Grant List
Represents Grant table in the DB
GET /v1/grants?page%5Bnumber%5D=1392&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=1393&sort=program_reference_codes", "prev": "https://cic-apps.datascience.columbia.edu/v1/grants?page%5Bnumber%5D=1391&sort=program_reference_codes" }, "data": [ { "type": "Grant", "id": "1319", "attributes": { "award_id": "2037374", "title": "RAPID: Bioinformatic Search for Epitope-based Molecular Mimicry in the SARS-CoV-2 Virus using Chameleon", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Computer and Information Science and Engineering (CISE)" ], "program_reference_codes": [ "096Z", "7914", "7923", "9102" ], "program_officials": [ { "id": 3392, "first_name": "Deepankar", "last_name": "Medhi", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2020-07-01", "end_date": "2022-06-30", "award_amount": 238798, "principal_investigator": { "id": 3397, "first_name": "Giri", "last_name": "Narasimhan", "orcid": null, "emails": "[email protected]", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 207, "ror": "https://ror.org/02gz6gg07", "name": "Florida International University", "address": "", "city": "", "state": "FL", "zip": "", "country": "United States", "approved": true } ] }, "other_investigators": [ { "id": 3393, "first_name": "Kalai", "last_name": "Mathee", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, { "id": 3394, "first_name": "Prem P", "last_name": "Chapagain", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, { "id": 3395, "first_name": "Ananda M", "last_name": "Mondal", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, { "id": 3396, "first_name": "Jessica", "last_name": "Liberles", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "awardee_organization": { "id": 207, "ror": "https://ror.org/02gz6gg07", "name": "Florida International University", "address": "", "city": "", "state": "FL", "zip": "", "country": "United States", "approved": true }, "abstract": "Cross-reactive immunity is a process by which an individual who was vaccinated by an unrelated vaccine or who has recovered from an infection from an unconnected pathogen in the past is seemingly protected against an infection by SARS-CoV-2. A key step for an individual to mount a successful immune response to a pathogenic infection is for a human antibody to recognize and bind to a specific epitope (fragment) from an antigenic protein from the infecting pathogen. Cross-reactivity by molecular mimicry may occur when an antibody fortuitously binds to an epitope from SARS-CoV-2 because of a structural similarity at the binding interface with the epitope for which it was intended. If verified, a rapid repurposing of drugs and vaccines designed for the other pathogens can be quickly validated and applied to the current pandemic. This project plans to use bioinformatic techniques to investigate how molecular mimicry may play a role in cross-reactive immunity.The software pipeline will use the high-performance computing resources in the Chameleon cloud computing platform to run computationally-intensive molecular dynamics simulations within a machine learning framework and help identify occurrences of molecular mimicry in SARS-CoV-2. The pipeline can be divided into two main parts. The first part involves extracting useful features from structures of known complexes available from public databases such as Protein Data Bank (PDB). The second part involves building machine learning models from these features so that molecular mimicry, if present, can be detected in SARS-CoV-2.The machine learning framework will result in reusable models of molecular mimicry and is expected to assist in vaccine development. If successful, the project can potentially (a) explain global disparities in hospitalizations and death rates; (b) lead to quick repurposing of drugs to fight the current pandemic; (c) be replicated for other pathogens; (d) lead to faster vaccine development; (e) impact development of novel bioinformatic strategies for the current and future pandemics.An interdisciplinary team with expertise in computational biophysics, bioinformatics, machine learning, evolutionary biology, infectious diseases, computational epigenetics, glycobioogy, high-performance computing and software engineering will drive this project. All results will be made available through the project website at: http://biorg.cs.fiu.edu/lemom, including examples of molecular mimicry, software for replicating the experiments, and performance benchmarking results on Chameleon.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.", "keywords": [], "approved": true } }, { "type": "Grant", "id": "1626", "attributes": { "award_id": "2033946", "title": "RAPID: Neighborhood-level U.S. Internet Accessibility Assessment through Dataset Aggregation and Statistical and Predictive Modeling", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Computer and Information Science and Engineering (CISE)" ], "program_reference_codes": [ "096Z", "7914", "7923", "9102" ], "program_officials": [ { "id": 4271, "first_name": "Deepankar", "last_name": "Medhi", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2020-07-01", "end_date": "2022-06-30", "award_amount": 149439, "principal_investigator": { "id": 4272, "first_name": "Elizabeth M", "last_name": "Belding", "orcid": null, "emails": "[email