cdc-rfa-ck-25-0018.pdf

Notice of Funding Opportunity

Application due March 19, 2025

National Center for Emerging and Zoonotic Infectious Diseases Division of Healthcare Quality Promotion

Modeling Infectious Diseases in Healthcare (MInD Healthcare) to Improve Pathogen Prevention and Healthcare Delivery Opportunity number: CDC-RFA-CK-25-0018

Contents Before you begin 3

Step 1: Review the Opportunity 4

Basic information 5

Eligibility 7

Program description 8

Step 2: Get Ready to Apply 42

Get registered 43

Step 3: Prepare Your Application 45

Application contents and format 46

Step 4: Learn about Review and Award 53

Application review 54

Award notices 62

Step 5: Submit Your Application 63

Application submission and deadlines 64

Application checklist 66

Step 6: Learn What Happens After Award 67

Post-award requirements and administration 68

Contacts and Support 73

Appendix: HAI Modeling Publications 75

Contents 2

Before you begin If you believe you are a good candidate for this funding opportunity, secure your SAM.gov and Grants.gov registrations now. If you are already registered, make sure your registrations are active and up-to-date.

SAM.gov registration (this can take several weeks) You must have an active account with SAM.gov. This includes having a Unique Entity Identifier (UEI).

See Step 2: Get Ready to Apply

Grants.gov registration (this can take several days) You must have an active Grants.gov registration. Doing so requires a Login.gov registration as well.

See Step 2: Get Ready to Apply

Apply by the application due date Applications are due by 11:59 p.m. Eastern Time on March 19, 2025.

To help you find what you need, this NOFO uses internal links. In Adobe Reader, you can go back to where you were by pressing Alt + Left Arrow (Windows) or Command + Left Arrow (Mac) on your keyboard.

Before you begin 3 https://sam.gov/ https://grants.gov/

Jump to a step

1. Review 2. Get Ready 3. Prepare 4. Learn 5. Submit 6. Award Contacts

Step 1:

Review the Opportunity

In this step

Basic information 5

Eligibility 7

Program description 8

Basic information Centers for Disease Control and Prevention (CDC)

National Center for Emerging and Zoonotic Infectious Diseases

Division of Healthcare Quality Promotion

Supporting and providing workforce development opportunities centered on new models to understand how antimicrobial resistant pathogens spread.

Have questions?

See Contacts and Support.

Key facts

Opportunity name:

Modeling Infectious Diseases in Healthcare (MInD Healthcare) to Improve Pathogen Prevention and Healthcare Delivery

Opportunity number:

CDC-RFA-CK-25-0018

Federal assistance listing: 93.084

Key dates

Application deadline:

March 19, 2025

Optional letter of intent deadline:

February 28, 2025

Informational call:

February 10, 2025

Expected award date:

July 18, 2025

Expected start date:

August 1, 2025

Summary The purpose of this Notice of Funding Opportunity (NOFO) is to support development of innovative and applied mathematical models and computational tools for:

• Modeling the spread of pathogens in healthcare settings, for example, healthcare-associated infections (HAIs) and antimicrobial resistant organisms (AROs).

• Predicting outbreaks of HAI pathogens and trends in the burden of antimicrobial resistant and susceptible HAIs.

• Assessing the effectiveness of intervention strategies.

This NOFO will increase training and workforce development opportunities about applied public health modeling, including collaboration with public health partners. Models developed by HAI and ARO leaders within the Modeling Infectious Diseases in Healthcare (MInD Healthcare) network could help public health partners make informed decisions and optimize evidence-based prevention strategies to respond to evolving public health needs and emergencies in healthcare settings.

1. Review 2. Get Ready 3. Prepare 4. Learn 5. Submit 6. Award Contacts

Step 1: Review the Opportunity 5

Informational call scheduled for February 10, 2025 3:00 - 4:30 PM is CANCELED

Funding details Type: Cooperative Agreement

Expected total program funding over the performance period: $16,500,000 to $19,500,000

Expected total program funding per budget period: $3,300,000 to $3,900,000

Expected awards: 4 to 6

Funding range per applicant per budget period: $550,000 to $650,000

Expected average award amount per budget period: $550,000 to $650,000

We plan to award projects for five 12-month budget periods, for a five-year period of performance.

The number of awards is subject to available funds and program priorities.

Funding strategy This NOFO will fund recipients with the necessary capabilities, including subject matter expertise; administrative infrastructure; regional, state, and local partnerships; and management capabilities to make substantial contributions to the activities and outcomes of the logic model. You will be required to respond to the two mandatory strategies outlined in this NOFO.

You must address all of the strategies in the Project Abstract Summary, Project Narrative, Work Plan, and Budget Narrative as described in this NOFO.

You can propose activities that are complementary to, but must not be duplicative of, work funded through any other mechanism or source during the period of performance.

We may deem any strategy that is not funded at the time of a new award “Approved but Unfunded (ABU).” We cannot guarantee funding for all strategies in a budget period. ABU components are subject to availability of funds.

If funded, continuation funds will be based on the availability of funds and agency priorities.

