investigates both beneficial innovations and liabilities arising in waste management industries, as they deploy AI technologies in response to the COVID-19 crisis. It develops a set of best practices for the coordination of human labor and AI to address the pandemic, transforming the future of work. The best practices will be presented as guidance on how to incorporate AI into critical economic institutions to mitigate the negative effects of COVID-19 on public health, society, and the economy. This guidance will regularly be communicated to workers, industry leaders, and the public through an open access toolkit, a workshop series, press releases, and social media. This will potentially benefit essential industries that employ or serve tens of millions of workers, including waste labor, shipping, manufacturing, retail, and food service.This project will be conducted through a multi-site ethnographic study, examining how two American waste management organizations negotiate the introduction of automated technologies, in an effort to mitigate risks associated with the COVID-19 pandemic. The first involves automated “floor care” robots at Pittsburgh International Airport. The second involves AI sorting systems in a single stream recycling plant, in Austin, Texas. By studying two sites, the research team is expected to gain comparative insight into how automation is introduced and attuned, according to professional, regional, and institutional norms. Data collection will include ethnographic fieldnotes, interview transcripts, and media materials. Extending theories of technological diffusion and invisible labor, the research team will qualitatively analyze the technology dissemination process, drawing insights from the actions and perspectives of workers as they negotiate the changing shape of their daily work. Through reflexive memos and “constant comparative” coding, the research will identify patterns of action and build a set of transferable observations. This is expected to yield (1) empirical findings on factors that promote or hinder rapid technological introduction in response to crisis, with specific insights on the human labor required to make automated technologies work (e.g., calibration, troubleshooting, and maintenance), (2) theoretical findings that contribute core understandings of the diffusion of innovation and how workplace technologies are reinvented through use, and (3) design recommendations for a variety of essential work sectors.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": "1935", "attributes": { "award_id": "2030425", "title": "RAPID: Development of a Nonlinear Activity Response Model for Coronavirus (COVID-19) Scenario Projections Based on the Observations of the Shutdown-Reopening Cycle of China", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Geosciences (GEO)" ], "program_reference_codes": [ "096Z", "7914" ], "program_officials": [ { "id": 5139, "first_name": "Sylvia", "last_name": "Edgerton", "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": 199811, "principal_investigator": { "id": 5140, "first_name": "Yuhang", "last_name": "Wang", "orcid": null, "emails": "[email protected]", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 294, "ror": "", "name": "Georgia Tech Research Corporation", "address": "", "city": "", "state": "GA", "zip": "", "country": "United States", "approved": true } ] }, "other_investigators": [], "awardee_organization": { "id": 294, "ror": "", "name": "Georgia Tech Research Corporation", "address": "", "city": "", "state": "GA", "zip": "", "country": "United States", "approved": true }, "abstract": "This RAPID project will investigate the cycle of shutdown and reopening of businesses in China due to COVID-19, and assess the usefulness of studying this cycle for providing guidance for policymaking in the U.S., European countries, and the countries where the medical testing capability is severely limited. The response to COVID-19 has varied significantly from the epicenter (Wuhan and Hunan province) to the other 25 provinces, 4 provincial-level megacities (Beijing, Shanghai, Tianjin, and Chongqing), and 5 autonomous regions for minority ethnic groups. The proxy data for response activities includes the processed TROPOMI tropospheric vertical NO2 column data and inverse modeling of daily NOx emissions in China. The hypotheses of this project are that (1) the COVID-19 infection data (including coronavirus positive test, hospitalization, and mortality data) and government policies largely shape the responses by the society and businesses; (2) near real-time monitoring of tropospheric column NO2 provide timely high spatiotemporal data for gauging the activity responses by the society and businesses, which are unavailable through conventional means; (3) with the large datasets of varying degrees of COVID-19 infection, governmental policy, and activity responses in different regions of China, a nonlinear response model will be developed and this model can later be corrected with data from US and other countries to provide policymaking guidance through scenario analysis.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": "1936", "attributes": { "award_id": "2029690", "title": "RAPID: Smart Ventilation Control May Reduce Infection Risk for COVID-19 in Public Buildings", "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": 5141, "first_name": "Bruce", "last_name": "Hamilton", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2020-05-01", "end_date": "2021-04-30", "award_amount": 125417, "principal_investigator": { "id": 5143, "first_name": "Zheng D", "last_name": "O'Neill", "orcid": null, "emails": "[email protected]", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 282, "ror": "", "name": "Texas A&M Engineering Experiment Station", "address": "", "city": "", "state": "TX", "zip": "", "country": "United States", "approved": true } ] }, "other_investigators": [ { "id": 5142, "first_name": "Qingsheng", "last_name": "Wang", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "awardee_organization": { "id": 282, "ror": "", "name": "Texas A&M Engineering Experiment Station", "address": "", "city": "", "state": "TX", "zip": "", "country": "United States", "approved": true }, "abstract": "During