Grant List
Represents Grant table in the DB
GET /v1/grants?page%5Bnumber%5D=1384&sort=-approved
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Effective water treatment is crucial due to society's rapid industrialization and urbanization, and drinkable water should be fecal and total coliforms free. This project aims to investigate the antibacterial activity of porous carbon materials that are doped with sulfur or/and nitrogen-containing species. It will open a new perspective on these metal-free materials in antibacterial studies. By linking the properties of the carbon to their performance in bacterial inhibition, the most effective features of the carbons will be identified. Why these species are effective will be explored too. This project will lay a solid foundation for the long-term material development of potent metal-free antibacterial materials. Metal-free materials offer the advantage of reduced cost for the water treatment process. Besides, they can be potentially applied to the existing filtration systems if they possess effective antibacterial effects. The project will provide an early-career faculty to develop a research lab and launch research programs. It will also help the undergraduate students achieve their educational and career goals, especially those from underrepresented minority groups. TECHNICAL SUMMARYThis interdisciplinary project is to investigate the antibacterial activity of heteroatom-doped porous carbon materials systematically. The relationship between the properties (surface chemistry and texture) of carbons and their performance in bacterial inhibition will be mainly studied. By using in-situ doping or post-treatment methods, a series of doped-porous carbons will be prepared and functionalized of doping sulfur or/and nitrogen groups. Escherichia coli (E. coli) and Staphylococcus aureus (S. aureus) are specifically selected for testing since they are the most prevalent species of gram-positive and gram-negative bacteria, respectively. The bacteriostasis rate of the carbons will be calculated. A typical inactivation pathway for bacteria on a carbon-based material is oxidation stress. The mechanism of the antibacterial process will be explored by identifying the types of reactive oxygen species (ROS) generated from the carbons. The materials will be intensively characterized by X-ray photoelectron spectroscopy analysis, thermal analysis, porosity analysis, potentiometric titration, and electron microscopy imaging. The project will focus on identifying dopant species that are efficient in bacterial inhibition, thus establishing carbon materials as effective antibacterial materials. For the photoactive carbons, their antibacterial activity and mechanism will also be evaluated under visible light radiation.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": "12461", "attributes": { "award_id": "2300188", "title": "National Information Technology Innovation Center (NITIC)", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Directorate for STEM Education (EDU)", "Advanced Tech Education Prog" ], "program_reference_codes": [], "program_officials": [], "start_date": "2023-09-01", "end_date": null, "award_amount": 0, "principal_investigator": { "id": 28401, "first_name": "Diane", "last_name": "Meza", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [], "awardee_organization": { "id": 1182, "ror": "https://ror.org/02s8rep32", "name": "Columbus State Community College", "address": "", "city": "", "state": "OH", "zip": "", "country": "United States", "approved": true }, "abstract": "The Information Technology (IT) industry is continuing to experience shortages of skilled workers, and COVID-19 has further exacerbated the sense of urgency and need for an IT skilled technical workforce. According to CompTIA’s 2022 Workforce and Learning Trends report, U.S. tech employment will grow an estimated 12.4 million net jobs in 2022 alone. Over the next ten years, technology occupations are expected to grow at twice the rate of overall employment. Yet the high demand for skilled workers is confounded by workers leaving the field. According to Microsoft’s 2021 Work Trends Index, more than 40% of employees were considering a job change, and 46% planned to make a significant career move or transition. While traditional areas of technology are experiencing an increasing demand, there are also emerging job clusters. These include, but are not limited to, the following fields: Artificial Intelligence (AI), Machine Learning (ML), IT Automation, Quantum Computing, DevOps, FinTech, Augmented Reality (AR), Virtual Reality (VR), Encryption Technologies, IoT/Edge Computing, 5G, and Blockchain Technology. Nationally, there are diversity gaps in the IT industry. Underserved and disadvantaged students are not well represented in pathways for careers in IT which often discourages these individuals from pursuing these career pathways. This project will bring together an experienced consortium of community colleges leaders in information technology collaborating with industry partners to create the National Information Technology Innovation Center (NITIC). The Center will create a future-focused community of practice with diverse perspectives and geographic distribution. NITIC will focus on emerging technologies while strengthening and promoting proven best practices from prior ATE IT centers. NITIC will create new deliverables that develop high-quality educational materials, curricula, pedagogy, and teaching resources while consolidating access for existing materials across broad areas of information technology. The center will leverage a mix of experienced and emerging community college partners to