Statutory authority Public Health Service Act, Section 317(k)(2), [42 USC 247b(k)(2)], as amended.

Step 1: Review the Opportunity 6

Eligibility Who can apply

Eligible applicants Only these types or organizations may apply:

• State governments

• County governments

• City or township governments

• Special district governments

• Public and state-controlled institutions of higher education

• Native American tribal governments (Federally recognized)

• Native American tribal organizations, other than federally recognized tribal governments

• Nonprofits having a 501(c)(3) status, other than institutions of higher education

• Private institutions of higher education

Other required qualifying factors Applicant organizations that request funding above the ceiling amount listed in the funding details for this NOFO will be considered non-responsive and will not be passed along for further review.

Cost sharing and matching funds This program has no cost-sharing requirement or matching funds requirement. If you choose to include cost-sharing funds as a contribution to the award, we won’t consider it during review of your application. If you receive an award, voluntary cost-sharing funds will be a requirement of your award and you will need to report on them.

Step 1: Review the Opportunity 7

Program description Background Overview Each year, healthcare-associated infections (HAIs) lead to substantial morbidity and mortality. Antimicrobial resistant (AR) pathogens within and outside of healthcare settings, including strains that are resistant to antibiotics used as treatments of last resort, are becoming a significant public health threat. Better understanding of the dynamics of pathogen transmission, within and between U.S. healthcare facilities and in the community, is crucial to informed decision-making and implementation of optimal evidence-based prevention strategies. Early in the coronavirus disease 2019 (COVID-19) pandemic, the importance of transmission of SARS- CoV-2 within nursing homes was recognized and associated with substantial morbidity and mortality.

Pathogen transmission in healthcare settings is complex, often involving multiple human actors (for example, healthcare workers, hospital patients, nursing home residents, visitors) and non-human actors (such as the environment). The most affected groups are people who experience frequent sickness and have immunodeficiencies. Studying transmission processes while accounting for health status of populations can present challenges.

Some populations are also placed at an increased risk for experiencing infection, severe illness, and death from pathogens in healthcare settings because of persistent health disparities in chronic diseases combined with social circumstances (for example, healthcare access) and historic and contemporary structural barriers (such as discriminatory practices).

Eliminating health disparities and advancing health equity are foundational to U.S. Department of Health and Human Services (HHS) Healthy People 2030.

Moreover, a greater understanding of how policies, systems, and environments may influence transmission processes and how intervention effectiveness can be supported across diverse healthcare settings is needed.

Limited resources preclude testing many intervention combinations across all healthcare settings (for example, acute care hospitals, long-term acute care hospitals, nursing homes) or evaluating which combinations are most effective in each setting.

Step 1: Review the Opportunity 8

Modeling the transmission of healthcare-associated or antimicrobial-resistant pathogens creates a virtual laboratory to identify and evaluate drivers of disease or pathogen spread and estimate the relative benefits of multiple prevention strategies in a timely and cost-effective manner. Epidemiological modeling provides insights into how HAI pathogens spread through healthcare settings and the community geospatially, temporally, and through patient transfer and social networks.

Modeling can enable a better understanding of the dynamics of pathogen spread and identify which interventions can maximize prevention and control.

The latter includes pathogen-specific response strategies and intervention bundles which could be effective across a range of organisms. Epidemiologic modeling can enhance the understanding of findings from previous epidemiologic trials, and aid in the design of future epidemiologic trials.

Individual and social behavior contributes greatly to the dynamics of infectious disease emergence and spread, and to public health policy compliance. Modeling can help assess the potential impact of behavioral interventions in different healthcare settings.

This Notice of Funding Opportunity (NOFO) aims to fund innovative and applied transmission modeling activities to expand knowledge and to develop and apply computational tools and mathematical methods to model the spread of healthcare-associated or antimicrobial-resistant pathogens in healthcare and community settings. This includes creating a network of multidisciplinary scientists and public health partners who will conduct computational, statistical, and mathematical work to improve the ability to prepare for, detect, control, and prevent antimicrobial resistant HAI pathogens in the United States. This network will be called the Modeling Infectious Diseases in Healthcare Network (MInD Healthcare Network) and will contribute to generalizable scientific knowledge through dissemination (for example, pre-prints, peer-reviewed studies, scientific presentations) and collaboration between mathematical modelers and public health partners.

Related work This NOFO builds on work from three prior NOFOs: CK17-001, CK20-003, CK22-008.

