Peter X. Song in Webinar Series: Data Science in Action in Response to the Outbreak of COVID-19
Dr. Peter X. Song, Professor, University of Michigan
An epidemiological forecast model and software assessing interventions on COVID-19 epidemic in China
We develop a health informatics toolbox that enables timely analysis and evaluation of the time-course dynamics of a range of infectious disease epidemics. As a case study, we examine the novel coronavirus (COVID-19) epidemic using the publicly available data from the China CDC. This toolbox is built upon a hierarchical epidemiological model in which two observed time series of daily proportions of infected and removed cases are generated from the underly- ing infection dynamics governed by a Markov Susceptible-Infectious-Removed (SIR) infectious disease process. We extend the SIR model to incorporate various types of time-varying quarantine protocols, including government-level ‘macro’ isolation policies and community-level ‘micro’ social distancing (e.g. self-isolation and self-quarantine) measures. We develop a calibration pro- cedure for under-reported infected cases. This toolbox provides forecasts, in both online and offline forms,as well as simulating the overall dynamics of the epidemic. An R software package is made available for the public, and examples on the use of this software are illustrated. Some possible extensions of our novel epidemiological models are discussed.
Peter Song is a Professor of Biostatistics at the Department of Biostatistics, School of Public Health, University of Michigan. He received his PhD in Statistics from the University of British Columbia in 1996. Prior to the appointment at the University of Michigan, he was a faculty member at the Department of Statistics and Actuarial Science, University of Waterloo (2004-2007) and a faculty member at the Department of Mathematics and Statistics, York University, Toronto (1996-2004). Peter Song's research interests include bioinformatics, longitudinal data analysis, missing data problems in clinical trials, statistical genetics, and time series analysis. He is interested in methodological developments related to modelling, statistical inference and applications in biomedical sciences. In particular, Dr. Song's research projects are strongly motivated from real world data analysis. In 2007 he published a monograph "Correlated Data Analysis: Modeling, Analytics and Applications" by Springer. Dr. Song is a Fellow of the American Statistical Association and an Elected Member of the International Statistical Institute.
Dr. Peter X. Song, Professor, University of Michigan
An epidemiological forecast model and software assessing interventions on COVID-19 epidemic in China
We develop a health informatics toolbox that enables timely analysis and evaluation of the time-course dynamics of a range of infectious disease epidemics. As a case study, we examine the novel coronavirus (COVID-19) epidemic using the publicly available data from the China CDC. This toolbox is built upon a hierarchical epidemiological model in which two observed time series of daily proportions of infected and removed cases are generated from the underly- ing infection dynamics governed by a Markov Susceptible-Infectious-Removed (SIR) infectious disease process. We extend the SIR model to incorporate various types of time-varying quarantine protocols, including government-level ‘macro’ isolation policies and community-level ‘micro’ social distancing (e.g. self-isolation and self-quarantine) measures. We develop a calibration pro- cedure for under-reported infected cases. This toolbox provides forecasts, in both online and offline forms,as well as simulating the overall dynamics of the epidemic. An R software package is made available for the public, and examples on the use of this software are illustrated. Some possible extensions of our novel epidemiological models are discussed.
Peter Song is a Professor of Biostatistics at the Department of Biostatistics, School of Public Health, University of Michigan. He received his PhD in Statistics from the University of British Columbia in 1996. Prior to the appointment at the University of Michigan, he was a faculty member at the Department of Statistics and Actuarial Science, University of Waterloo (2004-2007) and a faculty member at the Department of Mathematics and Statistics, York University, Toronto (1996-2004). Peter Song's research interests include bioinformatics, longitudinal data analysis, missing data problems in clinical trials, statistical genetics, and time series analysis. He is interested in methodological developments related to modelling, statistical inference and applications in biomedical sciences. In particular, Dr. Song's research projects are strongly motivated from real world data analysis. In 2007 he published a monograph "Correlated Data Analysis: Modeling, Analytics and Applications" by Springer. Dr. Song is a Fellow of the American Statistical Association and an Elected Member of the International Statistical Institute.