What to know
The Healthcare, Infectious Diseases, Research (HIRe) Modeling Fellowship builds mathematical modeling workforce capacity to support infectious disease and healthcare research.
Background
The American Rescue Plan Act (ARP) of 2021 invests significant resources in the healthcare and public health workforce. In alignment with this initiative, CDC's Division of Healthcare Quality Promotion (DHQP) Epidemiology, Research, and Innovations Branch (ERIB) has funded six institutions to enhance healthcare-associated infections (HAIs) modeling research capabilities within the public health workforce.
The partnering institutions will support pre-doctoral fellows' innovative research to develop and apply computational tools and mathematical methods for modeling the spread of pathogens in healthcare settings (e.g., SARS-CoV-2, HAIs, antimicrobial-resistant organisms [AROs]). This investment will expedite public health workforce improvements by focusing on expanding the number of highly qualified researchers generating research products and innovative analyses as a part of their training and enhancing the use of modeling to improve healthcare outcomes.
Current awardees
$1.7 million
For FY 2022-2025, approximately $1.7 million has been awarded to six recipients
Children's Hospital of Philadelphia
Characterizing dynamics of a pandemic and preparing for speedy and accurate response
Research aims
- Develop more precise models that are robust to estimation errors through modeling reporting patterns in case counts, and by allowing heterogeneity of the infectiousness function among the population.
- Develop models that permit a comparative analysis of transmissibility and virulence between variants of concern during the course of the pandemic.
- Develop a model to examine the variation in predictive disease transmission across counties in relationship to virus evolution (regional genomic characteristics), population immunity, public behavior, health policy decisions and population immunity, and to evaluate the mitigation effect of public health interventions.
Training goals
- Training on modeling of infectious disease, biology, computational methods, dissemination of results, and effective communication with policy and media to accurately understand the insights of scientific results.
University of Florida
Early warning, analysis and prediction of infectious disease outbreaks in healthcare centers and communities
Research aims
- Develop a transparent sequential learning algorithm for spatio-temporal disease surveillance and early detection of disease clusters.
- Develop a competing risks modeling framework for transmission dynamics of antimicrobial-resistant and antimicrobial-susceptible pathogens at the individual level in healthcare centers and at the population level in communities.
- Develop an agent-based model to assess 1) effectiveness of strategies combining early detection, antimicrobial intervention and patient management on containing both antimicrobial-sensitive and antimicrobial-resistant pathogens; and 2) optimal control strategies for vaccine-preventable infectious diseases.
Training goals
- Build fundamental knowledge base in statistical inference and computation, machine learning, epidemiology, mathematical and statistical modeling of infectious diseases, and phylodynamics.
- Develop skills in literature review, synthesis and quality control of data streams, and communication and presentation of scientific data and ideas.
- Develop both independent and collaborative research capacities in infectious disease modeling; learn how to interpret results and improve methods; understand the importance of responsible conduct of biomedical research.
- Develop writing skills for manuscripts and grant proposals as well as presentation and teaching skills; Be actively engaged in dissemination of research achievements.
University of Missouri - Kansas City
Midwest Virtual Laboratory of Pathogen Transmission in Healthcare Settings (MVL-PATHS)
Research aims
- Improve understanding of source, distribution and spread of Antimicrobial Resistant (AMR) Enterobacteriaceae.
- Use real-time contact and movement data to improve modeling and simulation of AMR pathogen transmission by asymptomatic spreaders and contaminated medical devices.
- Use SARS-CoV-2 pandemic data to advance health equity in nursing homes.
Training goals
- Enhance mathematical and computational modeling research capabilities of the public health workforce.
- Increase the number of junior modeling professionals that are trained and experienced in modeling transmission of pathogens in healthcare settings partly incorporated with health disparities.
University of North Carolina - Charlotte
Building Next-generation Mathematical Biology Modeling Workforce for HAI Control
Research aims
- Provide a systematic overview and quantification of multiple sources of heterogeneity and uncertainty, most importantly spatial, temporal, and individual heterogeneity, that occur in HAI systems.
- Provide a novel, universally designed modeling framework that 1) links within- and between-host dynamics, 2) integrates pathogen, hosts, and environment into one unified system, and 3) works across multiple scales from pathogen subpopulations to among healthcare facilities (HCFs).
- Explore data from multiple sources to create an integrative modeling framework to optimize HCF operation.
Training goals
- Prepare and transform our next-generation modelers with a deeper understanding of HAI and AR challenges, extensive knowledge about various heterogeneities and uncertainties in the complex HAI system.
- Provide a wide range of innovative and effective mathematical modeling techniques to increase the capacity of HAI and health research.
University of Utah
TRANSMIT: Training Research Acumen iN Students Modeling Infectious Threats
Research aims
- Quantify the phylogenetic relationships between community and long-term care facility SARS-CoV-2 lineages to understand the viral diversification attributable to healthcare settings.
- Identify the characteristics of the sub-population disproportionately impacted by co-infections with multi-drug resistant organisms (MDROs) for patients hospitalized with SARS-CoV-2.
- Evolution of antimicrobial resistance due to variable dose and off-target antibiotic use.
Training goals
- To train three pre-doctoral fellows to develop and apply novel mathematical, statistical, and computational methods to model the spread of key pathogens in healthcare settings including SARS-CoV-2 and MDROs.
- To train fellows in communicating models, their results, and the underlying uncertainty to key public health stakeholders.
- To develop and strengthen collaborations and improve modeling capacity at the state and local health departments in Utah.
Washington State University
Healthcare Modeling Workforce Development Through Washington State University's Resistance Epidemiology Modeling Initiative
Research aims
- Pursue both foundational and applied modeling results studying nosocomial amplification, specifically as it applies to the interaction between the healthcare system and the community in the face of a novel pathogen, such as SARS-CoV-2, as a motivating example but not an exclusive one.
- Assess the generalizability of existing mathematical and computational models of healthcare epidemiology to rural and community settings, as well as to contribute to the available parameter, models, and best practices involved in modeling infections in this setting.
- Develop rigorous statistical and/or machine learning methods to quantify, evaluate and simulate within- and between-hospital patient transfer networks, as well as make the results of this research available to a broader research audience.
Training goals
- To address questions surrounding how emerging pathogens can propagate from the community into the healthcare system, be amplified there, and then be sent back into the community. Identifying and evaluating what interventions are most impactful and where in the healthcare system they are best implemented.
- To assess how well our current evidence base applies to rural healthcare settings – and what steps might be taken to improve this, as well as how rural hospitals may best assess their own data to make evidence-based decisions in their specific context. To explore potentially unique features of rural healthcare, as it impacts both rural hospitals and the healthcare system as a whole, including relatively frequent long-distance patient transfers, a sicker catchment population, and frequent interactions across administrative public health boundaries.
- Estimation and simulation of transfer networks (a research question whose methods are undergoing convergent evolution in statistics and computer science), as well as having developed a library of networks and set of software tools to make analyzing healthcare epidemiology-related networks more broadly accessible to the network science community.