• Research Gaps in Patient and Healthcare Personnel Safety

• Biggest Threats and Data: 2019 Antibiotic Resistance Threats Report

(CDC)

• National Strategy for Combating Antibiotic Resistant Bacteria

1. Review 2. Get Ready 3. Prepare 4. Learn 5. Submit 6. Award Contacts

Step 1: Review the Opportunity 9 https://grants.gov/search-results-detail/285279 https://grants.gov/search-results-detail/320417 https://grants.gov/search-results-detail/337897 https://www.cdc.gov/healthcare-associated-infections/media/pdfs/DHQPs-Research-Questions-508.pdf https://www.cdc.gov/antimicrobial-resistance/data-research/threats/?CDC_AAref_Val=https://www.cdc.gov/drugresistance/biggest-threats.html https://www.cdc.gov/antimicrobial-resistance/data-research/threats/?CDC_AAref_Val=https://www.cdc.gov/drugresistance/biggest-threats.html https://www.cdc.gov/antimicrobial-resistance/programs/ar-actions-events.html?CDC_AAref_Val=https://www.cdc.gov/drugresistance/us-activities/national-action-plan.html

• U.S. Health and Human Services, National Action Plan to Prevent Healthcare-Associated Infections: Roadmap to Elimination

Go to our abbreviated list of HAI modeling publications.

Purpose For the purposes of this NOFO, “transmission modeling” is defined as statistical, computational, and mathematical methods employed to study the spread of healthcare-associated or antimicrobial-resistant pathogens that account for transmission from infectious to susceptible individuals.

Examples of transmission modeling include but are not limited to computational and mathematical methods to simulate the spread of infectious diseases (for example, compartmental models, agent-based models, network models, machine learning techniques) and statistical methods to quantify pathogen transmissibility (for example, estimate reproduction numbers, describe contacts between people relevant to transmission, conduct phylodynamic analyses).

To appropriately use modeling results, decision-makers must understand the limitations of the data and methods used, the assumptions employed, and how these issues affect the results (through complete model specification of compartmental models or use of the MInD-Healthcare Framework for describing agent-based or individual-based models).

Consequently, critical steps in the modeling process include uncertainty analyses, sensitivity analyses, and model validation, and their clear communication. Such analyses are especially important when there is little or no data to support specific assumptions or parameters.

“Uncertainty analyses” summarize the range of possible outcomes (for example, numbers of cases averted) due to imperfect knowledge about the true model structure (such as natural history of disease, demographics relevant to transmission and disease, risk factors) and parameter values corresponding to the real world. “Sensitivity analyses” quantify which parameters or aspects of the model structure most influence a model’s outcomes.

By identifying influential parameters and structural assumptions, models can be used to support development of research priorities for empiric data collection, thereby improving the value of future modeling analyses. “Model validation” compares model outputs to data not used in the construction of

1. Review 2. Get Ready 3. Prepare 4. Learn 5. Submit 6. Award Contacts

Step 1: Review the Opportunity 10 https://www.hhs.gov/oidp/topics/health-care-associated-infections/hai-action-plan/index.html https://www.hhs.gov/oidp/topics/health-care-associated-infections/hai-action-plan/index.html https://pubmed.ncbi.nlm.nih.gov/32155235/ https://pubmed.ncbi.nlm.nih.gov/32155235/ the model to help ensure that the model performs as expected. “Stochastic process” indexes a family of random variables against some other variable or a set of variables.

If funded, you will use existing or simulated datasets to conduct analyses and build computational models relevant to the MInD Healthcare Network goals.

Award funding may be used for generating simulations. Any anticipated need for collecting primary data to directly inform model calibration or validation throughout the 5-year period of performance should be outlined in your initial application and should not exceed 25% of the proposed annual budget.

Your application should focus on diverse transmission settings relevant to the United States with no more than 20% of each annual award focused on activities in low- and middle-income countries.

Approach Overview The following logic model includes the strategies and activities allowed under this NOFO. It also includes the program’s expected outcomes. The asterisked outcomes are those we expect you to achieve during the five-year period of performance.

Outcomes are the results that you intend to achieve and usually show the intended direction of change, such as increase or decrease.

Not all outcomes apply to all strategies. The table shows how they apply. You will use these outcomes as a guide for developing performance measures.

Step 1: Review the Opportunity 11

Program logic model The logic model shows the strategies and activities of the program along with the outcomes we expect over time. We will require you to report on the asterisked (*) outcomes as follows.

Table: Strategies and outcomes

Strategies and activities Short-term outcomes Intermediate outcomes Long-term outcomes

Strategy 1.

Conduct modeling activities focused on at least three of the thematic areas incorporating at least two healthcare-associated or antimicrobial resistant pathogens.

• Identification of high priority applied public health activities (for example, thematic areas focused on outbreak response, simulations of epidemiologic studies, surveillance), and relevant research questions.*

• Identification of influential modeling parameters (for example, determinants of transmission) and assumptions to develop priorities for future data collection.*

• Determination of groups of people or healthcare settings that have a disproportionate role in transmission, and/

• Improved understanding of the determinants of pathogen transmission and HAI risk, and how this can inform HAI prevention, control, and treatment.*

• Improved understanding of health inequities that contribute to the disproportionate occurrence of HAIs and antimicrobial resistance among groups of people or healthcare settings on whom interventions should be focused.

• Increased dissemination of findings from modeling activities to inform development of guidance for infection prevention and

• Reduced morbidity, mortality, and healthcare costs associated with HAI/AR pathogens because of modeling-informed improvements in patient safety.

• Increased dissemination, citation, translation, and integration of modeling evidence in consensus, evidence-based guidelines or policies for HAI/AR prevention and control.

