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Important information about NSF’s implementation of the revised 2 CFR

NSF Financial Assistance awards (grants and cooperative agreements) made on or after October 1, 2024, will be subject to the applicable set of award conditions, dated October 1, 2024, available on the NSF website. These terms and conditions are consistent with the revised guidance specified in the OMB Guidance for Federal Financial Assistance published in the Federal Register on April 22, 2024.

Important information for proposers

All proposals must be submitted in accordance with the requirements specified in this funding opportunity and in the NSF Proposal & Award Policies & Procedures Guide (PAPPG) that is in effect for the relevant due date to which the proposal is being submitted. It is the responsibility of the proposer to ensure that the proposal meets these requirements. Submitting a proposal prior to a specified deadline does not negate this requirement.

Dear Colleague Letter

Emerging Mathematical Tools applied to Ecology and Evolution of Infectious Disease

Encourages proposals that enhance our knowledge of pathogen transmission dynamics through novel technologies, such as remote sensing, precision treatment and mathematical or computational models, including artificial intelligence and machine learning.
Encourages proposals that enhance our knowledge of pathogen transmission dynamics through novel technologies, such as remote sensing, precision treatment and mathematical or computational models, including artificial intelligence and machine learning.

Dear Colleagues:

The Ecology and Evolution of Infectious Diseases (EEID) program (https://new.nsf.gov/funding/opportunities/ecology-evolution-infectious-diseases-eeid) supports research that provides predictive understanding of pathogen transmission dynamics. This program includes partnerships with the US Department of Agriculture, National Institute of Food and Agriculture (USDA/NIFA), National Institutes of Health (NIH), United Kingdom Research and Innovation (UKRI), United States-Israel Binational Science Foundation (BSF), and National Natural Science Foundation of China (NSFC). The intent of the program is discovery of principles of infectious disease (re)emergence and transmission and testing mathematical or computational models that elucidate infectious disease systems. Those models can be of any type that advance causal or mechanistic understanding, including, but not limited to, models that use artificial intelligence or machine learning. The Program also encourages projects that take advantage of emerging technologies, such as remote sensing and precision treatment, especially those that will enhance food security.

The history of the EEID program has shown that the most competitive proposals are those that are organized around an overarching conceptual framework that advances broad, conceptual knowledge that reaches beyond the specific system under study and that may be useful for understanding public, agricultural or ecosystem health, natural resource use and wildlife management, and/or economic development. Such proposals are typically interdisciplinary in their approach and/or the nature of the question(s) being addressed. Projects should bring together such areas as anthropology, behavior, bioinformatics, computational science, ecology, economics, epidemiology, evolution, food science, genomics, geography, global health, immunology, mathematics, medicine, microbiology, oceanography, plant science, population biology, sociology, physical environmental sciences, systems science, and veterinary medicine.

The next deadline for the EEID program is November 20, 2024. Consult the solicitation for information about proposal content and submission criteria.

Susan Marqusee
Assistant Director
Directorate for Biological Sciences

Alexandra Isern
Assistant Director
Directorate for Geosciences

Denise Caldwell
Acting Assistant Director
Directorate for Mathematical & Physical Sciences

Sylvia Butterfield
Acting Assistant Director
Directorate for Social, Behavioral & Economic Sciences

Organization(s)

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