protected]", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 320, "ror": "", "name": "University of California-Santa Barbara", "address": "", "city": "", "state": "CA", "zip": "", "country": "United States", "approved": true } ] }, "other_investigators": [], "awardee_organization": { "id": 320, "ror": "", "name": "University of California-Santa Barbara", "address": "", "city": "", "state": "CA", "zip": "", "country": "United States", "approved": true }, "abstract": "The U.S. has long suffered from digital inequities in multiple dimensions: rural and tribal regions are far less likely than urban cities to have high speed Internet access. Internet availability and quality within communities can often be predicted based on demographic and socioeconomic factors. The COVID-19 pandemic has brought to the forefront these inequalities; due to shelter-in-place orders, the lack of high quality Internet access has had dramatic impacts, including on the ability to participate in remote learning, remote work, and telehealth. While new government programs have been created to try to broaden access, a fundamental problem persists: no one accurately knows who does and does not have high quality access. There are many datasets of Internet measurements, but each on its own represents too incomplete a picture to provide the fine-grained information needed to discern which communities, or, ideally, neighborhoods lack quality Internet access. However, these datasets, when combined, is expected to provide a rich and geographically broad data source through which it may be possible to accurately assess Internet connectivity and performance. Furthermore, this study can let one learn trends from these datasets to predict Internet accessibility in regions for which no measurement data is currently available. The goal of this project is threefold: (i) to aggregate data from public and private sources to produce the most fine-grained analysis and detailed maps, to date, within states, at the community and, ideally, neighborhood level, of where fixed and mobile Internet access exists, where it does not, and where it is of too poor quality to be usable; (ii) to build statistical models that use demographic and other social variables to understand variation in Internet availability and quality; and (iii) to use what is learned to build predictive models of Internet service in areas for which there exist insufficient measurement data from available sources. This work will have broad impacts, including the informing of local, state and federal governments about where investments must be made to ensure all Americans have access to high quality mobile and/or fixed Internet. The project website, digitalaccess.cs.ucsb.edu, will contain information about research methodology and outcomes, including a report on what is learned about the state of California, the first state of focus for this award. Prediction models will also be made available.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.", "keywords": [], "approved": true } }, { "type": "Grant", "id": "1805", "attributes": { "award_id": "2027669", "title": "RAPID: Deciphering Within-host Diversity and Multi-strain Infections in 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", "7931" ], "program_officials": [ { "id": 4768, "first_name": "Mitra", "last_name": "Basu", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2020-05-15", "end_date": "2021-04-30", "award_amount": 100000, "principal_investigator": { "id": 4770, "first_name": "Mohammed", "last_name": "El-Kebir", "orcid": "https://orcid.org/0000-0002-1468-2407", "emails": "[email protected]", "private_emails": "", "keywords": null, "approved": true, "websites": "['https://github.com/elkebir-group', 'http://www.el-kebir.net']", "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 281, "ror": "", "name": "University of Illinois at Urbana-Champaign", "address": "", "city": "", "state": "IL", "zip": "", "country": "United States", "approved": true } ] }, "other_investigators": [ { "id": 4769, "first_name": "Jian", "last_name": "Peng", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "awardee_organization": { "id": 281, "ror": "", "name": "University of Illinois at Urbana-Champaign", "address": "", "city": "", "state": "IL", "zip": "", "country": "United States", "approved": true }, "abstract": "To facilitate real-time outbreak management and mitigation strategies, there is an urgent need to understand the spread of COVID-19. Researchers reconstruct the evolutionary and transmission history of the virus by applying algorithms to sequencing samples of COVID-19 patients. However, a key challenge is the presence of multiple strains of the virus within hosts, which is overlooked by current algorithms. This RAPID project will improve the nation’s COVID-19 response by developing algorithms to characterize the ongoing evolution and spread of the viral strains that coexist within patients. The developed algorithms will be applicable to future disease outbreaks.This RAPID project seeks to understand the impact of within-host viral diversity on the current spread of COVID-19. The project will identify the viral strains coexisting in patients through the development of algorithms that deconvolve COVID-19 sequencing samples. Subsequently, the project will assess whether or not such coexisting strains are the result of multiple infection events. Finally, the project will quantify the severity of the identified viral strains through a protein functional analysis of their mutations. Results will be disseminated through an online portal that enable labs and hospitals to upload their sequencing reads and generate annotations and characterizations of COVID-19 viral strains.In summary, the goal of this research is to understand SARS-CoV-2 by investigating the evolutionary origins of the virus and its genetic variation within host species in order to determine how molecular variation correlates with host range, and to evaluate risk of further disease emergence.