serve as leaders within their technology clusters to expand collaboration and develop future leadership for the ATE community. The Center will produce, implement, assess impact, and broadly share the following: 1) Innovation clearinghouse to encourage new emerging IT curriculum and materials driven by a Business & Industry Leadership Team (BILT) Model for high employer engagement; 2) A Community of Practice: IT Innovation Network (ITIN) to promote sharing and problem-solving; 3) Faculty Professional Development Model – Working Connections Virtual and In-Person Workshops to address continuing learning needs for faculty as technologies emerge; and 4) Dedicated models for increasing diversity and underserved population representation in IT, including veterans, women, and underserved student populations in STEM. This project is funded by the Advanced Technological Education program that focuses on the education of technicians for the advanced-technology fields that drive the Nation's economy.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": "12462", "attributes": { "award_id": "2318302", "title": "Collaborative Research: HSI Implementation and Evaluation Project: Developing a Wastewater-based Epidemiology Student Training and Education Program at CUNY", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Directorate for STEM Education (EDU)", "HSI-Hispanic Serving Instituti" ], "program_reference_codes": [], "program_officials": [], "start_date": "2023-09-01", "end_date": null, "award_amount": 0, "principal_investigator": { "id": 15387, "first_name": "Monica", "last_name": "Trujillo", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [], "awardee_organization": { "id": 1096, "ror": "", "name": "CUNY Queensborough Community College", "address": "", "city": "", "state": "NY", "zip": "", "country": "United States", "approved": true }, "abstract": "With support from the Improving Undergraduate STEM Education: Hispanic-Serving Institutions (HSI Program), this Track 2 project aims to establish a wastewater-based epidemiology training program at The City University of New York to respond to declining minority student retention and graduation rates in STEM. These declines hamper workforce development in industries clamoring for STEM talent and ultimately U.S. competitiveness in emerging technologies. The project will train students with wastewater-based epidemiology technologies and competencies, preparing the next generation of workers to face challenges posed by emerging infectious diseases. It is hypothesized that inspiring community college students with career prospects in an emerging technology, such as wastewater-based epidemiology, and providing academic, research, and social mentoring from role models that they can identify with will improve student retention, graduation rates, and career success. The program will generate interest in wastewater-based epidemiology as a career option, thus enhancing US pandemic preparedness. The program will also have ripple effects in different areas of society, such as water management, healthcare, public recreation and health, regulatory policy, science education, and economic mobility for graduates of the program and their families. By participating in the training program, students will be well-positioned for high-paying jobs in the public and private sectors or for graduate school.The specific aims of the project are: Aim 1- Advance the effectiveness of STEM education and workforce development programs, activities, and outreach through evaluation and assessment; Aim 2- Attract and train the Nation’s future STEM workforce through multiple pathways to educational and career opportunities; Aim 3- Increase participation of underserved and underrepresented groups in STEM education and workforce development programs, activities, and outreach; and Aim 4- Inspire community engagement in STEM education programs and activities to provide meaningful wastewater-based epidemiology learning opportunities for students and educators. The evaluation will include the use of surveys, interviews, observations, and career tracking to determine the impact of the program on students. The combination of pedagogical activities and hands-on experiential education, including internships and job training, will provide students with opportunities to commence STEM careers following graduation. The results of our work will be disseminated through publications, conference presentations, and through CUNY internal networks. The HSI Program aims to enhance undergraduate STEM education and build capacity at HSIs. Projects supported by the HSI Program will also generate new knowledge on how to achieve these aims.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": "12463", "attributes": { "award_id": "2317290", "title": "SBIR Phase II: Analog front end (AFE) platform for lightweight, long-term, cortical monitoring", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Technology, Innovation and Partnerships (TIP)", "SBIR Phase II" ], "program_reference_codes": [], "program_officials": [], "start_date": "2023-09-01", "end_date": null, "award_amount": 0, "principal_investigator": { "id": 28402, "first_name": "Mark", "last_name": "Myers", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [], "awardee_organization": { "id": 2107, "ror": "", "name": "NEURODYNE, INC.", "address": "", "city": "", "state": "TN", "zip": "", "country": "United States", "approved": true }, "abstract": "The broader impact/commercial potential of this Small Business Innovation Research (SBIR) Phase II project provides next generation ambulatory seizure data acquisition. The advent of mobile health advances during the COVID-19 