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The Ecology and Evolution of Infectious Diseases program supports research on the ecological, evolutionary, and socio-ecological principles and processes that influence the transmission dynamics of infectious diseases. The central theme of submitted projects must be quantitative or computational understanding of pathogen transmission dynamics. The intent is discovery of principles of infectious disease transmission and testing mathematical or computational models that elucidate infectious disease systems. Projects should be broad, interdisciplinary efforts that go beyond the scope of typical studies. They should focus on the determinants and interactions of transmission among humans, non-human animals, and/or plants. This includes, for example, the spread of pathogens; the influence of environmental factors such as climate; the population dynamics and genetics of reservoir species or hosts; the cultural, social, behavioral, and economic dimensions of disease transmission. Research may be on zoonotic, environmentally-borne, vector-borne, or enteric diseases of either terrestrial or freshwater systems and organisms, including diseases of animals and plants, at any scale from specific pathogens to inclusive environmental systems. Proposals for research on disease systems of public health concern to developing countries are strongly encouraged, as are disease systems of concern in agricultural systems. Investigators are encouraged to develop the appropriate multidisciplinary team, including for example, modelers, bioinformaticians, genomics researchers, social scientists, economists, epidemiologists, entomologists, parasitologists, microbiologists, bacteriologists, virologists, pathologists or veterinarians, with the goal of integrating knowledge across disciplines to enhance our ability to predict and control infectious diseases.

The Ecology and Evolution of Infectious Diseases program supports research on the ecological, evolutionary, and socio-ecological principles and processes that influence the transmission dynamics of infectious diseases. The central theme of submitted projects must be quantitative or computational understanding of pathogen transmission dynamics. The intent is discovery of principles of infectious disease transmission and testing mathematical or computational models that elucidate infectious disease systems. Projects should be broad, interdisciplinary efforts that go beyond the scope of typical studies. They should focus on the determinants and interactions of transmission among humans, non-human animals, and/or plants. This includes, for example, the spread of pathogens; the influence of environmental factors such as climate; the population dynamics and genetics of reservoir species or hosts; the cultural, social, behavioral, and economic dimensions of disease transmission. Research may be on zoonotic, environmentally-borne, vector-borne, or enteric diseases of either terrestrial or freshwater systems and organisms, including diseases of animals and plants, at any scale from specific pathogens to inclusive environmental systems. Proposals for research on disease systems of public health concern to developing countries are strongly encouraged, as are disease systems of concern in agricultural systems. Investigators are encouraged to develop the appropriate multidisciplinary team, including for example, modelers, bioinformaticians, genomics researchers, social scientists, economists, epidemiologists, entomologists, parasitologists, microbiologists, bacteriologists, virologists, pathologists or veterinarians, with the goal of integrating knowledge across disciplines to enhance our ability to predict and control infectious diseases.

The Ecology and Evolution of Infectious Diseases program supports research on the ecological, evolutionary, and socio-ecological principles and processes that influence the transmission dynamics of infectious diseases. The central theme of submitted projects must be quantitative or computational understanding of pathogen transmission dynamics. The intent is discovery of principles of infectious disease transmission and testing mathematical or computational models that elucidate infectious disease systems. Projects should be broad, interdisciplinary efforts that go beyond the scope of typical studies. They should focus on the determinants and interactions of transmission among humans, non-human animals, and/or plants. This includes, for example, the spread of pathogens; the influence of environmental factors such as climate; the population dynamics and genetics of reservoir species or hosts; or the cultural, social, behavioral, and economic dimensions of disease transmission. Research may be on zoonotic, environmentally-borne, vector-borne, or enteric diseases of either terrestrial or freshwater systems and organisms, including diseases of animals and plants, at any scale from specific pathogens to inclusive environmental systems. Proposals for research on disease systems of public health concern to developing countries are strongly encouraged, as are disease systems of concern in agricultural systems. Investigators are encouraged to involve the public health research community, including for example, epidemiologists, physicians, veterinarians, food scientists, social scientists, entomologists, pathologists, virologists, or parasitologists with the goal of integrating knowledge across disciplines to enhance our ability to predict and control infectious diseases.

This Funding Opportunity Announcement (FOA) is to invite applications for new applications for the Data Management and Coordinating Center (DMCC) for the Rare Diseases Clinical Research Network (RDCRN). Each consortium within the RDCRN is intended to advance the diagnosis, management, and treatment of rare diseases. The DMCC will facilitate and support the activities of each Rare Diseases Clinical Research Consortium (RDCRC) along with trans-network activities that broadly facilitate the advancement of rare disease research via four Cores; 1) Administrative; 2) Data Management; 3) Clinical Research and; 4) Engagement and Dissemination.