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": "2024", "attributes": { "award_id": "2027773", "title": "RAPID: Methods for Reconstructing Disease Transmissions from Viral Genomic Data with Application 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", "7931" ], "program_officials": [ { "id": 5415, "first_name": "Mitra", "last_name": "Basu", "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": 100000, "principal_investigator": { "id": 5416, "first_name": "Haris", "last_name": "Vikalo", "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": "The coronavirus causing COVID-19 was first detected in humans in November 2019 and rapidly developed into a pandemic. There is an urgent need to enhance the ability to precisely track and predict spread of the disease. However, analysis of classical epidemiological data such as the time of testing and lengths of exposure provides limited insight. This Rapid Response Research (RAPID) project aims to enable discovery of disease transmission patterns based on analysis of genomic data, provide accurate identification of transmission clusters, and enable detection of critical nodes in a network of pathogen hosts while also providing insight into pathogen-mutation processes that occur during the spread of the disease.The specific aims of this project are to: (1) Develop methods for the inference of a network of hosts based on genomic information about viral pathogens infecting them. In particular, this research thrust is focused on the reconstruction of a weighted directed graph whose nodes represent hosts and edge weights reflect evolutionary distance between corresponding pathogens. (2) Develop methods for the discovery of transmission clusters and identification of critical nodes in the host network. The focus of this research thrust is on deep-learning algorithms for the identification of transmission clusters, and discovery of the host network nodes that played a pivotal role in the disease outbreak. (3) Relying on the developed methods, analyze publicly available COVID-19 datasets. The results of the outlined work are expected to have an immediate impact on the understanding of the coronavirus transmission and spread.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": "1558", "attributes": { "award_id": "2029543", "title": "RAPID: Prediction of coronavirus infections and complications at the individual and the population levels from genomic, proteomic, clinical and behavioral data sources", "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", "7931", "9251" ], "program_officials": [ { "id": 4070, "first_name": "Mitra", "last_name": "Basu", "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": 116000, "principal_investigator": { "id": 4072, "first_name": "Judith", "last_name": "Klein", "orcid": null, "emails": "[email protected]", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 621, "ror": "https://ror.org/04raf6v53", "name": "Colorado School of Mines", "address": "", "city": "", "state": "CO", "zip": "", "country": "United States", "approved": true } ] }, "other_investigators": [ { "id": 4071, "first_name": "Hua", "last_name": "Wang", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "awardee_organization": { "id": 621, "ror": "https://ror.org/04raf6v53", "name": "Colorado School of Mines", "address": "", "city": "", "state": "CO", "zip": "", "country": "United States", "approved": true }, "abstract": "As of mid-April 2020, two million people are infected worldwide with the novel coronavirus that first appeared in Wuhan, China in December of 2019. Now, the USA is at the epicenter of this pandemic, where it has already killed 20,000 people. Approaches to slow the progression are urgently needed. This requires a better fundamental understanding of the factors affecting not only virus spread, but also who develops complications and ultimately dies from the infection. It is becoming clear that many factors are at play, including molecular, physiological, lifestyle, behavioral, demographic and socio-economic ones. In particular, co-morbidities such as diabetes and high blood pressure are known risk factors for COVID-19 complications and death but are likely only the tip of the iceberg. Molecular data indicates that as many as 100 co-morbidities exist. Given this complexity, statistical approaches are needed to integrate and account for all of these factors when predicting and assessing the health risks arising from coronavirus spread and infection. This project will create computational tools that will help individuals and healthcare professionals make decisions related to coronavirus, helping target