Grant List
Represents Grant table in the DB
GET /v1/grants?page%5Bnumber%5D=1424&sort=-approved
{ "links": { "first": "https://cic-apps.datascience.columbia.edu/v1/grants?page%5Bnumber%5D=1&sort=-approved", "last": "https://cic-apps.datascience.columbia.edu/v1/grants?page%5Bnumber%5D=1442&sort=-approved", "next": "https://cic-apps.datascience.columbia.edu/v1/grants?page%5Bnumber%5D=1425&sort=-approved", "prev": "https://cic-apps.datascience.columbia.edu/v1/grants?page%5Bnumber%5D=1423&sort=-approved" }, "data": [ { "type": "Grant", "id": "12486", "attributes": { "award_id": "2318301", "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": 28423, "first_name": "Olga", "last_name": "Calderon", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [], "awardee_organization": { "id": 367, "ror": "https://ror.org/01d03cj21", "name": "Research Foundation of The City University of New York", "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": "12487", "attributes": { "award_id": "2318739", "title": "AGS-PRF: Novel Detection of Accelerated Emissions of Nitrogen Oxides Following High Latitude Peat Fires", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Geosciences (GEO)", "Postdoctoral Fellowships" ], "program_reference_codes": [], "program_officials": [], "start_date": "2023-09-01", "end_date": null, "award_amount": 0, "principal_investigator": { "id": 28424, "first_name": "Alan", "last_name": "Gorchov Negron", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [], "awardee_organization": { "id": 2109, "ror": "", "name": "Gorchov Negron, Alan", "address": "", "city": "", "state": "MI", "zip": "", "country": "United States", "approved": true }, "abstract": "In this Postdoctoral Research Fellowship project, the PI will use remote sensing observations along with laboratory and field measurements to determine if peatland fires, and the associated nitrogen emissions, might fertilize nearby high-latitude soils resulting in an enhancement of microbial production of nitrogen oxides. These northern high-latitude soils are the largest reservoir of soil organic matter on the planet, and ongoing disruptions due to wildfire and other climatic changes could result in a substantial release of greenhouse gases and reactive gases such as nitrogen oxides, which are the focus of this work. The new knowledge gained during this project could enhance our understanding of an understudied and potentially substantial source of soil nitrogen emissions, which will be beneficial to atmospheric chemistry and climate research communities, as well as the broader public.The specific tasks include: (1) using satellite remote sensing data to document trends and emissions of nitrogen dioxide and carbon monoxide from high-latitude fires and soils; (2) conduct laboratory soil chamber experiments to quantify in-situ emissions from burning peatland soils; (3) deploy a network of nitrogen oxides analyzers to compare ambient nitrogen oxides concentrations nearby both burned and pristine soils; (4) combine these data with forward modeling in order to compare to satellite columns; and (5) conduct additional laboratory experiments to determine physical and chemical controls of soil nitrogen oxides. The end result will provide emission factors of nitrogen oxides from fires and soils in high-latitude land masses and will provide information that might explain the nitrogen oxides anomaly observed after peatland fires. The PI plans on joining and enhancing the Polar Science Early Career Community Office and mentoring an undergraduate student during the course of this project.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.", "keywords": [], "approved": true } }, { "type": "Grant", "id": "12488", "attributes": { "award_id": "2327797", "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": 28425, "first_name": "Shengjie", "last_name": "Lai", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [], "awardee_organization": { "id": 244, "ror": "", "name": "Virginia Polytechnic Institute and State University", "address": "", "city": "", "state": "VA", "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": "12489", "attributes": { "award_id": "2327798", "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": 28426, "first_name": "Robert", "last_name": "Holt", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [], "awardee_organization": { "id": 158, "ror": "https://ror.org/02y3ad647", "name": "University of Florida", "address": "", "city": "", "state": "FL", "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": "12490", "attributes": { "award_id": "2327815", "title": "Collaborative Research: IHBEM: Three-way coupling of water, behavior, and disease in the dynamics of mosquito-borne disease systems", "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": 28427, "first_name": "Alun", "last_name": "Lloyd", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [], "awardee_organization": { "id": 245, "ror": "https://ror.org/04tj63d06", "name": "North Carolina State University", "address": "", "city": "", "state": "NC", "zip": "", "country": "United States", "approved": true }, "abstract": "Complex behavioral responses to information from public health officials, social media, and elsewhere during the COVID-19 pandemic laid bare the limitations of the simplistic assumptions that epidemiological models have traditionally made about human behavior. The investigators of this project hypothesize that human behavior