Ebola modeling: behavior, asymptomatic infection, and contacts (2019-nCoV Admin Supplement)

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

Grant number: 3R01GM130900-01A1S1

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Key facts

  • Disease

    COVID-19
  • Start & end year

    2019
    2023
  • Known Financial Commitments (USD)

    $242,000
  • Funder

    National Institutes of Health (NIH)
  • Principle Investigator

    Pending
  • Research Location

    United States of America, Americas
  • Lead Research Institution

    UNIVERSITY OF CALIFORNIA-SAN FRANCISCO
  • Research Category

    Pathogen: natural history, transmission and diagnostics

  • Research Subcategory

    Pathogen morphology, shedding & natural history

  • Special Interest Tags

    Gender

  • Study Subject

    Non-Clinical

  • Clinical Trial Details

    N/A

  • Broad Policy Alignment

    Pending

  • Age Group

    Unspecified

  • Vulnerable Population

    Unspecified

  • Occupations of Interest

    Unspecified

Abstract

We propose to use statistical methods we have used for Ebola virus disease forecasting in order toproject COVID-19 transmission at the state and municipal level, producing testable forecasts. Thesewill feature continuously updated estimates of the reproduction number (number of cases per case)and permit us to assess the benefits of current interventions (social distancing, school closure). We also propose to conduct a close analysis of the fraction of cases traceable to known cases.This statistic can be useful because it can indicate transmission through unknown routes or throughasymptomatic cases, but it can also be influenced by the efficacy of contact tracing itself. Manycases that would have otherwise occurred are caught and prevented by contact tracing and isolation.We will conduct network simulations to determine when large values of this presage epidemic growth(depending on the reproduction number, and timeliness and yield of contact investigation). Finally, we will use detailed network simulation to yield a pandemic preparation road maplooking into the future. Specifically, specific events (rate of increase of cases, large number ofuntraceable cases, cases in varying geographic areas) will yield specific actions (school closures,mass gathering abrogation) despite uncertainty in the mode of transmission. These detailed modelswill also be used for real time assessment of the timing and duration of school closure.