OpportunityTitle: NCHHSTP Epidemiologic and Economic Modeling for HIV, Viral Hepatitis, STD, and TB
AdditionalInformationOnEligibility: N/A
Description: This notice of funding opportunity announcement will support modeling of disease dynamics and advanced data analytics to inform and improve HIV, viral hepatitis, STD, TB, and adolescent health interventions and programs. Epidemiologic and economic models have long been a part of NCHHSTP&#8217;s emphasis on data use for program improvement. Over the past 10 years, the Center has harnessed extramural collaborations to strengthen the use of data for understanding the efficiency, outcomes, impact, and cost-effectiveness of programs, policies, and activities. Prior modeling efforts by NCHHSTP and collaborators has provided insights into the development of guidelines, program strategy, needs assessment, and informed piloting, implementation and scale-up of programs. This announcement will build on the success of prior modeling efforts by continuing to enhance existing models that guide informed decision-making, provide tools and data for prioritizing public interventions and programs, and to guide the allocation of scarce program resources. The cooperative agreement will also expand the use of advanced data analytics using predictive models, machine learning and artificial intelligence, as well as allow for the development and implementation of research based on these modeling and advanced data analytic projects in alignment with NCHHSTP-related public health priorities. Scientifically valid models can help CDC to fulfill its mission of supporting federal, state, and local partners and increase the prevention effectiveness of public health interventions. This mechanism allows NCHHSTP to strengthen its capacity to conduct scientifically valid models of public health interventions and programs for HIV, viral hepatitis, STD, TB, and adolescent health.
GrantorContactEmailDescription: imb5@cdc.gov
GrantorContactText: Tamika Hoyte&lt;br/&gt;imb5@cdc.gov
CloseDateExplanation: Electronically submitted applications must be submitted no later than 11:59 pm ET on the listed application due date.