Seminar Bộ môn Toán tài chính, Khoa Toán - Thống kê Chủ đề: Epidemic Forecasting using Delayed Time Embedding

26/07/2024

Hôm nay, ngày 26 tháng 7 năm 2024, tại B1.108 Bộ môn Toán tài chính, Khoa Toán - Thống kê có buổi seminar với chủ đề "Epidemic Forecasting using Delayed Time Embedding". Báo cáo viên: TS. Hồ Sĩ Tùng Lâm, Dalhousie University, Halifax, Nova Scotia, Canada

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Nội dung trình bày:

Forecasting the future trajectory of an outbreak plays a crucial role in the mission of managing emerging infectious disease epidemics. Compartmental models, such as the Susceptible-Exposed-Infectious-Recovered (SEIR), are the most popular tools for this task. They have been used extensively to combat many infectious disease outbreaks including the current COVID-19 pandemic. One downside of these models is that they assume that the dynamics of an epidemic follow a pre-defined dynamical system which may not capture the true trajectories of an outbreak. Consequently, the users need to make several modifications throughout an epidemic to ensure their models fit well with the data. However, there is no guarantee that these modifications can also help increase the precision of forecasting. In this talk, I will introduce a new method for predicting epidemics that does not make any assumption on the underlying dynamical system. Our method combines sparse random feature expansion and delay embedding to learn the trajectory of an epidemic.

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