Abstract Number:
3845
Submission Type:
Contributed Abstract
Contributed Abstract Type:
Paper
Participants:
Idamawatte Gedara Idamawatta (1), Bong-Jin Choi (2)
Institutions:
(1) N/A, N/A, (2) North Dakota State University, N/A
Co-Author:
First Author:
Presenting Author:
Abstract Text:
There has been extensive research conducted on the transmission dynamics of COVID-19 disease. The SARS-CoV-2 virus primarily spreads through the respiratory tract. It is very common for the virus to spread rapidly during the incubation period. Further, asymptomatic carriers contribute to this rapid transmission. As an early detection method, wastewater surveillance can be used to detect viruses before they spread far and wide. Our study focused on collecting wastewater samples from treatment plants across various cities in North Dakota. Utilizing viral RNA copies, we compared the model predictions of K-Nearest Neighbor (KNN) regression, Quantile Regression (QR), and Long-Short-Term-Memory (LSTM) network models. To gauge its efficacy, we compared our models' predictions with those of the fundamental Susceptible-Infected-Recovered (SIR) model.
Keywords:
SARS-CoV-2 |K-Nearest Neighbor|Quantile Regression|Long-Short-Term-Memory|SIR model|
Sponsors:
Section on Statistics in Epidemiology
Tracks:
Disease Prediction
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