human and material resources where they are most needed. To decrease the numbers of people suffering from this pandemic, these tools are needed urgently.Integrating large numbers of risk factors through machine-learning approaches allows the building of statistical models that take all evidence into account. COVID-19 infections will be predicted at the individual and population levels. At the individual level, two binary (yes/no) classifiers will be built, (1) if an individual is likely infected with coronavirus, and if yes, (2) will the patient develop complications. As with all predictions, they cannot replace real data, but they can help prioritize who gets tested, who gets quarantined, who gets more closely monitored for signs of complications, and who gets personalized recommendations. Existing approaches include symptom-tracker apps, such as the coronavirus self-checker apps offered by the CDC, many healthcare providers and local government authorities and the National Early Warning Score (NEWS) and Modified Early Warning Score (MEWS), which determine the degree of illness of a patient. None of these approaches account for co-morbidities, and they lack the use of machine learning for data integration needed to predict individual outcomes. At the population level, possible routes of infection will be analyzed using graph analysis, through analysis of proximity, social interactions, and materials transport, taking the individual-level information into account where available. The project will be highly interdisciplinary, integrating biochemistry and computer science with ongoing input and feedback from healthcare professionals. This will ensure that the work will be relevant to the current crisis and easier to adopt by healthcare providers. Students and postdocs who participate in this research will be trained in interdisciplinary research and will be exposed directly to frontline workers in the pandemic. A publicly available, free app and a web interface will disseminate the predictions made in this project broadly in the hope it will find many users.In summary, the goal of this research is to understand how SARS-CoV-2 virus and host genomes interact to determine the full spectrum of disease outcomes, with the goals of identifying the cellular basis for host range and pathology, predicting morbidit,; and developing effective medical interventions.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": "1489", "attributes": { "award_id": "2028036", "title": "RAPID: A Controlled 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", "7935" ], "program_officials": [ { "id": 3871, "first_name": "Scott", "last_name": "Acton", "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": 200000, "principal_investigator": { "id": 3873, "first_name": "Massimo", "last_name": "Franceschetti", "orcid": null, "emails": "[email protected]", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 258, "ror": "", "name": "University of California-San Diego", "address": "", "city": "", "state": "CA", "zip": "", "country": "United States", "approved": true } ] }, "other_investigators": [ { "id": 3872, "first_name": "Behrouz", "last_name": "Touri", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "awardee_organization": { "id": 258, "ror": "", "name": "University of California-San Diego", "address": "", "city": "", "state": "CA", "zip": "", "country": "United States", "approved": true }, "abstract": "The forecast and containment of epidemics are central themes in public health and are well-studied in the literature through modeling and simulations. In the case of COVID-19, the lack of a vaccine, and the limited response to treatment, can lead to possible acute respiratory complications that may nevertheless be non-lethal, provided that patients have access to respiratory support in intensive-care units equipped with ventilation machines. Proactive planning and optimal allocation of resources are key challenges to face in the current emergency. In this scenario, the project studies the response to the pandemic from a network service point of view. Namely, it aims at determining actionable strategies under which the hospitals' response system can reach an equilibrium state where the spreading of the infection occurs, but the spreading rate does not lead to service overflow. There has been widespread recognition on the need to “flatten the curve,” representative of the infection spreading, and different strategies to achieve this objective have been enforced by governments across the world. The project’s research can be viewed in this context as a principled approach to predict how “the curve” will respond to different strategies, whether it can be kept below the hospitals' saturation threshold for a given amount of time, and what is the societal cost required to achieve this objective. Given a service-rate constraint dictated by hospital capacity and dynamic equations describing the spread of the infection, the project aims at deriving a control policy expressed in terms of public-health policies that meets the-service rate constraint of the local health-care system while minimizing their economic impact. Since societal constraints can make such an optimal strategy cost-prohibitive, or infeasible, the project will also study strategies that, given a certain cost budget, lead to the most desirable (time-varying) arrival rate that minimizes the global mortality rate of the population. While derived solutions are expected to be optimal in the context of the proposed dynamical-system model, results will also be validated using extensive simulations driven by real population data, in order to demonstrate their wider applicability in a practical setting.