• Increased knowledge and prevention gains resulting from this program to reduce the morbidity, mortality, and costs associated with HAI pathogens.

Step 1: Review the Opportunity 12

Strategies and activities Short-term outcomes Intermediate outcomes Long-term outcomes or to whom interventions should be prioritized to effectively reduce risk of HAIs.*

• Identification of data needs to improve the applicability of modeling studies for improving patient safety practices in healthcare settings.*

• Improved epidemiologic study design and application driven by novel modeling methodologies to answer high-priority questions related to transmission, risk factors, and other disease-related topics.

• Innovative analytic approaches and translational research to inform HAI and antimicrobial resistance prevention, control, and treatment.*

• Meaningful collaboration with control, particularly in antimicrobial resistant infections.*

• Increased availability of and access to modeling code, tools, and resources on commonly available and preferably non-proprietary platforms (for example, GitHub), consistent with the federal government source code policy.*

• Improved design of surveillance systems and interpretation of surveillance data.

• Documentation of lessons learned from failures of innovative analytic approaches and translational research.*

• Effective working relationships with state, tribal, local, or territorial (STLT), federal, or international public health organizations,

• Improved understanding of the risk factors and mechanisms for transmission of HAI pathogens.

• Improved prioritization and delivery of specific interventions that are most effective in combating HAIs and/or antimicrobial resistant HAIs.

• Improved health equity and decreased disparities among groups of people or healthcare settings that are disproportionately affected by HAIs and antimicrobial resistance, and increased understanding of how interventions can impact health inequities.

1. Review 2. Get Ready 3. Prepare 4. Learn 5. Submit 6. Award Contacts

Step 1: Review the Opportunity 13 https://obamawhitehouse.archives.gov/sites/default/files/omb/memoranda/2016/m_16_21.pdf https://obamawhitehouse.archives.gov/sites/default/files/omb/memoranda/2016/m_16_21.pdf https://obamawhitehouse.archives.gov/sites/default/files/omb/memoranda/2016/m_16_21.pdf https://obamawhitehouse.archives.gov/sites/default/files/omb/memoranda/2016/m_16_21.pdf outcomes Long-term outcomes academic, governmental, and healthcare practice partners to identify modeling topics, strategies, and products to address ongoing public health needs.

healthcare epidemiologists, and clinical partners to identify modeling topics, strategies, and products that address public health problems, and improve public health practice and population health.

Strategy 2.

Provide training and workforce development opportunities centered on public health modeling, including collaboration and embedding with public health partners.

• Recruitment, development, and retention of graduate student (master’s and predoctoral) and postdoctoral public health modelers from diverse academic and sociodemographic backgrounds.*

• Demonstrated capacity and willingness to develop and maintain continuing collaborations with STLT public health partners and within healthcare settings.*

• Increased knowledge among early-career modelers of infection prevention and control, particularly in antimicrobial resistant infections.

• Increased number of early-career modelers (including those who fit the NIH’s definition of “historically underrepresented populations” with expanded knowledge and capability to develop and apply computational tools and mathematical

• Improved knowledge of public health modeling of HAIs, and antimicrobial resistance within the scientific and public health communities.

• Increased diversity in backgrounds and experiences across the public health modeling workforce contributing to varied perspectives in model construction and more representative modeling results.

• Sustained HAI and antimicrobial resistance modeling capacity

1. Review 2. Get Ready 3. Prepare 4. Learn 5. Submit 6. Award Contacts

Step 1: Review the Opportunity 14 https://extramural-diversity.nih.gov/diversity-matters/underrepresented-groups https://extramural-diversity.nih.gov/diversity-matters/underrepresented-groups https://extramural-diversity.nih.gov/diversity-matters/underrepresented-groups https://extramural-diversity.nih.gov/diversity-matters/underrepresented-groups outcomes Long-term outcomes methods modeling the spread of pathogens in healthcare settings to fill critical gaps in workforce capacity and infrastructure.*

• Increased collaborations between professional mathematical modelers across federal and STLT government-funded public health programs, as well as with scientists focused on translational research.*

• Increased collaborations between professional modelers and academic partners focused on collaborative, translational research that leads to the prevention of HAIs and other adverse health outcomes.

for responding to routine and emerging public health threats.

* Indicates outcomes you are required to report on.

Step 1: Review the Opportunity 15

Strategies and activities This section elaborates on the strategies and activities described in the logic model and provides details on how you are expected to implement the

NOFO.

Proposed activities will be required to address the two strategies outlined within this NOFO.

• Strategy 1: You should use the following lists of thematic areas and pathogens of interest to construct your proposed workplans of non-research and research activities. Full workplans should include at least three or more of the following outlined thematic areas and at least two or more of the following identified pathogens. You should include a summary table highlighting selected thematic area(s) and pathogens(s) for each proposed activity.

• Strategy 2: Clearly identify how you will incorporate workforce development and training activities for junior and early-career modeling professionals throughout proposed non-research and research activities in Strategy 1. For this NOFO, junior and early-career modeling professionals include graduate students (master’s and predoctoral) and postdoctoral researchers.

