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Predicting COVID-19 symptoms and outcomes before infection, in a precise and personalized way

Grant number: unknown

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Key facts

  • Disease

    COVID-19

  • start year

    -99

  • Known Financial Commitments (USD)

    $0

  • Funder

    RIKEN

  • Principal Investigator

    N/A

  • Research Location

    Japan

  • Lead Research Institution

    N/A

  • Research Priority Alignment

    N/A

  • Research Category

    Clinical characterisation and management

  • Research Subcategory

    Prognostic factors for disease severity

  • 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

Even after the first wave of COVID-19 has passed, it is likely that we will face a second and third wave. But during the subsequent waves, it would be desirable to have a way to avoid economically crippling restrictions on going out. One way to allow normal daily life to continue during subsequent waves is to make precise, personalized predictions of symptoms and outcomes before infection, allowing us to know what makes some people particularly vulnerable. Such predictions could also save the lives of high-risk people by allowing us to provide them with preemptive care. Scientists at the RIKEN Medical Sciences Innovation Hub Program (MIH) are rising to this challenge. They have developed novel technologies for a precise, personalized prediction through a method known as "deep phenotyping" using "information geometry." They are hopeful these technologies could contribute to the development of personalized predictions for COVID-19.