Accelerating viral outbreak detection in US cities using mechanistic models, machine learning and diverse geospatial data

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

Grant number: unknown

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Key facts

  • Disease

  • Start & end year

  • Known Financial Commitments (USD)

  • Funder

    National Institutes of Health (NIH)
  • Principle Investigator

  • Research Location

    United States of America, Americas
  • Lead Research Institution

  • Research Category

    Epidemiological studies

  • Research Subcategory

    Disease transmission dynamics

  • Special Interest Tags


  • Study Subject


  • Clinical Trial Details


  • Broad Policy Alignment


  • Age Group


  • Vulnerable Population


  • Occupations of Interest



ABSTRACTSince early January 2020, our interdisciplinary research team has conducted several studies to elucidate theemerging threat of COVID-19 and support public health responses throughout the United States, resulting inpeer-reviewed publications, online COVID-19 forecasting tools, and extensive engagement with city, state and national decision makers. In our collaboration with the CDC to develop a national modeling resource for pandemic preparedness, we had recently developed a national model for evaluating multi-layered intervention strategies to contain and mitigate outbreaks in US cities. We adapted the model to COVID-19 by incorporating the latest estimates for age- and risk-group specific rates of transmission, disease progression, asymptomatic infections, and severity (including risks of hospitalization, critical care, ventilation and death). The model is designed to flexibly incorporate combinations of social distancing, contact tracing-isolation, antiviral prophylaxis and treatment, as well as vaccination strategies. Our Supplementary Aims propose to build a more granular and data-driven model of COVID-19 to elucidate the transmission, identify high-risk populations, surveillance targets and effective control of this and future epidemics within US cities. Aim S1: Focusing initially on the Austin-Round Rock metropolitan area in Texas,we will apply these models to improve real-time risk assessments and optimize the timing and extent of layeredsocial distancing measures. Aim S2: We will rapidly evaluate strategies for rolling out antiviral prophylaxis and therapy based on clinical trial data. Aim S3: We will develop user interfaces for our Austin and national modelsto support both scientific research and public health efforts to mitigate COVID-19 and plan for future pandemicthreats. These Aims are synergistic with Specific Aim 2 of our parent grant (R01 AI151176-01), in which we aredeveloping high-resolution models of viral transmission to improve the early detection and control ofanomalous respiratory viruses, particularly in at risk populations.