Strategy 1: HAI modeling activities Strategy 1 objectives are to develop and/or apply computational, statistical, and/or mathematical transmission modeling methods focused on three or more of the thematic areas to improve the understanding of major determinants of transmission and/or prevention of two or more healthcare-associated or antimicrobial-resistant pathogens.

The following lists are organized alphabetically and do not reflect any specific ranking or importance.

Choose two or more pathogens with substantial burden and/or clinical significance. This includes, but is not limited to:

• Candida auris: An emerging fungus first identified in 2009 in Japan that presents a serious global health threat. C. auris often does not respond to commonly used antifungal drugs, making infections difficult to treat. C.

auris clinical cases have steadily increased since 2015, growing by 60% in 2020 compared to 2019.

• Clostridioides difficile: Responsible for almost half a million infections and estimated to be associated with approximately 12,800 deaths in the United States annually.

Step 1: Review the Opportunity 16

• Enterobacterales: Enterobacterales with notable resistance mechanisms, such as extended-spectrum beta-lactamase (ESBL)-producing- Enterobacterales and carbapenem-resistant Enterobacterales (CRE), are of special concern because they can be challenging to treat.

• Nontuberculous mycobacteria (NTM): Mycobacteria other than M.

tuberculosis complex (the cause of tuberculosis), M. leprae complex (the cause of leprosy), and M. ulcerans (the cause of Buruli ulcers); the rest of the species are referred to as NTM. Healthcare-associated NTM infections and outbreaks can occur when environmental and infection control factors permit susceptible hosts to be exposed to NTM in the healthcare environment, such as a facility’s water system.

• Resistant Acinetobacter species: Outbreaks of Acinetobacter infections typically occur in intensive care units and healthcare settings housing very ill patients. Acinetobacter infections rarely occur outside of healthcare settings. More than 60% of Acinetobacter infections are resistant to at least three classes of antibiotics.

• Resistant Pseudomonas aeruginosa: Infections with P. aeruginosa are caused by strains of bacteria found widely in the environment.

• Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2):

Responsible for >6,800,000 hospitalizations and >1,180,000 deaths in the United States as of March 2024. It has been associated with substantial morbidity and mortality in long-term care settings. Activities and analyses focused primarily on community-onset SARS-CoV-2 are not sufficient.

• Staphylococcus aureus: S. aureus, including strains with notable resistance mechanisms such as methicillin-resistant S. aureus (MRSA), vancomycin-intermediate S. aureus (VISA), and vancomycin-resistant S.

aureus (VRSA), are of special concern because they can be challenging to treat.

Choose three or more of the following thematic areas for HAI modeling activities. Clearly indicate selections in the application using the following headings/embedded topics:

Antimicrobial Resistance

Each year in the United States, at least 2.8 million people become infected with bacteria that are resistant to antibiotics. Such cases can cause even greater concern than antimicrobial susceptible infections because effective treatment may be delayed, leading to increased morbidity, mortality, and economic costs. Modeling may contribute to better understanding:

• The evolution, sources, and spread of resistance in microbial populations.

Step 1: Review the Opportunity 17

• The dynamics of pathogen spread at the hospital, public health system, and regional levels.

• The patterns of antimicrobial use that have the largest impact on the dissemination of antimicrobial resistant strains.

• The interplay between susceptible and resistant strains.

Modeling can also help evaluate evidence regarding differences in transmission patterns of resistant and susceptible strains, and the effectiveness of interventions to reduce the incidence of antimicrobial resistant infections. This includes: antimicrobial stewardship (a priority aim of Healthy People 2030), rapid testing to identify pathogens and their antimicrobial susceptibility, screening to identify carriers, isolating carriers and/or infected persons, hand hygiene, barrier precautions, vaccination, interventions to reduce the load of organisms colonizing host anatomic sites, such as the skin and gastrointestinal tract (known as “pathogen burden reduction”), and other interventions.

Economic Modeling

Understanding potential changes in disease dynamics, with prevention strategies developed under realistic economic constraints, is vital for designing feasible prevention strategies. Activities may include:

• Estimating the costs and epidemiologic benefits of pathogen-specific or more general response strategies and intervention bundles, which can contribute to decision-making about implementation of prevention strategies.

• Modeling the development and implementation of incentives and penalties from both private and public healthcare payers, which can also provide important policy insights.

• Assessing the burden and cost of intervention strategies and incentive structures, which can aid in their design and implementation. For example:

◦ Adverse outcomes related to inappropriate outpatient antibiotic use and the potential impact of improving antibiotic use, which may be useful for strengthening the case for antibiotic stewardship in ambulatory healthcare settings, and

◦ Economic analyses for different use cases for transmission-based precautions.

