MOdeling Nursing homes to Affect Response to COVID-19 (MONARC)

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

Grant number: 5R01HS028165-02

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Key facts

  • Disease

    COVID-19
  • Start & end year

    2020
    2023
  • Known Financial Commitments (USD)

    $493,471
  • Funder

    National Institutes of Health (NIH)
  • Principal Investigator

    PROFESSOR Bruce Lee
  • Research Location

    United States of America
  • Lead Research Institution

    GRADUATE SCHOOL OF PUBLIC HEALTH AND HEALTH POLICY
  • Research Priority Alignment

    N/A
  • Research Category

    Epidemiological studies

  • Research Subcategory

    Impact/ effectiveness of control measures

  • Special Interest Tags

    N/A

  • Study Type

    Clinical

  • Clinical Trial Details

    Not applicable

  • Broad Policy Alignment

    Pending

  • Age Group

    Adults (18 and older)Older adults (65 and older)

  • Vulnerable Population

    Unspecified

  • Occupations of Interest

    Health Personnel

Abstract

PROJECT SUMMARY ABSTRACT Nursing homes have been hit particularly hard by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic. NHs may serve as epicenters of transmission that could continue to help fuel the overall pandemic because they are critical parts of complex, interconnected networks of health facilities in a region. However, determining how best to prevent/control the transmission of SARS-CoV-2 in nursing homes can be challenging. A NH itself is a complex system, consisting of NH residents/staff/visitors that mix with each other in different ways throughout a given day. Since NHs and the ecosystems that they sit within are complex systems, computational modeling that integrates economic, operational, and epidemiologic aspects of SARS- CoV-2 can provide decision makers with important insights on how best to prevent the spread of SARS-CoV-2 within NHs and throughout the surrounding region. The overall goal of this proposed project, MOdeling Nursing homes to Affect Response to COVID-19 (MONARC), is to develop agent-based models (ABMs) of the 70 NHs in OC and use these models to help design and evaluate various SARS-CoV2 policies and interventions (e.g., screening, testing, and cohorting strategies for NH residents, NH staff and visitors). Furthermore, the project will develop a new computational tool that NH administrators and public health officials and policymakers in other regions can then use to build models of their NHs to use to make decisions about COVID-19 prevention and response. This project will be led by two seasoned investigators and their teams who have worked together for over a decade on developing ABMs to prevent/control the spread of infectious diseases in healthcare facilities. Since 2007, this has included helping decision makers address nearly every major infectious disease threat to the U.S., including being embedded in Health and Human Services (HHS) during the 2009 H1N1 epidemic. This project will be a natural extension of our past projects and our current COVID-19 coronavirus modeling work. Specific Aim 1 will develop ABMs of the 70 NHs in OC to evaluate the impact of different SARS-CoV-2 symptom screening and COVID-19 testing strategies such as the timing, frequency, and test types. Specific Aim 2 will explore the value of various strategies to cohort COVID-19-positive NH residents and the staff who care for them, within and across different NHs. Specific Aim 3 will develop a computational tool that can simultaneously evaluate symptom screening, testing, and cohorting strategies to address COVID-19 in NHs, accounting for local prevalence, facility size, and adherence to infection prevention standards. The MONARC project will bring multiple innovations including: 1) addressing urgent but currently unaddressed questions about what NHs can do to prevent/control the spread of SARS-CoV-2, 2) determining how SARS-CoV-2 prevention and control strategies should be tailored by different NHs and NH resident and staff characteristics and 3) developing a computational tool that NHs can use to help determine the best strategies in response to SARS-CoV-2.