# data[].attributes.title
The Importance of Agriculture in the Economy: Impacts from COVID-19

# data[].attributes.grants[].title
CIF: Small: Group Testing for Epidemics Control

# data[].attributes.grants[].abstract
This project develops testing and intervention (quarantine) methods in the presence of a pandemic. COVID-19 has revealed the key role of  epidemiological models and testing in the fight against disease spreading. For any new virus or variant of the existing ones, society will always need to be able to expeditiously deploy strategies that allow efficient testing of populations and empower targeted interventions.  Group testing is a method that has recently attracted attention for efficient testing, as it allows to identify the infected individuals in a population with many fewer tests than the ones needed to test everyone individually. A main new observation in this project is that viral diseases like SARS-CoV-2 are governed by community spread, and taking into account (even partial) knowledge of  the community structure in an epidemics model (e.g., the distribution of students in classes of a school) can make such testing much more efficient and effective.Preliminary results indicate that it is possible to estimate the infection spread and evaluate the impact of interventions using a much smaller number of tests than  traditional techniques.Accordingly, the goal of the project is to leverage community structure and epidemic dynamics to enable real-time estimation of infection and intervention with the following attributes: (i) it is robust to model uncertainties; (ii) it offers provable theoretical performance guarantees and (iii) it achieves low complexity of operation. To do so, the proposal combines tools from coding theory and control, and proceeds in two steps. First, assuming complete and perfect knowledge of the true underlying dynamical model, it derives test designs and intervention strategies, as well as fundamental bounds on the number of tests and amount of intervention, for both a static and state-estimation problem formulation. Building on this first step, the proposal then considers approximations to the dynamic models either because the exact dynamics are not perfectly known, or for complexity-reduction reasons. In particular, the proposal develops approximations on the evolution of marginal probabilities for popular epidemic models, derives and analyzes discretized models, explores the effect of parameter uncertainty and investigate decomposable community models; in all cases, the goal is to understand how these approximations provably affect the associated fundamental bounds, test designs, and intervention strategies to contain the disease.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.

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Diversification and resilience of firms in the agrifood supply chain

# data[].attributes.grants[].title
What explains ambitious climate policy? Comparing updated climate targets and Covid-19 recovery packages and their drivers

# data[].attributes.grants[].abstract
Many countries updated their national climate targets or Nationally Determined Contributions (NDCs) during the COVID-19 pandemic.  At the same time, these countries introduced vast COVID-19 economic recovery packages and fiscal reforms that may either serve to strengthen or upset existing carbon-intensive economic systems. This project will study how long-term climate goals, as indicated in countries’ NDCs, and short-term implementation of climate policies through pandemic recovery spending plans affect each other and why some countries lead in climate policy implementation, while others lag.  The study is based on the collection of and analyses of large data sets across several countries.  This study, to be carried out by research teams from several countries, will help identify opportunities for policymakers and international financial institutions on how to promote and implement climate change policy ambitions.  In addition to helping to understand cross country differences in climate policy implementation, the results of this research will provide inputs into formulating efficient and innovative climate policies in the US.This research project will study two inter-related issues.  First, it studies the links between long-term national climate ambitions enshrined in NDC updates and short-term implementation plans embedded in COVID-19 recovery packages, and fiscal reform. Second, it investigates the political and economic drivers underlying differences in climate ambitions and implementation abilities in these types of policy intervention across countries. To do so, the project integrates political science and economics, focusing on the role of policy feedback and financing conditions in driving climate ambition and climate policy interventions. In a mixed-methods design, the project combines descriptive statistical analyses with qualitative comparative case studies in the three analytical tasks. The intellectual merit of this project lies in identifying patterns in the relationship of long-term climate targets and plans (NDCs) and short-term implementation (economic recovery spending) as well as the causes of cross-national variation in this relationship. The results of this research will provide inputs into formulating efficient and innovative climate policies in the US.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.

# data[].attributes.title
Farm labor supply and fruit and vegetable production

# data[].attributes.authors[].affiliations[].name
University of North Carolina at Chapel Hill

# data[].attributes.grants[].title
RAPID: Statistical inference of incidence of SARS-CoV-2 in the US using multiple data streams to identify levels of immunity and the impact of non-pharmaceutical interventions

# data[].attributes.grants[].abstract
The goal of this study is to integrate multiple, independent data sources to estimate the rate of SARS-CoV-2 infections across the US over time. Population-based SARS-CoV-2 serological assays are critical for understanding cumulative incidence and population-level immunity. The US CDC, in partnership with a number of laboratories, has conducted nationwide serosurveys which can help retrospectively assess the cumulative number of total infections. However, data from these surveys may be difficult to interpret due to heterogeneity in antibody response across individuals, by assay, and over time since infection. Reconciling patterns observed in seroprevalence with other data sources including reported COVID-19 cases and deaths can explain variation in seroprevalence across space and time in the US CDC. In addition, the project will estimate the proportion of the population with recent immunizing events (infection or vaccination) to understand the immunity landscape prior to the Omicron-variant-driven wave in 2021-2022 in the US. The project will develop tools to jointly analyze serology, caseand death data, and contribute to the training of a post-doctoral scholar.The primary objective in this study is to integrate multiple independent data streams using statistical and mechanistic models to estimate the rate of seroreversion in assays used in serosurveys across the US, and estimate seroprevalence and cumulative incidence over time by state. The model will provide information about SARS-CoV-2 transmission from case, hospitalization and death data by taking a multi-objective approach and adapting fast inference techniques that we have developed. Methods such as these have been applied to state-leveldata on COVID-19 incidence, including by this group. This project was funded in collaboration with the CDC to support rapid-response research projects to further advance federal infectious disease modeling capabilities.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.