health crisis have enabled new innovations to be considered. This effort provides a framework for the next line of remote neurological data acquisition capabilities for the implementation of military helmet designs that detect battlefield traumatic brain injuries, football helmet designs that detect sports-related brain injuries, caps for first responder teams that detect trauma, at-home monitoring headsets for remote migraine assessment, etc. A complete analogue front end (AFE) will be developed in order to provide digitized electroencephalograph (EEG) signals to the downstream stages. The project will have a major impact in several areas, namely, wearable bio-devices, data fusion, and neurological data extraction and visualization of complex biological systems. This device can be utilized for first responders at the scene of neurological trauma such as emergency medical technicians, battle front medical areas, and sports related events.This Small Business Innovation Research Phase II project will provide a robust mobile device that can be worn in an at-home setting for remote neurological monitoring. The solution will remove noisy artifacts from the electroencephalograph signal in order to perform neurological diagnoses and provide neurological reporting to the neurologist as an aid to quantify the patient’s seizure instances. The analogue front end (AFE) provides the foundation for a portable electroencephalograph (EEG) device for neurological data acquisition for the clinical, academic, and research communities. The ambulatory seizure monitoring device will enable an end-to-end system for robust, lightweight, data transmission to a cloud service, which will generate reports for the physician to analyze a patient neurological data for treatment. This system will extend the current rise of health devices into the complex environment of neurological states, as well as the eventual development of neuro-analytics.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": "12464", "attributes": { "award_id": "2327799", "title": "Collaborative Research: IHBEM: The fear of here: Integrating place-based travel behavior and detection into novel infectious disease models", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Mathematical and Physical Sciences (MPS)", "MATHEMATICAL BIOLOGY" ], "program_reference_codes": [], "program_officials": [], "start_date": "2023-09-01", "end_date": null, "award_amount": 0, "principal_investigator": { "id": 28403, "first_name": "Nicholas", "last_name": "Kortessis", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [], "awardee_organization": { "id": 1343, "ror": "https://ror.org/0207ad724", "name": "Wake Forest University", "address": "", "city": "", "state": "NC", "zip": "", "country": "United States", "approved": true }, "abstract": "When people change where, when, and why they travel, there are effects on infectious diseases. People’s movements determine who is at risk of the disease and whether new cases are counted by local public health agencies. For example, during the COVID-19 pandemic, people’s movements changed drastically and, in addition to COVID-19, influenza and Lyme disease cases also dropped nationwide. These drops in cases may be because people spent less time in high risk areas, or simply because people traveled to healthcare facilities less frequently, and so fewer cases are reported. Distinguishing between these alternatives is critical for understanding disease control and predicting disease spread, but is made difficult when travel patterns change dramatically. This problem is especially challenging because communities may modify travel patterns in response to local disease, which can, in turn, change how diseases spread in communities and how public health monitors disease. To determine the cause of case reductions as human movements changed, the Investigators will develop new mathematical models that account for the ways travel impacts both risk and detection, using data from mobile phones to inform transmission risk and using local surveys to inform underdetection rates. By developing this new collection of models, the Investigators will better understand how transmission and detection of various non-COVID-19 infections changed throughout the pandemic, recognize how this depends on the biology of the disease being considered, and predict how case numbers may change during future periods of significant community-level changes in travel.Community-level travel patterns have multifactorial effects on the dynamics of any infectious disease. Major changes to travel patterns affect both transmission, as people spend more or less time in high-risk places, and detection, as people change their propensity to visit healthcare facilities. These factors also influence individual behaviour, because local increases in reported cases can cause people to change their travel further. This creates critically important feedback loops between transmission, detection, and travel. Depending on the interactions between these factors, changes to travel or transmission could lead to undercounting of cases or a harmful population-level response that leads to communities being exposed to more infections. As changes in community-level travel patterns become more likely with global factors such as climate change and emerging infectious disease threats, it becomes increasingly important for models to integrate their effects on both detection and transmission. The project addresses this need by developing novel models that account for the ways in which travel can simultaneously affect both transmission and detection, and be affected by reported and perceived disease risk. The Investigators will combine the models with mobility data obtained from SafeGraph and use local surveys to inform underdetection rates of key notifiable diseases across the New River Valley Health District of Virginia, and to develop a framework for predicting transmission and detection changes during future large-scale changes in travel. Central Appalachia is a key region for this work, as it experiences relatively high incidence of respiratory and Lyme diseases, and intervention adherence was especially low during the later stages of the COVID-19 pandemic. This project is jointly funded by the Division of Mathematical Sciences (DMS) in the Directorate of Mathematical and Physical Sciences (MPS) and the Division of Social and Economic Sciences (SES) in the Directorate of Social, Behavioral and Economic Sciences (SBE).