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Spatial Modeling of Covid-19: Optimizing PDE and Metapopulation Models for Prediction and Spread Mitigation

Grant number: unknown

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Key facts

  • Disease

    COVID-19

  • start year

    -99

  • Known Financial Commitments (USD)

    $0

  • Funder

    C3.ai DTI

  • Principal Investigator

    Zoi Rapti, Yannis Kevrekidis, Panayotis G Kevrekidis,Kontou, Eleftheria
    …

  • Research Location

    United States of America

  • Lead Research Institution

    University of Illinois, Johns Hopkins University, University of Massachusetts

  • Research Priority Alignment

    N/A

  • Research Category

    Epidemiological studies

  • Research Subcategory

    Disease transmission dynamics

  • Special Interest Tags

    N/A

  • Study Type

    Unspecified

  • Clinical Trial Details

    N/A

  • Broad Policy Alignment

    Pending

  • Age Group

    Not Applicable

  • Vulnerable Population

    Not applicable

  • Occupations of Interest

    Not applicable

Abstract

This research proposal offers a comprehensive approach to the spatial dynamics of COVID-19 based on partial differential equation and metapopulation models. We aim to fill the modeling gap between studies with a detailed description of the disease dynamics, but lacking spatial dynamics, and those that while spatial in nature, do not account for the intricacies of the COVID-19 disease. We will use a diverse array of techniques ranging from dynamic traffic assignment models, which will inform the links in the metapopulation model, to manifold learning techniques for model parameterization. We will fully utilize the C3.ai platform to manage and integrate data, implement code, and build a user interface to increase research outcome accessibility.