the current COVID-19 pandemic, airborne transmission of the virus through exhaled aerosols is a likely explanation for the rapid rate of new infections. The risk of infection with COVID-19 could be reduced by employing feasible measures in public buildings, such as smart and enhanced Heating, Ventilation, and Air- conditioning (HVAC) design and operations, higher humidity levels, surface cleaning and hygiene protocols, revised spatial configuration, etc. This project aims to investigate a novel smart ventilation control strategy using a CO2-based indicator to operate under a normal mode and a pandemic mode as appropriate for common public buildings (e.g., office buildings, classroom buildings, retail stores). These buildings are designed and operated in normal conditions by default. The question to be studied is that, with the current HVAC equipment and systems already installed in existing public buildings, can operations be modified via smart ventilation control by diluting the air in a space with cleaner air from outdoors to reduce infection risk for occupants. Ventilation controls in public buildings under a pandemic represent significant challenges. In this project, the research team will look into the problem of potentially reducing infection risk with coronavirus through three objectives: 1) Obtain a minimum ventilation rate for different HVAC systems in most common public buildings to potentially reduce infection risk through a risk analysis with Computational Fluid Dynamics (CFD) simulations; 2) Establish a scientific correlation between CO2 concentration with the potential infection risk in spaces in public buildings to better monitor the infection risk with numerical studies and limited field experiments; and 3) Evaluate a novel smart ventilation control strategy that can switch between normal operation and operation under a pandemic through a co-simulation of energy performance and CFD simulations. COVID-19 has generated immense social-economic impact, which may be mitigated by the proposed smart ventilation control in public buildings to reduce the risk of being infected with COVID-19 during occupation of public buildingsThis 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": "1939", "attributes": { "award_id": "2028564", "title": "RAPID: COVID-19’s Impact on the Urban Environment, Behavior, and Wellbeing", "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": 5151, "first_name": "Mamadou", "last_name": "Diallo", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2020-05-01", "end_date": "2021-04-30", "award_amount": 199998, "principal_investigator": { "id": 5154, "first_name": "Rolf U", "last_name": "Halden", "orcid": "https://orcid.org/0000-0001-5232-7361", "emails": "[email protected]", "private_emails": "", "keywords": null, "approved": true, "websites": "['https://covid19.tempe.gov/', 'https://biodesign.asu.edu/environmental-health-engineering/human-health-observa…']", "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [ { "id": 5152, "first_name": "Matthew L", "last_name": "Scotch", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 147, "ror": "https://ror.org/03efmqc40", "name": "Arizona State University", "address": "", "city": "", "state": "AZ", "zip": "", "country": "United States", "approved": true } ] }, { "id": 5153, "first_name": "Arvind", "last_name": "Varsani", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "awardee_organization": { "id": 147, "ror": "https://ror.org/03efmqc40", "name": "Arizona State University", "address": "", "city": "", "state": "AZ", "zip": "", "country": "United States", "approved": true }, "abstract": "SARS CoV2, the coronavirus responsible for the global severe acute respiratory syndrome pandemic (COVID-19) represents a major threat to the health and welfare of the public. Currently the US death toll is nearing 30,000 and estimates exceeding a hundred thousand fatalities if the spread of the virus continues unchecked. Public health interventions including strict shelter in place rules have been put in place across the US and globally to limit the spread of the virus through communities. This has had a dramatic impact on the economy, with millions of people becoming unemployed in the immediate aftermath of the interventions. The goal of this RAPID project is to develop the science for rapid assessment of how public health interventions in response to the COVID-19 pandemic are impacting the environment, human behavior, and the wellbeing of the public. This goal will be achieved through detailed characterization of various biomarkers of environmental health and wellbeing in wastewater collected in the service area of people living in Tempe, Arizona. The information collected will be available to city officials, other community stakeholders, and the public using an online platform for emergency response. The resources developed in this project will inform the Nation of the impacts of shelter in place and assist crisis management and decision makers in managing these interventions.The goal of this project is to employ detailed high-resolution analysis of wastewater together with geospatial modeling to develop a rapid assessment of environmental health at the community level during the mandated health interventions in response to the global COVID-19 pandemic. The study will take place in Tempe, Arizona, a city of 185,000 people. Baseline data on environmental quality and human behavior and health have been collected by the research team for the past two years leading up to the pandemic. A transdisciplinary team consisting of an environmental engineer, a virologist, and a bioinformatician will study environmental and human health impacts associated with the global pandemic and the public health interventions implemented in the city in response to the outbreak using this unique data resource. Urban wastewater will be sampled daily during the shelter in place intervention for analysis of a broad spectrum of compounds and biomarkers using liquid chromatography tandem mass spectrometry. Analyses include the types and quantities of air pollutants, medications taken as a result of fever and viral infections (e.g., ibuprofen), allergy suppressants, stimulants and depressants (such as nicotine and alcohol), drugs of abuse, dietary markers (indicating potential food shortages), and general biomarkers suggestive of human wellbeing and health status (e.g., antidepressants). US census data will enable an interpretation of study findings using demographics and population-size data relevant for crisis management and evidence-based decision making by scientists, city planners, mayors, health agencies, healthcare providers, and policy makers.