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": "12465", "attributes": { "award_id": "2329987", "title": "Planning: Positive Mental Health in the Geosciences", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Geosciences (GEO)", "GOLD-GEO Opps LeadersDiversity" ], "program_reference_codes": [], "program_officials": [], "start_date": "2023-09-01", "end_date": null, "award_amount": 0, "principal_investigator": { "id": 28404, "first_name": "Jennifer", "last_name": "Nocerino", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [], "awardee_organization": { "id": 1276, "ror": "", "name": "The Geological Society of America, Inc.", "address": "", "city": "", "state": "CO", "zip": "", "country": "United States", "approved": true }, "abstract": "Geoscientists at all education and career levels experience mental health challenges, and are frequently isolated during fieldwork, in the laboratory, and in learning environments. COVID and the use of virtual lessons and meetings removed geoscientists from their support networks and normal daily structure, leading to negative impacts on the wellbeing of the community. The intersection of mental health and the geosciences is a relatively unexplored area of study. This planning effort will support a gathering of social scientists and geoscientists, who will engage in a dialogue and review materials created over a two-year time period, which will result in new knowledge about mental health in situations specific to the geosciences and will situate these within the broader STEM mental health context, and within the context of structural racism and other bias. By directly addressing both how to change institutional cultures around mental health and how to support geoscientists’ mental health resilience, the proposed work is intended to contribute to retention, improved wellbeing, and professional development for a wide variety of groups, including those who have been historically excluded from the geosciences. These outcomes could ultimately lead to a stronger geoscience workforce that appropriately represents the communities most affected by climate change and environmental injustice.The current interdisciplinary approach involves social scientists, experts interested in improving educational and workplace culture in the geosciences, and participants whose expertise may come primarily from lived experience. To create an initial community around mental health, a one-day in-person convening will be supported to gather experts from psychology and geology to share evidence-based approaches and lived experiences, discover community needs, and create a foundation for addressing mental health in the geosciences in a comprehensive way. This planning effort will be organized by the Geological Society of America (GSA) and facilitated by the Science Education Resource Center (SERC). Outcomes will be used to develop resources that will be widely disseminated through publications, presentations, and professional development activities for the cohort of participants. Through these efforts, the project seeks to create change agents in order to orient the geoscience community toward a coordinated proposal to develop a comprehensive program of mental health support and training.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": "12466", "attributes": { "award_id": "2318814", "title": "Label-free Detection of Opioids in Liquid Using Zinc Oxide Nanophotonic Sensor", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Engineering (ENG)", "CCSS-Comms Circuits & Sens Sys" ], "program_reference_codes": [], "program_officials": [], "start_date": "2023-09-01", "end_date": null, "award_amount": 0, "principal_investigator": { "id": 28405, "first_name": "Xiaojing (John)", "last_name": "Zhang", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [], "awardee_organization": { "id": 386, "ror": "https://ror.org/049s0rh22", "name": "Dartmouth College", "address": "", "city": "", "state": "NH", "zip": "", "country": "United States", "approved": true }, "abstract": "Illicit drug abuse has become another major national health crisis since the Covid-19pandemic started, due to long period of quarantine at home with significantly reduced socialinteractions. In 2022, U.S. drug overdose deaths hit the highest level in history: nearly 110,000people died from drug overdose according to US Centers for Disease Control and Prevention. Thetop overdose drugs are opioids, cocaine, psychostimulants, and methadone. Mixing multiple drugscan also cause drug-drug interactions which may increase the risk of death. The current drugdetection apparatuses typically require time-consuming, laborious sample preparation procedureand trained staff. These detection methods are not suitable for monitoring and profiling the currentdrug overdose crisis en masse. This project aims to develop a high throughput, label-free