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": "2010", "attributes": { "award_id": "2027997", "title": "RAPID: Accelerated Testing for COVID-19 using Group Testing", "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", "7936" ], "program_officials": [ { "id": 5374, "first_name": "Scott", "last_name": "Acton", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2020-05-01", "end_date": "2021-12-31", "award_amount": 114612, "principal_investigator": { "id": 5376, "first_name": "Krishna", "last_name": "Narayanan", "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": 5375, "first_name": "Anoosheh", "last_name": "Heidarzadeh", "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": "COVID-19 has resulted in an unprecedented global health crisis that may become even more widespread over the upcoming months. Extensive and immediate testing of symptomatic and asymptomatic people is known to be important for implementing containment policies and to ensure that medical resources can be apportioned to different geographic regions appropriately. Individual testing can provide the necessary information; however, this requires enormous amounts of medical and human resources. This project will facilitate widespread testing for COVID-19 while using fewer tests. The main approach is based on the idea of pooling samples from multiple patients and performing tests on combined samples. If the result of a test is negative, one can conclude that no one in the pool is infected, and if the result is positive, then further fine-grained testing can be performed. Pooling-based testing, also known as group testing, can be very effective in reducing the number of tests required for both identifying infected people in a population and for obtaining coarse-grained population-level information about infection rates. In this project, effective group-testing schemes that minimize the total number of tests required and/or the total time taken to conduct tests will be designed, and their performance will be analyzed. In this project, group-testing schemes that do not require precise knowledge of the infection rates and multi-stage group-testing schemes will be designed and optimized. The trade-off between the number of tests required and the total time taken to complete testing will also be characterized. The robustness of group-testing schemes to correlation in the infection status among the tested population and to errors in the tests will be studied. Using mathematical tools from group testing and hypothesis testing, strategies for rapid classification of infection rates will also be designed and analyzed. Finally, a smart-phone application which guides laboratory technicians through the group-testing process will be developed. The focus will be on small pool sizes and population sizes. Successful completion of the proposed activities in this project will advance the state of the art in the field of group testing, and provide practical and efficient solutions for COVID-19 testing.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": "1617", "attributes": { "award_id": "2028274", "title": "RAPID: CCF: Optimizing Resource Allocation to Combat Pandemics", "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", "7936" ], "program_officials": [ { "id": 4240, "first_name": "Phillip", "last_name": "Regalia", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2020-05-01", "end_date": "2022-08-31", "award_amount": 100000, "principal_investigator": { "id": 4241, "first_name": "Sanjiv", "last_name": "Kapoor", "orcid": "https://orcid.org/0000-0001-5637-9919", "emails": "[email protected]", "private_emails": "", "keywords": null, "approved": true, "websites": "['http://www.medrxiv.org']", "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 618, "ror": "https://ror.org/037t3ry66", "name": "Illinois Institute of Technology", "address": "", "city": "", "state": "IL", "zip": "", "country": "United States", "approved": true } ] }, "other_investigators": [], "awardee_organization": { "id": 618, "ror": "https://ror.org/037t3ry66", "name": "Illinois Institute of Technology", "address": "", "city": "", "state": "IL", "zip": "", "country": "United States", "approved": true }, "abstract": "Network mobility models are important in the analysis of the COVID-19 pandemic, and are especially useful for optimizing allocation of resources to combat the spread of infections. The recent pandemic highlights this, as well as the need for methods to determine timely decisions for strategic interventions that reduce the impact of the pandemic on populations. Use of network traffic models account for flow of the disease via carriers from the initial source of the pandemic and between centers of infections, and addresses the long-distance spread of the disease. Non-medical solutions that immediately attempt to reduce the spread of pandemic include intra-city restrictions and inter-city strategies that involve suppression