may also play a key role in why diseases transmitted by Aedes mosquitoes, such as dengue and Zika, have been so difficult to control. Aedes mosquitoes lay eggs in household water storage containers, meaning that behaviors related to water storage, water consumption, and water container management impact mosquito populations and, thereby, diseases transmitted by these mosquitoes. The central objective of this project is to understand how humans make decisions about preventive actions against Aedes-borne diseases and how those actions in turn affect disease dynamics and subsequent individual-level decision-making. The project will focus on the city of Ibagué, Colombia, where public health officials have long used behavioral approaches to intervene against Aedes-borne diseases. Empirical social science research will investigate how individuals respond to these interventions and characterize differences among individuals in their responses. Mathematical modeling research will estimate the effectiveness of these interventions at the population level. Throughout the project, a close connection with community members and local public health officials will be cultivated to ensure the effective translation of project outcomes. Training and capacity building activities will extend the impacts of the project to settings beyond Ibagué.This project aims to develop a mechanistic understanding of the role of behavior in infectious disease dynamics and mathematical modeling tools that are capable of accounting for those mechanisms, with the ultimate goal of enabling more effective use of public health interventions. The project will be grounded in empirical social science research in Ibagué, a city in Colombia with one of the highest urbanization rates and Aedes-borne disease transmission rates in the country. A combination of observational and experimental approaches will be used to characterize heterogeneity in the adoption of mosquito prevention behaviors in and around the home and to understand the cues that drive the adoption, or neglect, of those behaviors. These empirical findings will be used to develop a mathematical model of individual decision-making around the use of mosquito prevention behaviors in response to individual-level behavioral dispositions that change over time as cues arise and subside. This decision-making model will then be incorporated into an agent-based model of Aedes-borne disease transmission that will be used to infer the effectiveness of behavioral interventions that public health officials use to control Aedes-borne diseases in Ibagué. Finally, a suite of simpler macroscopic models will be developed and assessed with respect to their ability to capture effects of behavioral interventions on epidemiological dynamics simulated with the agent-based model. The ultimate outcome of the project will be the development and validation of minimally complex mathematical models that are capable of predicting responses of epidemiological dynamics to behavioral interventions.This project is jointly funded by the Division of Mathematical Sciences (DMS) in the Directorate of Mathematical and Physical Sciences (MPS), the Established Program to Stimulate Competitive Research (EPSCoR), 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": "12491", "attributes": { "award_id": "2338266", "title": "Conference: Gateways 2023", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Computer and Information Science and Engineering (CISE)", "Software Institutes" ], "program_reference_codes": [], "program_officials": [], "start_date": "2023-09-01", "end_date": null, "award_amount": 0, "principal_investigator": { "id": 13923, "first_name": "Sandra", "last_name": "Gesing", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [], "awardee_organization": { "id": 163, "ror": "https://ror.org/02mpq6x41", "name": "University of Illinois at Chicago", "address": "", "city": "", "state": "IL", "zip": "", "country": "United States", "approved": true }, "abstract": "Science gateways are a key part of NSF funded Cyberinfrastructure, and they are used by hundreds of thousands of researchers and students, supporting both publication-quality science and at-scale education. Science gateways involve a comprehensive set of research domains that has a broad impact on society, addressing considerable challenges such as pandemics, climate change, global sustainability on food, water, and land use driven by the growing population, rising per capita incomes, to name a few. In recognition of their importance, NSF has funded the Science Gateways Community Institute (SGCI) and more recently the SGX3 Science Gateways Center of Excellence to provide leadership for the science gateways community. The Gateways conference series is one of the of flagships of SGCI and SGX3 and the major event in the US to bring the science gateways community together. The conference series has existed since 2016 and has attracted each year between 100-170 participants. This year it has moved from an SGCI-organized conference to a community-driven conference with the first time the general chair being selected by a newly established advisory board for the conference and who is not part of the SGCI/SGX3 team. The goal is to attract additional research domains and tap into the chair's networks that are not already in contact with SGCI/SGX3. SGX3 continues to guide the conference. Gateways 2023 features different program formats