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Distinguished Lecture Series - Learning from COVID-19 Data on Transmission, Health Outcomes and Interventions

Distinguished Lecture Series - Learning from COVID-19 Data on Transmission, Health Outcomes and Interventions
Date & Time
Jan 14, 2021 (Thurs) | 10:30am (HKT)
Venue
ZOOM online lecture (https://bit.ly/2IY8Ajg)
Speaker
Professor Xihong Lin
Professor of Biostatistics and of Statistics at Harvard University

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COVID-19 is an emerging respiratory infectious disease that has become a pandemic.

In this talk, Professor Xihong Lin will first provide a historical overview of the epidemic in Wuhan, with the provision of analysis results of 32,000 lab-confirmed COVID-19 cases in Wuhan to estimate the transmission rates using Poisson Partial Differential Equation based transmission dynamic models. This model is also used to evaluate the effects of different public health interventions on controlling the COVID-19 outbreak, such as social distancing, isolation and quarantine. Professor Lin will present the results on the epidemiological characteristics of the cases, which show that multi-faceted intervention measures successfully controlled the outbreak in Wuhan.

Professor Lin will next present the transmission regression models for estimating transmission rates in USA and other countries, as well as factors including intervention effects using social distancing, test-trace-isolate strategies that affect transmission rates. She will present the analysis results of >500,000 participants of the HowWeFeel project on symptoms and health conditions in US, and discuss the risk factors of the epidemic.

Estimation of the proportion of undetected cases will also be discussed, including asymptomatic, pre-symptomatic cases and mildly symptomatic cases, the chances of resurgence in different scenarios, and the factors that affect transmissions. Professor Lin will also present the US county-level analysis to study the demographic, social-economic, and comorbidity factors that are associated with COVID-19 case and death rates, and will provide several takeaways and discuss priorities.

 

Revisit the lecture:

Professor Xihong Lin

Speaker Professor Xihong Lin

Professor of Biostatistics and of Statistics at Harvard University

  • Professor and Former Chairperson of the Department of Biostatistics
  • Professor of Statistics at Harvard University
  • Coordinating Director of the Programme in Quantitative Genomics of Harvard TH Chan School of Public Health
  • Associate Member of the Broad Institute of MIT and Harvard

Professor Lin’s research interests lie in development and application of scalable statistical and computational methods for analysis of massive data from genome, exposome and phenome, such as large scale Whole Genome Sequencing studies, integrative analysis of different types of data, biobanks, as well as complex epidemiological and observational studies, and statistical learning methods for big data. She has been active in COVID-19 research.

Awards and Achievements

  • Elected member of the US National Academy of Medicine
  • Mortimer Spiegelman Award from the American Public Health Association (2002)
  • Presidents’ Award (2006) and FN David Award from the Committee of Presidents of Statistical Societies (COPSS) (2007)
  • Recipient of the MERIT Award (R37) (2007-2015) and the Outstanding Investigator Award (R35) (2015-2022) from the National Cancer Institute
  • Former Chairperson of the COPSS (2010-2012)
  • Contact PI of the Harvard Analysis Center of the Genome Sequencing Program of the National Human Genome Research Institute
  • Founding Chairperson of the US Biostatistics Department Chair Group
  • Founding Co-chairperson of the Young Researcher Workshop of East-North American Region (ENAR) of International Biometric Society
  • Co-launched the Section of Statistical Genetics and Genomics of the American Statistical Association (ASA)
  • Former Coordinating Editor of Biometrics
  • Founding Co-editor of Statistics in Biosciences
  • Former member of the Committee of Applied and Theoretical Statistics of the US National Academy of Science