of population transfer. Critical actions include decisions on the level of suppression, the routes over which suppression has to be applied, and the time at which it has to be applied. Reducing the mitigation or suppression must critically account for the re-occurrence of the disease. The level of suppression has economic consequences with immediate and potential long term impacts on employment and economic growth, and can run counter to maintaining essential services such as food distribution, medical facilities, and first responders. This project will develop novel techniques to analyze pandemic models and design new optimization algorithms that provide decision strategies, accounting for costs, including the economic costs of suppression. This research has time urgency as there is a need for strategic analysis in the current pandemic and the project will utilize timely insights from the data available. Additionally, the insights will assist in determining decision strategies for future occurrences.This RAPID project will identify and refine network models of pandemic spread using a hierarchical model that incorporates the traffic network between major cities and countries at the first level. The subsequent levels will utilize local traffic networks and mobility patterns in centers of large populations. The models will include (i) subdivision of populations into classes that represent the current state of the pandemic, examples being population sets that are susceptible, infected, suppressed and recovered, all parameterized by time, (ii) multiple source and destination network flow models of infection flow, and (iii) geographic models of infection spread in local population clusters. This project will apply optimization techniques and network analysis to analyze the models and design algorithms for determining decision strategies. Evolution of the population sets, as modeled by differential equations solved using numerical methods and discrete analogs, will be investigated. Methods to determine parameters that regulate the transfer rates between populations will be designed. The model will be used to define mathematical programs in order to optimize decision parameters that include the level of suppression and the time at which to relax suppression. Network flow techniques will be used to minimize the flow of infection with multiple key objectives, especially to minimize the peak levels of the spread.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": "2149", "attributes": { "award_id": "2029044", "title": "RAPID: Active Tracking of Disease Spread in CoVID19 via Graph Predictive Analytics", "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", "7936" ], "program_officials": [ { "id": 5804, "first_name": "Phillip", "last_name": "Regalia", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2020-05-01", "end_date": "2022-04-30", "award_amount": 199449, "principal_investigator": { "id": 5808, "first_name": "Gautam", "last_name": "Dasarathy", "orcid": "https://orcid.org/0000-0003-2252-2988", "emails": "[email protected]", "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 } ] }, "other_investigators": [ { "id": 5805, "first_name": "Douglas", "last_name": "Cochran", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, { "id": 5806, "first_name": "Huan", "last_name": "Liu", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, { "id": 5807, "first_name": "Pavan K", "last_name": "Turaga", "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": "Corona Virus Disease 2019 (COVID-19) has emerged as a public health crisis of global proportions. As of April 10, 2020, there are approximately 1.7 million confirmed COVID-19 cases in more than 180 countries, with over 100,000 deaths. In the US, there are more than 500,000 confirmed cases and nearly 20,000 fatalities, and these numbers are continuing to rise sharply. There is a clear and acute need for ensuring the availability of infrastructure and critical services as the epidemic progresses. Current plans for controlling the epidemic are based on forecasts from well established “compartment” models for epidemic prediction. These models rely on differential equations based on assumptions of homogeneous populations, homogeneous mixing, and knowledge of several critical hyperparameters such as the base reproduction rate. It is well known among experts in infectious diseases and epidemic management that fitting observed data to the parameters of such models is an exercise in characterizing the epidemiology as opposed to generating valid and actionable predictions. Consequently, there is an urgent need to significantly update these models to account for the data collected on the ground from multiple data sources and locations. This is especially relevant in engineering preemptive interventions to check disease spread. Current COVID disease data are organized in a geospatial format, i.e., infected, deceased, and suspected cases indexed by geolocation, which can range from city-, county-, or state-level coarseness. This project aims to develop and demonstrate techniques that use the geospatial nature of the data, the temporal evolution of disease statistics (along with predictions), and synthesis of multiple sources of data to help rapidly and preemptively allocate available medical resources toward the