COVID-19 Pandemic: Epidemiological and Statistical Considerations and Findings


  • Nicholas P. Jewell, University of California, Berkeley
  • Neil Pearce, London School of Hygiene and Tropical Medicine
  • Jing Qin, National Institute of Allergy and Infectious Diseases
  • Peter Song, University of Michigan

Date and Time: Friday, May 1, 2020, 12:00 pm - 2:30 pm Eastern Time

Sponsor: Section on Statistics in Epidemiology, American Statistical Association

Contact: Jing Cheng, jing.cheng@ucsf.edu, 2020 Chair of ASA Section on Statistics in Epidemiology

Registration: Free. Registration is limited and will be handled on a first-come, first-served, basis.


In the global pandemic of COVID-19, numerous epidemiologists and statisticians, along with basic scientists, computer scientists, economists, medical professionals and public health officers, have responded rapidly to better understand COVID-19 and its impacts on public health and economics. In this webinar, four speakers will share their considerations and findings from different perspectives.

Dr. Jewell, Professor at the University of California, Berkeley and Past-Chair of ASA Section on Statistics in Epidemiology, will introduce concepts and statistical methods for infectious disease epidemiology, and current challenges and opportunities in COVID-19 research compared to other infectious diseases such as HIV/AIDS, Ebola and SARS.   

Dr. Pearce, Professor at London School of Hygiene and Tropical Medicine and Past-President of International Epidemiological Association, will discuss considerations on appropriate statistics for policy guidance and decisions, and the use of test-negative case-control design and standard matched case-control design to identify risk factors for just COVID-19 and for both COVID-19 and other respiratory infections.

Dr. Qin, Mathematical Statistician at the National Institute of Allergy and Infectious Diseases, will discuss challenges in accurately estimating the incubation period of COVID-19 and introduce their method using disease onset forward time to estimate the incubation period unbiasedly based on probability renewal theory. 

Dr. Song, Professor at the University of Michigan, will introduce their health informatics toolbox for the evaluation of the time-course dynamics of the COVID-19 infection. Their toolbox is based on a hierarchical epidemiological model with two observed time series of infected and recovered cases to predict community-level risk of the COVID-19 infection for 3109 counties in the continental US.