COVID-19: High Risks, High Prevalence, High-level Evidence, and High-stake Decisions

John Ioannidis, PhD
Professor of Medicine, Stanford University

Professor Ioannidis's presentation will delineate efforts to understand the magnitude of risk conferred by COVID-19 and the gradient of risk across different populations and settings. It will also present data that try to assess the prevalence of the infection, since many infected people are asymptomatic or have very limited symptoms (and thus not tested). Furthermore, it will also provide an overview of the research agenda on rigorous testing and evaluation of interventions against COVID-19 and the caveats thereof. Finally, the implications of emerging evidence for high-stake decisions will be discussed.

John P.A. Ioannidis, MD, DSc holds the C.F. Rehnborg Chair in Disease Prevention at Stanford, where he is Professor of Medicine, Professor of Epidemiology and Population Health, and Professor (by courtesy) of Biomedical Data Science at the School of Medicine, Professor (by courtesy) of Statistics at the School of Humanities and Sciences, and co-Director of the Meta-Research Innovation Center at Stanford (METRICS). He is the recipient of many awards and he has been elected as Einstein fellow at the Berlin Institute of Health. He has been inducted in the Association of American Physicians, the European Academy of Cancer Sciences, the American Epidemiological Society, the European Academy of Sciences and Arts, and the US National Academy of Medicine. He has received honorary titles from FORTH and U Ioannina, honorary doctorates from U Rotterdam, U Athens, and U Tilburg and multiple honorary lectureships.

Measuring COVID-19 and Influenza in the Real World via Person-Generated Health Data

Luca Foschini, PhD
Co-Founder and Chief Data Scientist, Evidation Health

Dr. Foschini will present preliminary findings of an Evidation study, in collaboration with researchers at UW, UCSB, and the Bill & Melinda Gates Foundation, on 85k US participants with the goal of characterizing COVID-19 symptoms presentation in home settings, outside the clinic walls. We'll explore longitudinal symptoms reports from 230 confirmed COVID-19 cases and compare those with 6.7k confirmed flu cases. We identify unique patterns of COVID-19 symptoms that display increased severity and duration as compared to flu. Additionally, we observe a significantly increased fraction of participants with elevated Resting Heart Rate measurements passively collected from commercial wearable devices (e.g., Fitbit) in the 2 days surrounding COVID-19 (and flu) symptoms onset.

Tweetorial: https://twitter.com/calimagna/status/1267250624678096896?s=20

Medrxiv preprint: https://bit.ly/3eO6rRN

Luca Foschini is the Co-founder and Chief Data Scientist at Evidation Health, responsible for data analytics and research and development. At Evidation he has driven research collaborations resulting in numerous publications in the fields of machine learning, behavioral economics, and medical informatics. Previously, Luca held research positions in industry and academic institutions, including Ask.com, Google, ETH Zurich, and UC Santa Barbara. He has co-authored several papers and patents on efficient algorithms for partitioning and detecting anomalies in massive networks. Luca holds MS and PhD degrees in Computer Science from UC Santa Barbara, and ME and BE degrees from the Sant’Anna School of Pisa, Italy.

Please join us for this exciting seminar series. Sign up here to receive updates by email about this series.

Sponsored by UCSB's Interdisciplinary Research Centers, NOVIM, and Cottage Health.