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

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ABSTRACT Since early January 2020, our interdisciplinary research team has conducted several studies to elucidate the emerging threat of COVID-19 and support public health responses throughout the United States, resulting in peer-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 layered social 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 models to support both scientific research and public health efforts to mitigate COVID-19 and plan for future pandemic threats. These Aims are synergistic with Specific Aim 2 of our parent grant (R01 AI151176-01), in which we are developing high-resolution models of viral transmission to improve the early detection and control of anomalous respiratory viruses, particularly in at risk populations.