1. Review 2. Get Ready 3. Prepare 4. Learn 5. Submit 6. Award Contacts

Step 1: Review the Opportunity 18 https://health.gov/healthypeople/objectives-and-data/browse-objectives/health-care-associated-infections/reduce-inappropriate-antibiotic-use-outpatient-settings-hai-d01

Genomics

Genetic sequences can provide highly detailed information on pathogen relatedness, which can inform transmission inferences when combined with traditional epidemiologic data (for example, demographics, common exposures, dates, locations of patients sampled, healthcare environment sampling, wastewater sampling). Combining these data sources can improve characterization of the spread of pathogens across timescales and multiple geographic levels (for example, within hospitals, across state and other borders). This information can increase our understanding of disease dynamics, including:

• How patient and healthcare worker movement patterns affect disease transmission.

• Patient and environmental factors that affect the risk of transmitting or acquiring HAI and related pathogens.

• The extent of transmission across settings (for example, short-term acute care hospitals, long-term acute care hospitals, nursing homes, wider community) which may not accurately reflect where disease diagnoses occur.

• The role of endogenous versus exogenous flora in healthcare-associated infections and colonization.

Additionally, this information can inform:

• Defining cutoffs for using sequencing analyses to determine which cases are part of an outbreak and are likely associated with a particular transmission pathway.

• Where interventions should be prioritized to interrupt transmission.

• The evolution and emergence of strains of interest (for example, antimicrobial resistant strains, hypervirulent strains), including how such information can be used to better understand and reduce HAI pathogen transmission.

• The fraction of total pathogen transmission that occurs from asymptomatically colonized versus sick individuals.

• The amount of transmission that occurs due to long-lasting environmental contamination (for example, hospital transmission that occurs after an infectious patient has been discharged due to contamination of rooms, sinks, or equipment).

• Calculation of parameters useful for simulations (for example, generation times, serial intervals, reproduction numbers). Read about sequence data from CDC surveillance systems and outbreak investigations.

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Step 1: Review the Opportunity 19 https://www.ncbi.nlm.nih.gov/bioproject/531911 https://www.ncbi.nlm.nih.gov/bioproject/531911

Health Equity

As highlighted through CDC strategy, health equity is achieved when everyone has a fair and just opportunity to attain their highest level of health. This requires addressing injustices and overcoming obstacles to reduce the disproportionate burden of HAIs among populations at increased risk for infection, severe disease, and death.

For example, persistent disparities in chronic diseases (for example, hypertension, obesity, diabetes, asthma) combined with circumstances such as barriers to accessing care, quality education, safe and affordable housing, as well as historic and contemporary structural barriers that have resulted in discriminatory practices and policies, have placed some population groups (for example, racial and ethnic minorities, people who live in rural settings) at higher risk for experiencing infection, severe illness, and death from pathogens in healthcare settings.

This thematic area focuses on use of scientific, innovative, and data-driven intervention strategies and development of mathematical models that address environmental, place-based, occupational, policy, and systemic factors that impact outcomes and address drivers of disparities. Models might include a variety of determinants impacting transmission of healthcare-associated or antimicrobial-resistant pathogens. Those determinants may be at the individual (for example, patient, clinician), healthcare facility (for example, setting, access, quality), community (for example, geography and resources), or national level (for example, national and state policies, historical context, structural inequities).

However, modeling approaches may be limited by the availability of data, and more research is needed to determine if additional populations who are under-studied (people with disability) may also be put at higher risk of experiencing infection.

Healthcare Systems Resilience

Healthcare systems resilience is the cyclical ability of healthcare workers, organizations, or systems to prepare for and prevent expected and unexpected events, absorb and adapt to maintain structure and function during an event, and to recover and review from crises, shocks, or stressors.

Long-term Care Facilities

It is estimated that one to three million serious infections occur every year in nursing homes, skilled nursing facilities, and assisted living facilities. Recent data suggest that prevalence of AR pathogen colonization is high in these

1. Review 2. Get Ready 3. Prepare 4. Learn 5. Submit 6. Award Contacts

Step 1: Review the Opportunity 20 https://www.cdc.gov/health-equity/ settings, and that transmission in such facilities may play an important role in amplifying and sustaining spread of multidrug-resistant organisms (MDROs).

This area focuses on understanding the transmission dynamics within facilities across the post-acute care spectrum, including in long-term acute care hospitals and long-term care facilities (for example, skilled nursing facilities, assisted living facilities, rehabilitation centers). Of particular interest is the role of high-acuity skilled nursing facilities that provide ventilator services in facilitating spread of MDROs, and the role of nursing homes in broader MDRO transmission dynamics across regions.

One Health

Although this announcement focuses on healthcare-associated human disease, animals can act as reservoirs, hosts, and vectors of infectious agents for humans. For example, animals could serve as a source of novel strains of antibiotic-resistant bacteria and other pathogens that often cause HAIs.

Studies of animal populations should focus on issues or modeling approaches directly relevant to the colonization and/or infection of people in the communities surrounding healthcare facilities and those within healthcare facilities. Examples of relevant modeling activities include analyses of genetic data to assess the role of transmission from animals to humans in the emergence of novel strains of HAI pathogens that spread widely among humans.