This FOA invites applications for the Microphysiological Systems (MPS) for Disease Modeling and Efficacy Testing Program to develop highly reproducible and translatable in vitro models for preclinical efficacy studies through discovery and validation of translatable biomarkers, development of standardized methods for preclinical efficacy testing and definitive efficacy testing of candidate therapeutics using best practices and rigorous study design. An essential feature will be a multidisciplinary approach that brings together experts in bioengineering, microfluidics, material science, "omic" sciences, computational biology, disease biology, pathology, electrophysiology, pharmacology, biostatistics and clinical science.

This FOA invites applications for the Microphysiological Systems (MPS) for Disease Modeling and Efficacy Testing Program to develop highly reproducible and translatable in vitro models for preclinical efficacy studies through discovery and validation of translatable biomarkers, development of standardized methods for preclinical efficacy testing and definitive efficacy testing of candidate therapeutics using best practices and rigorous study design. An essential feature will be a multidisciplinary approach that brings together experts in bioengineering, microfluidics, material science, "omic" sciences, computational biology, disease biology, pathology, electrophysiology, pharmacology, biostatistics and clinical science.

The purpose of this initiative is to support Genomic Centers for Infectious Diseases (GCID) to promote broad use and expand the application of genomics technologies and computational analysis to understand infectious diseases, with an emphasis on pathogens, their interaction with the host and microbiome, and to aid in the development of novel genomics-based tools to diagnose, prevent and treat infectious diseases. The GCID will support innovative technology development in all aspects of genomics, including the use of synthetic and genome editing technologies as well as functional genomics to address basic, translational, and clinically relevant questions in host-pathogen interactions. The knowledge generated, including research data, analytical software tools, computational models, experimental protocols, and reagents, is expected to be widely disseminated to the scientific community through publicly accessible databases and reagent repositories.

The National Institute of Arthritis and Musculoskeletal and Skin Diseases (NIAMS) requests applications for the NIAMS Core Centers for Clinical Research (CCCR) (P30) within its mission. The CCCRs will provide avenues to advance the methodological sciences that support clinical research within and across the NIAMS' portfolio of diseases. The overall goals of the CCCR are to advance prevention, diagnosis and treatment of musculoskeletal, rheumatologic, and skin diseases by developing and fostering the implementation of novel methods, metrics, and outcome measures that address critical existing and emerging clinical research needs. 

This Funding Opportunity Announcement (FOA) solicits applications from single institutions, or consortia of institutions, to participate in a network of research groups developing computational models of immunity to infectious diseases other than HIV/AIDS. Applications are sought to develop, refine and validate computational models of immune responses (1) during or following infection, and/or (2) before and after vaccination against an infectious disease, through an iterative approach involving computational studies and immunological experimentation. The main goal of this FOA is to advance our understanding of the complex immune mechanisms triggered by infection and/or vaccination through the development and application of computational models of immunity, coupled with immunological experimentation to validate and improve the utility and robustness of the computational models.Another goal of this FOA is to make the computational models and data developed under this initiative readily available to the broader research community for further refinement or direct use in biological experimentation. This program will also support pilot projects, workshops, and symposia to foster the use of computational models of immunity by the broader research community.

This Funding Opportunity Announcement (FOA) solicits applications from single institutions, or consortia of institutions, to participate in a network of research groups developing computational models of immunity to infectious diseases other than HIV/AIDS. Applications are sought to develop, refine and validate computational models of immune responses (1) during or following infection, and/or (2) before and after vaccination against an infectious disease, through an iterative approach involving computational studies and immunological experimentation. The main goal of this FOA is to advance our understanding of the complex immune mechanisms triggered by infection and/or vaccination through the development and application of computational models of immunity, coupled with immunological experimentation to validate and improve the utility and robustness of the computational models.  Another goal of this FOA is to make the computational models and data developed under this initiative readily available to the broader research community for further refinement or direct use in biological experimentation. This program will also support pilot projects, workshops, and symposia to foster the use of computational models of immunity by the broader research community.  

The purpose of this funding opportunity announcement (FOA) is to invite applications for a broad range of research efforts in computational genomics, data science, statistics, and bioinformatics relevant to one or both of basic or clinical genomic science, and broadly applicable to human health and disease. This FOA supports fundamental genomics research developing innovative analytical methodologies and approaches, early stage development of tools and software, and refinement or hardening of software and tools of high value to the biomedical genomics community. Work supported under this FOA should be enabling for genomics and be generalizable or broadly applicable across diseases and biological systems. All applications should address how the methods would scale to address larger and larger data sets.