such as keynotes, presentations, tutorials, demos, posters and this year the first time Bring Your Own Portal. Accepted submissions are published in open-access proceedings and accepted papers are invited to a special issue in a journal. SGCI/SGX3 has an impressive record of underrepresented minority involvement within the science gateway community. The travel grant allows to involve more students at Gateways 2023 and they are selected under consideration of diversity, equity and inclusion.Gateways 2023 is the major event for the science gateway community in the US to discuss challenges and solutions in the area, to identify new issues, to shape future directions for research, foster the exchange of ideas, standards and common requirements and push towards the wider adoption of science gateways. The topics covered by the Gateways conference series range from technical topics to use cases to related content such as usability or sustainability of science gateways. The knowledge transfer can be transformative between different research domains and technical content. The building blocks of science gateway frameworks are re-usable in diverse research areas evident in widely used frameworks such as Hubzero and Tapis. The Gateways conference series sets the stage for learning, engaging and empowering the different stakeholders in the community who are science gateway users, developers and providers as well as funders and decision makers. Providing travel grants for students allows to include a diverse audience and support underrepresented minorities.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": "12492", "attributes": { "award_id": "2318300", "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": 27558, "first_name": "John", "last_name": "Dennehy", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [], "awardee_organization": { "id": 722, "ror": "", "name": "CUNY Queens 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": "12493", "attributes": { "award_id": "2327817", "title": "Collaborative Research: IHBEM: Three-way coupling of water, behavior, and disease in the dynamics of mosquito-borne disease systems", "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": 5291, "first_name": "Hayriye", "last_name": "Gulbudak", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [], "awardee_organization": { "id": 707, "ror": "https://ror.org/01x8rc503", "name": "University of Louisiana at Lafayette", "address": "", "city": "", "state": "LA", "zip": "", "country": "United States", "approved": true }, "abstract": "Complex behavioral responses to information from public health officials, social media, and elsewhere during the COVID-19 pandemic laid bare the limitations of the simplistic assumptions that epidemiological models have traditionally made about human behavior. The investigators of this project hypothesize that human behavior may also play a key role in why diseases transmitted by Aedes mosquitoes, such as dengue and Zika, have been so difficult to control. Aedes mosquitoes lay eggs in household water storage containers, meaning that behaviors related to water storage, water consumption, and water container management impact mosquito populations and, thereby, diseases transmitted by these mosquitoes. The central objective of this project is to understand how humans make decisions about preventive actions against Aedes-borne diseases and how those actions in turn affect disease dynamics and subsequent individual-level decision-making. The project will focus on the city of Ibagué, Colombia, where public health officials have long used behavioral approaches to intervene against Aedes-borne diseases. Empirical social science research will investigate how individuals respond to these interventions and characterize differences among individuals in their responses. Mathematical modeling research will estimate the effectiveness of these interventions at the population level. Throughout the project, a close connection with community members and local public health officials will be cultivated to ensure the effective translation of project outcomes. Training and capacity building activities will extend the impacts of the project to settings beyond Ibagué.This project aims to develop a mechanistic understanding of the role of behavior in infectious disease dynamics and mathematical modeling tools that are capable of accounting for those mechanisms, with the ultimate goal of enabling more effective use of public health interventions. The project will be grounded in empirical social science research in Ibagué, a city in Colombia with one of the highest urbanization rates and Aedes-borne disease transmission rates in the country. A combination of observational and experimental approaches will be used to characterize heterogeneity in the adoption of mosquito prevention behaviors in and around the home and to understand the cues that drive the adoption, or neglect, of those behaviors. These empirical findings will be used to develop a mathematical model of individual decision-making around the use of mosquito prevention behaviors in response to individual-level behavioral dispositions that change over time as cues arise and subside. This decision-making model will then be incorporated into an agent-based model of Aedes-borne disease transmission that will be used to infer the effectiveness of behavioral interventions that public health officials use to control Aedes-borne