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": "1942", "attributes": { "award_id": "2032467", "title": "RAPID: COVID-19 diagnostics for limited resource settings via improved sample preparation", "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": 5162, "first_name": "Christina", "last_name": "Payne", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2020-07-15", "end_date": "2020-12-31", "award_amount": 140939, "principal_investigator": { "id": 5163, "first_name": "Rustem F", "last_name": "Ismagilov", "orcid": null, "emails": "[email protected]", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 211, "ror": "https://ror.org/05dxps055", "name": "California Institute of Technology", "address": "", "city": "", "state": "CA", "zip": "", "country": "United States", "approved": true } ] }, "other_investigators": [], "awardee_organization": { "id": 211, "ror": "https://ror.org/05dxps055", "name": "California Institute of Technology", "address": "", "city": "", "state": "CA", "zip": "", "country": "United States", "approved": true }, "abstract": "COVID-19 continues to be a global challenge. A critical aspect of the disease is that up to half of all people infected with the SARS-CoV-2 virus are asymptomatic, rendering screening and containment strategies based solely on clinical presentation impossible. Society cannot fully return to work and school unless both symptomatic and asymptomatic infected individuals can be screened regularly. There is therefore an urgent need for a universally accessible, rapid point-of-care (POC) diagnostic with accurate, reliable results that can be deployed in an affordable way on an unprecedented global scale. However, the diagnostics field has struggled for decades to make these complex tests compatible with POC settings. Great progress has been made developing nucleic-acid amplification assays that are ultrasensitive and rapid; yet the core technology for the initial RNA-extraction step remains largely unimproved since 1990. The RNA extraction step is the key bottleneck to developing a globally deployable COVID-19 diagnostic. This project will bring advances in interfacial engineering to COVID-19 diagnostic technology to decentralize molecular diagnostics from the laboratory, which is needed to reopen the US economy and to protect the vulnerable members of society.The ultimate impact of this project will be to improve the performance and availability of SARS-CoV-2 RNA testing so that these tests can be run at the POC by minimally trained users. There are two goals: (1) reduce the logistical burden associated with relying on supply-chains and centralized labs and (2) simplify the RNA extraction step to eliminate the need for complex equipment. Standard RNA extraction follows a complex, multi-step protocol based on solid-phase extraction (SPE). The protocol requires centrifugation, and it suffers from lowered assay performance due to the carryover of inhibitory buffers. This project will directly address both bottlenecks associated with RNA extraction by integrating an innovative approach: a two-phase wash (TPW) that reduces inhibitors while maintaining the RNA yield during the extraction step. The TPW technology integrates a wash buffer immiscible with water. TPW removes contaminants from the extraction column by leveraging the combination of solid-liquid and liquid-liquid interfacial properties and solubility of the inhibitory components. Extra purity obtained via TPW will improve assay sensitivity and reduce cost and will enable the use of lyophilized reagents and isothermal amplification, eliminating refrigeration requirements and reducing testing time (from hours to minutes). Finally, using TPW with a pressure-based RNA-extraction technology will eliminate the need for centrifugation and will improve the speed and accessibility of RNA extraction. Additionally, this project will leverage interfacial engineering via TPW to develop sample-preparation modules that can be used as stand-alone components and combined (plug-and-play) with other state-of-the-art amplification and readout technologies, such as those designed by industrial collaborators and other sensing/detection technologies currently under development in the RAPID program. The technologies developed in this project can be immediately adopted by commercial and pre-commercial diagnostic manufacturers.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": "1943", "attributes": { "award_id": "2034150", "title": "Collaborative Research: RAPID: Mitigating the Impact of Forced Remote Learning of ECS Due 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": 5164, "first_name": "Jeffrey", "last_name": "Forbes", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2020-07-15", "end_date": "2022-06-30", "award_amount": 24793, "principal_investigator": { "id": 5165, "first_name": "Andrew", "last_name": "Rasmussen", "orcid": null, "emails": "[email protected]", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 702, "ror": "https://ror.org/022q4qx22", "name": "Chicago Public Schools", "address": "", "city": "", "state": "IL", "zip": "", "country": "United States", "approved": true } ] }, "other_investigators": [], "awardee_organization": { "id": 702, "ror": "https://ror.org/022q4qx22", "name": "Chicago Public Schools", "address": "", "city": "", "state": "IL", "zip": "", "country": "United States", "approved": true }, "abstract": "The Learning Partnership, in collaboration with the Chicago Public Schools (CPS), and Education Development Center (EDC), will conduct research and develop teacher support programs that will mitigate the impact that the global COVID pandemic is having on high school computer science students in CPS. There are significant concerns that the variability in implementation of remote learning policies in response to