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Seminar: Grzegorz Rempala - Mathematical Models of Epidemics: Tracking Coronavirus using Dynamic Survival Analysis

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March 24, 2020
10:30AM - 11:25AM
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Add to Calendar 2020-03-24 10:30:00 2020-03-24 11:25:00 Seminar: Grzegorz Rempala - Mathematical Models of Epidemics: Tracking Coronavirus using Dynamic Survival Analysis Grzegorz Rempala Professor of Biostatistics, College of Public Health, The Ohio State University As the outbreak of COVID-19 in the city of Wuhan appears to be the beginning of a global pandemic, there is much public interest in predicting both the dynamics and the size of the ongoing regional outbreaks in different countries. It is also important to ascertain the potential effects of early interventions such as school closures and mandatory or self-imposed quarantines. To answer some of these questions, we propose a general framework for analyzing the ongoing outbreak trend using data from a partially observed epidemic curve under minimal assumptions that are clearly speci- fied. In particular, this framework does not assume any specific infectious or recovery periods (which are often unknown) or observable prevalence of the disease (allowing, for instance, for silent infectives). We show that this analysis can help anticipate both the likely temporal trends of an ongoing epidemic as well as its final size in a commu- nity with or without social distancing. We use our approach to predict the trajectory of the epidemic curve from Wuhan city in Hubei province, which is most detailed one available to date from the COVID-19 outbreak.        Participate virtually Mathematical Biosciences Institute mbi-webmaster@osu.edu America/New_York public

Grzegorz Rempala

Professor of Biostatistics, College of Public Health, The Ohio State University


As the outbreak of COVID-19 in the city of Wuhan appears to be the beginning of a global pandemic, there is much public interest in predicting both the dynamics and the size of the ongoing regional outbreaks in different countries. It is also important to ascertain the potential effects of early interventions such as school closures and mandatory or self-imposed quarantines. To answer some of these questions, we propose a general framework for analyzing the ongoing outbreak trend using data from a partially observed epidemic curve under minimal assumptions that are clearly speci- fied. In particular, this framework does not assume any specific infectious or recovery periods (which are often unknown) or observable prevalence of the disease (allowing, for instance, for silent infectives). We show that this analysis can help anticipate both the likely temporal trends of an ongoing epidemic as well as its final size in a commu- nity with or without social distancing. We use our approach to predict the trajectory of the epidemic curve from Wuhan city in Hubei province, which is most detailed one available to date from the COVID-19 outbreak. 

 

 

 

 

 

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