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Biostat Student Seminar

October 9 @ 1:00 am - 2:00 am

Andy ShiTitle: Two biostatistical approaches to the COVID-19 pandemicAbstract: Despite the widespread implementation of public health measures, coronavirus disease 2019 (COVID-19) continues to spread in the United States. In this talk, I will highlight two projects that aim to facilitate a better response to the pandemic. First, I will discuss How We Feel, a web and mobile application that collects longitudinal self-reported survey responses on health, behaviour and demographics. Here, we report results from over 500,000 users in the United States from 2 April 2020 to 12 May 2020. We show that self-reported surveys can be used to build predictive models to identify likely COVID-19-positive individuals. We find evidence among our users for asymptomatic or presymptomatic presentation; show a variety of exposure, occupational and demographic risk factors for COVID-19 beyond symptoms; reveal factors for which users have been SARS-CoV-2 PCR tested; and highlight the temporal dynamics of symptoms and self-isolation behaviour. These results highlight the utility of collecting a diverse set of symptomatic, demographic, exposure and behavioural self-reported data to fight the COVID-19 pandemic. Second, I will present a unified framework for estimating the smoothed daily effective reproduction number, case rate, and death rate of COVID-19 based on Poisson log-linear models in a region. This flexible approach allows for the quantification of uncertainty and accounting for lag in data reporting and disease onset. We apply this framework to characterize COVID-19 impact at multiple geographic resolutions, including by US county and state as well as by country, demonstrating the variation across resolutions and the need for harmonized efforts to control the pandemic. We provide a tool for daily reporting of these metrics, which are important considerations when quantifying the impact of COVID-19 and making informed policy decisions. Our code is open-source and hosted in a dashboard http://metrics.covid19-analysis.org/.


October 9
1:00 am - 2:00 am
Event Category: