Webinar: https://yorku.zoom.us/j/98615589444?pwd=S1JYcVA0R291blBoZzBnRkhDdW56dz09

Also, see the announcement at http://cdm.yorku.ca/content/mathematics-and-covid-19

http://cdm.yorku.ca/content/covid-19-transmission-characteristics-implic... .

The COVID-19 pandemic has been a major public health challenge around the world. Mathematical models are powerful tools for prediction and intervention evaluation. A key component of mathematical modeling is parameter estimation. In this talk, we will look at how to use data to estimate disease parameters such as the distributions of the incubation period and serial interval, exponential growth rate and reproduction number, using the case descriptions published by Chinese provinces, and the outbreak data in BC, Canada. We will also discuss what parameters can be robustly estimated from the available data, and what cannot. We will summarize the implications of our results on the development of COVID-19 models. Dr. Junling Ma’s main research interests include the mathematical modeling of infectious diseases such as influenza and HIV, the study of disease dynamics on contact networks, and their application to real diseases.