Outbreak Response

Correctly identifying and characterizing an outbreak is a critical step to public health response. The speed and accuracy of a response (for example, provision of antimicrobials, contact tracing, isolation) may determine whether an outbreak will be contained.

Technological advances in disease detection will continue to provide considerable data, and statistical and modeling methods are needed to search for, and characterize, signals that reliably identify outbreaks in a timely manner in the presence of highly variable background information. These issues are relevant at many levels of organization, from individual hospitals, to communities, metropolitan areas, and countries.

Additionally, modeling can help refine approaches for limiting the spread of new or rare forms of antibiotic resistance by detecting threats early and with sufficient specificity (for example, regional prevention strategies).

1. Review 2. Get Ready 3. Prepare 4. Learn 5. Submit 6. Award Contacts

Step 1: Review the Opportunity 21 https://www.cdc.gov/healthcare-associated-infections/php/preventing-mdros/mdro-containment-strategy.html?CDC_AAref_Val=https://www.cdc.gov/hai/mdro-guides/containment-strategy.html

Simulations of Epidemiologic Studies

Healthcare-associated and antimicrobial-resistant pathogens are difficult to study using standard epidemiologic methods. For example, colonization often does not lead to clinical symptoms and therefore is not captured in electronic health records or standard surveillance systems, making it difficult to identify risk factors for transmission or acquisition of HAI pathogens and to design effective public health intervention strategies to reduce their burden.

There can also be strong confounding (for example, case-fatality rates can be biased because very ill people are susceptible to acquiring an HAI pathogen and already have an elevated risk of death) and selection bias (for example, healthier people are discharged from the hospital more quickly than very ill people, thereby reducing their risk of acquiring an HAI pathogen and potentially creating informative censoring; health status could also affect the duration of colonization and/or the risk of progressing to disease; durations of colonization can be both left-censored and right censored).

Simulations can investigate alternative ways to design data collection and/or conduct analyses with goals that include obtaining more reliable estimates of the following:

• Risk factors for colonization and/or infection.

• Understanding the natural history of HAIs, including:

◦ Case-fatality rates

◦ Incubation periods

◦ Generation times

◦ Asymptomatically colonized individuals

◦ Symptomatic individuals

• Effectiveness of individual-level, facility-level and/or regional interventions.

• Estimating unintended consequences of intervention strategies.

• Attributable proportion of total transmission due to environmental contamination.

• Healthcare worker interactions.

• Direct patient-to-patient contact.

• The ability to identify the appropriate model structure for a pathogen’s transmission and accurately estimate relevant model parameter values.

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Surveillance

We can improve the effectiveness of public health interventions if we have an accurate understanding of the overall incidence and burden of disease, groups at high risk of infection, primary modes of transmission, and settings in which transmission commonly occurs.

Burden of disease estimates also help in setting public health priorities and gauging progress in the control of nosocomial pathogens. Consequently, accurate and efficient surveillance systems are crucial to combating the spread of HAI pathogens and AR pathogens.

Modeling can help identify efficient strategies for designing or informing public health surveillance and estimating the incidence or prevalence of carriage and the disease burden of established pathogens. The early identification and accurate threat assessment of emerging nosocomial infections, including their transmissibility, case fatality rates, average numbers of transmissions and infections that occur for each clinical case identified, is important.

Transmission Dynamics

Pathogens that cause HAIs, and AR pathogens in particular, can be acquired anywhere healthcare is delivered, such as nursing homes (see Thematic Area:

Long-term care facilities) and outpatient settings (for example, clinics, ambulatory surgical centers, end-stage renal disease facilities). Patients may have interactions with other patients, healthcare workers, and the environment that could result in transmission of infectious diseases.

Additionally, AR pathogens may circulate in the community and HAIs may be introduced into the community once a patient leaves the healthcare setting, which complicates prioritizing interventions to reduce pathogen burden.

Studying where infection onset occurs may not be the best way to understand where transmission is occurring; there are often long delays between acquisition of a pathogen and detection of colonization or infection, and patients may have accessed healthcare at multiple locations during this period.

Application of infectious disease modeling within and among healthcare facilities and the surrounding community focusing on transmission dynamics and pathogen burden reduction interventions can contribute to understanding how the movement of healthcare workers and patients, their interactions with the healthcare environment, and their interactions with the surrounding community affect HAI pathogen transmission dynamics and how to most effectively interrupt transmission.

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Such modeling activities should provide actionable information to help public health decision-making at the levels of federal, state, or local health departments and within individual facilities.

Strategy 2: Training and workforce development opportunities The purpose of Strategy 2 is to provide training and workforce development opportunities centered on public health modeling and including collaboration and embedding with public health partners.

This is intended to increase the number of junior and early-career modeling professionals from diverse academic, sociodemographic, and/or underrepresented backgrounds who are trained and experienced in modeling transmission of pathogens in healthcare settings at the local, state, and national levels of applied public health.

This is particularly important to ensure diverse perspectives are represented in model construction, application, and dissemination to address disparities in HAI and antimicrobial-resistant HAI burden, risk factors, transmission, and intervention development.