The purpose of this Funding Opportunity Announcement (FOA) is to stimulate translation of scientific discoveries and engineering developments in imaging, data science and/or spectroscopic technologies into methods or tools that address contemporary problems in understanding the fundamental biology, potential risk of development, diagnosis, treatment, and/or disease status for cancer or other disease.

This Research Fund is opening a call for projects in the following areas: * Protecting vulnerable populations from epidemics and catastrophes, including COVID-19: be it migrants, informal settlements, workers in the informal economy, isolated people, people with disabilities, the elderly * Improving data collection and quality in health: how can data and technology help us get out of the crisis, understand it and mitigate it? How can it inform future containment and epidemic control? * Understanding the effects of confinement and social distancing: what are the effects of confinement and social distancing on society and households? What are the mental health consequences? The social and domestic repercussions? * Early warning and preparedness: how do we re-enforce our health infrastructure and ecosystem (including medical devices and drug supply) to be better prepared and how do we protect our health workers and caregivers? * Preserving the environment and our health: connections between climate change, biodiversity loss and the origin of viral disease including socioeconomic dynamics leading to infectious disease outbreaks and sanitary crisis; Learnings from COVID-19 for mitigating future related crises in climate and biodiversity

The goal of the Smart and Connected Health (SCH) Program is to accelerate the development and use of innovative approaches that would support the much needed transformation of healthcare from reactive and hospital-centered to preventive, proactive, evidence-based, person-centered and focused on well-being rather than disease. Approaches that partner technology-based solutions with biobehavioral health research are supported by multiple agencies of the federal government including the National Science Foundation (NSF) and the National Institutes of Health (NIH). The purpose of this program is to develop next generation health care solutions and encourage existing and new research communities to focus on breakthrough ideas in a variety of areas of value to health, such as sensor technology, networking, information and machine learning technology, decision support systems, modeling of behavioral and cognitive processes, as well as system and process modeling. Effective solutions must satisfy a multitude of constraints arising from clinical/medical needs, social interactions, cognitive limitations, barriers to behavioral change, heterogeneity of data, semantic mismatch and limitations of current cyberphysical systems. Such solutions demand multidisciplinary teams ready to address technical, behavioral and clinical issues ranging from fundamental science to clinical practice.

The goal of the Smart and Connected Health (SCH) Program is to accelerate the development and use of innovative approaches that would support the much needed transformation of healthcare from reactive and hospital-centered to preventive, proactive, evidence-based, person-centered and focused on well-being rather than disease. Approaches that partner technology-based solutions with biobehavioral health research are supported by multiple agencies of the federal government including the National Science Foundation (NSF) and the National Institutes of Health (NIH). The purpose of this program is to develop next generation health care solutions and encourage existing and new research communities to focus on breakthrough ideas in a variety of areas of value to health, such as sensor technology, networking, information and machine learning technology, decision support systems, modeling of behavioral and cognitive processes, as well as system and process modeling. Effective solutions must satisfy a multitude of constraints arising from clinical/medical needs, social interactions, cognitive limitations, barriers to behavioral change, heterogeneity of data, semantic mismatch and limitations of current cyberphysical systems. Such solutions demand multidisciplinary teams ready to address technical, behavioral and clinical issues ranging from fundamental science to clinical practice.

In light of the emergence and spread of the coronavirus disease 2019 (COVID-19) in the United States and abroad, the National Science Foundation (NSF) is accepting proposals to conduct non-medical, non-clinical-care research that can be used immediately to explore how to model and understand the spread of COVID-19, to inform and educate about the science of virus transmission and prevention, and to encourage the development of processes and actions to address this global challenge. NSF encourages the research community to respond to this challenge through existing funding opportunities. In addition, we invite researchers to use the Rapid Response Research (RAPID) funding mechanism, which allows NSF to receive and review proposals having a severe urgency with regard to availability of or access to data, facilities or specialized equipment as well as quick-response research on natural or anthropogenic disasters and similar unanticipated events. Requests for RAPID proposals may be for up to $200K and up to one year in duration. Well-justified proposals that exceed these limits may be entertained.