areas of greatest need. Modeling the COVID-19 epidemic and designing interventions are significant challenges. This project looks at the problem through the lens of graph analytics. In particular, it seeks to use similarity information between geospatial regions of interest to improve epidemic predictions and to design effective interventions. As a first step, the problem of epidemic prediction is being modeled as the reconstruction of a high-dimensional dynamical system from low-dimensional observations. The estimates of a model thus learned will be enhanced by leveraging similarity information between the localities of interest. While the geospatial proximity graph is a natural candidate for the graph of similarities, it fails to capture long-range statistical dependencies between geographical regions based on other factors such as the sociological and biological features of a population. Using techniques from graphical modeling, this project will develop new techniques for learning statistically meaningful graphs for epidemic modeling during an ongoing pandemic. Furthermore, the accurate time-series prediction generated will be combined with the graph-based similarity measures to design effective interventions to check the spread of the epidemic. This is being approached using a stochastic formulation and emerging methods for anomaly detection on graphs with time series observations; optimal policies based on these paradigms will be translated into interventional strategies for an evolving pandemic. The project leverages partnerships with local community stakeholders in Maricopa County and the State of Arizona through the Knowledge Exchange for Resilience (KER) to implement the methodologies developed, and to ensure its technical advances can produce meaningful insights that can generalize nationally and globally. 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": "1729", "attributes": { "award_id": "2031215", "title": "RAPID: Higher Accuracy and Availability of COVID-19 Testing and Monitoring via Post-CT Image Boosting and Analysis", "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", "7942" ], "program_officials": [ { "id": 4528, "first_name": "Almadena", "last_name": "Chtchelkanova", "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": 150000, "principal_investigator": { "id": 4530, "first_name": "Wuchun", "last_name": "Feng", "orcid": null, "emails": "[email protected]", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [ { "id": 4529, "first_name": "Guohua", "last_name": "Cao", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "awardee_organization": { "id": 244, "ror": "", "name": "Virginia Polytechnic Institute and State University", "address": "", "city": "", "state": "VA", "zip": "", "country": "United States", "approved": true }, "abstract": "The COVID-19 pandemic has caused an unprecedented health crisis in the United States. Given the lack of an effective vaccine or drug in the short term, testing techniques with high accuracy and availability are needed to mitigate the COVID-19 outbreak through expansive deployment. However, the current genetic-based test for COVID-19 involves many different materials (e.g., swabs, tubes, and chemical solutions), of which certain ones are in short supply at different times in different places across the United States. Furthermore, the test is a multi-step process that is error-prone, resulting in low accuracy. To address these shortcomings, this project seeks to deliver an alternative COVID-19 test that can be widely available and deliver results in minutes with high accuracy. By realizing, deploying, and continually improving a high-performance software tool to facilitate early and accurate testing and monitoring of COVID-19 via post-image boosting and analysis of computed tomography (CT) scans, which use computer-processed combinations of many X-ray measurements to produce cross-section images of the chest (in particular, the lungs) this research will facilitate accurate COVID-19 diagnosis in real time. The project leverages and extends recent advances in artificial intelligence and high-performance computing to create a high-performance software tool to significantly enhance the quality of chest CT images. These enhanced CT images, in turn, facilitate more accurate analysis and identification of the hallmark features of COVID-19, including consolidation, bilateral and peripheral disease, linear opacities, “crazy-paving” patterns, and the “reverse halo” sign. Specifically, we realize a novel deep-learning neural network that enhances the resolution and reduces the artifacts of chest CT images. It does so by modeling the image-formation processes in chest CT to deliver a super-resolution and deblur-based iterative framework for CT images. The neural network only learns the relevant blur kernels, appropriate weighting factors, and penalty functions of the regularization terms in the optimal solution for the CT super-resolution task. All told, this enabling approach will mitigate the negative effects of COVID-19 on public health, society, and the economy by delivering a highly accurate and highly available test for the rapid diagnosis and monitoring 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 } } ], "meta": { "pagination": { "page": 1392, "pages": 1419, "count": 14186 } } }