andportable sensor that can quantitatively detect multiple drugs (opioids, cocaine, psychostimulants,and methadone) in a liquid sample via a single test. The samples can be collected in the diverseforms of biofluids such as saliva, urine, sweat and blood. Successful development of this automatic,accurate, point-of-care platform will greatly simplify and accelerate the drug screen process.The main module of the sensing platform consists of a silver (Ag) or gold (Au) nanoparticledecorated Zinc Oxide nanorod coated silica nanofiber matrix (Ag/AuNP-ZnONR-SNFnanosensor). Machine learning algorithm will be incorporated to achieve the automatic,quantitative analysis of multiplex detection of the drugs without trained expertise. The objectiveof this project will be achieved by accomplishing the following three research tasks: (1)Development and characterization of the nanosensor material to experimentally demonstrate thefeasibility of surfaced enhanced plasmonic sensing of drugs using the device. The device isfabricated by electrospinning of the silica nanofiber as the supporting matrix, hydrothermal growthof the ZnO nanorod coated on the silica nanofiber, and Ag and Au nanoparticles synthesized byUV irradiation or seed mediated growth method, respectively, on the surface of the ZnONR-SNFmatrix. (2) Optimization of the sensing performance, including the sensitivity, limit of detection(LoD), repeatability and stability of the sensor by tuning the geometries, dimensions, and structureof the nanomaterials-based sensing module with respect to different biofluidic samples. (3)Development of machine learning (ML) algorithms using prior-embedded deep neural networkmodels trained by many data samples obtained using our sensor to identify and quantify multipledrugs from different sample sources. The successful implementation of the algorithm will allowfor an accurate, automatic, quick, and multiplex detection of the drugs. This project will providenew methodologies and data to address the challenges in understanding and monitoring the currentdrug overdose crisis.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": "12467", "attributes": { "award_id": "2318204", "title": "Collaborative Research: HNDS-I: Cyberinfrastructure for Human Dynamics and Resilience Research", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Social, Behavioral, and Economic Sciences (SBE)", "Human Networks & Data Sci Infr" ], "program_reference_codes": [], "program_officials": [], "start_date": "2023-09-01", "end_date": null, "award_amount": 0, "principal_investigator": { "id": 28406, "first_name": "Yi", "last_name": "Qiang", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [], "awardee_organization": { "id": 235, "ror": "https://ror.org/032db5x82", "name": "University of South Florida", "address": "", "city": "", "state": "FL", "zip": "", "country": "United States", "approved": true }, "abstract": "Disaster resilience research is concerned with learning how people prepare for and deal with damage to their communities because of events such as floods, fires, pandemics, and other existential threats. The concepts of risk, vulnerability and sustainability are very important because of the impact they have on individuals’ lives and well-being. However, advancing science-based resilience research is hard because the data that are used to measure disaster resilience come from many different sources and are difficult to merge. Even for available data there are few tools available to analyze them, especially for researchers who do not have extensive quantitative training. This project overcomes these difficulties by developing a national cyberinfrastructure called the Human Dynamics and Resilience infrastructure that contains large-scale data and analytic tools to support and advance human dynamics and resilience research. Knowledge gained from visualizing and analyzing the data using the infrastructure can better inform policies to increase community resilience. The project accomplishes four objectives. First, it develops four new databases containing integrated data from around the nation, including social media and cell-phone mobility data. Second, it constructs visualization and analytical tools, such as maps, statistics, and dynamic modeling and simulations to facilitate both exploratory and in-depth research. Third, it implements a training and feedback module to help engage researchers, practitioners, and the public to explore and conduct analyses that should lead to a better understanding of resilience. Finally, it includes three case studies that demonstrate how the infrastructure can lead to new knowledge about the relationship between human mobility and resilience. The project is a collaboration of multidisciplinary investigators from four universities, including Louisiana State University, University of South Florida, Texas A&M University, and Saint Louis University.