Regional Healthcare Ecosystem Analyst (RHEA) Modeling the Environment (MODE): SARS-CoV-2

  • Funded by National Institutes of Health (NIH)
  • Total publications:0 publications

Grant number: unknown

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Key facts

  • Disease

    COVID-19
  • Start & end year

    2020
    2024
  • Known Financial Commitments (USD)

    $322,626
  • Funder

    National Institutes of Health (NIH)
  • Principle Investigator

    Pending
  • Research Location

    United States of America, Americas
  • Lead Research Institution

    City University of New York
  • Research Category

    Infection prevention and control

  • Research Subcategory

    Barriers, PPE, environmental, animal and vector control measures

  • Special Interest Tags

    Gender

  • Study Subject

    Non-Clinical

  • Clinical Trial Details

    N/A

  • Broad Policy Alignment

    Pending

  • Age Group

    Not Applicable

  • Vulnerable Population

    Not applicable

  • Occupations of Interest

    Not applicable

Abstract

PROJECT SUMMARY ABSTRACTWith the ongoing COVID-19 coronavirus pandemic, the potential environmental transmission of severeacute respiratory syndrome coronavirus 2 (SARS-CoV-2) is of significant concern, especially inhospitals. Choosing and coordinating the right approaches (e.g., environmental cleaning and monitoring,airflow regulation) in the complex hospital environment can be challenging, given frequent patient and staffturnover, limited resources, and the potential rapid spread of SARS-CoV-2. Further, developing newapproaches requires guidance for design and implementation. Computational modeling with economic,operational, and epidemiologic components can assess the value of approaches with various features andefficacies to guide design and implementation in complex systems. Our Regional Healthcare EcosystemAnalyst (RHEA) Modeling the Environment (RHEA-MODE) project already will be developing agent-basedmodels (ABMs) to help better understand and prevent the environmental transmission of methicillin-resistantStaphylococcus aureus (MRSA) and vancomycin-resistant enterococci (VRE), two pathogens that commonlycause healthcare-associated infections (HAIs). This offers a key opportunity to ask and answer similarquestions about SARS-CoV-2. Therefore, the goal of this proposed RHEA-MODE: SARS-CoV-2supplemental project is to develop ABMs of hospitals to help better understand the role of the hospitalenvironment and environmental cleaning and monitoring methods in preventing and controlling thespread of SARS-CoV-2. While there may be some similarities with MRSA and VRE, the characteristics (e.g.,contact, air transmission) and consequences (e.g., various COVID-19 outcomes) of SARS-CoV-2 are different,requiring different representations in the ABMs. The virus also requires different interventions (e.g., N95 maskuse) and potentially different environmental cleaning (e.g., more aggressive standard disinfectant use, newprocedures like ultraviolet light irradiation, air filtering) and monitoring (e.g., checking compliance with cleaningprotocols and for the presence of virus in the air and on surfaces). Our team is led by Bruce Y. Lee, MD MBA,who has been part of the Models of Infectious Disease Agent Study (MIDAS) network for over 12 years andhas over two decades of experience in industry and academia leading large mathematical and computationalmodeling projects to better understand, prevent, and control infectious diseases, including being embedded inthe U.S. Department of Health Human Services during the H1N1 flu pandemic to assist the national response.Specific Aim 1 for this project will develop detailed computational representations of sample hospitals andtheir environments and determine the role of the hospital environment in the transmission of SARS-CoV-2under various conditions and circumstances. Specific Aim 2 will explore how various environmental cleaningand monitoring products, methods, approaches, and strategies can reduce SARS-CoV-2 transmission, spread,and associated health and economic outcomes based upon the simulation models from Aim 1.