Modeling Infectious Diseases in Healthcare Network (MInD - Healthcare)

Supporting Research that Models the Spread of Healthcare-Associated Infections (HAIs) and Antibiotic Resistant (AR) Infections

Healthcare-associated infections (HAIs) are linked with high rates of disease and death. In the last few decades, HAIs, including those that are increasingly resistant to antibiotics, are becoming much more common and a significant public health threat.

In order to develop evidence-based prevention strategies, we must understand how HAIs are transmitted within healthcare facilities and in the community. MInD-Healthcare will support innovative transmission modeling research to expand our knowledge of what drives the spread of HAIs and estimate the benefits of preventive measures.  The 6 newly awarded sites will work collaboratively to increase our understanding and response to HAIs and antibiotic-resistant (AR) infections in the US. Each site has also received supplemental funding to conduct additional work to support the Coronavirus disease 2019 (COVID-19) response efforts. Additional information concerning COVID-19 modeling can be found here (https://www.cdc.gov/coronavirus/2019-ncov/covid-data/mathematical-modeling.html.

What is CDC doing to better understand the transmission of HAIs and assess prevention strategies?

CDC funds innovative research to:

  1. Develop tools and methods to understand the spread of HAIs and AR infections
  2. Predicting outbreaks and burden trends of HAI pathogens
  3. Assess the potential effects of intervention strategies

Researchers and public health workers: Explore the projects below to learn about each recipients’ research project.

Policymakers: Consider using this research to inform the development of relevant evidence-based policy.

Awardees:

Over $20 million for a five-year period has been awarded to six recipients:

Center for Disease Dynamics, Economics, and Policy (CDDEP)

In silico Randomized Control Trial Framework for Assessing Infection Control and Prevention Interventions in the Hospital

AR and HAI aims:

  • Provide decision-support system using electronic health record data to rapidly assess patients at risk of colonization and infection in the hospital
  • Develop a within-hospital model of transmission of multi-drug resistant organisms for assessing the impact of interventions
  • Evaluate the potential effectiveness of infection prevention and control interventions including randomized controlled trials at the individual, hospital unit, and hospital levels

COVID-19 supplement aims:

  • Provide a clinical COVID-19 decision support tool to aid disposition decision-making by emergency department clinicians
  • Support resource allocation optimization of intensive care unit (ICU) beds across a hospital system using predicted infection levels
  • Forecast COVID-19 cases, deaths, and hospitalizations and assess policy choices regarding both pharmaceutical and non-pharmaceutical interventions

Columbia University

Interference, Forecasting and Optimal Control of HAIs

AR and HAI aims:

  • Infer key epidemiological parameters and asymptomatic carriage probabilities using sparse observations
  • Predict outbreaks of HAI pathogens in healthcare facilities
  • Evaluate and optimize intervention combinations against HAIs cost and logistical constraints

COVID-19 supplement aims:

  • Build in-hospital COVID-19 networks, identify bacterial transmission events, and analyze resource use patterns
  • Estimate the risk and spread of bacterial infection
  • Assess the impact of altering COVID-19 patient care on SARS-CoV-2 and bacterial infection risk and healthcare resources

North Carolina State University

Multi-scale Modeling and Phylodynamics for HAIs

AR and HAI aims:

  • Develop improved approaches for inferring routes of acquisition of HAIs and optimizing HAI surveillance and control
  • Develop and apply methods to explore the fitness effects of antibiotic-resistant traits on pathogen phylogenies and speed the methods to quantify fitness for large numbers of strains
  • Develop both agent- and equation-based models that account for multi-scale dynamics of resistance transmission to evaluate interventions to mitigate antibiotic resistance

COVID-19 supplement aims:

  • Quantify the impact of COVID-19 infections on healthcare burden and resources including the co-incidence of bacterial and fungal infections
  • Project hospitalizations and healthcare use in a region by developing and implementing zip code level, risk-structured compartmental models of COVID-19 transmission
  • Project the effects of varying patient management and flows in healthcare delivery by developing and implementing microsimulation models of healthcare resources and patient flows and agent-based models of HAIs to evaluate interactions between COVID-19 and HAIs

University of Iowa

Contact Network Transmission Modeling of HAIs

AR and HAI aims:

  • Develop and validate longitudinal models of HAI risk that incorporate the individual patient’s history prior to hospitalization, including community and healthcare exposures. Assess the effects of latent HAI transmission and transmission by asymptomatic carriers.
  • Develop and validate fine-grained models of HAI transmission that overlays all aspects of hospital operations on top of a detailed spatial model of the hospital, and evaluate the effectiveness of non-pharmaceutical interventions through agent-based simulations that are sufficiently fine-grained to demonstrate emergent behaviors such as combinatorial cascades
  • Develop and validate statistical and mathematical models of regional HAI transmission that reflect the underlying patterns observed in practice and creating publicly available tools to guide surveillance and effective regional outbreak response

COVID-19 supplement aims:

  • Modeling and simulation to explore and quantify the impact of non-pharmaceutical interventions specifically developed for COVID-19 in healthcare settings
  • Applying deep learning methods to forecasting demand on ICU beds based on facility operational features, patient demographics, and diagnostic test results
  • Developing, prototyping, implementing and deploying new technology to measure the acquisition, natural history, and transmission of COVID-19 in healthcare facilities

University of Utah

Modeling and Simulation to Support Epidemiological Decision-Making in Healthcare Settings

AR and HAI aims:

  • Evaluate intervention strategies to reduce transmission of antibiotic-resistant bacteria across different healthcare settings
  • Use evolutionary models to examine how antibiotics select for multiply resistant bacteria
  • Predict trends in antibiotic resistance using population-level data on antibiotic use

COVID-19 supplement aims:

  • Forecast non-ICU and ICU bed demand for patients with COVID-19 infections.
  • Estimate the frequency of transmission of SARS-CoV-2 between nursing home staff and residents.
  • Evaluate strategies for regional control of COVID-19 that account for spread within and between healthcare facilities

University of California San Francisco

Modeling of Infectious Network Dynamics for Surveillance, Control and Prevention Enhancement (MINDSCAPE)

AR and HAI aims:

  • Calculate the patient-specific risk of acquiring or transmitting a HAI
  • Prevent invasive methicillin-resistant Staphylococcus aureus (MRSA) infections by developed targeted algorithms for screening and decolonization
  • Control the spread of Clostridioides difficile infections (CDI) by pre-emptive assessment of need for contact precautions and enhanced environmental decontamination

COVID-19 supplement aims:

  • Produce generalizable tools for COVID-19 patients that provide real-time predictions of the probability of severe disease
  • Develop a microsimulation platform to evaluate the effectiveness of surveillance, screening and preventive measures for reducing transmission of SARS-CoV-2 in the workplace
  • Determine how changes in personal protective equipment, environmental decontamination, and patient cohorting policies impact the risk of secondary hospital-acquired infections for SARS-CoV-2 patients

These investments are part of CDC’s Antibiotic Resistance Solutions Initiative and empower the nation to combat AR and the threat it brings to people, modern medicine, and to the healthcare, veterinary, and agriculture industries. These investments work toward meeting national goals to prevent drug-resistant infections. Learn more about CDC’s ongoing innovative work to protect Americans from AR threats in the AR Investment Map (Fiscal Year 2019).