| Mon, Mar 06, 2023
|
Graduate Student Seminar
4:30 PM
MSCS 509 | | Data-Driven Modeling: Prediction, Detection, and Diagnosis of Transmission of Worldwide Mpox Virus
Haridas Kumar Das, OSU
Host: Siddiqur Rahman
| | | Abstract: A trustworthy prediction model can minimize an epidemic’s social
and economic impacts on a country. In other words, forecasting results help prompt prevention policies and remedial action. Motivated
by the rising trend of Monkeypox(Mpox) in recent times, we aim to
forecast Mpox transmission using deep learning models in this talk.
We also analyze the spatial pattern of global Mpox data. We implement 1D-CNN, LSTM, and hybrid CNN-LSTM to see the Mpox time
series trend. Mpox is a virus generally spread through close or intimate contact, with symptoms including a rash and fever, so analyzing the Mpox symptoms is essential and required. We use various classifier methods, and finally, a decision tree classifier is used to identify the major and minor symptoms of Mpox. The current study presents the classic SIR model for data-fitting on the worldwide Mpox data, which estimates important epidemiological parameters and outcomes. The model can also provide reasonable short-term (one-month) projections. We also use the ARIMA model to fit our data. |
|
|
|