COVID-19 school closures could exacerbate educational inequities in access and quality of experience for students taking Exploring Computer Science (ECS). These concerns are particularly acute in CPS where computer science is a graduation requirement and ECS is the primary course that students take to fulfill the requirement. Inequalities in access and implementation of computer science in CPS can have consequences for students’ high school graduation. In order to develop effective teaching materials for the 2020-2021 academic year, the project team will study the overall impact of remote learning by comparing outcomes for the 2019-20 implementation of ECS in CPS relative to the previous three years of ECS implementation as well as whether inequities have emerged within CPS by examining differences in school plans for remote learning, student access to technology, participation in remote learning experiences by race and ethnicity and whether those differences correlate with differences in learning outcomes. This project extends the work of the Chicago Alliance for Equity in Computer Science (CAFECS), a long-standing partnership between university computer science faculty, educational researchers, and CPS teachers and administrators, to ensure that all CPS high school students engage in high quality, engaging computer science education. CAFECS will develop strategies develop strategies on how to emulate effective high-touch teacher facilitation strategies online from their experience with face-to-face classrooms. The project will be guided by three research questions: (1) How can we design online professional development to support ECS teachers’ transition to teach the course fully or partially online during the 2020-21 school year? (2) How can the CAFÉCS coaching model be adapted to support teachers in moving ECS to a remote learning format and to provide remote coaching when school access is restricted? (3) What are the characteristics of remote learning policies that foster student engagement and best support student online collaboration? The teacher professional development model will be guided by the Desimone and Garet framework to provide a structure for drawing conclusions about the contribution of the professional development in mitigating the impact COVID-19 school building closures have on student outcomes. The results of this research will have a direct impact on hundreds of CPS teachers and, ultimately, thousands of CPS high school students. The results will significantly contribute to the knowledge base of how to effectively teach computer science for underserved students in computer science and how schools can mitigate the impacts of COVID-19.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": "1944", "attributes": { "award_id": "2037885", "title": "RAPID: Optimal allocation of COVID-19 testing based on context-specific outbreak control objectives", "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": 5166, "first_name": "Samuel", "last_name": "Scheiner", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2020-07-01", "end_date": "2023-06-30", "award_amount": 180234, "principal_investigator": { "id": 5167, "first_name": "Katriona", "last_name": "Shea", "orcid": null, "emails": "[email protected]", "private_emails": "", "keywords": null, "approved": true, "websites": "['https://kshealab.wordpress.com/', 'https://midasnetwork.us/mmods/']", "desired_collaboration": null, "comments": null, "affiliations": [] }, "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 purpose of this project is to design a framework for objective-driven, context dependent disease surveillance strategies, designed to deal with sampling errors and biases. This framework will be applied to the allocation of COVID-19 testing based on multiple outbreak scenarios, employing the use of multiple models to improve decision-making for COVID-19 surveillance and control. This work will improve the response to the COVID-19 pandemic. Results will be presented to key federal agencies, so that they can be considered as part of the decision-making process for the COVID-19 outbreak. These methods will also be highly applicable to optimal vaccine allocation especially during the early stages of vaccine availability when supplies will be limited. In addition, it will provide a framework for future outbreak testing response. One postdoctoral researcher will be trained in the theory and methods of applied epidemiological research. With a growing, but still limited number of imperfect tests available, the way in which tests are allocated critically determines what we can learn and in turn, what inferences can be made with respect to managing the disease for individuals and populations. This poses an optimal allocation problem for limited resources. The context-dependent nature of allocating a limited number of tests introduces additional potential sources of error and bias, making the question of optimal testing allocation a unique challenge. Current modeling efforts focus necessarily on disease dynamics and the efficacy of intervention strategies, but few consider testing, contact tracing, and isolation strategies explicitly. Unlike shelter-in-place or social distancing mandates, the impact of test allocation on management strategy decision-making is density-dependent. While testing remains limited, it is critical to explicitly model test allocation and strategies that involve both monitoring and management. The key to successful surveillance is to actively design surveillance strategies for a specific objective. The project will use a principle of effective monitoring based on two steps. First, identify the objective of the monitoring? Second, tailor the sampling design to achieve that objective, in this case selecting groups of individuals in a nonrepresentative way and to separately estimate the probabilities that a randomly sampled individual would appear in these groups. Misclassification of disease state (e.g., false positives/negatives) due to the specificity and sensitivity of different tests, the performance of different test platforms and population-level incidence will also be addressed.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": 1383, "pages": 1419, "count": 14184 } } }