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Optimized City Operations in the Face of COVID-19: A Hybrid Complex Network Theoretic-Machine Learning Approach

Grant number: unknown

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Key facts

  • Disease

    COVID-19

  • Start & end year

    N/A

  • Funder

    Roche Holding AG (Roche)

  • Principal Investigator

    Wael El-Dakhakhni

  • Research Location

    Canada

  • Lead Research Institution

    McMaster University

  • Research Priority Alignment

    N/A

  • Research Category

    Secondary impacts of disease, response & control measures

  • Research Subcategory

    Economic impacts

  • Special Interest Tags

    N/A

  • Study Type

    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

In this project, a novel City Dynamic Network Analysis (CityDNA) model will be developed to generate optimal city operation schemes in the face of COVID-19. By leveraging state-of-the-art techniques in network science, machine learning, systems analysis, and multi-objective optimization, the proposed CityDNA model will provide a unique decision support tool for optimizing the operation and/or reopening of municipal facilities (e.g. transit systems, parks, community centers, and schools) under complex constraints due to current and future COVID-19 pandemics. This project intends to provide innovative solutions to mitigate the risk of lasting economic and social damages caused by COVID-19 in cities across Canada.