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A call for justice work in agricultural and applied economics

# data[].attributes.authors[].affiliations[].name
University of Georgia Research Foundation Inc

# data[].attributes.grants[].title
Collaborative Research: Parenting, Housework, Well-being, and the COVID-19 Pandemic

# data[].attributes.grants[].abstract
The COVID-19 pandemic has dramatically altered family life in the United States. This project studies how parents’ engagement in domestic labor and paid work has changed throughout the pandemic and what factors may be driving these changes. It also investigates long-term consequences of the pandemic for the division of household labor between mothers and fathers, and the impacts of the pandemic on parents’ well-being. Starting in the first months of the pandemic and continuing for five years, this project illuminates how family and work life have changed over the course of the COVID-19 pandemic. Insights from this study inform decisionmakers to better meet the needs of working parents and families.This project uses longitudinal survey data from a sample of partnered U.S. parents. Parents were first surveyed in April 2020 and asked about their work and household activities prior to and one month after the pandemic began. These parents were surveyed again in November 2020 and October 2021, along with a new set of respondents at each wave. Follow-up surveys result in six waves of data spanning the period from March 2020 through September 2025. Approximately 6,500 parents are surveyed at least once during the study. These data are used to assess (a) changes in parents’ divisions of domestic labor, (b) the factors driving these changes, (c) effects on mothers’ labor force participation, and (d) changes in parents’ well-being and relationship quality. The novel nature of these data are uniquely situated to assess the long-term consequences of the COVID-19 pandemic for parents’ work and family life and their well-being.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.

# data[].attributes.authors[].affiliations[].name
University of Georgia Research Foundation Inc

# data[].attributes.grants[].title
Building Capacity to Develop Community-Engaged STEM Learning as an Institutional Transformative Mechanism

# data[].attributes.grants[].abstract
This project aims to serve the national interest by integrating STEM learning with community-oriented problem solving, evidence-based decision making, critical thinking, and the development of civic duties. The project plans to build institutional capacity to create an immersive environment of community-engaged learning (CEL) and to transform STEM education. Working collaboratively with community partners, students will use their knowledge and skills in STEM fields to address issues facing many minority communities, particularly inequity issues related to the COVID-19 pandemic and post-pandemic rejuvenation of affected communities. This project will directly tackle the challenges faced by minority and other underserved students, including food insecurity and mental health issues, to enhance their resilience, confidence, and persistence to succeed in STEM fields. The project will have a positive impact on minority and immigrant communities in areas of health and healthcare disparity, environmental and nutritional inequity, social justice, and equal rights. The project will engage an interdisciplinary team of STEM faculty, social scientists, and student affairs staff to provide students with an immersive environment through CEL courses in both STEM and humanities, arts, and social science (HASS) fields, which will lead to sustained change and a powerful advancement in STEM education. Interdisciplinarity will be a key component in this project, as the HASS courses will allow students to understand complex societal and community issues as they apply STEM knowledge to address those issues. The STEM solutions will be placed in the social and cultural contexts of the communities. The objectives of this project include 1) to create an immersive CEL environment at NJCU that offers broad CEL-focused courses and curricula in STEM and HASS to transform the student experience in undergraduate STEM education; 2) to build a holistic support system to empower faculty, students, and communities to embrace CEL and bolster students’ success in STEM; 3) to promote NJCU as a prominent “steward of place” that benefits and contributes to the minority communities with scientific and evidence-based approaches to meet the community needs; and 4) to conduct research on interdisciplinarity and community-institution partnership, establishing evidence-based best practices for CEL and catalyzing sustainable transformation across the university and surrounding communities. The NSF IUSE: EHR Program supports research and development projects to improve the effectiveness of STEM education for all students. Through the Institutional and Community Transformation track, the program supports efforts to transform and improve STEM education across institutions of higher education and disciplinary communities. This project is also supported by the NSF IUSE: HSI program, which seeks to enhance undergraduate STEM education, broaden participation in STEM, and build capacity at HSIs.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.

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Modelling during an emergency: the 2009 H1N1 influenza pandemic

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CAREER:  Discourse Processing and Content Generation for Document Simplification

# data[].attributes.grants[].abstract
This award is funded in whole or in part under the American Rescue Plan Act of 2021 (Public Law 117-2).Simplification is the process of making a text more accessible to a target audience, e.g., language learners, children, and individuals with language impairments, while preserving its meaning and content. The lack of accessible material can exacerbate social issues, for example, the complexity of language used in college admission and financial aid applications has contributed to the lagging access to higher education among emergent bilingual students; the WHO has recognized the urgency of accessible technical information, given the rise of medical misinformation especially in the wake of the COVID-19 pandemic. While there has been much work on sentence simplification, very few datasets are large enough to train supervised models; simplifying a document also involves different operations from those at the sentence level, including content addition, and how sentences connect with each other. This project aims to develop new resources and data-driven approaches for document simplification, with the potential to address information transparency and fair access across a range of high-stake domains. This project will also support the education and training of a diverse body of undergraduate and graduate students across disciplines.To substantially advance document simplification, this CAREER project will tackle several key issues in existing simplification work, including corpora diversity, explanation generation, and document-level approaches. This is achieved by the following research activities: (1) introducing new corpora that tackle the pressing challenge of data diversity in simplification research and enable new application scenarios, especially in the accessibility of technical and jargon-laden texts; (2) tackling content addition and elaboration during simplification---a previously little-explored challenge, and propose a novel, linguistically-informed framework that characterizes and generates elaborations; (3) develop models for document simplification that are informed by structures of discourse, using both coherence structure and entity salience. The innovative ways to integrate discourse target a larger challenge for models to take stretches of discourse into account.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.

# data[].attributes.title
EMERGENCE OF ONLINE LEARNING ON COVID 19