• Proposed activities should support the training and professional development of graduate students (master’s and predoctoral) and postdoctoral researchers in modeling the spread of pathogens in healthcare settings.

• Your application should include plans for internships or collaborations for early career modelers in at least one of the following settings of applied mathematical modeling: state, tribal, local, territorial (STLT), federal, international public health institutions, hospitals, and long-term care facilities.

Outcomes This section includes outcomes you are expected to report progress on and achieve within the performance period.

Strategy 1 The anticipated outcomes of the proposed projects for Strategy 1 include the following:

• Identification of high priority applied public health activities (for example, thematic areas focused on outbreak response, simulations of epidemiologic studies, surveillance), and relevant research questions.

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• Identification of influential modeling parameters (for example, determinants of transmission) and assumptions to develop priorities for future data collection.

• Determination of groups of people or healthcare settings that have a disproportionate role in transmission, and/or to whom interventions should be prioritized to effectively reduce risk of HAIs.*

• Identification of data needs to improve the applicability of modeling studies to improving patient safety practices in healthcare settings.

• Innovative analytic approaches and translational research to inform HAI and antimicrobial resistance prevention, control, and treatment.*

• Improved understanding of the determinants of pathogen transmission and HAI risk, and how this can inform HAI prevention, control, and treatment.*

• Increased dissemination of findings from modeling activities to inform development of guidance for infection prevention and control, particularly in antimicrobial resistant infections.*

• Increased availability of and access to modeling code, tools, and resources on commonly available and preferably non-proprietary platforms (for example, GitHub), consistent with the federal government source code policy.*

• Documentation of lessons learned from failures of innovative analytic approaches and translational research.*

Strategy 2 The anticipated outcomes of the proposed projects for Strategy 2 include the following:

• Recruitment, development, and retention of graduate student (master’s and predoctoral) and postdoctoral public health modelers from diverse academic and sociodemographic backgrounds.*

• Demonstrated capacity and willingness to develop and maintain continuing collaborations with STLT public health partners and within healthcare settings.*

• Increased number of early-career modelers (including those who fit the NIH’s definition of “historically underrepresented populations” ) with expanded knowledge and capability to develop and apply computational tools and mathematical methods modeling the spread of pathogens in healthcare settings to fill critical gaps in workforce capacity and infrastructure.*

1. Review 2. Get Ready 3. Prepare 4. Learn 5. Submit 6. Award Contacts

Step 1: Review the Opportunity 25 https://obamawhitehouse.archives.gov/sites/default/files/omb/memoranda/2016/m_16_21.pdf

• Increased collaborations between professional mathematical modelers across federal and STLT government-funded public health programs.*

Focus populations Modeling activities proposed under this NOFO should prioritize populations at risk for HAIs, including, but not limited to, pediatrics, persons admitted to hospitals or long-term care facilities, and persons receiving care in ambulatory settings.

Your application should focus on transmission settings relevant to the United States with no more than 20% of each annual award focused on activities in low- and middle-income countries.

Equal opportunities This NOFO, including funding and eligibility, is not limited based on, and does not discriminate on the basis of race, color, national origin, disability, age, sex (including gender identity, sexual orientation, and pregnancy) or other constitutionally protected statuses.

Health disparities The goal of health equity is for everyone to have a fair and just opportunity to attain their highest level of health. Health disparities are often caused by social determinants that influence which populations are most disproportionately affected by health conditions.

A health disparity is a difference in health burdens between groups of people with differing social determinants of health.

Social determinants of health are conditions in the environments where people are born, live, learn, work, play, worship, and age. These determinants affect a wide range of health, functioning, and quality-of-life outcomes and risks.

You should consider using data, including social determinants data, to identify communities that are disproportionately affected by infectious diseases, since experiencing chronic disease, injuries, and other conditions or events can increase their risk of infection with a healthcare-associated or antimicrobial-resistant pathogen. Identify activities to help eliminate identified health disparities. In collaboration with partners and appropriate community sectors, you should consider social determinants of health in the development, implementation, and evaluation of specific efforts and use culturally appropriate interventions and strategies that are tailored for their intended communities.

1. Review 2. Get Ready 3. Prepare 4. Learn 5. Submit 6. Award Contacts

Step 1: Review the Opportunity 26 https://www.cdc.gov/about/priorities/why-is-addressing-sdoh-important.html

Organizational capacity Describe how you will address the organizational capacity requirements. You should have expertise in:

• The dynamics of HAIs, antimicrobial resistance, and/or methodological expertise that is relevant and scientifically appropriate for addressing the prevention of HAIs or related antimicrobial resistance.

• Infectious disease transmission modeling as evidenced by a significant track record of high-quality communication products such as peer reviewed publications.

In addition, you should have the capacity to:

• Define modeling questions, generate hypotheses, conduct literature searches, determine needed data inputs, construct models individually and in collaboration with other recipients, disseminate findings, and evaluate the impact of their work.

• Access sufficient institutional support, equipment and other physical resources, and computational resources to perform the planned transmission modeling activities.

• Work effectively with STLT, federal, or international public health organizations; healthcare…