diseases in Ibagué. Finally, a suite of simpler macroscopic models will be developed and assessed with respect to their ability to capture effects of behavioral interventions on epidemiological dynamics simulated with the agent-based model. The ultimate outcome of the project will be the development and validation of minimally complex mathematical models that are capable of predicting responses of epidemiological dynamics to behavioral interventions.This project is jointly funded by the Division of Mathematical Sciences (DMS) in the Directorate of Mathematical and Physical Sciences (MPS), the Established Program to Stimulate Competitive Research (EPSCoR), 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": "12494", "attributes": { "award_id": "2315534", "title": "Collaborative Research: The Economics of Port Infrastructure", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Social, Behavioral, and Economic Sciences (SBE)", "Economics" ], "program_reference_codes": [], "program_officials": [], "start_date": "2023-09-01", "end_date": null, "award_amount": 0, "principal_investigator": { "id": 28428, "first_name": "Giulia", "last_name": "Brancaccio", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [], "awardee_organization": { "id": 167, "ror": "https://ror.org/0190ak572", "name": "New York University", "address": "", "city": "", "state": "NY", "zip": "", "country": "United States", "approved": true }, "abstract": "This award funds a project in the economics of transportation infrastructure that focuses on the role of shipping ports in international trade. Such infrastructure is crucial for the smooth functioning of international trade. Because more than 80% of traded goods (amounting to 11 billion tons and about $20 trillion) are carried by ships, shipping ports are the literal gateway to international trade. The advent of global sourcing, whereby firms source their inputs from further away suppliers, as well as just-in-production, whereby firms expect their inputs to arrive at the moment they need them, has made ports even more pivotal in recent decades. This became painfully apparent during the “Great Supply Chain Disruptions” induced by the Covid-19 pandemic, which upended supply chains, and generated enormous costs to importers and exporters worldwide, who faced massive delays in obtaining their goods. In this uncertain environment, what are the returns to investing in transportation infrastructure? And how should funding be coordinated and spent? What are the drivers of port performance and why are ports so prone to disruption? This project seeks to provide an answer to these questions, by combining a collection of novel data sets on ports, with a state-of-the-art modeling framework for port technology and demand for port services. The results of this project will be useful for making policy decisions about infrastructure investments, including both where ports should be located, what kinds of investment has the highest returns, and whether or not these decisions should be decentralized. The researchers construct a unique database on global ports that relies on data collected by private providers, as well as their own data collecting efforts. The data includes satellite vessel position data, detailed port charges, as well as union membership. They create a dataset on port infrastructure by manually collecting historical satellite images of world ports from Google Earth. In order to evaluate the role of transportation infrastructure one needs the following ingredients: infrastructure technology to understand both the dynamics of congestion, as well as how investment in infrastructure affects congestion; and a demand system for transportation services. This is necessary both because demand endogenously responds to infrastructure improvements, but also in order to quantify their welfare gains. Using insights from queueing theory, the researchers construct and estimate a micro-model of port technology that takes port inputs, such as infrastructure, labor, cranes, and productivity, and produces output, namely time at port. That setup endogenously generates convex costs of congestion. The researchers then estimate a demand system for port services: potential exporters/importers decide which port to use and obtain utility from lower time at port, shorter distance to their origin and destination, lower port prices, as well as other characteristics of the port. The researchers combine their estimates of technology with their estimates for port demand to estimate the returns to port infrastructure investment. The research advances knowledge by shedding light on how ports operate and how they contributed to the Great Supply Chain Disruption. It takes a deep look into port technology and shows that it renders ports inherently disruptive. This also illustrates the role of uncertainty and the impact of different inputs on resilience.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": "12495", "attributes": { "award_id": "2318206", "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": 28429, "first_name": "Heng", "last_name": "Cai", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [], "awardee_organization": { "id": 342, "ror": "https://ror.org/01f5ytq51", "name": "Texas A&M University", "address": "", "city": "", "state": "TX", "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 } } ], "meta": { "pagination": { "page": 1424, "pages": 1442, "count": 14420 } } }