The goal of the Smart and Connected Health (SCH) Program is to accelerate the development and use of innovative approaches that would support the much needed transformation of healthcare from reactive and hospital-centered to preventive, proactive, evidence-based, person-centered and focused on well-being rather than disease. Approaches that partner technology-based solutions with biobehavioral health research are supported by multiple agencies of the federal government including the National Science Foundation (NSF) and the National Institutes of Health (NIH). The purpose of this program is to develop next generation health care solutions and encourage existing and new research communities to focus on breakthrough ideas in a variety of areas of value to health, such as sensor technology, networking, information and machine learning technology, decision support systems, modeling of behavioral and cognitive processes, as well as system and process modeling. Effective solutions must satisfy a multitude of constraints arising from clinical/medical needs, social interactions, cognitive limitations, barriers to behavioral change, heterogeneity of data, semantic mismatch and limitations of current cyberphysical systems. Such solutions demand multidisciplinary teams ready to address technical, behavioral and clinical issues ranging from fundamental science to clinical practice.

The goal of the Smart and Connected Health (SCH) Program is to accelerate the development and use of innovative approaches that would support the much needed transformation of healthcare from reactive and hospital-centered to preventive, proactive, evidence-based, person-centered and focused on well-being rather than disease. Approaches that partner technology-based solutions with biobehavioral health research are supported by multiple agencies of the federal government including the National Science Foundation (NSF) and the National Institutes of Health (NIH). The purpose of this program is to develop next generation health care solutions and encourage existing and new research communities to focus on breakthrough ideas in a variety of areas of value to health, such as sensor technology, networking, information and machine learning technology, decision support systems, modeling of behavioral and cognitive processes, as well as system and process modeling.

The goal of the Smart and Connected Health (SCH) Program is to accelerate the development and use of innovative approaches that would support the much needed transformation of healthcare from reactive and hospital-centered to preventive, proactive, evidence-based, person-centered and focused on well-being rather than disease. Approaches that partner technology-based solutions with biobehavioral health research are supported by multiple agencies of the federal government including the National Science Foundation (NSF) and the National Institutes of Health (NIH). The purpose of this program is to develop next generation health care solutions and encourage existing and new research communities to focus on breakthrough ideas in a variety of areas of value to health, such as sensor technology, networking, information and machine learning technology, decision support systems, modeling of behavioral and cognitive processes, as well as system and process modeling.

The purpose of the Mentored Quantitative Research Career Development Award (K25) is to attract to NIH-relevant research those investigators whose quantitative science and engineering research has thus far not been focused primarily on questions of health and disease. The K25 award will provide support and protected time for a period of supervised study and research for productive professionals with quantitative (e.g., mathematics, statistics, economics, computer science, imaging science, informatics, physics, chemistry) and engineering backgrounds to integrate their expertise with NIH-relevant research. Prospective candidates are encouraged to contact the relevant NIH staff for IC-specific programmatic and budgetary information: Table of IC-Specific Information, Requirements and Staff Contacts.

The Common Fund Program - Accelerating Translation of Glycoscience: Integration and Accessibility - aims to develop accessible and affordable new tools and technologies for studying carbohydrates that will allow biomedical researchers to significantly advance our understanding of the roles of these complex molecules in health and disease. This program will enable investigators who might not otherwise conduct research in the glycosciences, to undertake the study of carbohydrate structure and function. In support of these aims, this FOA seeks applications for a community-driven project to develop computational and informatics tools for the manipulation, analysis, interpretation, and integration of glycoscience data. The product of this research will be accessible resources for analysis of carbohydrate and glycoconjugate structural, analytical, and interaction data, and integration of that information within the context of comparable gene, protein, and lipid data and databases.

The mission of BTO is to leverage biology as a technology to solve intractable problems. BTO seeks to leverage advances in engineering and computer science to drive and reshape biotechnology for national security. To achieve this vision, BTO is interested in a range of emerging technical areas, including but not limited to human-machine interfaces, human performance, infectious disease, and synthetic biology. The overarching goal is to develop, demonstrate, and transition biologically-based technologies as part of the national security toolkit.

Our Data-Driven Discovery Initiative seeks to advance the people and practices of data-intensive science, to take advantage of the increasing volume, velocity, and variety of scientific data to make new discoveries. Within this initiative, we're supporting data-driven discovery investigators - individuals who exemplify multidisciplinary, data-driven science, coalescing natural sciences with methods from statistics and computer science. These innovators are striking out in new directions and are willing to take risks with the potential of huge payoffs in some aspect of data-intensive science. Successful applicants must make a strong case for developments in the natural sciences (biology, physics, astronomy, etc.) or science enabling methodologies (statistics, machine learning, scalable algorithms, etc.), and applicants that credibly combine the two are especially encouraged. Note that the Science Program does not fund disease targeted research. It is anticipated that the DDD initiative will make about 15 awards at ~$1,500,000 each, at $200K-$300K/year for five years.