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": "12468", "attributes": { "award_id": "2327792", "title": "Collaborative Research: IHBEM: Multidisciplinary Analysis of Vaccination Games for Equity (MAVEN)", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Mathematical and Physical Sciences (MPS)", "MSPA-INTERDISCIPLINARY" ], "program_reference_codes": [], "program_officials": [], "start_date": "2023-09-01", "end_date": null, "award_amount": 0, "principal_investigator": { "id": 28407, "first_name": "Guillermo", "last_name": "Alvarez Pardo", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [], "awardee_organization": { "id": 2108, "ror": "https://ror.org/0425phg59", "name": "Cuesta College", "address": "", "city": "", "state": "CA", "zip": "", "country": "United States", "approved": true }, "abstract": "The MAVEN (Multidisciplinary Analysis of Vaccination Games for Equity) project addresses the global health threat of vaccine inequity in the fight against emerging infectious diseases. This project aims to provide a comprehensive understanding of vaccination coverage and identify key drivers of vaccine uptake. This will reduce the risk of future pandemics by enabling targeted interventions to increase vaccine acceptance among vulnerable populations, in particular racial/ethnic/sexual minority and rural populations. The research will also help to address vaccine inequities in the United States that are currently disproportionately affecting minority subgroups and clinical subpopulations (e.g., men with HIV infection). Ultimately, this research will have implications for public health policy and practice, contributing to the global effort to predict and mitigate the impacts of pandemics. In addition to the scientific contributions, the project will provide valuable training opportunities for over ninety undergraduate and graduate students and cultivate a diverse pool of talent equipped to adequately respond to current and future pandemics.The project will answer three questions: Q1) What are the structural, social, and individual factors related to vaccine uptake for common diseases (e.g., influenza), newer pandemics/epidemics, e.g., COVID-19, mpox (monkeypox), and future diseases? Q2) How does population heterogeneity affect vaccination behavior in a community context? Q3) How does individual behavior feedback into mpox (monkeypox) disease dynamics? The project will combine expertise from mathematical epidemiology and social and behavioral sciences to (1) develop a general framework of vaccine uptake that incorporates individual, social, and structural factors by analyzing a variety of secondary datasets, (2) collect primary survey data and use the framework to develop universal vaccine uptake (as well as vaccine refusal) models that are broadly applicable to mpox (monkeypox) and future outbreaks, and (3) use traditional and modern economic models of individual decision-making under uncertainty. Methodologically, the project will develop a new epidemiological-behavioral system of ordinary differential equations, using multiple data sources (observational, survey, and experimental) and mixed methods to estimate people’s vaccination preferences. The project will also integrate the investigators' empirical findings into the new epi-model, and use the parameterized epi-model to conduct retrospective and prospective vaccine acceptance and hesitance/refusal models.This award is jointly funded by the Division of Mathematical Sciences and the Division of Social and Economic Sciences in the Directorate of Social, Behavioral and Economic Sciences.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": "12469", "attributes": { "award_id": "2319438", "title": "URoL:ASC: What rules of life allow collectives to effectively manage risk? Understanding the rules underlying risk management across systems to increase societal resilience", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Biological Sciences (BIO)", "URoL-Understanding the Rules o" ], "program_reference_codes": [], "program_officials": [], "start_date": "2023-09-01", "end_date": null, "award_amount": 0, "principal_investigator": { "id": 28408, "first_name": "Theodore", "last_name": "Pavlic", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [], "awardee_organization": { "id": 147, "ror": "https://ror.org/03efmqc40", "name": "Arizona State University", "address": "", "city": "", "state": "AZ", "zip": "", "country": "United States", "approved": true }, "abstract": "Societies’ capacities to effectively manage risk, such as the threats arising from natural disasters, have not kept up with the world’s ecological changes. Previously very rare events, such as large floods or long-lasting droughts, are becoming more frequent, and the rapid dissemination of information on the internet is contributing to the spread of misinformation about hazards, risks, and how to manage them. To better deal with these risks, this project builds new risk management strategies that are based on biological Rules of Life. These rules are used by living systems to preserve and protect the life in those systems, including those based on altruism, community growth, communication, and enforcement of community rules. Biological systems that exploit these rules include bacterial colonies, hives of social insects, schools of fish, and herding animals.This project combines gamification of the Rules of Life with narrative storytelling to develop new strategies for collectively managing risk of natural disasters, infrastructure challenges, pandemics, and other shocks. The researchers use a practice-based co-design process that conducts science with involvement of individuals in at-risk communities. Story- and play-based activities that require solving cooperation and coordination dilemmas create a variety of experiences and products that uncover new solutions to societal challenges, encourage cooperation and collective risk management, determine new ways to encourage people to engage collective risk management strategies, and develop new outreach activities, such as museum exhibits and workshops. The project will benefit vulnerable low-income communities struggling to deal with disasters and water managers in the desert southwest trying to increase the resilience of the water supply.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.", "keywords": [], "approved": true } } ], "meta": { "pagination": { "page": 1384, "pages": 1405, "count": 14046 } } }